<p><center><a href="https://www.datacamp.com/webinars/train-your-workforce-to-thrive-in-a-data-driven-age"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603892941/webinar_banner_hedvoq.png" /></a><br></p>
<p>As organizations produce more data and digitize products and processes, a data-driven workforce has never been more critical. This is why learning and development has become central to business strategies, especially initiatives focused on building data science capabilities across organizations.</p>
<p>Next week, DataCamp’s Data Science Evangelist, Adel Nehme, will host a <a href="https://www.datacamp.com/webinars/train-your-workforce-to-thrive-in-a-data-driven-age">webinar</a> with Sheil Naik, Global Data Technical Trainer at Bloomberg, on how Bloomberg uses blended learning to teach data analysis with Python, and how these analysis techniques can be used to evaluate behavioral change and the success of upskilling initiatives.</p>
<h2 id="blended-learning-at-bloomberg">Blended learning at Bloomberg</h2>
<p>A data-driven workforce at Bloomberg uses data science to make data-driven business decisions, improves processes with data, and creates data-driven news stories. Sheil Naik leads a company-wide blended learning program focused on Data Analysis with Python which incorporates 12 to 20 hours of learning via DataCamp per quarter, three live 1.5-hour classroom sessions led by technical experts, and a final project using Bloomberg data as the final deliverable.</p>
<p><center><a href="https://www.datacamp.com/webinars/train-your-workforce-to-thrive-in-a-data-driven-age"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603894417/datadriven1_sjgl2a.png" /></a><br></p>
<p>The webinar will dive deep into the benefits of blended learning. The combination of DataCamp courses and virtual in-person classroom sessions allows for greater consistency and
flexibility across regional and scheduling constraints and increases the capacity of class sizes across the world.   </p>
<h2 id="evaluating-upskilling-success">Evaluating upskilling success</h2>
<p>A methodology for evaluating direct return on investment for training programs is the Kirkpatrick Model of Evaluation. In brief, the Kirkpatrick Model proposes four different evaluation levels: the initial reaction following a training program, learning evaluation, behavioral change, and the business impact of gained skills.</p>
<p><center><a href="https://www.datacamp.com/webinars/train-your-workforce-to-thrive-in-a-data-driven-age"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603898647/Train_Your_Workforce_to_Thrive_in_a_Data-Driven_Age_nwfelw.png" /></a><br></p>
<p>In the <a href="https://www.datacamp.com/webinars/train-your-workforce-to-thrive-in-a-data-driven-age">webinar on November 4</a>, Sheil will deep dive into how Bloomberg operationalized the Kirkpatrick Model of Evaluation by measuring and evaluating post-training behavioral change for all learners in the program.</p>
<h2 id="reap-the-benefits-of-data-upskilling">Reap the benefits of data upskilling</h2>
<p>This only scratches the surface of Adel’s and Sheil’s discussion on creating a data-driven workforce. To find out more about how your organization can scale personalized learning, evaluate training success, and tie training to business objectives, <a href="https://www.datacamp.com/webinars/train-your-workforce-to-thrive-in-a-data-driven-age">join our webinar on November 4th</a>.</p>
<p><center><a href="https://www.datacamp.com/webinars/train-your-workforce-to-thrive-in-a-data-driven-age"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603892941/webinar_banner_hedvoq.png" /></a><br></p>


https://www.datacamp.com/community/blog/blended-learning

Bloomberg’s Global Data Technical Trainer, Sheil Naik, breaks down how a blended learning environment can catalyze a data-driven workforce.

Creating a Data-Driven Workforce with Blended Learning

<p>R is a free, open-source programming language and software environment for statistics, bioinformatics, and visualization. It’s one of the most popular languages in 2020 and is widely used in finance, business, and academia. Many data scientists and data analysts <a href="https://www.datacamp.com/community/tutorials/r-or-python-for-data-analysis">choose to learn R or Python</a> (or both!). For a complete beginner, it’s possible to learn and start programming in R within a couple of weeks. Here’s how to get started.</p>
<h2 id="1-consume-r-content">1. Consume R content</h2>
<p>Stack Overflow is a great resource for any aspiring data practitioner—they have <a href="https://stackoverflow.com/tags/r/info">the largest collection of links about R</a> and an <a href="https://stackoverflow.com/questions/tagged/r-faq?tab=Votes">r-faq</a> tag, which contains important questions and answers for learning R. Reading through these questions is a great way to learn about how to solve common tasks and avoid common pitfalls.</p>
<p>CRAN provides a similar <a href="https://cran.r-project.org/doc/FAQ/R-FAQ.html">FAQ resource</a>, as well as <a href="https://cran.r-project.org/web/views">Task Views</a>, which list all the R packages that are used in particular areas. For example, the <a href="https://cran.r-project.org/web/views/Finance.html">Finance Task View</a> lists all the packages for Applied Finance.</p>
<p>The main place to find blogs about R is <a href="https://www.r-bloggers.com">R-bloggers</a>. The best (and free!) introductory book on learning R is <a href="https://r4ds.had.co.nz">R for Data Science</a> by Garrett Grolemund and Hadley Wickham.</p>
<p>For visual learners, YouTube has many great explainer videos to get you started. The <a href="https://www.youtube.com/c/RProgramming101">R Programming 101</a> channel has a really enthusiastic presenter. Start with the <a href="https://www.youtube.com/watch?v=9kYUGMg_14s">Why you should use R</a> video. I also like the <a href="https://www.youtube.com/c/RProgrammingDDS">Dynamic Data Script R series</a>, which has a longer <a href="https://www.youtube.com/watch?v=BvKETZ6kr9Q">R Programming for Beginners</a> tutorial.</p>
<h2 id="2-take-an-online-course">2. Take an online course</h2>
<p>Obviously, at DataCamp we are huge fans of online courses for learning data science! An important thing to know about R is that its functionality is split across packages. There is a core set of packages known as &quot;base-R&quot; developed by the R Core Team. These are included when you download R. Other packages can be created by anyone—the R ecosystem is a community driven effort. One set of packages of particular importance is called the &quot;tidyverse.&quot; These packages are designed to work well together, and make data manipulation and visualization easier.</p>
<p>DataCamp&#39;s R curriculum begins with a quick introduction to base-R through <a href="https://www.datacamp.com/courses/free-introduction-to-r">Introduction to R</a>, but the majority of our curriculum is built on top of the tidyverse packages, beginning with <a href="https://www.datacamp.com/courses/introduction-to-the-tidyverse">Introduction to the Tidyverse</a>, taught by the inimitable David Robinson (former Chief Data Scientist at DataCamp). You can start learning R for free on DataCamp since the first chapter of every course is free.</p>
<p><center><a href="https://datacamp.com/courses/free-introduction-to-r"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603206105/tidyverse_np3ouw.png" /></a></center><br></p>

