<center><a target="_blank" href="https://www.datacamp.com/resources/tools/take-datacamps-data-maturity-assessment"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1634662968/Take_DataCamp_s_Data_Maturity_Assessment_j7wywo.png" /></a></center><br>

<p>Becoming a data-driven organization is a long and arduous process that requires continuous investment and efforts across different dimensions. In early 2021 we released a white paper titled <a target="_blank" href="https://resources.new.datacamp.com/resources/static/ad3091e99aad70f919a383bfc68bb5f3/Your_Organization-s_Guide_to_Data_Maturity__2_.pdf">“Your organization’s guide to data maturity”</a> outlining the different stages of the data maturity spectrum, and actionable steps organizations can take to progress from one data maturity stage to another.</p>
<p>The path to data literacy consists of a four-stage spectrum that starts with Data Reactive, Data Scaling, Data Progressive, and finally, Data Literate. In order to advance from one stage to the next, there are five key levers that require continuous monitoring and investment. These five levers are what we call the IPTOP framework:</p>
<ul>
<li><strong>Infrastructure:</strong> A scalable data infrastructure that ensures data is collected, discoverable, reliable, understood, compliant, and actionable throughout the organization.</li>
<li><strong>People:</strong> Forging a data-driven culture where all employees understand the value of data and have the skills to work with data regardless of role (that does not mean everyone needs to code!).</li>
<li><strong>Tools:</strong> The tools, software, and systems data practitioners use, and how to drive further data democratization with frameworks that reduce entry barriers to working with data.  </li>
<li><strong>Organizations:</strong> How data talent is organized, developed, and fostered, and adoption of organizational models that promote scalable data science throughout the organization.</li>
<li><strong>Processes:</strong> The processes data experts and teams adopt to make their work more predictable and collaborative, and to ensure alignment with business objectives.</li>
</ul>
<p>Our white paper struck a chord with readers, and after receiving feedback from both readers and organizations, we learned that a common challenge is understanding where one’s team or organization fits on the data maturity spectrum. This is why we’re launching our data maturity assessment.</p>
<h2 id="what-is-the-data-maturity-assessment-">What is the data maturity assessment?</h2>
<p><a target="_blank" href="https://www.datacamp.com/resources/tools/take-datacamps-data-maturity-assessment">The Data Maturity Assessment</a> is a quick and simple to use survey,  consisting of a series of questions that focus on the IPTOP framework. Given the importance of data culture, data skills, and governed data access, we have deliberately adjusted the scoring criteria to make sure we prioritize the importance of infrastructure and people.</p>
<h2 id="how-you-can-make-use-of-the-data-maturity-assessment">How you can make use of the data maturity assessment</h2>
<p>It really depends on your objectives and overview of data maturity within your organization. We know that having a bird’s eye view of data maturity can be challenging for large organizations, so we adapted the assessment to provide insights on a team, department, and organizational level. We also know that 29 questions cannot capture the full dimensions of organizational data maturity, so we hope you’ll use the results as a conversation starter for the strategies and initiatives to prioritize when driving data literacy.</p>
<h2 id="take-the-data-maturity-assessment-now">Take the data maturity assessment now</h2>
<p>Anyone can take this assessment (free, of course), but with that said,  we do encourage participation among senior leaders, executives, and managers who have a comprehensive view of different dimensions of data maturity. Take the <a target="_blank" href="https://www.datacamp.com/resources/tools/take-datacamps-data-maturity-assessment">assessment now</a> and learn where you rank on the data maturity spectrum.</p>
<br><center><a target="_blank" href="https://www.datacamp.com/resources/tools/take-datacamps-data-maturity-assessment"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1634662968/Take_DataCamp_s_Data_Maturity_Assessment_j7wywo.png" /></a></center>


https://www.datacamp.com/community/blog/how-data-mature-are-you

How Data-mature are you? Take our new data maturity assessment, and find out! 

