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0:00
- If you've ever used an app to find the best way through traffic or received product recommendations while shopping online, you're already familiar with data analytics from the consumer end. According to some estimates, each person creates at least 1.7 megabytes of data per second on average. That roughly translates into over 2.5 quintilian megabytes of data being produced every single day worldwide. As such, there's a huge demand now and for the foreseeable future
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for people who can organize data and interpret the stories locked within. As you approach this career pathway, you're likely to bring practical experience in problem solving, decision making, allocating resources, time management, and many of the skills that are particularly suited for the job of a data professional. Many companies are searching for candidates to fill positions in this fast-growing high-paying field. My name is Cassie and I've been a data scientist
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since before we called it data science. I lead decision intelligence here at Google and I'll be your instructor for the first course of this certificate program. Before I became Google Cloud's Chief Decision Scientist, I worked as a data scientist in Google Research where I was involved with over 400 projects all across Google. One of my favorite things about the data science career is the tremendous variety, especially if like me, you're a naturally curious person.
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There are so many different flavors of project and challenge. Some of us choose to work on one project for years. Others get involved with several new projects every week. The possibilities are endless. A data professional is a term used to describe any individual who works with data and/or has data skills. At a minimum, a data professional is capable of exploring, cleaning, selecting, analyzing, and visualizing data. They may also be comfortable with writing code
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and have some familiarity with the techniques used by statisticians and machine learning engineers, including building models, developing algorithmic thinking, and or building machine learning models. Machine learning is an alternative approach to automation, expressing the way you want a task done by using data instead of explicit instructions. Machine learning is an important component of the modern data professionals toolkit. To train a machine learning model,
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specialists put a bunch of potential data inputs through algorithms, tweak the settings, and keep iterating until promising outputs are produced. But training a model is only one small step in the professional machine learning journey. Machine learning techniques can also be used for data analytics and exploration with far fewer steps, and that's what you'll learn in this program. You'll discover additional opportunities to explore machine learning through the course resources,
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so do check those out. Data professional work spans a wide range of industries and impact affects a multitude of products and services. As we'll discuss later, there are also lots of different roles and titles that focus on data professional work. Think of them as data detectives, analyzing and interpreting their findings to reveal the stories within. I'm excited for you to get to meet some of them in this program.
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Google Career Certificates are designed by industry professionals with decades of experience here at Google. You'll have a different expert from Google to guide you through each course in this program. We'll share our knowledge in videos, help you practice with hands-on activities, and guide you through scenarios that you might encounter on the job. If you work on the certificate part-time, so this program is really flexible. You can complete all of the courses on your own terms
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and at your own pace. Throughout the program, we'll give you resources that will prepare you to advance in your career as a data professional. As you progress, you'll also build a repository of portfolio projects and a comprehensive capstone project that will showcase your abilities beyond your resume. You'll also have a supportive network of peer learners taking the certificate with you and you can connect with them in the discussion forums. This program is designed to give you experience
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by building upon the knowledge and skills that you've developed to this point. Regardless of your experience with data and analytics, as you begin the program, you'll learn about different experiences that are relevant and helpful for starting or advancing your career. In addition to building your skillset, we'll examine how teams of data professionals collaborate and contribute in the workplace. By the end of this program, you'll be ready to pursue a position in the data career space.
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By completing a Google Career Certificate like this one, you'll develop the skills and knowledge necessary for a job in this expanding career field. Once you graduate, you can connect with hundreds of employers who are interested in hiring Google Career Certificate graduates. Whether you're seeking to switch careers, level up your skills or start your own business, the Google Career Certificates can help you take that next step. Throughout this first course,
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I'll be here to help you gain foundational knowledge needed to succeed in the field. Again, I'm so glad you're here. I'm excited for you to take these next steps forward in your career. I'll see you in the next video.
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Now that you have a general understanding about this program overall, let's talk a bit more specifically about what you can expect in this course. We'll start with the basics and a little background on what the data field offers. While you may already be familiar with work within the data field, I'm excited to dig deep into some history that shows us where we've been, where we are, and where we're going in this field. These key developments and applications
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will really showcase all the opportunities this program will help prepare you for. In this first course, we'll discuss the specific skills and characteristics that organizations look for in future employees. You'll develop the core skills necessary to advance on your journey as a data professional and integrate these skills with your own preexisting abilities. We'll focus specifically on technical and workplace expectations. There will also be plenty of chances to practice along the way.
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Then we'll explore your job market opportunities. You'll learn about the variety of roles and positions that match your skillset. You'll also investigate the responsibilities and ethics that underpin all roles within the data career space. With the increasing number of industries that are turning to the data professions, you're bound to find a role that fits your interests and we'll also explore some of these industries so you can see where data professionals are employed
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to make more informed decisions. Then we'll examine how larger organizations create teams of data professionals to approach larger scale projects. We'll also peek into the future of data careers and the trajectory of the field in general so that you have a good sense of what the future holds for you after completing this program. We'll also investigate elements of effective communication and discover how it can empower you as a data professional. Throughout this course and the whole program,
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you will see how effective communication can elevate productivity and promote general understanding during the data analytics process. As you progress, you will gain hands-on data analysis experience through your portfolio projects beginning with the first one in this course. We've designed a few different options for you to apply your data skills to actual scenarios with data shared by our industry partners and you can use these projects to showcase your skills to future employers.
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Lastly, our instructors will provide a few career tips and tactics to guide you on your journey. That's a short preview of what to expect later in this course. Next, you'll have the opportunity to go over some resources that'll help you get the most out of this program. I'll see you in the next video.
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Hello again. Let's discuss some of the course items you'll encounter in your learning journey. In this program, you'll code in Python, discover the stories that data holds, develop data visuals, use statistical tools, build models, and even dabble with some machine learning. Along the way, you'll build a portfolio full of data projects in addition to this program's capstone. Whether you're looking to switch careers, start a new career, improve your skills,
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or advance beyond your current role in a company, the Google Career Certificates can help guide you as you take steps towards new opportunities. We've gathered some amazing instructors to support you on your journey and they'd like to introduce themselves now. - Hello, I'm Carter and I'm a Developer Advocate at Google. Together we will explore one of the fastest growing programming languages, Python. You'll learn the basics which will help you write scripts
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that perform a number of key mathematical operations on data sets, all designed to help you unlock the stories within data. - Hi there. I'm Rob. I'm a Consumer Product Leader. I work on marketing projects here at Google. I'm excited to talk to you about how to tell stories using data. We'll discuss the six practices of exploratory data analysis and how to identify the trends and patterns in it. We will also learn about the importance of designing
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and presenting data visualizations using Python and Tableau, which can help you understand your data and convey it to others. - Hello, my name is Evan. I'm an economist and I consult with various teams across Google. Statistics helps you generate more complex ideas from the data itself. In our time together, you'll discover how you can generate insights, draw conclusions, make inferences, create estimates, and make predictions.
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- Hello, I'm Tiffany and I'm a Marketing Science Lead and I work with marketing data here at Google. I will guide you through the process of modeling relationships between variables. Together we'll explore different regression models and hypothesis tests. We'll also talk about model assumptions, construction, evaluation, and interpretation as the means for answering data-driven questions. - Hello, I'm Susheela. I'm a Data Scientist and I work on projects for YouTube here at Google.
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I'll guide you through building systems that can learn and adapt without a specific set of instructions. We'll discuss how machine learning is transforming the process of data analysis as you construct your own models. - Hello, I'm Tiffany and I lead teams focused on building AI responsibly here at Google. I'll introduce you to career resources and portfolio projects and guide you through the capstone course at the end of the program. I'll assist you with different opportunities and tools
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that will set you up for success on the job market.
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- And of course, you already know I'll be guiding you through course one. This is such a great time to grow and advance your career as a data professional. Your path to a career full of new opportunities awaits.