<p>While DataCamp has the most comprehensive R curriculum around with over 150 courses, there are many providers of introductory R courses, so you may also wish to check out Codeacademy’s <a href="https://www.codecademy.com/learn/learn-r">Learn R</a> and Coursera’s <a href="https://www.coursera.org/learn/r-programming">R Programming</a>.</p>
<h2 id="3-set-up-your-r-environment">3. Set up your R environment</h2>
<p>To work with R, we recommend installing R, RStudio, and git, and you may need to customize RStudio and your R profile as well. Watch my <a href="https://www.datacamp.com/resources/webinars/setting-up-your-r-environment">live training recording</a> for a step-by-step tutorial on how to do this.</p>
<p>You can also follow the <a href="https://www.datacamp.com/community/tutorials/installing-R-windows-mac-ubuntu">beginner guide to installing R on Windows, Mac OS X, and Ubuntu</a> on our Community if you prefer to follow written instructions.</p>
<h2 id="4-work-on-r-projects">4. Work on R projects</h2>
<p>There’s no substitute for hands-on experience with R using real data—you’ll probably want to build your own portfolio of data science projects.</p>
<p>DataCamp has 40+ <a href="https://www.datacamp.com/search?q=&amp;tab=projects&amp;facets%5Btechnology%5D%5B%5D=R">R projects</a> to guide you through everything from <a href="https://www.datacamp.com/projects/166">Visualizing Inequalities in Life Expectancy</a> to the <a href="https://www.datacamp.com/projects/435">Rise and Fall of Programming Languages</a>.</p>
<p><center><a href="https://www.datacamp.com/projects/435"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603206124/project_programminglanguages_jk31ys.png" /></a></center><br></p>

<p>If you’d like to download your own data and build skills in data cleaning, exploratory data analysis, and data visualizations, the <a href="https://www.rfordatasci.com/">R4DS Online Learning Community</a> has a great project called <a href="https://www.tidytuesday.com/">Tidy Tuesday</a> which gives you a new dataset to try analyzing each week. More experienced users may wish to download and import public datasets from <a href="https://www.kaggle.com/data">Kaggle</a>.</p>
<p>Of course, if you have access to real data from your company, you should use that. It’s best to work with data that you find interesting, or that matters to you in your career.</p>
<h2 id="5-keep-on-expanding-your-r-skills">5. Keep on expanding your R skills</h2>
<p>Keep building and expanding your R skills—but watch out for common pitfalls. <a href="https://www.burns-stat.com/documents/books/the-r-inferno">The R Inferno</a> by Patrick Burns is a classic text about common pitfalls and a pleasant, short read.</p>
<p>The <a href="https://www.r-project.org/mail.html">R mailing lists</a> are a good place to ask questions if you are stuck. Although email lists may feel totally antiquated now, the main benefit is that a lot of people who’ve been using R for decades are on the list to reply to you, including the R-Core team that develops R. Signing up for the R-help mailing list is a good idea if you’re serious about learning R.</p>
<p>As with any other language, you’ll need to practice and refine your R skills to get comfortable and become fluent. Study up on Indeed’s <a href="https://www.indeed.com/career-advice/interviewing/r-programming-interview-questions">35 R Programming Interview Questions and Answers</a> as you prepare to become production-ready. DataCamp also has courses on <a href="https://datacamp.com/courses/practicing-statistics-interview-questions-in-r">Practicing Statistics Interview Questions in R</a> and <a href="https://datacamp.com/courses/practicing-machine-learning-interview-questions-in-r">Practicing Machine Learning Questions in R</a>. Stay positive, keep at it, and you’ll be well on your way to landing a job in data science and analytics.</p>


https://www.datacamp.com/community/blog/how-to-learn-r

A beginner-friendly guide on building R skills for data science and analytics.

How to Learn R

<p><div class="wistia_responsive_padding" style="padding:56.25% 0 0 0;position:relative;"><div class="wistia_responsive_wrapper" style="height:100%;left:0;position:absolute;top:0;width:100%;"><iframe src="https://fast.wistia.net/embed/iframe/smoi8raepm?videoFoam=true" title="Introducing DataCamp's new look Video" allow="autoplay; fullscreen" allowtransparency="true" frameborder="0" scrolling="no" class="wistia_embed" name="wistia_embed" allowfullscreen msallowfullscreen width="100%" height="100%"></iframe></div></div>
<script src="https://fast.wistia.net/assets/external/E-v1.js" async></script></p>
<p>We’ve grown a lot since we started seven years ago, and it was time to make our branding part of that growth and evolution as well. We’ve grown to nearly seven million learners strong in more than 180 countries, with 1,600+ companies and 3,000+ academic institutions all learning on DataCamp.</p>
<p>Our platform has evolved too: We’ve launched <a href="https://www.datacamp.com/groups/business/">DataCamp for Business</a> and our adaptive assessment tool <a href="https://www.datacamp.com/signal/">DataCamp Signal™</a>, we’ve increased our <a href="https://www.datacamp.com/courses/">course library</a> by more than 500%, we’ve added new technologies like <a href="https://www.datacamp.com/courses/introduction-to-tableau">Tableau</a> and <a href="https://www.datacamp.com/courses/introduction-to-power-bi">Power BI</a>, as well as <a href="https://www.datacamp.com/tracks/foundational-data-skills-for-business-leaders">non-coding learning content to grow your data skills for business</a>—and much more. We’ve also expanded the ways learners can build data skills online using our proven learning methodology—Assess, Learn, Practice, Apply—with learning tools like our new <a href="https://www.datacamp.com/projects/">unguided projects</a>.  </p>
<p>It only makes sense that our branding catches up to where we are today, and keeps us pointed toward the future as we pursue our mission of democratizing data fluency for everyone.</p>
<p>Let’s take a closer look.</p>
<p>Our old logo just wasn’t working for us: The image was confusing, contained a lot of detail, and didn’t work well in smaller formats.</p>
<p><center><a href="https://www.datacamp.com"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603223608/DC_Old_f89eiw.png" /></a></center><br></p>
<p>Each learning journey is unique. Our new logo—while depicting the “D” and “C” of DataCamp—represents the different directions each learning journey can take.</p>
<p><center><a href="https://www.datacamp.com"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603223608/DC_New_mugdv8.png" /></a></center><br></p>
<p>The new logo is simple, modern, and dynamic, and lends itself to new and creative ways to visually represent our brand.</p>
<p><center><a href="https://www.datacamp.com"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1602853788/3_zdz9jo.gif" /></a></center><br></p>
<p>Our new color palette is an evolution of our previous brand colors, and embodies a more vibrant and energetic spirit.</p>
<p><center><a href="https://www.datacamp.com"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1602853785/4_cw5tot.jpg" /></a></center><br></p>
<p>The typography is crisp and modern, yet friendly and warm.</p>
<p><center><a href="https://www.datacamp.com"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1602853783/5_uqi3ry.jpg" /></a></center><br></p>
<p>Inspired by data, our new visual style offers an endless array of playful shapes, colors, and forms that celebrate the many ways we can visualize data.</p>
<p><center><a href="https://www.datacamp.com"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1602854102/6_vhvl9e.jpg" /></a></center><br></p>
<p><center><a href="https://www.datacamp.com"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1602854099/7_prx0fw.jpg" /></a></center><br></p>
<p><center><a href="https://www.datacamp.com"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1602854096/8_zclb33.jpg" /></a></center><br></p>
<p>This fresh new look reflects DataCamp today: bold, unique, forward-looking. DataCamp continues to provide the same best-in-class experience for learners and organizations building data skills for the future—we’re just doing it in style.</p>
<p>—The DataCamp Team</p>


https://www.datacamp.com/community/blog/new-brand

This week we launched a fresh new DataCamp look.