<h2 id="why-did-we-launch-data-science-competitions-">Why did we launch data science competitions?</h2>
<p>We started DataCamp to help everyone build their data skills, through learning by doing. We’re constantly innovating, making it easier for learners to go from learning to applying their skills to solve real-world challenges. For example, we’ve created over 80 different projects, we’ve launched our in-browser tool Workspace, and we’ve designed two of our certification’s assessments to be based upon how well learners can apply what they know.</p>
<p>We are now pleased to announce the launch of <a href="https://app.datacamp.com/learn/competitions">data science competitions</a>, in which you’ll perform an analysis based on a dataset and problem statement. This data analysis will be performed and presented in a Workspace notebook, and it will then be assessed through an upvote system and a panel of experienced data scientists. There are huge prizes to be won and these will be a regular occurrence, starting today!</p>
<br><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1634599264/competitionsprizes_gd4a8t.png" /></center><br>

<h2 id="what-is-the-first-datacamp-data-science-competition-">What is the first DataCamp data science competition?</h2>
<p>Our first data science competition, &quot;<a href="https://app.datacamp.com/learn/competitions/russian-alcohol-promotions">Designing a promo strategy for a drinks company</a>&quot;, is now live, so if you’re curious go check it out.</p>
<br><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1634599265/Screenshot_2021-10-18_at_15.09.45_dplfq6.png" /></center><br>

<p>In this challenge, you’re asked to create promotion recommendations for a drinks company based on previous promotions and historical alcohol consumption data in various regions of Russia.</p>
<h2 id="what-entering-datacamp-competitions-can-do-for-your-career-">What entering DataCamp competitions can do for your career?</h2>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1634599270/wes-hicks-4-EeTnaC1S4-unsplash_fendqd.jpg" /></center><br>

<p>DataCamp Competitions are designed to help users gain real-world data science experience—similar to what a data analyst would typically encounter in the workplace. All learners stand to strengthen their data science experience and coding skills by participating, as well as the opportunity to build a strong portfolio whose contents will be given an upvote score as part of the judging process. Furthermore, non-participants will have the opportunity to learn from submitted publications by seeing how others have found creative solutions to complex data problems.</p>
<h2 id="what-competitions-entail">What competitions entail</h2>
<p>Every week a new challenge is launched based upon a real-world dataset and problem statement drawn up by our team. Once a competition is launched, learners have two weeks to complete their analysis and publish it using Workspace, our online data science notebook.</p>
<p>Once you’ve submitted your analysis you can share your publication with your friends and on your social media to get upvotes on it. This will also enable them to upvote it early, before voting to the wider public begins.</p>
<p>After the submission deadline, the judging process begins. This step is two-fold:</p>
<ul>
<li>DataCamp learners and other data science enthusiasts give publications upvotes, and the most popular will be reviewed by the judges.</li>
<li>The judges will spend the following week evaluating these publications and selecting their own top five out of these.</li>
</ul>
<p>In the case of “Designing a promo strategy for a drinks company&quot;:</p>
<ul>
<li>Submissions will close on October 31, 2021 at 11:59 PM PT and voting will start on November 1. On this date all workspace notebook publications will be opened to the wider public for upvoting, and you’ll be able to upvote your favorite publications to boost their chances of winning.</li>
<li>Voting will end on November 7.</li>
<li>Judges will determine 5 winners, then announce them by November 12.</li>
</ul>
<h2 id="what-skill-level-datacamp-competitions-are-intended-for-">What skill level DataCamp competitions are intended for?</h2>
<p>DataCamp competitions are open-ended challenges intended to test your analytical, storytelling, and visualization skills. As a recommendation, we would say complete the skill track relevant to the tool you’ll be using in your analysis. DataCamp competitions are similar in certain respects to Kaggle, although they will have more of a focus on real-world analytics so are more accessible to a wider audience.</p>
<h2 id="what-else-is-coming-up-">What else is coming up?</h2>
<p>If you haven’t already, be sure to follow DataCamp on Facebook, Twitter, LinkedIn, and Instagram, where every week we will announce the launch and topic of new competitions!</p>
<br><center><a href="https://app.datacamp.com/learn/competitions"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1634599264/Group_1127_koksuy.png" /></a></center><br>

