<center><a target="_blank" href="https://www.datacamp.com/groups/business"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1624651228/schedule_a_demo_bfhlx1.png" /></a></center><br>
<p>In a recent episode of <a target="_blank" href="https://www.datacamp.com/community/blog/the-data-storytelling-skills-data-teams-need">DataFramed</a>, Andy Cotgrave, technical evangelist at Tableau, shared the importance of data storytelling in driving change with analytics. In this two-part blog post, we deep dive into seven concrete tips that Andy provided on telling better data stories.</p>
<p>Read <a target="_blank" href="https://www.datacamp.com/community/blog/seven-tricks-for-better-data-storytelling-1">Part I</a> if you haven't already!</p>
<h2 id="4-use-a-narrative-structure-in-data-stories-">4. Use a narrative structure in data stories.</h2>
<p>Stories generally follow a common arc – the protagonist faces a challenge, goes on to resolve it, then returns to normalcy. An example of this arc is the Hero’s Journey.</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643320567/image8_grp6ly.png" />Figure 1: The hero’s journey</center><br>

<p>The audience is <a target="_blank" href="https://www.forbes.com/sites/kateharrison/2015/01/20/a-good-presentation-is-about-data-and-story/?sh=7599eca6450f">22 times more likely</a> to remember a fact when told a story. Clearly, data visualizations stand to benefit from adopting the age-old narrative structure.</p>
<p>Adopting the narrative structure, a data story starts with a background on the current situation, continues with evidence that builds up to the central insight, and ends with a call to action. (Figure 2)</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643320742/image4_nnm9k9.png" />Figure 2. The narrative structure of a data story</center><br>


<p>An excellent example of a data story, the <a target="_blank" href="https://www.gatesfoundation.org/goalkeepers/report/2021-report/#LongTermInvestmentsPayOff">Goalkeeper’s Report by the Gates Foundation</a> demonstrated how the pandemic exacerbated inequality. (Figures 3A, 3B, and 3C)</p>
<br><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643320251/poverty_s2wd18.gif" />Figure 3A: To set the scene, the data story first established the impact of COVID-19 on poverty-stricken countries. [<a target="_blank" href="https://www.gatesfoundation.org/goalkeepers/report/2021-report/#LongTermInvestmentsPayOff">Source</a>]</center><br>

<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643320508/image7_bmdtdx.png" />Figure 3B: The data story provided rising insights by highlighting the widening chasm between the haves and the have-nots pre-pandemic and post-pandemic.</center><br>

<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643319219/image2_lyw5ro.png" />Figure 3C: The data story closes with a call to action–for greater investments in women, communities, and innovations.</center>

<h2 id="5-adapt-data-stories-to-the-medium">5. Adapt data stories to the medium</h2>
<p>It is tempting to reuse existing charts across multiple mediums. Data storytellers who do so are doing themselves a disservice.</p>
<p>Charts that are not optimized for their medium might fail to deliver their message effectively. A chart originally designed for email might contain minuscule texts that cannot be read on a screen.</p>
<p>The Economist portrayed how data stories can be adapted from print media to social media. The printed chart (designed for serious readers) was dense in information. It was streamlined for Instagram (designed for casual scrollers).</p>
<br><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643320591/image5_qjtkiu.png" />Figure 4: The Economist’s printed chart (left) is adapted for social media (right) [<a target="_blank" href="https://medium.economist.com/charting-new-territory-7f5afb293270">Source</a>]</center>

<h2 id="6-declutter-your-data-stories">6. Declutter your data stories</h2>
<p>Clutter makes data stories harder to read. The additional cognitive load distracts the audience from the main message.</p>
<p>The onus is on the data storyteller to identify and remove superfluous elements that do not contribute to the intended message. The result of decluttering is striking.</p>
<p>In particular, our whitepaper “<a target="_blank" href="https://www.datacamp.com/resources/whitepapers/8-rules-for-better-data-storytelling">8 Rules for Better Data Storytelling</a>” suggests data storytellers to:</p>
<ul>
<li>Remove chart borders</li>
<li>Remove gridlines or axes</li>
<li>Clean up axis labels</li>
<li>Label data directly (as opposed to using a legend)</li>
<li>Remove data markers</li>
<li>Use special effects (bold, underline, italics, shadows) sparingly</li>
</ul>
<h2 id="7-explain-data-stories-in-stages">7. Explain data stories in stages</h2>
<p>When presenting complex data stories, one practical tip is to reveal the elements of the chart gradually. When the information is presented in digestible pieces, the audience is more likely to engage with the presentation.</p>
<p>A prime example is the talk “<a target="_blank" href="https://www.youtube.com/watch?v=jbkSRLYSojo">The Joy of Stats</a>” by Hans Rosling. Watch how Rosling masterfully told his data story.</p>
<iframe width="560" height="315" src="https://www.youtube.com/embed/jbkSRLYSojo" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
<h2 id="data-storytelling-is-here-to-stay">Data storytelling is here to stay</h2>
<p>Data storytelling will soon <a target="_blank" href="https://www.forbes.com/sites/brentdykes/2020/01/22/why-data-storytellers-will-define-the-next-decade-of-data/?sh=56a32d24fb9c">become a necessary skill set</a> for all in the coming decade as data insights become a cornerstone of organizations. To learn more tips and tricks on data storytelling, listen to Andy’s podcast, or learn from his most recent webinar on DataCamp.</p>
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https://www.datacamp.com/community/blog/seven-tricks-for-better-data-storytelling-2