Meet the new DataCamp! 

<p><em>This article was written by <a href="https://www.linkedin.com/in/rosanaogomes/">Rosana de Oliveira Gomes</a>, Lead Machine Learning Engineer at Omdena, and <a href="https://www.linkedin.com/in/josephitopa/">Joseph Itopa A</a>, Junior Machine Learning Engineer at <a href="https://omdena.com/community">Omdena</a>.</em></p>
<p>Transitioning into a new career can feel like boarding a plane that’s already taking off. The data science profession is relatively new, which means that many data scientists and machine learning engineers didn’t start their careers on this path. They switched from other fields like we did, and perhaps like many of you reading this.
So let’s talk about what tools and skills you need to transition into data science—we’ll highlight possible challenges and give you practical advice on how to overcome them.</p>
<p><center><a href="https://www.datacamp.com/groups/business"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1602824693/shutterstock_1702515205_dqjydl.jpg" /></a></center><br></p>
<h2 id="technical-challenges-and-how-to-overcome-them">Technical challenges and how to overcome them</h2>
<h3 id="1-brush-up-on-math-and-programming">1. Brush up on math and programming</h3>
<p>Data science doesn’t require advanced math knowledge beyond what’s required for any science degree. But every artificial intelligence algorithm is based on some mathematical structure which you will need to understand. This usually involves linear algebra and some concepts from calculus. To interpret results, you will need to conduct statistical analysis, which also requires knowledge of probability and statistics.</p>
<p>While math provides the concepts, programming languages are the tools to make those concepts tangible. This means that you have to <a href="http://datacamp.com/community/blog/technologies">choose a programming language</a> to learn, which in this field usually is either <a href="https://www.datacamp.com/community/blog/when-to-use-python-or-r">Python or R</a>, likely combined with SQL and Bash. A <a href="https://www.kdnuggets.com/2018/05/poll-tools-analytics-data-science-machine-learning-results.html">KDnuggets</a> poll on programming software used by data scientists reveals that Python has surpassed R as the tool of choice.</p>
<p>But the choice of a programming language essentially boils down to the task at hand and style preferences. Python is easy to pick up for someone who has experience with programming, and is widely used across industries and specialties such as data science and machine learning. R is a good choice too if you have a statistics background and will be working mostly on analysis. It also has built-in tools and libraries to communicate results through reports. Our advice is to stick with one language and start building something after you are done with the basics.</p>
<p>Speaking from experience, we’ve learned that to acquire the necessary skills in data science, you should choose only one learning provider at a time and stick with it. The worst thing you can do is to keep learning the same things over and over.</p>
<h3 id="2-become-a-problem-solver">2. Become a problem solver</h3>
<p>You can view the science of data science as the ability to solve problems with creative and logical thinking. This requires knowledge of programming and an understanding of algorithms gained through practice.</p>
<p>After acquiring some basic knowledge of programming, you can begin solving real-world problems by practicing via courses or platforms. <a href="https://www.geeksforgeeks.org/">GeeksforGeeks</a> provides hands-on projects for competitive coding, Python, JAVA, and SQL. Solving some <a href="https://www.kaggle.com/">Kaggle</a> competition problems can also boost your problem-solving skills, as you can easily leverage real-world data to practice with and find a lot of help in the community. DataCamp’s <a href="https://www.datacamp.com/community/blog/unguided-projects">unguided projects</a> are a great way to find your own solutions to open-ended projects.</p>
<p>It’s important that you find enjoyment in these accomplishments to pursue a data science career. In a <a href="https://omdena.com/blog/learn-data-science/">recent Omdena webinar</a>, data science influencer Eric Weber said, “Don’t optimize for income only but for what brings you joy; otherwise, you may burn out quickly.”</p>
<h3 id="3-join-collaborative-projects">3. Join collaborative projects</h3>
<p>After working with some algorithms and practicing on a few projects, you will be ready for more advanced projects. This is where collaborative platforms come in. Collaborative data science programs rely on communities to develop projects in a diverse and productive way.
The ruggedness and fun in <em>street coding</em> is best found in collaborative projects. You will learn from others as they learn from you. You will make friends while struggling with unstructured and messy data—there’s nothing like it.</p>
<p>Inspiring collaborative initiatives include <a href="https://www.datakind.org/">Data Kind</a>, <a href="http://www.s2ds.org/">Science to data science</a>, and <a href="https://www.dssgfellowship.org/">Data Science for Social Good</a>. These options are often location-specific and may be costly or competitive due to limited availability and an extensive application process.</p>
<p>An alternative to collaborative projects is <a href="https://omdena.com/">Omdena</a>, which launches several projects per month and applies a principle of volunteering to solve real-world problems through online collaboration. Learners work with domain experts who will help them stay motivated with webinars, courses, books, and blog posts.</p>
<p><center><a href="https://www.datacamp.com/groups/business"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1602824647/shutterstock_1324818206_jltvty.jpg" /></a></center><br></p>
<h2 id="potential-non-technical-challenges-and-how-to-overcome-them">Potential non-technical challenges and how to overcome them</h2>
<h3 id="1-organization-and-time-management-skills">1. Organization and time management skills</h3>
<p>Changing careers is a project. It requires a strategic plan, a timeline, and specific (and realistic) milestones.
Ask yourself these questions:</p>
<ul>
<li>Why do I want to be a data scientist and what subjects am I passionate about?</li>
<li>Will I quit my job and take the time to learn the skills I need or will I make the transition in parallel with my current work?</li>
<li>What am I good at? What are my weaknesses?</li>
<li>How much time and money am I willing to spend on changing careers?</li>
<li>What are the new skills and qualifications that I need to acquire for the new career path?</li>
<li>What is my learning style?</li>
</ul>

<p>Once you answer these truthfully, you need a plan. In the end, you have to find what works for you. Here are some things you can do:</p>
<ul>
<li>Print a weekly plan and stick it on your wall.</li>
<li>Organize your daily schedule on a Google calendar.</li>
<li>Identify what works and what needs to be readjusted in your plan. Maybe you underestimated how much time you needed for a course or the learning resource you chose is not quite as you imagined. </li>
<li>Don’t be afraid to change your strategy. Go to online forums and search for a better resource. Or read some articles on time management and read on how to set <a href="https://www.mindtools.com/pages/article/smart-goals.htm">SMART goals</a>.</li>
<li>Keep checking tasks off your to-do list. This momentum will push you out of that initial frustration period of learning something new.</li>
</ul>