<p>Curious? You can learn more about our <a href="https://support.datacamp.com/hc/en-us/articles/4410791195031">data science competitions here</a>, and if you’d like some inspiration you can check out the winning publications of our Workspace competition.</p>
<ul>
<li><a href="https://tinyurl.com/5cfv44k7">First place—Loan Data by Kok Kwang Ng</a></li>
<li><a href="https://tinyurl.com/4sz37tww">Second place—Loan Data by Nikolaos Christoforidis</a></li>
<li><a href="https://tinyurl.com/sazjdemc">Third place—Optimizing Gower Weights in Feature Engineering by Patrick Coulombe</a></li>
<li><a href="https://tinyurl.com/xcfx3aks">Fourth place—Internet News and Consumer Engagement by Renaat de Rudere</a></li>
<li><a href="https://tinyurl.com/cm6nxsbx">Fifth place—Internet News and Consumer Engagement</a></li>
</ul>


https://www.datacamp.com/community/blog/introducing-data-science-competitions

DataCamp’s data science competitions are now live! You’ll be able to put your data science skills into practice for the chance to win prizes, get new additions to your portfolio, and learn new approaches to a single problem from other people who solved it.

DataCamp’s data science competitions are now live! You’ll be able to put your data science skills into practice for the chance to win prizes, get new additions to your portfolio, and more!

Introducing DataCamp Competitions!

https://www.datacamp.com/community/blog/scaling-ai-adoption-in-financial-services

In this episode of DataFramed, we speak with Shameek Kundu,  Chief Strategy Officer & Head of Financial Services at TruEra Inc about Scaling AI Adoption throughout financial services.

Scaling AI Adoption in Financial Services

<h2 id="what-will-i-learn-">What will I learn?</h2>
<p><a href="https://www.datacamp.com/courses/building-dashboards-with-dash-and-plotly">Building Dashboards with Dash and Plotly</a> will introduce you to the go-to tools for creating visualizations within Python. Dash, an open-source framework, is one of the most popular tools available within Plotly, and enables you to create interactive visualizations. It’s a simple tool to use, with many of its lines of code pre-written.</p>
<p>The first chapter (which, as always on DataCamp, is free for registered users) provides you with a concise overview of this course’s content, introducing you to HTML and CSS, and building multi-graph dashboards based on data from an e-commerce organization.</p>
<p>Subsequent chapters will dive deeper: you’ll learn more about using HTML and CSS to create individual charts and add interactivity to your dashboards in the form of filters and tooltips. You’ll learn how to create filters that are applied at the individual visualization and the dashboard level.</p>
<h2 id="why-should-i-take-this-course-">Why should I take this course?</h2>
<p>Visualizing data is the best way to communicate the insights within your dataset, and as you use Python to extrapolate more complex data you will want to display your findings in a way that people can understand and act upon. There are various different chart types, and dashboards allow you to combine them to get an overall picture of what the data is telling you.</p>
<p>Additionally, this course’s instructor Alex Scriven is a Senior Data Scientist for the New South Wales government. With a history of lecturing at universities, creating Masters-level courses, and applying data science to commercial settings, he makes for the ideal teacher—plus, if you love Australian accents, you’re in luck!</p>
<h2 id="are-there-any-prerequisites-">Are there any prerequisites?</h2>
<p>Before taking this course, you should have an <a href="https://www.datacamp.com/courses/intermediate-python">intermediate-level</a> understanding of Python, and you should also take our course on <a href="https://www.datacamp.com/courses/introduction-to-data-visualization-with-plotly-in-python">data visualization in Plotly</a>. If you did the latter a while back, don’t worry: before dashing straight to Dash, the first chapter will provide a recap of Plotly.</p>
<h2 id="what-s-in-this-course-">What’s in this course?</h2>
<h4 id="chapter-1">Chapter 1</h4>
<iframe width="560" height="315" src="https://www.youtube.com/embed/Hw7XXzvZtuE" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