Seven Tricks for Better Data Storytelling: Part II

<center><a target="_blank" href="https://www.datacamp.com/groups/business"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1624651228/schedule_a_demo_bfhlx1.png" /></a></center><br>
<p>With proper design skills, data teams have the power to frame arguments and persuade with data. That was the key takeaway from a recent episode of <a target="_blank" href="https://www.datacamp.com/community/blog/the-data-storytelling-skills-data-teams-need">DataFramed</a> featuring Andy Cotgrave, the technical evangelist at Tableau. In this two-part blog post, we distill seven concrete tips for better data storytelling from the podcast.</p>
<h2 id="1-craft-data-stories-with-a-message-in-mind-">1. Craft data stories with a message in mind</h2>
<p>Subtle differences in design can profoundly impact the audience’s interpretation of a data visualization. These designs can come in the form of color choice, title, and annotations.</p>
<p>One chilling example is Simon Scarr’s Iraq Bloody Toll that emphasizes the death toll from the US military engagement in Iraq. The downward bar chart colored in red is strongly evocative of the bloodbath in Iraq, making the visual metaphor highly memorable. Interestingly, a simple reorientation and color change alters the message entirely.</p>
<br><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643385426/image7_tafrhv.gif" />Figure 1: Iraq’s Bloody Toll by Simon Scarr [<a target="_blank" href="http://www.simonscarr.com/iraqs-bloody-toll">Source</a>]</center><br>



<p>Data storytellers ought to wield this power to convey arguments responsibly and deliberately. When delivering a supposedly objective data story, data storytellers should pay attention to details in charts that might amplify biases. For instance, axes should start with zero to avoid misleading correlations.</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643319150/image1_ybw363.png" />Figure 2. The same data points can convey dramatically different results [<a target="_blank" href="https://www.datasciencejunction.com/2019/01/how-to-be-cautious-about-misleading.html">Source</a>]</center>


<h2 id="2-position-elements-in-a-data-story-appropriately">2. Position elements in a data story appropriately</h2>
<p>The Gutenberg principle states that a user’s eyes travel according to a Z-shaped path from top-left to the bottom-right when reading content.</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643319221/image6_tdj1dv.png" />Figure 3. The Gutenberg principle [<a target="_blank" href="https://www.oreilly.com/library/view/practical-game-design/9781787121799/f77e030b-0f77-4774-9025-000c0d7732e6.xhtml">Source</a>]</center><br>

<p>Applied to data stories, this principle tells us that the most important information should be on the top left. More granular details should be fleshed out towards the bottom of the chart. A call-to-action can also be included in the bottom-right of the chart where the terminal section is.</p>
<h2 id="3-understand-your-audience-using-design-principles">3. Understand your audience using design principles</h2>
<p>Design principles can be used to understand an audience’s experience with data stories. In the book Design of Everyday Things, the author Don Norman argues that end-users process all objects on <a target="_blank" href="https://www.interaction-design.org/literature/article/norman-s-three-levels-of-design">three levels of emotions</a>–visceral, behavioral, and reflective.</p>
<ul>
<li><p>The visceral level:
Within the first second of seeing a chart, users judge a chart based on its appearance. That is the visceral level of processing, which refers to the ingrained and automatic qualities of human emotions. As such, a chart’s aesthetic is key to keeping an audience’s attention on the data story.</p>
</li>
<li><p>The behavioral level:
The user then judges a visualization based on its usefulness, which is referred to as the behavioral level of processing. In other words, the audience must be able to see the author’s point with the visualization.</p>
</li>
<li><p>The reflective level:
After interacting with the visualization, users reflect on their experience. For instance, a user is likely to reuse a data dashboard if they find their experience pleasant upon reflection.</p>
</li>
</ul>
<h2 id="more-tips-on-data-storytelling">More Tips on Data Storytelling</h2>
<p>This article covered how data teams can enhance data stories with thoughtful designs. In the <a target="_blank" href="https://www.datacamp.com/community/blog/seven-tricks-for-better-data-storytelling-2">second part of the blog post</a>, we will provide more actionable tips for presenting excellent data stories.</p>
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https://www.datacamp.com/community/blog/seven-tricks-for-better-data-storytelling-1