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Earlier I referenced the tremendous amount of data generated each day. It has become a byproduct of modern life. For companies and organizations all this data can provide insight into the ways they operate and ultimately interact with their users and consumers. To obtain these insights, organizations need people with the ability to access, interpret and share the stories within their data. Organizations understand that data can inform decision-making and explain
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consumer trends and user behavior. Data professionals use data insights to optimize products or services. There's a common phrase in data-driven decision making that references the untapped potential of data. The phrase is, "Imagine if we knew what we know?" Basically, it's a way of asking the following question, how can we take all that data that may already exist and translate it into meaningful and actionable insights? To gain insights, businesses rely on data professionals
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to acquire, organize, and interpret data, which helps inform internal projects and processes. Businesses seek those who can access data and understand its metrics. As a reminder, metrics or methods and criteria used to evaluate data both unnecessary before creating predictive models that can identify trends and inform best practices, and that's where you'll come in. The combination of all these skills from statistics and scientific methods to data analysis and artificial intelligence
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all fall within the category of data science. Data science is the discipline of making data useful. To me, the idea of usefulness is tightly coupled with influencing real world actions. Some individuals with these skills may work on developing business insights and supporting strategic decision makers. Others may use data skills to fuel automation, testing, and analytic tool development. Still, others may focus on the analytic process itself by adapting modeling approaches
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to incorporate new and emerging technologies. Data is the foundation for making future decisions. It's through our actions and decisions that we affect the world around us. Businesses and organizations need people like you who can think critically and analytically about how to directly address challenges and opportunities through data focused projects. The work of data professionals can provide businesses and organizations with details about their practices
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that can promote new approaches and innovation. This might make a little more sense if we take a closer look at an example, a global delivery company, for instance. Generally speaking, delivery companies are responsible for transporting goods to consumers. A company as complex as this is going to have a number of different data inputs or streams that influence impact and affect the ways the business operates. These data inputs and streams may include,
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but are not limited to: weather and traffic patterns which affect when deliveries are projected to arrive, gas price trends and fuel economy, which affect shipping costs and profit margins, truck loading times in relation to the number of workers available, which affects the time it takes for the delivery to reach its final destination, how users interact with the company's app to track their packages, which affects the customer experience and the company's ratings,
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and whether users engage with marketing emails sent after they make specific purchases which impacts future and repeat sales. My point is each of these variables affects the way organizations harness data to transform decisions. Automating and adapting machine learning where applicable. The ability to unlock transformative information within data is a skill that businesses seek. As you progress within this program, you'll discover how data professionals
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can make meaningful contributions to almost any organization by finding action-oriented solutions within data.
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All professions require a certain set of tools for success and data-driven work is no different. In this video, we'll open our analytics toolbox and look at some of the most common items. Before we begin, I wanna emphasize that each of the items serves their own individual purpose. However, when used together, they help build and tell stories with data which can then inform, influence and impact business decisions. Programming languages are the first tools we'll investigate.
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They allow data professionals to work efficiently within and dissect large data sets. Most languages have been developed over time and each data professional has their own preferences. We'll mention two in this video that have become very popular for data analysis. The R programming language and Python. R is a programming language that's used extensively by researchers and academics. It was my primary language during graduate studies in statistics and some people say that R captures
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the statisticians mindset. I'd say there's something to that sentiment. If you're after implementations of the latest statistical breakthroughs, R is a great place to look, but it's used for more than statistics. You'll find many new technologies and ideas programmed with it. One of the best futures of R is that you can create complex statistical models from just a few lines of code. If you're curious about R or need a refresher, be sure to check out our Google Data Analytics Certificate
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also offered here on this platform. This program teaches the Python programming language. It's a great choice for a few reasons. First of all, it emphasizes readability making it one of the easiest programming languages to learn and write. Second, unlike R, Python wasn't born in the data community. While this might sound like a minus, it can also be a huge plus. In the modern world, data is used in increasingly creative ways, so this is a massive advantage to learning a programming language
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that's capable not only of handling the data side of things but can also be used to build and deploy the applications that data will be fueling. Although R was my first love, these days I find that I lean more heavily on Python because of its flexibility. Python can perform a wide variety of data-related tasks, which makes it very popular among data professionals. If you're a novice or new to coding completely, Python is a very approachable language. It's formatting is visually uncluttered.
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It's one of the most beginner-friendly languages and it has enormous online communities and plenty of resources to help you if you get stuck. We will interact with Python within a web-based computing platform, also called Jupyter Notebooks, which allows you to run code in real time and helps identify errors easily. To visualize the stories in the data, we're going to teach you how to share complex data through a graphical interface. Those who experienced our data analytics program
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will be familiar with a platform called Tableau. In this program, we'll take a more detailed look at how this powerful tool can help others understand the results of your analysis. Additionally, we'll look at effective communication in data-driven careers. At first glance, it might seem like less of a concern, but describing the sometimes complex processes of data analytics to non-technical stakeholders may be one of the most important skills a data professional can have.
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Since communication is something we all do regularly, it's easy to forget about the importance of how data professionals share and process data stories. Our goal here is to strengthen the communicative skills that you already possess so that you can leave this program equipped to excel. In this course specifically and across other segments of this program, communication will be a key component that is directly tied to the work you'll do as a data professional.
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Programming languages allow data professionals to interact with and interpret data. Visual data tools like Tableau enrich the stories within data with visual elements that bring attention to specific details, but the most important element of any story is the storyteller. That's you. Your prior experiences and knowledge inform your storytelling abilities and your distinct background is what will say to you apart from others in these roles. Regardless of your eventual career path,
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remaining determined and developing the proper skills is essential to personal and professional transformation and the tools we're offering you in this program will also help you along the way. I'm thrilled to continue alongside you in your journey. The best is yet to come. I'll see you soon.
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Congratulations on completing the first part of this program. You've officially begun your journey to new opportunities in the data field. Let's revisit what we've covered so far. First, we covered the basic logistics of what's included in the certificate. You met each of your instructors and we previewed some of the different course topics you'll encounter. Next, we looked at the basics of data-focused work and some of the industries that are incorporating data insights.
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We also discussed the future of data-driven careers. In addition to exploring what it's like to work in the data field, we also discussed the data professionals toolbox and the skills this program will help you develop. Congratulations on your progress so far. I can't wait to see you in the next video.
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As you know, this program asks you to complete a greater assessment at the end of each section and course, and now's the time to prepare for your first one. This assessment will effectively verify your understanding of key data analytics concepts. It will also help build confidence in your understanding of data analysis while identifying any areas where you can continue to improve. Assessments can sometimes feel overwhelming, but approaching them with a strategy makes them more manageable.
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Here's a list of tips to set yourself up for success. Before taking an assessment, review your notes and the videos, readings and the most recent glossary to refresh yourself on the content. During the assessment, take your time, review the whole test before filling in any answers. Then answer the easy questions. Skip the ones you don't know the answer to right away. For multiple choice questions, focus on eliminating the wrong answers first. Also, it's a good idea to read each question twice.
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They're often clues that you might miss the first time. If you start to feel anxious, calm yourself with some mental exercises. One way to do that is by completing a simple math problem in your head or spelling your name backwards. This also helps you recall information more easily. Before you submit the assessment, check your work, but be confident. Sometimes people change an answer because it feels wrong, but it's actually correct. Your first instinct is often the best one.
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Finally, remember to trust yourself. Often people know a lot more than they give themselves credit for. Everyone learns at different speeds and in different ways, but it's important to maintain your momentum, so take the time you need and when you feel ready, keep moving ahead. You've got this.
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Great to have you back. I'm so delighted to be your guide as you continue on your way to becoming a data professional. As you've been discovering, data can be used in many ways, but no matter how it's applied, the key thing to keep in mind is that knowledge is power, the power to improve your business, your work, your personal life and the world around you. With all of the data surrounding us, there's just so much potential waiting to be unlocked. I'm so glad that you've chosen to learn more
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and to be a part of it. And speaking of your learning journey, in this section of the course, we'll start by finding out more about data careers. We'll explore different industries and examine some direct applications of data-driven work and we'll investigate where data careers are headed in the future. It's such an impactful and rewarding field and it just keeps getting better. I'm so excited for you to get started. Let's go.
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Data professionals are so valuable to their companies. They determine which data streams are most important to specific business projects, challenges and initiatives. They identify key goals for the future and they give their organizations the ability to take meaningful action by re-imagining processes and improving operations. To do all of this, data teams require individuals with diverse skills, knowledge and interests. Therefore, they're countless different data focused roles,
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responsibilities, and project types which are further differentiated by the industries and businesses they support. Among all of these possibilities, data careers can be loosely categorized into two complimentary types of work, technical and strategic. In this video, we'll investigate both. First, let's find out about the folks whose work requires a heavy emphasis on technical skills. Some examples of these professionals are machine learning engineers and statisticians.