<h3 id="2-become-a-better-communicator">2. Become a better communicator</h3>
<p>Another must-have skill for data science is communication. A data scientist translates tons of data into actionable insights for decision makers and stakeholders. However, not all the people that you will need to communicate with are data scientists or have a background in STEM. If you’re an introvert, you may not be super excited about public speaking or constant communication. Communication in data science is not only about being a good speaker but also about building these habits and skills:</p>
<ul>
<li>Provide good explanations of complex concepts to non-experts.</li>
<li>Document code and analysis so other team members can learn from your work in the future (or even your future self!).</li>
<li>Become a good writer by making a habit of writing about everything you do in your career path.</li>
</ul>
<p>Documentation is power, so write and keep writing. In the very near future, you will need to demonstrate the skills that you put on your resume, and you can back them via nicely documented code repositories (for example, on <a href="https://github.com/">GitHub</a>), blog posts, and webinars or talks about your work. The earlier you start, the faster you’ll improve.</p>

<h3 id="3-be-prepared-to-fight-imposter-syndrome">3. Be prepared to fight imposter syndrome</h3>
<p>One common challenge experienced by many career movers is imposter syndrome. It’s never easy having an established career and suddenly becoming a newbie. In this case, it’s all a matter of mindset: keep yourself motivated and excited about all the new things you are about to learn! Omdena has hosted a <a href="https://omdena.com/blog/impostor-syndrome-data-science/">webinar</a> on how to overcome imposter syndrome as a data scientist, which includes knowing the skills gap that you need to fill and identifying the skills you already learned from your previous career.</p>

<h3 id="4-build-your-network">4. Build your network</h3>
<p>Networking on your job hunt doesn’t have to be awkward. In fact, statistically, the majority of job opportunities come from an individual’s network, not from applications (check the references <a href="https://www.businessinsider.com/at-least-70-of-jobs-are-not-even-listed-heres-how-to-up-your-chances-of-getting-a-great-new-gig-2017-4?r=DE&amp;IR=T">here</a> and <a href="https://www.linkedin.com/pulse/study-reveals-85-jobs-filled-networking-sandy-hutchison">here</a>). When you network, you’re simply connecting to people who have similar interests as you, getting their take on data science topics, and getting a peek into their careers. <em>These people may become your future colleagues.</em> The absolute worst thing that can happen is not getting any response—what do you have to lose?</p>

<p>One of the easiest ways to network is through collaborative projects, where you have the opportunity to share knowledge, work with experienced practitioners, and gain insights from people in different roles, and find leads for jobs.</p>

<p>Another good way to network is by following authorities in data science such as:</p>
<ul>
<li><a href="https://omdena.com/">Omdena</a></li>
<li><a href="https://www.womenwhocode.com/">Women Who Code</a></li>
<li><a href="https://www.womeninai.co/">Women in AI</a></li>
<li><a href="https://www.superdatascience.com/">Super data science</a></li>
<li><a href="https://support.datacamp.com/hc/en-us/articles/360001915993-The-DataCamp-Slack-Community-An-Overview">DataCamp Slack Community</a></li>
</ul>

<p>Contact any interesting people you encounter—a speaker from a podcast you liked, a teacher from an online course, or a blogger whose posts you enjoy. The golden rule is: <strong><em>Put yourself out there and ask questions.</em></strong> That’s the best way to get feedback.</p>
<p><em>Joseph is currently transitioning into data science from Engineering, and Rosana from Astrophysics. Contact them and continue this conversation on LinkedIn: <a href="https://www.linkedin.com/in/josephitopa/">Joseph</a> and <a href="https://www.linkedin.com/in/rosanaogomes/">Rosana</a>.</em></p>


https://www.datacamp.com/community/blog/switching-into-data-science

Six powerful lessons we wish we’d known before switching into data science six months ago.

How to Switch Into a Data Science Career

<p>By now, you know about all the benefits of SQL and are a seasoned data analyst, data engineer, or data scientist. You know about the different SQL dialects, you’re proficient in importing SQL data, and you’re fluent in writing basic SQL queries. But do you want to grow your skills to the next level? Here’s how.</p>
<h2 id="intermediate-sql-concepts">Intermediate SQL concepts</h2>
<h3 id="know-the-sql-sublanguages">Know the SQL sublanguages</h3>
<p>There are <a href="https://www.quora.com/What-are-the-SQL-sub-languages">four primary sublanguages</a> in SQL—they refer to different tasks, but are all part of the SQL language:</p>
<h4 id="1-ddl-data-definition-language-">1. DDL (Data Definition Language)</h4>
<p>These statements are used to create tables and databases and define field properties or table properties. Examples of commands in this category include CREATE, ALTER, and DROP.</p>
<h4 id="2-dml-data-manipulation-language-">2. DML (Data Manipulation Language)</h4>
<p>The statements that fall under this category are used to update data or add or remove data from tables. Commands in this category include UPDATE, DELETE, and INSERT.</p>
<h4 id="3-dcl-data-control-language-">3. DCL (Data Control Language)</h4>
<p>These SQL statements are used to control who accesses the data. The commands that fall under this category are GRANT and REVOKE.</p>
<h4 id="4-tcl-transaction-control-language-">4. TCL (Transaction Control Language)</h4>
<p>This language is used to commit data and restore data. COMMIT and ROLLBACK fall under this category.</p>
<h4 id="5-dql-data-query-language-">5. DQL (Data Query Language)</h4>
<p>This is to retrieve data from SQL server. SELECT statement falls into this category.</p>
<p>These may look intimidating, but don’t worry! SQL is a very elegant, straightforward language to learn, especially because you cannot use SQL to create data visualizations—it’s all about creating tables, organizing and cleaning data, and learning to ask the right questions.</p>
<h3 id="unions-and-joins">Unions and joins</h3>
<p>A lot of the time, the data you need will not be stored in one giant table but will be spread out across multiple tables. Using SQL, you can merge these tables together to combine all the data you need in one place. Since UNION and JOIN deal with tables, they go in the FROM statement.</p>
<p>A UNION will basically stack two tables with identical columns on top of each other. This is useful for sales transaction data that may be broken into different tables by month, quarter, or year.</p>
<p>There are several kinds of commonly used JOINS—Inner, Outer, Left, and Exception. To put it simply, these will return different combinations of rows from the tables being joined together.</p>
<p>Additional resources:</p>
<ul>
<li><a href="https://www.w3resource.com/sql/joins/sql-joins.php">SQL Joins | Mode</a></li>
<li><a href="https://www.diffen.com/difference/Inner_Join_vs_Outer_Join | Diffen">Inner vs. Outer Joins | Diffen</a></li>
</ul>