<p>Take Plotly to the next level with Dash. After briefly revising Plotly, you&#39;ll dive straight into building your first Dash app. You&#39;ll then get your first taste of HTML and how Dash harnesses this vital web development language to structure larger Dash apps. Finally, you&#39;ll put all this together in your first multi-graph dashboard for a global e-commerce organization.</p>
<h4 id="chapter-2">Chapter 2</h4>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1634045079/sales_dashboard2_hmptlc.png" /></center>

<p>In this exercise, you’ll use your data skills to respond to a series of requests from management, including placing charts side-by-side and making the organization’s logos more prominent.
Take control of the elements in your Dash apps with HTML and CSS. Learn what each of these is and how they are at the core of modern web development, then incorporate their powerful abilities in your Dash apps to change your objects’ size, color, and placement.</p>
<h4 id="chapter-3">Chapter 3</h4>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1634045079/reusablecomponent_kdvwxk.png" /></center>

<p>Creating reusable components is one way to ensure that your code can be neatly packaged as much as possible, and in this exercise you’ll learn how to apply this to the organization’s logo.</p>
<p>Turn your Dash apps into user-driven experiences by mastering interactive components. Learn how to include dropdowns, date pickers, and event-free text fields to modify elements on your Dash apps and build truly self-service dashboards your users can explore.</p>
<h4 id="chapter-4">Chapter 4</h4>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1634045080/endchapterdash2_nuja6a.png" /></center>

<p>In this exercise, you’ll add more interactivity to your dashboards by allowing the viewer greater control over its tabular elements.</p>
<p>Unleash the full power of Dash app interactivity by triggering changes to one graph when a user clicks on or hovers over, another graph. Additionally, you&#39;ll learn the basics of Dash&#39;s Data Table module for creating tables that can be easily filtered, sorted, and paginated; as well as trigger changes in other graphs as well.</p>
<p>Are you ready to bring your Python data to life through visualizations in Dash? Click <a href="https://www.datacamp.com/courses/building-dashboards-with-dash-and-plotly">here</a> to take this course!</p>


https://www.datacamp.com/community/blog/building-dashboards-with-dash-and-plotly

You’ve learned how to use Python to collect data—now it’s time to learn how to present it! With our new course in Dash, you’ll learn the ins and outs of building dashboards with Python’s most popular visualization tool!

New Course: Building Dashboards with Dash and Plotly

<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1633628944/florencenightingale_mnid7a.png" /></center><br>

<p>Data visualization is often associated with business meetings and shiny boardrooms, but its origins were very different. While pinpointing a sole inventor or source of data visualization is difficult, one of its pioneers was a woman who is widely renowned in the UK as the founder of modern nursing: Florence Nightingale.</p>
<p>The idea that a hospital should be clean and hygienic might seem like a given from a 21st-century perspective. However, in the Victorian era, before reforms were implemented thanks to the work of various doctors and nurses, such as Nightingale herself, the opposite was the case. In October 1854 Nightingale was sent to Istanbul as a military nurse. Ending up in the military hospital here was its own kind of death sentence: beds had filthy sheets, often unchanged and unwashed between different occupants, and soldiers who arrived in full hospitals would have to share the equally filthy floors with the resident rats. As a result, <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2920984/">these hospitals claimed 10 times more lives than the battlefields</a>. All this was considered normal at the time: many influential politicians and officers, including England’s Chief Medical Officer, believed that deaths caused by contagious diseases were unavoidable and that there existed no link between poor hygiene and these deaths.</p>
<p>Nightingale was convinced that improving the quality of care would reduce these deaths and famously set to work on tending to the soldiers, becoming known as “The Lady with the Lamp.” Her caring nature continues to go down in history, but one less well-known aspect of this story is the improved bookkeeping Nightingale instituted. Nightingale had had a passion for statistics since she was a child, and when she found that the hospital had been losing track of its soldiers’ deaths, she tasked specific individuals with record-keeping. This would prove invaluable upon her return to England.</p>
<p>Nightingale sought to push for widespread reforms to healthcare, feeling that her accomplishments in Turkey had not done enough. As is the norm in stories of change, she would have to face skeptics—such as the Chief Medical Officer—and she knew this herself.</p>
<p>Data she collected during the war and charts she produced afterward were her weapons. She understood that the intended audience of civil servants and the public would have an easier time making sense of visualizations as opposed to tables of data:</p>
<blockquote>
<p>“Printed tables and all-in double columns, I do not think anyone will read. None but scientific men ever look in the Appendix of a Report. And this is for the vulgar* public.”</p>
<p>*In Nightingale’s era, the word vulgar would have been understood to mean ordinary.</p>
</blockquote>
<br><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1633628942/2560px-Nightingale-mortality_hjien8.jpg" /></center><br>