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These people provide high effort solutions to specific problems. Through their expertise in mathematics, statistics and computing, they build models and make predictions using tools such as R and Python, they help their teams extract value from business data sets. The result is a solution that has a direct positive impact. Another highly technical role is the expert data analyst whose work involves exploring vast and complex data sets to identify directions worth pursuing in the first place.
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They ensure that an organization's data science efforts are directed as efficiently as possible. Bridging the gap between other technical data professionals and the strategic work will cover shortly. Like most technical data professionals, you'll learn how to acquire, scale, organize, structure, and manipulate data so that it's packaged in a way that others can work with. In other words, you'll know how to transform raw data into something useful for decision making.
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Okay, now let's consider data professionals on the more strategic side. These people include business intelligence professionals and technical project managers, to name a couple. Strategic data professionals use their skills to interpret information that affects an organization's operations, finance, research, and development and so much more. Their work aligns closely to the overall business strategy and involves seeking solutions to problems through data analytics.
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In short, strategic data professionals maximize information to guide how a business works. Sometimes you'll find a company has roles that blend specialist technical knowledge with strategic data expertise often in unusual and very creative ways. Soon we'll learn more about some of these opportunities as well as the more specialized technical and strategic roles, and of course we'll discover some proven ways to tap into them as a data professional. Lots more to come.
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Nowadays, it seems that data is generated everywhere. Computer functions have been integrated into a multitude of everyday technologies, smart home voice assistance, step trackers, TV streaming apps, even some coffee makers are connected. All of these touchpoints create data that businesses can use to understand trends and advance their products and services. And with every update, the ability to gather environmental and user data expands even further. Within this massive reserve of data
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is a wealth of untapped potential awaiting data professionals and the organizations they support.
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That's why these people are so in demand. Companies need them to use data to refine business strategies, meet consumer preferences, react to emerging trends and realign internal efforts. Let's consider some examples of how data professionals use their expertise to transform industries. First, the world of big finance was an early adopter of the power of data science and with the way information drives this industry, it's easy to understand why. Data professionals help financial institutions
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assess investment risks, monitor market trends, detect anomalies to reduce fraud and create a more stable financial system overall. Hundreds of millions of financial transactions occur in the financial world each day and data is part of each and every one of them.
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As another example, data analytics is key in healthcare and here the data benefits can actually be in lifesaving. For instance, the information collected by smartwatches is making a huge difference in the lives of many people. Sensors in these devices record biodata such as heart rate and oxygen levels, and of course all this information can be shared with healthcare professionals. Together, the patient and the physician can better understand sleep trends, stress levels, and much more.
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Then individualized wellness plans can be created and modified for the patient's wellbeing. Plus, on a larger scale, data analytics is helping healthcare organizations process large amounts of clinical data, which supports the early detection of a health condition and leads to more precise diagnoses. Thirdly, data has a big impact in manufacturing. Data professionals predict when to perform preventative maintenance to avoid production line breakdowns,
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use data to maximize quality assurance and defect tracking and artificial intelligence models help respond to logistical issues and reduce delivery truck miles on the road, advancing key sustainability goals. And in a time when supply chains reach every corner of the world, data enables clear and near real-time communication with suppliers, retailers, and other network partners. It also helps supply chains maintain optimal levels of inventory to avoid stockouts and empty retail shelves.
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Data professionals are also advancing approaches to agriculture. With data insights, farmers develop new ways to approach crop production, livestock care, forestry and aquaculture. The inclusion of autonomous machinery, tractors and irrigation systems is improving harvesting technologies as well. If you'd like to keep learning about how various industries use data analytics, refer to the course resources on this topic. And here's a little piece of advice from me: don't miss an opportunity
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to learn from someone in real life. I love asking business owners, store managers and client support professionals about how they use data each and every day. Who knows, one of these conversations could open a door to a future opportunity for you.
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Recently, you've been learning about how businesses use data to guide decision making, answer questions and solve problems. In this video, we'll investigate how nonprofits use data analysis to pursue their unique goals.
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Nonprofit groups are created to further a social cause or provide benefit to the public. As the name suggests, their main purpose is not about profit, but to foster a collective public or social advantage. There are some very rewarding and inspiring opportunities for data professionals in the nonprofit sector. In particular, data can be applied in order to help these groups more effectively anticipate and respond to the greatest areas of need.
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For instance, maybe a US charity that provides bicycles for children would like to determine which neighborhoods are most in need. They could ask their data professional to access the US Census Bureau. The professional would use their talents to navigate the Census database, identify key metrics and summarize findings with analyses and data visualizations. This report would highlight where there are larger numbers of school-aged children in need who would benefit
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from the resources of this program, and there you go. Data insights led to informed decisions about where this nonprofit can do the most good. Now, nonprofits do more than use data. Many of them also collect it. As you likely know, public entities and government agencies can be excellent resources for useful data, and much of it is open data that's available for general use. As you likely know, open data is data that is available to the public. It's free to use and guidance is provided
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to help navigate the data sets and acknowledge the source. While sourcing open data is a good way to interact with data on your own, there are other opportunities that enable you to refine your skills while helping others. Data volunteers contribute to many projects that help nonprofits benefit communities all over the world. To find out more, here are some organizations to check out. First, the Data Science for Social Good Foundation was launched at the University of Chicago back in 2013.
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In 2020, they trained forces with UNICEF to analyze various aspects of air pollution around the world to help monitor children's health. DataKind launched in 2011 in New York City with chapters in the United Kingdom, Bangalore, San Francisco, Singapore, and Washington, D.C. This organization analyzes the cost of environmental cleanup in different underserved communities to guide restorative efforts. You can view both foundation's latest efforts through the links in the transcript for this video.
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Another option for putting your data skills to good use are hackathons. A hackathon is an event where data professionals and programmers come together and collaborate on a particular project. The goal is to create a solution to an existing problem using technology. Some examples include developing better tools for predicting extreme weather events, creating tech to help elementary school kids learn important reading skills and identifying ways that community development groups can use their data
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to advance home accessibility and affordability. Volunteering your data skills to public projects is an excellent way to contribute to the greater good while gaining experience and networking with others in your field. Coming up, we'll take a deeper look at some data-oriented projects in the public sector and how they're making an impact around the world.
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One of the most important responsibilities for those of us in data-centered careers involves how we protect our organizations, manage and protect data. This has a lot to do with communication exchanges between a company and its customers. As you've been learning, almost all communication generates data, whether it's a shopping receipt, confirmation of an order, or even earning customer loyalty points. And businesses have a big responsibility to their customers,
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especially when it comes to maintaining and protecting user privacy. Any data gathered from individuals or consumers is referred to as personally identifiable information or PII. PII permits the identity of an individual to be inferred by either direct or indirect means. This includes things like biometric records, usernames and social security, or national identification numbers.
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Because this information is often associated with medical, financial and employment records, PII is sensitive and must be managed with great care. After all, when someone's personal data is improperly handled, they become vulnerable to identity theft, fraud, and other issues. Recently, there have been great efforts to take a wider view of data collection practices and protect individuals. Industries are trending towards aggregate information. This is data from a significant number of users
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that has eliminated personal information. By aggregating the data and removing PII, this protects people and gives them more control over their own data. Similarly, as more industries become interconnected, the amount of data available to them increases, and just as with aggregate information, the more data collected, the more likely it is that it will be representative of a wider population, rather than a single user. A key thing to keep in mind is that data gathering
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is a task managed by humans, and that process can be informed by different backgrounds, experiences, beliefs, and worldviews. These and other types of biases can affect the way that data is communicated and how the results are shared, which in turn can have an impact on business decisions. Effective data professionals know that whether collecting, analyzing, interpreting or communicating sensitive data bias should always be considered, so be very careful when interpreting data
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where there is a clear source of bias and be on the lookout for subtle biases as well.
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In addition to thinking through bias in the data, data professionals should also try to emphasize that there can be a multitude of possible interpretations for every data insight. So the main trick is avoid jumping to conclusions until you've really done your homework. One method of addressing bias is to make sure that the data that you are working with has the same characteristics as the greater population that you're interested in. In data analytics, this is called a sample.