<h2 id="intermediate-sql-syntax">Intermediate SQL syntax</h2>
<p>By now, you know the basics of <a href="https://www.w3schools.com/sql/sql_syntax.asp">SQL syntax</a>, including the functions SELECT, FROM, WHERE, GROUP BY, HAVING, ORDER BY, and LIMIT. You’re proficient in <a href="https://www.datacamp.com/courses/database-design">Database Design</a> and you also understand the <a href="https://www.datacamp.com/community/blog/how-to-learn-sql">SQL order of execution</a>—that queries are written differently from how they’re processed.</p>
<p>Now it’s time to learn more advanced syntax.</p>
<h3 id="aggregations-select-group-by-and-having">Aggregations: SELECT, GROUP BY, and HAVING</h3>
<p>Similar to Excel, you can use SQL to aggregate your data by sum, average, maximum, minimum, and count. Aggregations can be useful when initially exploring your data, analyzing, or when presenting it to your audience. You can do these in the SELECT statement to return a simple aggregation, or use a GROUP BY statement to create an Excel “pivot table” out of your data.</p>
<p>Using aggregations in the SELECT statement will return just a simple calculation, for instance, if you were using SUM(column_name), your query would return one value that represent all the values in that column added up.</p>
<p>You can also introduce aggregations by categorical data. For instance, if you had a table of fruit sales data across the country, you could group the sum of all sales by product type, or the average price by state. This requires a GROUP BY statement, which comes after the WHERE statement. You will need to specify the categorical column you want your aggregations to be “grouped by”, and then include those columns in your select statement as well.</p>
<p>After your data has been neatly grouped, with an aggregation paired to a categorical variable, you can then filter on those aggregations by using a HAVING statement, which comes after GROUP BY. This is useful if you wanted to narrow your query even further, for example, if you wanted to select only those fruit stores with an average sales total over $100,000.</p>
<p>Additional resources:</p>
<ul>
<li><a href="https://www.geeksforgeeks.org/sql-string-functions/">SQL | String Functions | GeeksforGeeks</a></li>
<li><a href="https://mode.com/sql-tutorial/sql-aggregate-functions/">SQL Aggregate Functions | Mode</a></li>
<li><a href="https://www.w3schools.com/sql/sql_groupby.asp">SQL GROUP BY | W3 Schools</a></li>
<li><a href="https://www.tutorialspoint.com/sql/sql-having-clause.htm">SQL HAVING | TutorialsPoint</a></li>
</ul>

<h3 id="case-statements">Case statements</h3>
<p>A SQL CASE statement is similar to an Excel IF() function—if the data in a column matches a set criteria, then return “this”. This can be useful when reclassifying quantitative data as categorical, such as when breaking down a set of values into “High”, “Medium”, or “Low”. CASE statements go in the SELECT statement.</p>
<p>See also:</p>
<ul>
<li><a href="https://mode.com/sql-tutorial/sql-case/">Case statements | Mode</a></li>
</ul>

<h3 id="subqueries">Subqueries</h3>
<p>Subqueries may be used in several different ways, but are usually found in either the FROM or the WHERE statement. They create a small, temporary table from your data that you can then use as a new table to query from in a different way.</p>
<p>See also:</p>
<ul>
<li><a href="https://www.essentialsql.com/get-ready-to-learn-sql-server-22-using-subqueries-in-the-from-clause/">Subqueries | Essential SQL</a></li>
</ul>

<h3 id="dates-and-times">Dates and times</h3>
<p>As with many other data languages, dealing with dates and times can be tricky. Sometimes dates act like “strings”, or pieces of information. Sometimes they can be treated as actual dates, with SQL syntax that can break the data down into units such as months or years. Since there are many different ways to do this, these functions are considered more advanced SQL syntax.</p>
<p>See also:</p>
<ul>
<li><a href="https://www.sqlite.org/lang_datefunc.html">Date and Time Functions | SQLite</a></li>
</ul>

<h2 id="advance-your-sql-thinking">Advance your SQL thinking</h2>
<h3 id="sql-guidelines-to-follow">SQL guidelines to follow</h3>
<p>When you’re working in the real world, business questions won’t likely translate neatly into SQL queries. That’s why you need to carefully assess what’s needed before even writing a single line of code. Here are some good guidelines to follow when practicing SQL.</p>
<h4 id="rewrite-your-business-question-as-a-comment-first">Rewrite your business question as a comment first</h4>
<p>Use code commenting to “brainstorm” your query first—what is the question you’re trying to answer? What pieces and elements do you need to focus on? This will guide your SQL query.</p>
<p>There are two kinds of commenting, inline and multi-line.</p>
<p>--Inline is just two hyphens and it lives on one line of code—good for short, quick notes</p>
<p>/*Multi-line commenting looks like this.</p>
<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;It’s good for creating banners that explain a query’s purpose, author, etc*/</p>
<h4 id="sketch-out-your-query">Sketch out your query</h4>
<p>Before you even start typing, you should figure out what pieces you’ll need for your query. You will always need a SELECT and FROM statement—but what else might you need? Do you need to filter the data using a WHERE statement? Will you need to use aggregate functions?</p>
<h4 id="understand-the-different-ways-to-achieve-your-objective">Understand the different ways to achieve your objective</h4>
<p>When possible, try to have multiple ways of doing the same thing. That way, you can quality check yourself and validate if you can get the same results. For example, you can find the highest value in a column either by using the MAX() function in the STATEMENT, or by using ORDER BY to sort the data and then LIMIT to limit so only one row is returned.</p>
<h4 id="test-your-query">Test your query</h4>
<p>Build your query one line at a time and run it frequently to check that it works. Adding pieces one at a time also helps prevent you from having to go back through your code line by line in case it doesn’t work.</p>
<h4 id="profile-your-table">Profile your table</h4>
<p>In order to start performing queries, you first must understand the data elements within it. Start out by viewing the entire database; then, a few columns at a time. Find the range of values, both categorical and quantitative, to get an idea of what information is contained in the table.</p>
<h4 id="keep-a-list-of-assumptions">Keep a list of assumptions</h4>
<p>When writing any kind of data code, you should always make a list of assumptions to easily track your limitations and dependencies.</p>
<h4 id="keep-a-data-dictionary">Keep a data dictionary</h4>
<p>Compile a central repository for the data elements you’re using, containing descriptions of the different tables and fields used within the analysis. This can include the different data types in each column (character, integer, money, date, etc), and a brief description of the column itself.</p>
<h4 id="always-allocate-time-to-perform-data-quality-checks">Always allocate time to perform data quality checks</h4>
<p>The analysis doesn’t end when you’ve finished writing your query. You still have to understand your results and perform quality checks to make sure your analysis is correct. Try different ways of achieving the same result to see if you get something different—this is another time when inline commenting comes in handy!</p>
<h3 id="practice-in-a-sandbox">Practice in a sandbox</h3>
<p>One of the best ways to learn any coding language is to find a “sandbox” to play around in. You’ll be able to test out your code, see if it runs, modify it, run it again, over and over until you feel comfortable with the coding principles and syntax. To start out, you may want a little more guidance—try finding a project with clear instructions and solution code to check your work.</p>
<p>Here are a few good practice resources:</p>
<ul>
<li><a href="https://www.hackerrank.com/domains/sql">Solve SQL | HackerRank</a></li>
<li><a href="http://sqlfiddle.com/">SQL Fiddle</a></li>
<li><a href="http://mystery.knightlab.com/">The SQL Murder Mystery | Knightlab</a></li>
</ul>

<p>That’s all for now. Hope this was useful to you—keep practicing and learning!</p>


https://www.datacamp.com/community/blog/blog-how-to-become-a-sql-expert

Here’s a comprehensive guide on more advanced SQL concepts and syntax.