<p>Her most famous visualization was one she produced early on: a <a href="https://campus.datacamp.com/courses/intermediate-data-visualization-with-ggplot2/coordinates?ex=15">rose chart</a> of deaths in the British Army in Crimea and their causes. It demonstrated, through the color scheme and patterns, that a reduction in hospital deaths would lead to thousands of lives saved. The collection and process of visualizing the underlying data was also a revelation to Nightingale: soldiers’ deaths had fallen sharply after the government sent out a sanitation committee in March 1855, which had cleaned up the hospital’s drinking water and ventilation. In her subsequent publications, she would place heavy importance on improving hygiene.</p>
<br><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1633628940/florencenightingalemortalitychart2_aixnwg.jpg" /></center><br>

<p>Already an iconic figure during the war, Nightingale’s work proved hugely popular among readers, from ordinary citizens to Queen Victoria herself. <a href="https://www.smithsonianmag.com/history/surprising-history-infographic-180959563/">This was one of the first instances in which data visualization was used to persuade policymakers of the need for social reforms</a>. As a result of these efforts, hygiene within the army’s hospitals improved dramatically. By the end of the 19th century, their average mortality rates had fallen to <a href="https://www.astmh.org/ASTMH/media/Documents/Thomas-Mackie-Presidential-Address.pdf">half of those in civilian hospitals, where beforehand it had been consistently higher</a>. Nightingale would go on to use her influence and passion for data to drive healthcare reforms across the world and in a variety of settings, from maternity wards in India to doctors’ tents in the American Civil War.</p>
<blockquote>
<p>This was one of the earliest instances in which data visualization was used to persuade policymakers of the need for societal reforms.</p>
</blockquote>
<p>Nightingale was a data scientist in a time before <a href="https://www.datacamp.com/courses/intro-to-python-for-data-science">Python</a>, <a href="https://www.datacamp.com/courses/free-introduction-to-r">R</a>, <a href="https://www.datacamp.com/courses/introduction-to-sql">SQL</a>, <a href="https://www.datacamp.com/courses/introduction-to-tableau">Tableau</a>, <a href="https://www.datacamp.com/courses/introduction-to-power-bi">Power BI</a>, or even computers themselves existed. In this day and age, it can take mere minutes to collect enormous amounts of data and construct charts from it. What hasn’t changed, however, is the underlying concept of using visualizations to drive data literacy: healthcare organizations to this day continue to be visualization-driven, using <a href="https://www.datacamp.com/courses/designing-and-analyzing-clinical-trials-in-r">R</a> and <a href="https://www.datacamp.com/courses/biomedical-image-analysis-in-python">Python</a> extensively to improve patient care and create a healthier society.</p>
<p>Just like the days of Nightingale releasing her datasets to the public in a way they could easily understand, visualizations continue to be a valuable tool in democratizing data.</p>
<p>If you would like to improve your visualization skills to communicate data insights, we offer:</p>
<ul>
<li>Courses on visualization packages including: <a href="https://www.datacamp.com/courses/introduction-to-data-visualization-with-matplotlib">Matplotlib</a>, <a href="https://www.datacamp.com/courses/introduction-to-data-visualization-with-seaborn">Seaborn</a>, <a href="https://www.datacamp.com/courses/introduction-to-data-visualization-with-plotly-in-python">Plotly</a>, <a href="https://www.datacamp.com/courses/introduction-to-data-visualization-with-ggplot2">ggplot2</a>, and <a href="https://www.datacamp.com/courses/data-visualization-with-lattice-in-r">lattice</a>.</li>
<li>Courses in visualization tools such as <a href="https://www.datacamp.com/courses/data-visualization-in-spreadsheets">Spreadsheets</a>, <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>.</li>
<li>Real-world healthcare projects, including <a href="https://www.datacamp.com/projects/166">lifespan expectancy in R</a> and the <a href="https://www.datacamp.com/projects/870">spread of COVID-19 in R</a>.</li>
<li>A <a href="https://www.datacamp.com/projects/132">project</a> that involves using Python to emulate the work of another famous data-driven Victorian doctor: Dr John Snow.  </li>
</ul>
<h4>Sources:</h4>
<ul>
<li><a href="https://www.sciencenews.org/article/florence-nightingale-birthday-power-visualizing-science">https://www.sciencenews.org/article/florence-nightingale-birthday-power-visualizing-science</a></li>
<li><a href="https://medium.com/nightingale/florence-nightingale-is-a-design-hero-8bf6e5f2147">https://medium.com/nightingale/florence-nightingale-is-a-design-hero-8bf6e5f2147</a></li>
<li><a href="https://www.sciencenews.org/article/florence-nightingale-passionate-statistician">https://www.sciencenews.org/article/florence-nightingale-passionate-statistician</a></li>
<li><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2920984/">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2920984/</a></li>
<li><a href="https://www.astmh.org/ASTMH/media/Documents/Thomas-Mackie-Presidential-Address.pdf">https://www.astmh.org/ASTMH/media/Documents/Thomas-Mackie-Presidential-Address.pdf</a></li>
<li><a href="https://www.smithsonianmag.com/history/surprising-history-infographic-180959563/">https://www.smithsonianmag.com/history/surprising-history-infographic-180959563/</a></li>
</ul>