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A good sample is a segment of a population that is representative of that entire population. Here's an example. A clothing company is analyzing sales in their highest growth market. They want to determine what color shirts will be most popular in the upcoming season. One person notes that red and blue shirts accounted for 80% of their sales in this market over the past three months. This is a big number, so they suggest ordering lots of red and blue shirts, but another person points out
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that the local sports teams colors are red and blue, and this team had recently won a championship. It's very likely that sales of red and blue shirts will have spiked as consumers purchase tees to support the local team. Plus, they note that although this market is high growth, it only represents 40% of the retailer's total sales. With all this information in mind, decision makers at this retailer, instead, choose to evaluate color popularity over a full year and across all markets.
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This will provide a much more complete picture. We'll investigate more about bias later in this program, and as you progress, you'll discover many more strategies for ensuring that you're aware of bias and proactively working to counter it in all of your data work.
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When investigating a possible new career path, one of the most important things to consider is its outlook and potential for growth. Predictions about careers related to data analysis show that there is no shortage of need for professionals in this field. Over the last decade, data focused careers have searched. According to estimates by LinkedIn the data science field grew by over 650% between 2012 and 2017. Many experts believe that we have not yet seen the full potential of these careers.
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In fact, the US Bureau of Labor Statistics stated that data science is one of the fastest growing career fields in the United States projecting a 30% increase over the next decade. Among the data science professions, one of the fastest growing is artificial intelligence and machine learning, and we've seen significant advances in these areas in recent years. At its core, artificial intelligence, or AI, is the development of computer systems able to perform tasks
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that normally require human intelligence. Thanks to growth in the data sciences, AI is now becoming more commonplace. These technologies will continue to evolve and provide more accurate results and richer insights. And as AI increasingly becomes an essential component of data work, it's important to be aware of the human bias that can be imprinted within your work. To counter this, organizations benefit most from building diverse teams of professionals
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from different backgrounds and different life experiences. Incorporating a wide range of perspectives and worldviews promotes wider representation and yields more accurate results. As we study the future of the data professions, I wanna emphasize that data professionals have yet to realize the full potential of artificial intelligence. As these types of technological innovations continue to evolve, we can expect that organizations will grow and adapt their business practices accordingly.
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With wider and wider adoption of data analysis techniques, the most likely area for growth is in specialization and we expect to see further subdivision of roles within data focused teams. Ultimately, what I want you to keep in mind is this: the world is generating more and more data every year, so it's reasonable to expect labor that extracts business value from it to be able to earn its keep. More data means more demand for the three main activities covered by the data professions:
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statistical inference, machine learning, and data analytics. So those skills will stay very relevant, though their names might evolve over time. In addition, constant innovation in the field offers you the opportunity for perpetual learning, growth and development. As you may already know, being a data professional means that your growth and success in this field depend on a desire to keep learning. In fact, that just might be the reason you enrolled
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in this program and for that I'm so proud of you. Continue to explore opportunities to evolve throughout your career. Be proactive in acquiring new skills, keep growing, and you will always be ready for the future.
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In this section of the course, we explored many different facets of data careers. You learned that data professional is a broad term that encompasses different roles and responsibilities in the data space. You discovered that the work we do in this field involves countless possibilities, such as determining important data streams, identifying and focusing on future business goals and re-imagining internal and external processes. You also thought about some of the ways
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that organizations are being transformed by data professionals and how these talented individuals use their skills to positively impact communities around the world. You've come so far already, but there is so much more to learn. Thanks for joining me on this exciting exploration and I'll catch up with you again soon.
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Welcome back. I'm really excited to share this section with you. We'll start by looking at some of the skills needed in the data field today. We'll look at what happens after you land a data position and what to expect in those first few weeks on the job. We'll investigate some of the roles within the data professions and take a closer look at the general categories that you'll encounter. Then we'll explore the importance of networking and building relationships within an organization.
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We'll also look at the responsibilities of different data professionals, so let's get started. See you in the next video.
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All data professionals share a love of data and a desire to solve problems. While wearing their analytics hat, data professionals lay out the story that they're attempted to tell and then they poke it from several angles with follow up investigations to see if it holds water before bringing it to their decision makers. In doing so, they rely on their programming and investigative skills to guide others towards informed decisions. Data professionals also combine a knowledge
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about how to do practical tasks with an awareness of what makes communication and collaboration successful. Later, we'll dig deeper into the elements of communication and discuss the ways communication enhances and structures your work as a data professional. For now, let's examine some skills and attributes that are applicable across data-driven careers. Working in data analytics requires a mix of business sense and knowledge in gathering, manipulating and analyzing data.
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Our goal is to prepare you to develop the competencies needed to succeed. Let's start by discussing some interpersonal skills. Often these are referred to as people skills. They focus on communicating and building relationships. Interpersonal skills are critical. In this field, there's a high degree of interaction between stakeholders. This is especially relevant now with team members often working collaboratively across the globe. Very often, work conversations are the starting point
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and the fuel that drives projects, and because of the cyclical processes within data analysis, communication is always ongoing. Another important skill is active listening. This means allowing team members, bosses, and other collaborative stakeholders to share their own points of view before offering responses so that each exchange improves mutual understanding. You can actually practice active listening. Next time you speak with someone, put extra effort into listening beyond their words,
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focus on what they're trying to communicate. Your listening and communication skills will play a huge role in helping you capture effective insights and informed decisions. We'll take a closer look at communication a little later in this course. There are other things you'll need to consider. As a data professional, you'll search for information hidden within large amounts of data by applying critical thinking skills. Along the way, you'll investigate the connections
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between a variety of different data sources as you search for trends and indicators. Think of yourself as a data detective. Project data can come directly from your organization or from other sources. You might be lucky and receive a well formatted spreadsheet or database, but quite often you will need to prepare the data to get started. This process is known as data cleaning. This is where the data is reorganized and reformatted. The goal is to remove anything
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that could create an error during analysis. This process includes tagging and consolidating duplicates, irrelevant entries, structural errors, and empty space. Once you have everything in the proper format, you can then filter out unwanted material. Now your data is ready to be analyzed. It's time to look for trends and tendencies. Often it's very helpful to render the data visually to reveal additional insights through charts, dashboards, and reports.
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Graphic tools will be very useful in identifying patterns as well as in sharing information with others. You will explore this in greater detail later and have opportunities to practice compiling visualizations too. You'll also learn about more advanced skills like building models and machine learning algorithms. These tools will help you and other data professionals assess information accuracy, analyze specific data segments, and predict future business outcomes.
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Your hard work will assist leaders and other decision makers in your company providing them access to a rich variety of perspectives on different sets of information. With demand for data analytics increasing across all types of companies and businesses, you will likely find opportunities in an industry that you are personally interested in. Next, we'll take a look at working in the data field.
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You can learn a lot about a career by looking at job postings. If you've searched for opportunities in the data space, you may have noticed different data related job titles with similar responsibilities or postings with similar titles, listing different responsibilities. Here's an example. At one company, the role of data analyst will focus on using statistics and models to craft insights that inform business decisions. Another job with the same title at a different company
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may focus on optimizing the tools and products that automate analytical processes. One reason for these inconsistencies is that data tasks and responsibilities are dependent on an organization's data, team structure and how they make use of insights and analytics. As such, some organizations choose to be very specific with responsibilities. Others leave job tasks quite broad in scope. That's why this program refers to the field as a career space. With that said,
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when you're comparing positions that have similar titles, I encourage you to classify them based on the skills used in their day-to-day activities. Two of the most common titles are data analyst and data scientist. These can cover a wide range of job responsibilities, many of which you'll gain experience with in this program. Traditionally, a data scientist was expected to be a three in one expert in data analytics, statistics and machine learning, but not all employers use these conventions
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when writing their job descriptions. Generally, any role that includes analytics expects candidates to be able to function as technically skilled social scientists looking for patterns and identifying trends within big data sets. Also, they develop new inquiries and questions as they uncover the stories inside their data. Their hard work can help steer a company's future actions and guide decision making. They allow their organizations to keep a finger
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on the pulse of what's going on in the business, interpreting and translating key information into visualizations such as graphs and charts, allowing every stakeholder to understand their findings. At times, they may be tasked with creating computer code and models to recognize patterns in the data and make predictions. When you investigate job postings, you'll encounter other titles with similar responsibilities. For example, junior data scientist,
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data scientist entry level associate data scientist or data science associate. All of these roles include a mix of technical and strategic skills to help others make informed decisions. In your career, you might encounter other professionals in roles that use data and analytical skills. Some of these may overlap with the skills you will learn, but these roles are specific to certain tasks or are supervisory positions. Let's take a look at a few.