How to Become a SQL Expert

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<p>On October 15, we&#39;re hosting a webinar on <a href="https://www.datacamp.com/webinars/what-l-and-d-leaders-need-to-know-about-data-fluency">What L&amp;D Leaders Need to Know About Data Fluency</a>. One key focus area is building the right skills for each of your key data personas. Here&#39;s a sneak preview on what you&#39;ll learn.</p>
<h2 id="the-challenges-in-upskilling-for-data-fluency">The challenges in upskilling for data fluency</h2>
<p>Data fluency is a methodology for answering business questions rather than a singular skill to be taught and learned, like traditional learning and development initiatives. When scaling learning and development programs for data fluency, learning journeys will vary depending on the level of interaction different individuals may have with data. For example, a marketing analyst who regularly works with Excel may need to learn R or Python to succeed at their job, while a manager or leader may only need to know how to make educated decisions using data.</p>
<h2 id="why-a-persona-driven-methodology-is-a-way-to-go">Why a persona-driven methodology is a way to go</h2>
<p>A role-based, persona-driven learning journey is more effective at scaling data fluency training programs. While each organization and the data they produce is different, there are commonalities in the different relationships individuals have with data. A useful way of thinking about and scaling data-focused upskilling efforts is with data personas. Each data persona has a different relationship with data and requires different data fluency competencies to become empowered to do their best work. Organizations can then map different roles within the organization to that persona and create a curated, personalized learning experience depending on what they need to learn.</p>
<h3 id="1-data-consumers-and-leaders">1. Data Consumers and Leaders</h3>
<p>Data Consumers and Leaders often work in non-technical roles, but they consume data insights and analytics to make data-driven decisions. They often need to have conversations with data professionals and should be able to distinguish when data can and cannot be used to answer business questions.</p>
<h4 id="commonly-used-technology-and-tools">Commonly used technology and tools</h4>
<p>Spreadsheets: Google Sheets, Microsoft Excel
Business Intelligence: Power BI, Tableau</p>
<h4 id="example-job-titles">Example job titles</h4>
<p>Chief Marketing Officer, Human Resources Manager, Head of Sales</p>
<h3 id="2-business-analysts">2. Business Analysts</h3>
<p>Business Analysts are responsible for tying data insights to actionable results that increase profitability or efficiency. They have deep knowledge of the business domain and often use SQL alongside non-coding tools to communicate insights derived from data.</p>
<h4 id="commonly-used-technology-and-tools">Commonly used technology and tools</h4>
<p>Spreadsheets: Google Sheets, Microsoft Excel
Business Intelligence: Power BI, Tableau
SQL: PostgreSQL, SQL Server, Oracle SQL</p>
<h4 id="example-job-titles">Example job titles</h4>
<p>Business Analyst, Supply Chain Analyst, Operations Analyst, Financial Analyst</p>
<h3 id="3-data-analysts">3. Data Analysts</h3>
<p>Similar to Business Analysts, Data Analysts are responsible for analyzing data and reporting insights from their analysis. They have a deep understanding of the data analysis workflow and report their insights through a combination of coding and non-coding tools.</p>
<h4 id="commonly-used-technology-and-tools">Commonly used technology and tools</h4>
<p>Programming languages: Python, R
Spreadsheets: Google Sheets, Microsoft Excel
Business Intelligence: Power BI, Tableau
SQL: PostgreSQL, SQL Server, Oracle SQL</p>
<h4 id="example-job-titles">Example job titles</h4>
<p>Data Analyst, Business Analyst, Supply Chain Analyst, Operations Analyst, Financial Analyst</p>
<h3 id="4-data-scientists">4. Data Scientists</h3>
<p>Data Scientists investigate, extract, and report meaningful insights with the organization’s data. They communicate these insights to non-technical stakeholders and have a good understanding of machine learning workflows and how to tie them back to business applications. They work almost exclusively with coding tools, conduct analysis, and often work with big data tools.</p>
<h4 id="commonly-used-technology-and-tools">Commonly used technology and tools</h4>
<p>Programming languages: Python, R, Scala
SQL: PostgreSQL, SQL Server, Oracle SQL
Big data tools: Airflow, Spark</p>
<h4 id="example-job-titles">Example job titles</h4>
<p>Data Scientist, Data Analyst, can include a “citizen data scientist” (i.e., someone who performs the tasks of a data scientist, but does not have the title “Data Scientist”).</p>
<h3 id="5-machine-learning-scientists">5. Machine Learning Scientists</h3>
<p>Machine Learning Scientists are responsible for developing machine learning systems at scale. They derive predictions from data using machine learning models to solve problems like predicting churn and customer lifetime value, and are responsible for deploying these models for the organization to use.</p>
<h4 id="commonly-used-technology-and-tools">Commonly used technology and tools</h4>
<p>Programming languages: Python, R, Scala
SQL: PostgreSQL, SQL Server, Oracle SQL
Big data tools: Airflow, Spark
Command-line tools: Git, Shell</p>
<h4 id="example-job-titles">Example job titles</h4>
<p>Data Scientist, Research Scientist, Machine Learning Scientist, Machine Learning Engineer</p>
<h3 id="6-statisticians">6. Statisticians</h3>
<p>Similar to Data Scientists, Statisticians work on highly rigorous analysis, which involves designing and maintaining experiments such as A/B tests and hypothesis testing. They focus on quantifying uncertainty and presenting findings that require exceptional degrees of rigor, like in finance or healthcare.</p>
<h4 id="commonly-used-technology-and-tools">Commonly used technology and tools</h4>
<p>Programming languages: Python, R
SQL: PostgreSQL, SQL Server, Oracle SQL</p>
<h4 id="example-job-titles">Example job titles</h4>
<p>Quantitative Analyst, Inference Data Scientist, Data Scientist</p>
<h3 id="7-programmers">7. Programmers</h3>
<p>Programmers are highly technical individuals that work on data teams and work on automating repetitive tasks when accessing and working with an organization’s data. They bridge the gap between traditional software engineering and data science and have a thorough understanding of deploying and sharing code at scale</p>
<h4 id="commonly-used-technology-and-tools">Commonly used technology and tools</h4>
<p>Programming languages: Python, R, Scala
Command-line tools: Git, Shell</p>
<h4 id="example-job-titles">Example job titles</h4>
<p>Software Engineer, Data Scientist, Dev-Ops Engineer</p>
<h3 id="8-data-engineers">8. Data Engineers</h3>
<p>Data Engineers are responsible for getting the right data in the hands of the right people. They create and maintain the infrastructure and data pipelines that take terabytes of raw data coming from different sources into one centralized location with clean, relevant data for the organization.</p>
<h4 id="commonly-used-technology-and-tools">Commonly used technology and tools</h4>
<p>Programming languages: Python, R, Scala
SQL: PostgreSQL, SQL Server, Oracle SQL
Command-line tools: Git, Shell
Big data tools: Airflow, Spark
Cloud Platforms (e.g., Amazon Web Services)</p>
<h4 id="example-job-titles">Example job titles</h4>
<p>Software Engineer, Data Engineer, Dev-Ops Engineer</p>
<h2 id="going-beyond-personas">Going beyond personas</h2>
<p>Personas only scratch the surface of how to scale data fluency learning and development programs. If you want to learn more about data personas, data fluency competency areas, and what initiatives learning and development leaders should apply while scaling data fluency programs, <a href="https://www.datacamp.com/webinars/what-l-and-d-leaders-need-to-know-about-data-fluency">join our webinar on October 15th.</a></p>
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https://www.datacamp.com/community/blog/eight-personas

Scaling data fluency learning programs successfully is no small feat. Learn about data personas and how a persona-driven methodology can help you scale your data training program.