Seven Tricks for Better Data Storytelling: Part I

<ol>
<li>Introduction</li>
<li>What are the responsibilities of a data engineer?</li>
 <ul>
<li>ETL Pipelines</li>
<li>Data Warehouses</li><br>
</ul>
<li>Data engineering vs data science</li>
<li>Why has data engineering increased in popularity?</li>
<li>What are the skills of a data engineer?</li>
<li>How to learn data engineering</li>
</ol>
<h1 id="introduction">Introduction</h1>
<p>Data is everywhere. In recent years, with the growing number of connected devices and the expansion of their use, the world has been producing a vast amount of data that cannot be handled with traditional methods anymore.</p>
<p>According to Statista, as of 2021 there are <a target="_blank" href="https://www.statista.com/statistics/1101442/iot-number-of-connected-devices-worldwide/">23.8 billion devices connected to the internet</a> and 58% of those are IoT devices (smart home devices, connected self-driving cars, industrial devices, etc.) and the remaining 42% are non-IoT devices (laptops, smartphones, etc.). This means that nowadays data can come from various sources. Some of these are:</p>
<ol>
<li><strong>User activities:</strong> Any user action produces data and even the data that reflects where and how often we tap on the mobile applications we use is valuable. Every piece of data that contains this kind of information needs to be kept and distributed to the applications’ data centers.</li>
<li><strong>“Internet of Things” devices:</strong> These devices produce a huge amount of information, such as sensor data generated in many different locations. All this data needs to be retrieved and sent to a data pool for further analysis.</li>
<li><strong>Program logs:</strong> Today’s computer applications consist of many components and all those components produce program logs. These logs are sent to a data engineering pipeline for further analysis.</li>
</ol>
<p>All the different structured data that the world produces needs to be delivered to other components in the software structure, or visualized and interpreted by business analysts and data scientists. However, this delivery is not a simple task. There are some crucial requirements on how to deliver the data:</p>
<ol>
<li>Data needs to be delivered in a standardized structure</li>
<li>The data should not present any conflicts</li>
<li>Data duplication may need to be removed</li>
<li>The data needs to be reliable</li>
<li>The data delivery must be done with minimum delay</li>
</ol>
<p>Therefore, the delivery of this information is a complex process and, if it is not carried out properly, it can be very problematic, especially for medium and large-scale companies. This is where data engineers come into play.</p>
<p>In this article you will learn the basics of data engineering, which are:</p>
<ol>
<li>What data engineering is and why to ensure a robust data pipeline</li>
<li>The difference between data science and data engineering</li>
<li>The reason behind the increasing popularity of data engineering</li>
<li>The skills of a good data engineer</li>
<li>How DataCamp can help you become a data engineer</li>
</ol>
<h1 id="what-are-the-responsibilities-of-a-data-engineer-">What are the responsibilities of a data engineer?</h1>
<p>To put it simply, data engineers:</p>
<ol>
<li>Aim to make data easily accessible and available for data scientists and business intelligence engineers (or anyone working with data)</li>
<li>Optimize the big data system architecture of companies</li>
<li>Design and configure databases</li>
<li>Collaborate with teams of business intelligence engineers, data scientists, and data analysts</li>
<li>Explore and transform data
In order to perform all these tasks, data engineers first need to set up the ETL pipelines.</li>
</ol>
<h1 id="etl-pipelines">ETL pipelines</h1>
<p>ETL pipelines receive complex data at regular time intervals and process it with the aim of storing the data in a usable form. It is the responsibility of the data engineers to establish and maintain these pipelines.</p>
<p>There are 3 types of ETL pipelines:</p>
<ol>
<li><strong>Extract</strong>
A vast number of data created by data sources is collected in different formats.</li>
<li><strong>Transform</strong>
Since the data comes in different formats and due to its inconsistent sources, the data is processed to become as standardized as possible. This increases the discoverability and usability of the data for the next steps.</li>
<li><strong>Load</strong>
Once the data is in a discoverable and usable format, it is usually stored in data warehouses. This makes the data available at all times for further analysis.</li>
</ol>
<h1 id="data-warehouses">Data warehouses</h1>
<p>Data warehouses are where standardized data is stored. They are optimized for filtering and reading large volumes of data. In modern data warehouses, both structured and unstructured data can be stored, because it is also important to store the data that cannot be categorized, such as photos and videos.</p>
<h1 id="data-engineering-vs-data-science">Data engineering vs data science</h1>
<p>The differences between the roles of a data engineer and a data scientist are important. On the one hand, data scientists have an important role in companies because they contribute to data-driven decision making. Nevertheless, the success of data scientists is only as good as the data that they have available.</p>
<p>On the other hand, data engineers are the ones who design the systems that provide data to data scientists in an organized and consistent manner. Once the data engineers make the information available for them, data scientists then try to find patterns in the data to reach conclusions, using the format provided by the data engineers.</p>
<p>Data engineers and data scientists clearly work closely together. We can think of it as a football match. In football, it is the coach who develops the strategies that will create goal opportunities, and it is the coach who sets the players in certain positions. In this context the coach is the data engineer, so we can consider that the ball is the data and the player who scores is the data scientist. The rest of the team is the infrastructure.</p>
<h1 id="why-has-data-engineering-increased-in-popularity-">Why has data engineering increased in popularity?</h1>
<p>According to research conducted on the <a href="https://www.burning-glass.com/products/nova/">Burning Glass Nova</a> platform, job postings for &quot;data engineer&quot; increased by 88.3% in 2019. According to this research, demand for data engineers has been on the rise since 2016. Another interesting piece of information is that the number of job openings for data engineers is almost 5 times higher than the job openings for data scientists. The salary of a data engineer is also 20-30% higher than the salary of a data scientist.</p>
<p>As stated above, data scientists are the final link in the data processing chain. They need the data to be processed by a data engineer in order to perform their job. As the number of data sources and data types multiply, the need for a data engineer grows. For this reason, companies have recently started to invest more in the extraction and distribution of data.</p>
<p>In Figure 4.1, a pyramid chart displays the hierarchy of needs of data science. In this hierarchy, the tasks assigned to data engineers are at the base of the pyramid, which explains how crucial data engineers are for companies.</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1640794050/image3_kje7by.png" />Figure 4.1: The data science hierarchy of needs.</center>