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Data scientists depend on systems within their companies to collect, organize, and convert raw data. Designing and maintaining these processes are some of the most important responsibilities of a data engineer. Their goal is to make data accessible so that it can be used for analysis. They also ensure that the company's data ecosystem is healthy and produces reliable results. These positions are highly technical and typically deal with the infrastructure for data
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usually across an entire enterprise. You also need to have the ability to get data before it even makes sense to talk about data analysis. Most of the technical work leading up to the birthing of the data may comfortably be called data engineering and everything done once some data have arrived is data science. Similar to how a data engineer oversees the data infrastructure, there are data roles that manage all aspects of data analytics projects for a company.
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Insight managers or analytics team managers often supervise the analytical strategy of the team or of the organization as a whole. As a data analyst, you will likely report to someone working in this capacity. They're often responsible for managing multiple groups of customers and stakeholders, and they're often a hybrid between the data scientist and the decision maker. Since this combination of skills is rare, these positions are often more difficult to fill. This role can have other titles
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like analytics team director, head of data or data science director. You may encounter another job role in your scan of job postings, business intelligence engineer or business analyst. This role is highly strategic, focused on organizing information and making it accessible. BI analysts synthesize data, build dashboards and prepare reports to address specific needs for a business or requests from leadership. If you're interested in learning
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more about business intelligence and its opportunities, I encourage you to look into the Google Business Intelligence Certificate. Now that you have some idea of the roles found within the data analytics career space, we'll begin to take a closer look at how data professionals function within their larger organizations.
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Compared with many other professions, the data career space is relatively young. This application of data-driven work in organizations has grown exponentially in the last several decades, which means there are many different opportunities and much job security for you in the future. Now that organizations have the technical capacity to take on their own data focused work, they're looking for people like you with the right skills to fill these jobs. Traditionally, companies have filled jobs
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in the data career space with those from computer computer engineering backgrounds or from statistics. Increasingly, there's been a shift towards de-emphasizing engineering and instead, promoting analytical skills. These skills can be learned in different forums like the program that you're currently enrolled in. Let's look at a scenario. Let's say that an enthusiastic and enterprising person, that's you, is starting a new position at a company as a data professional.
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Your company is a recognized leader in its industry. Its workforce spans the globe and you are its newest member. It's your first day on the job and you are ready to start working. During your orientation, your company grants you systems access and onboarding documentation. You're starting to have a clearer picture of how information is generally shared with employees. You still have many questions about the responsibilities of the position.
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Later, you watch a video from the quarterly review meeting led by a company executive. Watching the presentation, you get insight into the quarterly budget, recent client interactions and some general information on an upcoming project. You now have a broad understanding of the company. At this point, you still lack details about your specific responsibilities. During your first week, you're invited to a virtual meeting of the data professionals involved on the project
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that you've been assigned to. As each data professional outlines their job responsibilities, you take note of the differences among them. After each participant speaks, you begin to realize that not all data tasks are universal and that many data professionals end up adapting to meet the needs of the current project and the needs of the data. When you're new to a job, I would discourage you from over specializing immediately. Instead, taking on a variety of tasks within a project
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is a great way for newer data professionals to continue developing their skillset. As a member of a larger group of data professionals, you're able to observe and learn from your team members. Once the analytical process is complete, the results of the project will need to be shared, allowing everyone in the organization to have access to the information. This includes building user-friendly interfaces and communicating the findings to different departments. Working for a large company means
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that there is a good chance that you will be dealing with vast amounts of information. This will require more work than a single data professional can reasonably provide. Because of this, you might encounter scenarios where organizations have created teams of data professionals. Throughout the rest of this section, you'll take a closer look at how complex organizations are incorporating data professionals through data teams and the division of responsibilities within these teams.
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- Hi, I'm Tiffany and I lead teams focused on building AI responsibly here at Google. I've served in the United States Army, worked as a consultant and have worked as a program manager in privacy and machine learning fairness. Data and having a rich understanding of data has always been an important part of my job. Today, we have more data available to us than ever before, and it's important to be able to derive insights to help decision makers make the best possible decisions.
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I'm so glad you're here and I really hope this program is giving you all kinds of new possibilities to think about. You've already learned so much. We've covered the basics of data-driven fields and looked at career roles, how data professionals are being used by different industries and how those in the field can make a valuable contribution. You're gaining a vast range of knowledge and skills, which is going to be extremely valuable as you prepare
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to join us in the amazing field of data-driven careers. At this point in the program, I encourage you to take some time to reflect on how your experiences so far are setting you up for a great career, and one way to do that is by enhancing your current online presence. In the Google Data Analytics Certificate, we covered numerous job related materials including how to create an effective resume and LinkedIn profile. This video is about improving your existing career assets.
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Those of us who were involved in the Google Data Analysis Certificate always love receiving learner feedback, especially when it has to do with someone else's professional success. I remember one person who took the initiative to refine her LinkedIn profile as soon as she began the program. She noted that she was currently working through her program and she added to her profile many of the technologies she had become familiar with. Well, not long after,
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she saw an advertisement for her dream job, even though she was early on in her DA education, she decided to apply for it and she got it. The hiring manager told her that the fact that she had familiarity with those data tools really set her apart from other candidates. There are tons of stories just like this one that proved the value of having a compelling and professional LinkedIn presence. So let's get into that now.
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A professional online presence enables you to better connect with others in the field. You can share ideas, ask questions, or provide links to a useful website or an interesting article in the news. These are great ways to meet other people who are passionate about data focused jobs. Even if you're already part of the community, strengthening your network makes it even more dynamic. LinkedIn is an amazing way to follow industry trends, learn from thought leaders and stay engaged
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with the global data analytics community, and of course, it has job boards and recruiters who are actively looking for data professionals for all sorts of organizations and industries. So it's a good idea to always keep your profile up to date and to be sure to include a professional photo. Beyond that, consider including a link to some of the relevant projects you've done in data analytics such as the portfolio project you'll work on during this program.
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As you continue expanding your online presence to represent the work you're doing in data analytics, the connections you make will be an important part of having a truly fulfilling networking experience. Plus, there are also many rewarding in-person networking opportunities, which will explore soon. See you then.
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Recently, you learned about the value of maintaining a professional online presence and connecting with others in the data field. As I noted, there are many professional networking sites such as LinkedIn that are well worth your time and involvement, but here's something that many people don't realize. Some of the best opportunities are never actually shared on a networking site. Sometimes there are professional opportunities that are not publicly advertised by the employer.
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There are lots of reasons why some positions are not posted. Maybe an employer is concerned about revealing details about confidential projects to its competitors through a job posting, or perhaps the company HR department doesn't have the resources to review a flood of applications. Often a business may choose to use a recruiter instead of posting jobs. So let's start exploring how you can increase your visibility to access more opportunities through building valuable relationships.
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After all, the more people you connect with professionally, the greater your chances are for being referred. Be sure to follow best in class organizations and visionary business leaders on Twitter, Facebook, and Instagram. Interact with them and share their content. If there's a post you like, consider commenting with response or a thank you. You can also search for data field webinars featuring interesting speakers, and many of these events are free. This is another fascinating way
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to learn while connecting with peers, colleagues, and experts. And there are also lots of blogs in online communities that focus on data fields. Data and tech companies will often talk about what's new and important from their point of view, but there's a growing community of bloggers and podcasters who offer great perspectives of their own. Now, let's move to in-person networking opportunities. The easiest way to find events is by simply searching
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for data science or data analytics events in your area. You'll likely discover a wide range of engagement opportunities for more formal conferences and seminars to casual meetups and get togethers. Nonprofit associations are also wonderful resources and may offer free or reduced rate memberships for students. In addition to networking, learning from a mentor can positively influence your career and life. As you may know, a mentor is someone who shares knowledge, skills and experience
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to help you grow both professionally and personally. Mentors are trusted advisors and valuable resources. The first step in finding a mentor is to determine what you're looking for to narrow down your potential list. Think about any challenges you face or foresee and how to address them in order to advance professionally. Then consider who can help you grow in these areas as well as fortify your existing strengths. Share these things openly when you formally ask someone to be your mentor.