Eight Personas Found in Every Data-Driven Organization

<p><center><a href="https://www.datacamp.com/groups/business"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603720084/nautilus_brnaku.png" /></a></p>
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<h2 id="data-transformation-underpins-digital-transformation">Data transformation underpins digital transformation</h2>
<p>Over the past two decades, digital-first startups such as Uber, Amazon, Airbnb, and Stripe have disrupted vital industries such as transportation, commerce, travel, and banking. Organizations across all industries have recognized the need for digital transformation to compete in the new information economy. This is especially true of the COVID-19 economy, which has been accelerating the digitization of their processes and services (<a href="https://www.pwc.com/sk/en/current-press-releases/the-covid-19-crisis-has-accelerated-the-digital-transformation.html">PwC</a>). Despite massive digitalization investments, the painful truth is that approximately 70% of digital transformation initiatives fail to reach their stated goal (<a href="https://www.mckinsey.com/industries/retail/our-insights/the-how-of-transformation">McKinsey</a>).</p>
<p>While there may be many culprits for why digital transformation programs may fail, a key reason is not recognizing that having sustainable data transformation is a prerequisite for successful digital transformation. <a href="https://www.gartner.com/en/newsroom/press-releases/2019-10-23-gartner-says-traditional-data-and-analytics-strategies-cannot-satisfy-digital-business-demands">Gartner</a> finds that fewer than 50% of documented corporate strategies mention data analytics as a critical lever for delivering enterprise-wide value.</p>
<blockquote>
<p>Leaders need to look at data first to succeed in their digital initiatives, rather than treating them as an afterthought to help with ad hoc projects. —<a href="https://www.gartner.com/analyst/37069">Mike Rollings</a>, Research Vice President at Gartner</p>
</blockquote>
<p>There are many levers for <a href="https://www.datacamp.com/resources/webinars/scaling-data-science-at-your-organization-part-1">successful data transformation</a>—from investing in data infrastructure, to data tools, to improved processes and agile organizational structures. However, none of these levers would be effective at ushering in data transformation without investing in <a href="https://hbr.org/2020/05/digital-transformation-is-about-talent-not-technology">organization-wide data fluency skills</a>.</p>
<p>Many organizations have tried to cultivate data fluency skills by creating data science and analytics teams. But merely hiring data scientists isolates data science as a service center, and won’t usher in organization-wide data fluency. Moreover, as <a href="https://business.linkedin.com/content/dam/me/business/en-us/talent-solutions/emerging-jobs-report/Emerging_Jobs_Report_U.S._FINAL.pdf">demand for data scientists grows</a>, hiring one’s way out of addressing the data fluency skills gap is unsustainable.</p>
<p>In a data-driven organization, data science—and more broadly, data fluency—is an inclusive methodology for answering organizational questions where everyone is equipped to answer questions with data. The key differentiators between the disruptors and the incumbents is not technology-based but in their data-driven culture, the insights they draw from data while examining and iterating upon their services, and the data fluency skills they foster.</p>
<p>In short, the success of your digital transformation pivots on having the appropriate data fluency skills across the organization.</p>
<h2 id="addressing-the-data-fluency-skills-gap">Addressing the data fluency skills gap</h2>
<p>Indeed, many organizations have recognized the need to address their data fluency skill gaps. A <a href="https://www.mckinsey.com/business-functions/organization/our-insights/beyond-hiring-how-companies-are-reskilling-to-address-talent-gaps">McKinsey survey</a> of over a thousand businesses from various industries found that the most pressing skill gap to be addressed was data analytics—with 43% of respondents believing it to be the most urgent priority when it comes to upskilling. Similarly, <a href="https://www.pwc.com/gx/en/ceo-survey/2019/report/pwc-22nd-annual-global-ceo-survey.pdf">PwC’s 2019 annual CEO survey</a> found that 34% of CEOs believe skill gaps in data analytics are the most crucial threat for their organization.</p>
<p>Forward-thinking organizations are already pouring in investments to upskill their people to compete in the digital age. For example, <a href="https://corporate.marksandspencer.com/media/press-releases/2018/marks-and-spencer-partners-with-decoded-to-create-retail-data-academy">Marks &amp; Spencer</a> created a retail data academy to upskill over 1,000 employees. Amazon launched a <a href="https://aws.amazon.com/blogs/machine-learning/amazons-own-machine-learning-university-now-available-to-all-developers/">Machine Learning University</a> to equip their engineers with the skills needed to deploy machine learning at scale in their products and services. Airbnb developed its own <a href="https://medium.com/airbnb-engineering/how-airbnb-democratizes-data-science-with-data-university-3eccc71e073a">Data University</a> to provide every level of the organization with the skills to make data-driven decisions. <a href="https://hbr.org/2016/10/atts-talent-overhaul">AT&amp;T</a> embarked on a $1 billion, 10-year long project to upskill more than half of its 250,000 people workforce.</p>
<p>On aggregate, data fluency upskilling efforts are paying off. Another <a href="https://www.mckinsey.com/business-functions/organization/our-insights/beyond-hiring-how-companies-are-reskilling-to-address-talent-gaps">McKinsey study</a> found that 70% of organizations that invested in upskilling efforts are reporting positive business impacts that exceed the initial investment in upskilling. For example, 48% of organizations have reported moderate to significant positive effects on bottom-line growth due to upskilling—and 73% of organizations have reported moderate to substantial improvements in employee satisfaction. A <a href="https://www.datacamp.com/resources/whitepapers/what-300-l-and-d-leaders-have-learned-about-data-fluency">DataCamp survey of over 300 L&amp;D leaders</a> across industries found that companies that have invested in mature data fluency competencies are also outperforming their less data fluent counterparts in revenue growth, market share, profitability and more.</p>
<h2 id="the-challenges-of-l-d-when-building-data-fluency">The challenges of L&amp;D when building data fluency</h2>
<p>Unlike traditional learning &amp; development initiatives that often take the shape of one-off training for specific skills, data fluency is a methodology for answering business questions that organizations should hone over time. Learning journeys will vary depending on the level of interaction different individuals may have with data. For example, a marketing analyst who regularly works with Excel may need to learn R or Python to succeed at their job, while a manager or leader may only need to know how to make educated decisions using data. In short, there are no one-size-fits all when it comes to data fluency.</p>
<p>This is why a role-based, persona-driven learning journey is more effective at scaling data fluency training programs. Every persona has a different relationship with data and would need to acquire competencies in different tools, and grow different skills to thrive in the digital age.</p>
<p>At DataCamp, we have identified the different types of tools needed to succeed when working with data. More importantly, we have found 8 key data personas found in every data-driven organization alongside the different skills they need to possess, and the tools they most commonly use.</p>
<p>If you want to learn more about these data personas and how they could fit into your organization, download our white paper, <a href="https://www.datacamp.com/resources/whitepapers/l-and-d-guide-to-data-fluency">The L&amp;D Guide to Data Fluency</a>.</p>
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https://www.datacamp.com/community/blog/blog-data-upskilling

Scaling organization-wide data fluency training is not an easy task. Learn how to scale your data program and ensure the success of your digital transformation initiatives. 