https://www.datacamp.com/community/blog/florence-nightingale-pioneer-of-data-visualization

Did you know that data visualization has origins in military healthcare reforms? In this blog post, we discuss how the famous nurse Florence Nightingale used charts to demonstrate her life-saving work to her target audience.

Florence Nightingale: Pioneer of Data Visualization

<h2 id="what-will-i-learn-">What will I learn?</h2>
<p><a href="https://www.datacamp.com/courses/marketing-analytics-for-business">Marketing Analytics for Business</a> is our latest no-code course, introducing you to the role of marketing analysts and how they leverage data to answer questions such as:</p>
<ul>
<li>What effect will customer lifetime value (LTV) have on my business?</li>
<li>How much of an effect will seasonality have upon sales?</li>
<li>What will our return on marketing investment be?</li>
<li>How can I accurately analyze customer sentiment towards my marketing activities?</li>
<li>Should we apply last-touch attribution (LTA) or multi-touch attribution (MTA)?</li>
<li>How can I improve the impact of future marketing campaigns?</li>
</ul>
<h2 id="why-should-i-take-this-course-">Why should I take this course?</h2>
<p>Through working with real-world marketing data, you’ll gain a solid foundation of knowledge in one of the most widespread uses of data science, which will enable you to become a more data-driven marketer. This course will show you what this role will entail, from dealing with messy data to predicting the effects of marketing activities. It will also show you the effect marketing analytics will have on your workplace. Remember—their success is also yours!</p>
<p>Additionally, because this course is so recent, it will give you an up-to-date understanding of recent developments in marketing analytics such as the introduction of GDPR and other privacy legislation.</p>
<p>Furthermore, if you are already confident in your data science skills, learning how to apply these to a marketing setting will boost your employability. Seeking out this career path, your skills in R, Python, SQL, or BI tools will be highly sought after by employers, and showing an understanding of marketing in addition to these will make you a strong candidate for any marketing analyst listing.</p>
<h2 id="are-there-any-prerequisites-">Are there any prerequisites?</h2>
<p>Because there is no coding involved, this course is completely beginner-friendly. Taking Marketing Analytics for Business is a good idea if you have a data background and would like to explore marketing as a career option, or if you come from a marketing background and are looking to apply data to your profession. Furthermore, it is the first course in the Data Skills for Business track, so is an excellent starting point in multiple ways.</p>
<h2 id="what-is-in-this-course-">What is in this course?</h2>
<h4 id="-chapter-1-https-www-datacamp-com-courses-marketing-analytics-for-business-"><a href="https://www.datacamp.com/courses/marketing-analytics-for-business">Chapter 1</a></h4>
<p>What does a marketing analyst do? Why is this role in high demand? In this chapter, you’ll learn about roles and business questions that a marketing analyst encounters. From revenue impact to campaign insights—you&#39;ll find that marketers will never run out of problems to analyze.</p>
<iframe width="560" height="315" src="https://www.youtube.com/embed/KTDL0Co7X6s" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