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It's also helpful to note any common experiences. Perhaps you grew up in the same city. Maybe you both worked in the same industry. Your mentor doesn't have to be someone you work with currently. Many people find mentors on LinkedIn and association mentorship program or at a mentor matching event.
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This really taught me the value of mentorship. I also learned that having a successful mentorship experience requires effort and investment in time. Whether you're preparing to ask the right questions, internalizing the feedback or scheduling follow-up sessions, but it's well worth it. Always be open to connecting with new people. You never know where a single conversation will lead.
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Congrats on your progress so far and on taking meaningful action to advance your career. I wanted to let you know about some of the great career building activities and resources you'll encounter in the rest of this program. In the next course and those that come after it, you'll have the chance to complete a number of hands-on activities based on data-driven scenarios. They'll let you put what you're learning into practice and help you discuss your skills with hiring managers in a concrete way.
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Be sure to save your work from these activities. They'll be useful to you as you near the end of the program and start thinking about the next stage of your data-driven career. When you get to the last course in the program, we'll go in depth on preparing for a job search. We'll cover how to find and apply for jobs that interest you. I'll also share some tips to help you prepare for the interview process so you'll know what to expect going in. You'll learn how to put together an online portfolio
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that will help you demonstrate your knowledge and experience. You'll also complete a scenario-based project from beginning to end that you can put in your portfolio and use to present your working process to potential employers. With your past working and educational experiences, your career journey will be unique to you, but whatever path you choose, the knowledge and resources you gain from this program will give you a strong start. You've accomplished so much already
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and there's so much more to come. Good luck on the next part of your journey. I'm excited to meet up with you again soon.
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- As you approach the end of this section. Let's take a few moments to review some key concepts before moving ahead. We saw that the data career space has experienced amazing growth over the last decade. Future predictions indicate that this should continue.
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You discovered that all kinds of companies and organizations are looking to drive their future decisions with data, which is creating opportunities for data professionals. Additionally, we saw that innovation is the engine driving this continued growth. You also discovered that data skills and tools are becoming more universal. At the same time, experts foresee a continued specialization of roles within the different fields. We were introduced to artificial intelligence
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and saw how it has become an important tool for data professionals. We also took a brief look at some of the common roles and responsibilities in today's data professions. While we reflect on the information we've covered so far, please remember that you're not taking these steps of personal and professional growth alone. One of the most beneficial resources available to you, is our discussion board. Take advantage of the opportunity to interact with like-minded learners and gain knowledge
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from their experience. Coming up, we'll take a closer look at the skills needed by data professionals and we'll investigate how larger organizations incorporate data analysis. I'm looking forward to joining you as we continue your journey. I'll see you in the next video.
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We began our journey into the data professional world by covering the basics of data science, exploring careers, and discussing skills needed to succeed. In this section, we'll investigate the workflow within data-driven careers. You'll be introduced to a helpful organizational tool called the PACE model that can provide structure while allowing great flexibility when working on projects. We'll explore the elements of effective communication
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and some best practices when sharing your ideas with others. All of these skills and tools will help you prepare for your upcoming portfolio project. I know you're excited to continue, so let's get started.
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The most important part of any project is preparation. That involves thinking through all the necessary steps and anticipated tasks. Let's say you are planning a dinner party. You would start by planning the theme, menu, the guest list, and all the other details. Next, you might check the reservation list, dietary restrictions, or take a trip to the grocery store for ingredients. Afterward, you would return home to prepare your dishes, clean your space, set up the table, and get ready.
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Because you set up so much beforehand, the evening will be awesome. While this scenario may be fictional or not, it offers some wise advice. As a data professional, being able to visualize data, predict outcomes and quickly pivot away from obstacles makes you a problem solver and a great asset to any organization. Benjamin Franklin once said, "by failing to prepare, you are preparing to fail." After almost three centuries,
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it still applies to something as simple as a dinner party or as complex as a deep space mission.
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Regardless of the project, having a structural framework in place for how to get that work done can be immensely beneficial. I've done a lot of data science consulting in my time, and one of the most common problems I've seen is teams coming to me thinking that they need advice on which tool or equation to use and all of us finding out during the meeting that they're solving entirely the wrong problem. All the math in the universe won't help you if you don't point it in the right direction,
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but it's easy to get excited about those nitty gritty details and rush headlong into a waste of time. The best teams I've worked with have adopted a framework to help them focus on the most impactful actions in the most efficient order, and they've had the discipline to use it to stay on track instead of running off into the weeds. For those of you who completed the Google Data Analytics Certificate, you'll recall a structure for the data workflow that was divided into six phases.
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Ask, prepare, process, analyze, share, and act.
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This framework is quite useful for a multitude of projects, but often with bigger data sets, you need more freedom and flexibility. The PACE framework designed for this program offers the same workflow and structure, but in a simpler way. PACE is a framework developed with input and feedback from our team of data professionals. The intent of PACE is to provide an initial structure that will help guide you through projects. The goal is to lay a foundation
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upon which you will develop your own workflow practices. The PACE framework helps you solve problems and make judgements quickly and efficiently. PACE is an acronym. Each one of the letters represents an actionable stage in a project. Plan, analyze, construct, and execute. In the plan stage, you'll define the scope of your project. You'll begin by identifying the informational needs of your organization. This is where you'll wanna ask yourself questions like the following:
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what are the goals of the project? What strategies will be needed? What will be the business or operational impacts of this plan? Taking inventory of the project and the tasks required will help you get a better understanding of the context of your work and prepare for success. During analyze, you'll engage with the data. You'll start by preparing it for your project. Here you'll acquire the necessary data from primary and secondary sources, and then you'll clean it, reorganize it,
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and transform it for analysis. Then you'll conduct a methodological examination of the data. You'll also engage in exploratory data analysis or EDA for short. This will involve converting the data into usable formats, assessing the quality of the data, and then diving into the data to find as many potentially useful insights and directions as possible.
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You'll then work with your stakeholders to see which of those areas are worth pursuing in more detail which brings us to the construct stage where you are going to pursue a limited subset of all those potential insights that looked interesting to you in your EDA and here is when you will work with other data professionals, potentially statisticians and machine learning engineers, to do things like building machine learning models and revising those, uncovering relationships within your data
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and doing statistical inference about those relationships. Finally, in execute stage, you'll share the result of your analysis and your collaboration with your stakeholders as well as the value that you've unlocked from your data. Here you will present findings to internal and external stakeholders answering questions and considering different viewpoints. You'll also have an opportunity to present recommendations based on what you found in the data. You may discover that you revisit the planning
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and analysis stages as you refine models and incorporate feedback. A good way to visualize the PACE framework is as a completed circuit. Each of the four stages must be engaged for it to function correctly. The electricity or flow of energy in the PACE circuit is the communication between you, your team, and all the other stakeholders and collaborators involved. When you look at the stages in this manner, you might think that communication only flows one way.
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Well, you always do start with planning, but don't be afraid to go back and iterate. With PACE, new information and feedback can be incorporated in any part of the process. You might need to return to analyzed to clarify some aspect of the data and then jump back to execute to present this aspect to your stakeholders without the need to construct new models or dashboards. Along the way, you'll see how the PACE framework can be scaled to fit within the scope of any project.
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The model's adaptability will prepare you for a dynamic profession that requires a high degree of professional flexibility and communication. Regardless of where your career takes you, the PACE framework is a tool that provides a clear foundation and structure. Through the continued application of the PACE framework, you'll prepare for each course's portfolio project. Then you'll have the opportunity to practice your evolving skills. Each portfolio project will introduce opportunities
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to develop and strengthen your organizational methods. As you develop your own intuitive workflow, the PACE framework can be a great organizational tool. Next, we'll look at how communication is so crucial.
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When I was a kid, I used to love to take things apart so that I could see how everything came together on the inside to make something work. I actually still love doing that today, and this is so much easier to do with tangible things that you can hold and look at like a pen, for example. I can twist the tip to pull each section apart. From there, I can pull the ink cartridge out and check out the way that the spring works in tandem with the top part of the pen to make it click.