Why Data Upskilling is the Backbone of Digital Transformation

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<p>One of the main reasons behind the rapid innovation and advances in data science has been the proliferation of open-source code-centric tools. This has allowed data scientists and software engineers to build powerful abstractions that make it really easy to analyze data.</p>
<p>For example, R packages like dplyr and tidymodels allow users to manipulate data and build sophisticated machine learning models with a few lines of code. Similarly, Python packages like tensorflow and pytorch allow anyone to build a deep learning model with little effort.</p>
<p>While code-driven tools have served data work well, the future of data work will be much broader, and involve graphical user interfaces (GUIs) that will let a broader audience engage in data science using point-and-click tools. Note that this does not by any means imply the death of the coding data scientist. Similar to the industrial revolution, this move will allow coders to redirect their focus to solving more complex problems, and building interfaces to tackle problems, leaving the domain-specific work to the domain experts. This is already happening across different slices of data work: descriptive, predictive, and prescriptive analytics.</p>
<h2 id="descriptive-analytics-is-accessible-with-business-intelligence">Descriptive analytics is accessible with business intelligence</h2>
<p>Descriptive analytics has been made accessible to a wider audience by the drag-and-drop business intelligence (BI) tools that are everywhere now. Just look at last year’s $2.6 billion acquisition of Looker, a very popular BI tool, by none other than Google. Also last year, Salesforce completed the acquisition of Tableau, another very popular BI tool. Microsoft has invested in its own tool, Power BI. (DataCamp offers courses on both <a href="https://datacamp.com/courses/introduction-to-tableau">Tableau</a> and <a href="https://datacamp.com/courses/introduction-to-power-bi">Power BI</a>.)</p>
<p>Business intelligence tools make data discovery accessible for all skill levels—not just advanced analytics professionals. They are one of the simplest ways to work with data, providing the ability to collect data in one place, gain insight into what will move the needle, forecast outcomes, and much more.</p>
<h2 id="how-drag-and-drop-interfaces-reduce-the-barrier-to-entry-for-predictive-and-prescriptive-analytics">How drag-and-drop interfaces reduce the barrier to entry for predictive and prescriptive analytics</h2>
<p>The democratization of data work is evolving to also include predictive and prescriptive analytics. For example, <a href="https://www.alteryx.com/">Alteryx</a> is a $10 billion dollar company that provides a drag-and-drop data science platform that allows business analysts to manipulate data and build predictive models using a drag-and-drop interface.</p>
<p>Google is investing a huge amount in their AutoML tool, which makes “AI as simple as drag and drop.” We see that <a href="https://techcrunch.com/2019/05/02/microsoft-launches-a-drag-and-drop-machine-learning-tool-and-hosted-jupyter-notebooks/">Microsoft is also entering the space</a>:</p>
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<p>Above is an example of a basic regression model with a data pipeline. You can see that the data flows from rectangle to rectangle. First data is ingested, then columns are selected, the data is cleaned, and the model splits and trains the data.</p>
<p>There are many potential use cases for drag-and-drop machine learning. This can be reframed as: what type of people in an organization would want to use ML tools that aren&#39;t coders? Perhaps your customer success team wants to model customer churn. They’ll be able to do this using automated, drag-and-drop tools to forgo writing new models and new code every time a new customer comes in. Similarly, your Chief People Officer and HR team will be using more and more automated machine learning models in hiring flows, including screening resumes (we’ll get to the potential perils of this in a minute). And your marketing team may be interested in using such tools for the marketing funnel, which changed with the advent of ML tools for programmatic ad buying. One other telling example is supply chain optimization, which can greatly reduce costs within an organization and is relatively low-hanging fruit.</p>
<p>This trend can also be observed in complex spaces like deep learning. For example, <a href="https://lobe.ai/about">Lobe</a> is a startup that makes deep learning accessible to all, allowing users to build models using a GUI. Lobe was acquired by Microsoft even before they came out of a beta, which is testament to the importance of GUI tools in shaping the future.</p>
<p>Another example is an <a href="https://ai.googleblog.com/2019/07/building-smily-human-centric-similar.html">image search engine</a> built by Google that lets medical doctors explore model predictions and fine-tune them further with human intelligence. Such tools can increase adoption with great effect by opening up the black box and supplementing ML models with human intelligence.</p>
<p>We&#39;re going to see more drag-and-drop interfaces that will reduce the barrier to entry. This will allow us all to think more about which types of tools we want to build in-house and which ones we want to source from vendors.</p>
<h2 id="algorithmic-marketplaces-allow-non-coders-to-purchase-models">Algorithmic marketplaces allow non-coders to purchase models</h2>
<p>We’ll see more and more marketplaces for these ML products. Booz Allen’s Modzy is a point-and-click marketplace where you can buy models that others have created to incorporate into your tech stack.</p>
<p>There are dangers to allowing non-coders and non-statisticians to use these tools. Namely, we must ensure that everyone knows of the inherent risks associated with these tools and how to mitigate them. Increasingly, product development advancements in point-and-click tools may focus on helping people recognize when they’re given biased results, and not to take them at face value.</p>
<h2 id="beware-of-the-danger-zone-of-drag-and-drop-ml">Beware of the danger zone of drag-and-drop ML</h2>
<p>An interesting educational game out of Mozilla Labs is Survival of the Best Fit, about hiring bias in AI. You get to play a CEO who attempts to scale the hiring process with an automated tool that causes hiring practices to go wrong.</p>
<h2 id="recap">Recap</h2>
<p>We&#39;ll see more data work done in GUIs, via drag-and-drop and point-and-click interfaces. The space is getting more and more competitive with key investments by big players like Google and Microsoft, with much more to come. And even if a lot of your data science work is currently done in-house, more data work is increasingly shifting to vendors—which means there needs to be widespread education of potential risks and consequences to adopting AI.</p>
<h2 id="call-to-action">Call to action</h2>
<p>Write down how much of the day-to-day work in your organization currently happens in code. You could write this in human hours or how much it actually costs your organization. Then calculate how much is happening in GUIs, point-and-click, and drag-and-drop interfaces. How do you see this changing over the next 24 months?</p>
<p>Find out more about building your data program for the future of work in <a href="https://www.datacamp.com/resources/ebooks/definitive-guide-to-machine-learning-for-business-leaders">The Definitive Guide to Machine Learning for Business Leaders</a>.</p>
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We'll see more data work done in GUIs, via drag-and-drop and point-and-click interfaces.

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