<p><i>Like all of DataCamp’s content, this course mixes instruction with hands-on exercises to help you gain a better understanding of the subject matter.</i></p>
<h4 id="chapter-2">Chapter 2</h4>
<p>Marketing analytics is not for the faint of heart! Between numerous data sources and ever-increasing privacy legislation, marketing analysts must consider multiple factors in reporting. Add in measuring the indirect impact of marketing, and you’ll see why marketing analysts get extremely comfortable with imperfect data.</p>
<br><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1633454931/marketing-analytics-blog-post_1_cfqvog.png" /></center><br>

<p><i>An exercise in spotting and flagging legal risks with regards to customer data</i></p>
<h4 id="chapter-3">Chapter 3</h4>
<p>Now that you know more about the marketing analytics landscape, let&#39;s talk about how you can use it to assess campaign performance. To do this, you’ll dive into text and sentiment analysis, audience segmentation, and predictive analytics. Marketers and executives rely on analytics to send the right message to the right people at the right time.</p>
<br><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1633454931/marketing-analytics-blog-post_2_jtf5gu.png" /></center><br>

<p><i>An exercise in understanding sentiment analysis from the human perspective</i></p>
<h4 id="chapter-4">Chapter 4</h4>
<p>The big question every marketer asks is &quot;did my marketing efforts make any money?&quot; To answer that question you&#39;ll have to consider: seasonal patterns, business model, campaign goals, and ROI. Most importantly, you’ll be responsible for setting and measuring KPIs to evaluate marketing effectiveness.</p>
<br><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1633454931/marketing-analytics-blog-post_3_wu2kc0.png" /></center><br>

<p><i>An exercise to test your understanding of the differences between LTA and MTA</i></p>
<p><i>Are you ready to start making your marketing activities more data-driven? Interested in working in marketing analytics? Go ahead and start learning this course <a href="https://learn.datacamp.com/courses/marketing-analytics-for-business">here</a>!</i></p>


https://www.datacamp.com/community/blog/marketing-analytics-blog-post

Become a data-driven marketer: Learn about our newest no-code course, which will show you how as a marketer you can use data to better understand your customers and drive business growth.