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Studying the way things work becomes much harder when we start thinking about abstract concepts like communication. And you're probably wondering why it's important to talk about it in the first place. In your role as a data professional, you are the direct connection between the information inside the data and other project stakeholders. So let's take a closer look at some key considerations when crafting a message. All the communicative exchanges have three key elements
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that we need to keep in mind: purpose, receiver and sender. When we think about purpose, I want you to think about the reason why the communication is taking place. In analytically oriented settings, you might find yourself in situations where technical pockets of information require analysis or reporting. On the other hand, there may be contexts that depend on strategic insights, which will be used to direct a company's financial or organizational efforts. The receiver is your audience.
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When you think about the receivers of your messages, I want you to think about who you are talking to. It's helpful when crafting communications to ask, what does my audience already know? What do they need to know? And it's important to keep in mind that every exchange can cause a rippled chain of events. As a data professional, you're often working as part of a distributed team across an organization, and that's why a message shared with one receiver
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may be used for reporting with or to others. The sender is the person responsible for crafting that message or communication. Yes, you, you're the sender. The sender is a crucial part of any communicative exchange. As the sender, I encourage you to think about the following, what's your relationship to the receivers? What's your role in this exchange? Are you reporting insights? Are you pitching ideas or are you identifying potential data inputs? Also, what personal biases
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might affect the message you're trying to share. At the heart of the relationship between purpose, receiver, and sender is the message or communication that you intend to share, which is impacted by all three elements. For this reason, the same message might be shared in dramatically different ways when the purpose and the receivers change from scenario to scenario. For example, you might know a data professional working on a complex project. They've been involved since the project was sparked
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by an idea in a meeting. The way they articulate the project pitch structure, research, models and findings will change depending on whom they're talking to. With non-technical audiences, they're much less likely to focus on details about the code used to program the model, and instead, they'll focus on the impact of the project. When the audience shifts to other colleagues, your friend may instead choose to be very detailed about the code and project logistics. In both cases,
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the overall message exchanged, that is, information about the project was the same, how it was crafted, the details included, and the way the information was organized, that's what was different. Now that we've examined the key elements of communication, we can really start to think about some of the best practices that will set you up for success when you communicate in your future job.
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Have you ever stopped to think about what makes communication successful? Surely you can think of a time when you witnessed a communication failure. I know I can. Communication is one of the most essential components in the exchange of ideas and concepts. It's a large part of everyday life and the foundation of all human interaction. It's also an important element of analytically focused work that isn't discussed as often as technical skills. In this video,
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I'm gonna share a few tips for successful communication. My hope is that you'll rely on this advice in your future role as a data professional. Having a clear vision of why you're communicating is the first thing you need to consider. Your why depends on the context set by the business or organization that you work for. As we discussed in earlier videos, your why is also informed by the project's mission statement, that is, the goals orienting the project.
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And when crafting any form of communication, use your why to guide the main ideas so that your audience can identify how to act or respond with purpose. Time is a currency in the professional world, it's very important to be efficient. As you draft or prepare to communicate, I encourage you to use direct language, minimize wordiness and avoid unnecessary details. You should always strive for clarity. Use proper grammar and punctuation. Regarding vocabulary, keep it simple.
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Choosing specific terms and avoiding technical language where possible. Break complex ideas into shorter sentences, especially when addressing a diverse audience of professionals from outside data analytics. This will make the points within your communication easier to understand and remember. You may recall from earlier videos that I mentioned, the three key elements of communication. These are the sender, receiver, and purpose. Now I want to add to that list and consider the setting
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for your communication as well. Are you having a discussion at lunch with a coworker? Are you creating an email to update all the stakeholders about your progress or presenting the results of your analysis to a boardroom of executives? Where and how you'll be delivering your message will have a direct impact on the way you shape it. Let's use a simple example to help illustrate effective communication like using a hose to water a plant. The straighter the hose, the better the water flows.
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Now bend the hose or tie a knot into it and the flow of water is interrupted. In this analogy, the water is like the intended message. The hose is whatever method you've selected to deliver your message. Knots in the hose symbolize misunderstandings within the mind of your audience. When the knots are created, the free flow of information stops even if you continue to deliver information. As a data professional, you may find that a significant aspect of your work involves your ability
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to communicate with non-technical audiences. Your goal should be to meet your audience where they are. What I mean by that is you wanna be efficient in your communication and tailor it to the people you're sharing information with. Depending on how familiar they are with what you're sharing, you'll likely wanna break down technical concepts into simpler terms, avoid jargon, acronyms, and technical buzzwords. When everyone understands the intended message,
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you can avoid the knots and the flow of ideas can continue. Carry these practices through all types of interactions, email, messaging, and both virtual and in-person communications. As you continue throughout the program, consider ways that you would communicate the technical concept you're acquiring to different audiences. Also remain focused on the purpose and setting of your communication. All these factors add up and make a big difference
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to how effectively you communicate as a data professional.
1:21:42
Previously, you were introduced to the PACE framework. As a reminder, PACE has four stages: plan, analyze, construct, and execute. We also examine the rationale and key elements involved in effective communication. Now let's investigate how they can be integrated together within data projects.
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At first glance, the PACE framework easily enables a wide view of an entire project's workflow. That is, each stage from planning to executing is logically sequenced with the beginning, middle, and end of a data project. As you may recall though, PACE is incredibly flexible and it doesn't need to be employed in a linear progression. Often iteration makes sense in projects. So let's unpack that with an example. For reference, we can compare it to the construction of a building.
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First, a blueprint is created. This is where the planning happens. Next, we lay the foundation. We can do this once we've analyzed the lot space and considered other variables like cost. Then we add the frame. After that, the roof can be installed. By the time the structure is ready for move-in, the builder will have progressed through the entire workflow, planning analyzing constructing and executing the client's vision. In practice, the PACE workflow is meant to serve as a navigational tool.
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We created it with the goal of helping you understand the data professional's workflow and as an aid you can consult in your future role. Now let's return to the example. With the roof completed, that makes it possible to begin the inside construction, like installing drywall, electrical components and plumbing systems. Each of these jobs also has its own pace workflow, demonstrated on its own planning document within the larger blueprint, each requiring planning, analyzing,
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constructing, and executing. Just like the building example we've been discussing, as data projects move into the construct and execute stages on the global level, you may need to return to the earlier stages to incorporate additional data or feedback from other stakeholders. Even while the global project is transitioning into a new phase of pace, there can still be upcoming tasks that are just beginning their PACE cycles. Regardless of where you might be in the pace workflow,
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communication is what drives the framework to the realization of the project. At each stage within the framework, there will always be a need for communication to improve the workflow. This could be asking questions about your data, gathering additional sources, updating stakeholders on progress or presenting findings and receiving feedback. One of the most important considerations behind the development of PACE was providing a flexible structure
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that allows you to adjust changes within a project. Let's return to our building example. Let's say that during the installation of the electrical system, the property owner has communicated that they wish to have an additional charging port for electrical vehicles added to the plan. To facilitate the change, you would revisit the PACE framework to plan, analyze, construct, and execute this new request. Just like in this example, requests from other stakeholders can come in at any time.
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Regardless of the timing of an additional request or task, data professionals need to be available and accessible throughout the entire project cycle. Sometimes you may need to speak at a meeting or participate in a progress update. Additionally, you may update your progress within a tracking system. Email conversations and chat discussions will keep others involved and up to date with where you are in your workflow. I'm excited for you to develop some hands-on experience,
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practicing different communication strategies through each stage of PACE. You'll have opportunities to do this later in the program. For now, I want you to remember that a good data professional is a proactive communicator who responds to questions in a timely fashion. Keeping other stakeholders up to date with clear explanations can make you the most effective data professional that you can be.
1:25:47
You're nearing the end of your first course and we've discussed some key concepts, especially in the areas of communication and data workflow through the PACE framework. As you're preparing for your first portfolio project, I'd like to point out a vital consideration when using the PACE framework. Each stage of the workflow is not mutually exclusive. Different roles, teams, projects and workflows may put emphasis on different stages of the PACE framework. So although you may be working on a task
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that is primarily focused on, for example, analyzing data, there will still be elements from the other stages of the PACE framework that will affect your work as well. Let's take a look at the way this program applies the PACE framework. We can take a global perspective and say that the earlier courses are heavier on planning and analysis and the later courses are for construction and execution, but that's a very wide view. If you take a closer look, you'll discover that each course is operating
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within its own PACE framework. In your Python course, you'll be acquiring knowledge to enable you to use the language for data analysis. Because you're acquiring new skills, much of the course may be classified in the planning stage of PACE. As you move on to that course's portfolio project, you'll shift into analysis, but this will have elements of planning, constructing, and even execution as you create the final product. Next, you'll learn how to prepare data to reveal the stories within.
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Here, you'll lean into the analyze stage. As you become familiar with the foundations of statistics, you'll continue analyzing data and you'll construct some new tools for your toolbox that you'll apply in your portfolio project. While you're exploring regression, you'll extend your experience in the analysis stage and put it into practice by constructing data models. You'll also practice executing by summarizing the results and insights to provide value to your stakeholders.
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Advanced modeling will further expand your analysis and construction skills within that particular portfolio project, and even while you're expanding your knowledge of career resources, you'll be operating in the planning stage as you collect information, the analysis stage, while processing information about the job market and the constructing stage while assembling your resume and portfolio. You'll have an opportunity to bring all your newly acquired knowledge
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and skills together in the Capstone project. Here, you'll have the chance to use the entire PACE framework through scenarios and data provided by our industry partners. Even though some stages are more prominent than others in each course, you'll still see evidence of all the stages throughout. You could look at PACE as the scaffold that surrounds the exterior of a building. With this scaffold in place, different tasks within the building can use the same workflow structure
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without disrupting the entire project. As you can see, the PACE framework is an excellent way to help guide you as you gain knowledge and confidence. Understanding how to classify tasks and proactively interact will help you develop good habits and eventually develop your own professional workflow.
1:29:01
You've reached the end of the data workflow overview. You've covered a lot of material and concepts. Great job. You saw how the PACE framework can be used to help structure and guide you through projects. Next, we learned about effective communication and how it can be used within the PACE framework. We looked at the external factors that can have an impact on data analysis. We also looked at the external factors that can have an impact on data analysis, like bias within data.
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As you are preparing for the weekly challenge, don't forget that you can review any of the materials we covered. In the next section, you'll begin working on your first portfolio project. I wish you the best of luck.
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- Attending a class, watching an instructional video or reading information are all great ways to gain new knowledge. However, there's nothing like applying that knowledge. When you actually do something, this really helps confirm that you understand what you've learned. This concept is called experiential learning, which simply means understanding through doing. It involves immersing yourself in a situation where you can practice what you've learned,
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further develop your skills and reflect on your education. Experiential learning gives a broader view of the world, provides valuable insights into your particular interests and passions and helps build self-confidence. In the context of this program, experiential learning will give you the opportunity to discover how organizations use data analysis every day. This type of activity can help you identify the specific types of industries and projects
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that are most interesting and gain the confidence necessary to discuss them with potential employers. This can really help you stand out during a job search. Soon, you'll put experiential learning into practice by working on a portfolio project. A portfolio is a collection of materials that can be shared with potential employers. Portfolios can be stored either on a public or personal website. They can be linked within your digital resume or any online professional presence you may have,
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such as your LinkedIn. Your portfolio project for this course will involve using the PACE model to set up tasks of a project. Creating a portfolio project is a useful opportunity since companies will often ask you to complete some type of project during the interview process. Employers commonly use this method to assess you as a candidate and gain insight into how you approach common business challenges. Completing this portfolio will prepare you if you encounter the situation when applying
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for data focus jobs. Coming up, you'll be introduced to the specifics involving your portfolio project. You'll also receive clear instructions to follow. As you begin working, consider the knowledge and skills you've acquired in this course and how they can be applied to your project. Within each portfolio project, you'll prepare a PACE strategy document. This will help you identify key points within each project to share with the hiring a manager
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such as the many transferrable skills you've gained. A transferrable skill is a capability or proficiency that can be applied from one job to another. Highlighting your transferrable skills is especially important when changing jobs or industries. For instance, if you learned how to solve customer complaints while working as a host at a restaurant, you could emphasize the transferrable skill of problem solving when applying for a job in the data field.
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Or maybe you learned how to meet deadlines, take notes, and follow instructions while working in administration at a non-profit organization, you could discuss how your organizational skills are transferrable to the data analysis field. The point is you've developed the ability to problem solve or keep things organized in one role. You can apply that knowledge anywhere. There are all kinds of transferrable skills that you can add to your resume. Reflecting on your transferrable skills
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and the notes you take in your PACE strategy document will help you consider how to convey technical concepts clearly. This will also help you demonstrate how you would apply your expertise across all kinds of tools and scenarios in the data career space. And by the time you're done, you'll not only have a very useful data analysis process document, but also a comprehensive set of artifacts for your portfolio. Sounds exciting, doesn't it? Let's get going.
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When I interview people for jobs here at Google, I love checking out their online portfolios. I find that I feel more confident in candidates who can demonstrate their knowledge in a clear and compelling format. Having a portfolio has become incredibly common in the data field. During a job hunt, it's so valuable to showcase your ability to understand business scenarios, communicate effectively and use tools to solve complex problems. Your portfolio can really help you stand out
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from other candidates. So far in this course, you gain lots of knowledge and job-ready skills to help you excel. You've discovered the role of data professionals within an organization and typical career paths. You've explored core analysis practices and tools and witnessed how data professionals use them to make a positive impact. All of these things will help you successfully complete your portfolio project. In addition, you will apply what you've learned about team members, stakeholders,
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and clients such as their particular roles or priorities. You'll begin by reading the specific project you'll be working on. This reading will describe the type of organization you're working with, the people involved, the business problem to be solved, and other key details. This will enable you to further define the project, understand the stakeholders, and consider key questions to answer in order to achieve a successful result. Then you will create a PACE strategy document
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outlining the project's purpose, stakeholders, deliverables, and much more. In this document, you will begin to integrate the PACE model to identify steps at each stage in the project. For each portfolio project, you will continue to use the PACE model to guide you. By completing each PACE strategy document, you'll be well on your way to developing your own data analytic workflow. Then in later courses, you will continue working on your portfolio project
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and continue using the PACE model to guide your process and by the time you're done, you'll have designed something that you can use to really impress hiring managers. Plus, you'll have a dynamic example of your data analytics skills, demonstrating your thought process, approach to the problems, the key skills you've gained and lots more. These are all great things to talk about during an interview. Alright, let's get started. It's time to discover how you will help an organization
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advance through the exciting world of data.
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You've completed the first portfolio project, congrats. Planning and documenting your approach to the example situation is a useful experience as you begin thinking about your future job hunt. Soon you'll be able to impress hiring managers by discussing your data professional experience, including understanding stakeholder requests, establishing a clear and straightforward project plan and completing an effective strategy document. In addition, you'll understand how to plan out
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and organize the necessary workflow. This is a big part of a data professionals process. Also, as you've learned, it's helpful to communicate about your transferrable skills with potential employers. The information you've added to your PACE strategy document will be valuable during job interviews. There will be another opportunity to complete a portfolio project at the end of the next course. At that point, you'll use the pay strategy document that you started here
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to continue developing your skills as a data professional. Then at the end of the program, you'll bring everything together in order to finalize your unique approach to this example situation. The goal is to have a great example of your work that clearly demonstrates your skills to potential employers. Congratulations again and I hope you have a really rewarding experience as you continue working on your portfolio project throughout this program.
1:37:14
- Congratulations on finishing this first course. You've already learned so much and now you're ready to take your new knowledge and skills and keep moving forward. But remember, if one day you feel like you need a refresher or just some extra practice, these videos, readings, and activities will still be here whenever you need them. Now I'm excited for you to begin working with the instructor for the next course. They're ready to help you take your next step towards finishing this program
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and continuing your journey as a data professional. This course builds directly on only interesting topics you've learned so far. It will give you more insight into the Python programming language and how it can be used to enable data analytics. As you progress, you'll continue building your data professional skillset, and by the end of the course, you'll be prepared to take the next step towards your portfolio project. Before you get started though,
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I'd like to thank you for joining me in this course and choosing to pursue this exciting learning opportunity. I strongly believe that education is a lifelong journey, and I have no doubt that all the time and effort you put into experiences like this one will better equip you for anything you choose to pursue. You've come a long way, so take a moment to celebrate everything that you've already accomplished, and then when you're ready, head on over to the next course.
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