New Course: Marketing Analytics for Business

<p>Congratulations! You’ve taken the courses, you’ve done the timed assessments, you’ve written the case study, and you’ve completed the presentation. And now, before you on your screen, sits the email from DataCamp. “Congratulations, you’re now certified as a Professional Data Scientist by DataCamp!”</p>
<p>Here is what to do next!</p>
<h2 id="1-share-your-data-science-certificate-on-linkedin">1. Share your data science certificate on LinkedIn</h2>
<p>DataCamp makes it very easy to share your data science certificate on LinkedIn—simply follow the links provided within your email. On your personal certification page, you can share your achievement by clicking on the social icon of your choice.</p>
<h2 id="2-celebrate">2. Celebrate</h2>
<p>You did it! Getting a data science certification on DataCamp is a huge achievement, so give yourself some time doing whatever you love, be it a night on the town, or Netflix and your couch!</p>
<h2 id="3-get-in-touch-with-our-career-services-team">3. Get in touch with our career services team</h2>
<p>Available to all certified data scientists, this can be thought of as a constant step. They can assist you in many of the subsequent steps on this list, including offering you resume and cover letter advice, and holding mock interviews.</p>
<h2 id="4-develop-a-broad-data-science-career-plan">4. Develop a broad data science career plan</h2>
<p>Before you begin the search for your dream data science job, you should first consider the direction you want to take. Data science is used in many professions, including healthcare and finance. An understanding and appreciation of the underlying field you will be working in as a data scientist can help tremendously in matters such as communication with nontechnical colleagues (remember that part of the Certification process?) and feeling enthusiastic about your work.</p>
<h2 id="5-brush-up-your-cv">5. Brush up your CV</h2>
<p>Your CV is the main thing potential employers will read, and many will skim-read it. Have a convincing bio and/ or a small set of bullet points highlighting certain parts of your CV in a prominent place, and provide further details on your education, skills, and experience in clearly labeled sections.</p>
<h2 id="6-get-good-at-writing-cover-letters">6. Get good at writing cover letters</h2>
<p>A good cover letter can be the difference between getting a job as a data scientist and not getting one. Cover letters should not be thought of as your CV reworded, but rather as an explanation for why this particular role would be ideal for you—and you, for it.</p>
<h2 id="7-get-onto-glassdoor">7. Get onto Glassdoor</h2>
<p>Glassdoor is best-known for two things: interview questions and employee reviews. Regarding the first, knowing what kind of questions you will be asked in your data science interview can help to calm your nerves and prepare some sample answers. Employee reviews can give you an indication of what kind of work environment exists within the company you are applying to, and Glassdoor keeps these anonymized to provide greater authenticity. A word of caution, though: unsatisfied employees are more likely to leave bad reviews than satisfied ones, so bear this in mind.</p>
<h2 id="8-get-a-mock-data-science-interview-set-up">8. Get a mock data science interview set up</h2>
<p>To be able to sell yourself you need to be prepared for questions that may require more thought, or may be more surprising. Interviewing is a skill of its own, and the more practice you get in it, the better you will be. Getting it right the first time is extremely rare, so mock interviews are a great way to give yourself a practice round. Additionally, you will have a guarantee of detailed feedback afterward, which will help you identify any areas of improvement to your interview technique.</p>
<h2 id="9-build-up-a-list-of-data-science-jobs-you-d-like-to-apply-for">9. Build up a list of data science jobs you’d like to apply for</h2>
<p>Devote some time to browsing through jobs you are sufficiently qualified for and listing them, whether through saving them to the platform you’re searching on (LinkedIn, for example, allows this) or adding them to a spreadsheet.</p>
<h2 id="10-apply-for-these-data-science-jobs">10. Apply for these data science jobs</h2>
<p>This step varies from one listing to another. Every listing will require your contact details and a CV, at minimum. Some will also require a cover letter, or a completed application form. Make a good first impression at this stage, and you may get invited for a data science interview. Don’t take it too personally if you are rejected—the more practice you get in applications and interviews, the better you will get. It should also be said that data science skills are in high demand, so by getting certified you have already set yourself in good stead!</p>
<p>Good luck!</p>
<p><h4 id="whats-the-difference-between-a-data-science-certification-and-a-statement-of-accomplishment">FAQ: ”What’s the difference between a data science certification and a statement of accomplishment?”</h4>
<p>A data science certification is only available to learners who have gone through the Certification process, while a statement of accomplishment is provided to everyone who completes a course on DataCamp. As Certification does not provide answers or hints, and is timed, it is intended for people who have already completed a sizable number of courses.</i></p>
<p><i>DataCamp also has courses specifically designed to help you with interview preparation: click <a href="https://www.datacamp.com/search?q=interview">here</a> to check them out!</i></p>


https://www.datacamp.com/community/blog/what-to-do-after-getting-certified-as-a-data-scientist

From how to share your data science certificate on LinkedIn to other data science job hunting tips, find out how to get a data science job after getting certified!

What to do after becoming a certified data scientist through DataCamp

https://www.datacamp.com/community/blog/building-high-performing-data-teams

In this episode of DataFramed, we speak with Syafri Bahar, VP of Data Science at Gojek about building high-performing data teams, and how data science is central to Gojek’s success. 

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