<center><a target="_blank" href="https://medium.com/hackernoon/the-ai-hierarchy-of-needs-18f111fcc007">Source: hackernoon</a></center>

<h1 id="what-are-the-skills-of-a-data-engineer-">What are the skills of a data engineer?</h1>
<p>The skills of data engineers are generally intertwined with the skills of software engineers and data scientists, as shown in Figure 5.1.</p>
<br><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1640794050/image2_rqg5yb.png" />
Figure 5.1: Data roles and skill sets</center>
<center><a target="_blank" href="https://ryanswanstrom.com/2016/11/28/data-scientists-data-engineers-software-engineers-the-difference-according-to-linkedin/">Source: RyanSwanstrom</a></center>

<h1 id="is-the-role-of-a-data-engineer-closer-to-the-one-of-a-software-engineer-or-to-the-one-of-a-data-scientist-">Is the role of a data engineer closer to the one of a software engineer or to the one of a data scientist?</h1>
<p>The previous occupations of a group of data engineers are displayed in Figure 5.2.  According to this figure, over 40% of them have a software engineering background. Not only does this mean that data engineering and software engineering are significantly closer than we may have first thought, but also that software engineers nowadays are much more likely to switch to data engineering than any other field.</p>
<br><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1640794050/image5_d4vwny.png" />
Figure 5.2: Data engineers by prior role (top 10).</center>
<center><a href="https://www.stitchdata.com/resources/the-state-of-data-engineering/">Source: stitchdata</a></center><br>

<p>At DataCamp we believe the following to be the skills of a good data engineer:</p>
<ol>
<li><strong>Software engineering background</strong>
Data engineers need to have a software engineering background, since they need to approach data engineering challenges with their knowledge of object-oriented programming, data structures and algorithms. Data engineers must be experienced in Python, Scala or Java.</li>
<li><strong>Tools</strong>
Data engineers need to be comfortable with data engineering tools like Airflow, Apache or Kafka.</li>
<li><strong>Database Technologies</strong>
A data engineer should have knowledge of multiple kinds of databases.</li>
<li><strong>Cloud Technologies</strong>
Data engineers should be comfortable using multiple cloud platforms like Amazon Web Services, Google Cloud Platform and Microsoft Azure. They need to have experience in cloud architecture and DevOps tools as well, since they are responsible for the automation of the data flow part of the system.</li>
</ol>
<h1 id="how-to-learn-data-engineering">How to learn data engineering</h1>
<p>If you want to improve in the data engineering field, you can start by completing the following recommended DataCamp courses. By completing these courses, you will likely become a more suitable candidate for internships or job positions in the field of data engineering.</p>
<ol>
<li><a target="_blank" href="https://www.datacamp.com/courses/intro-to-python-for-data-science">Introduction to Python</a></li>
<li><a target="_blank" href="https://www.datacamp.com/courses/intermediate-python">Intermediate Python</a></li>
<li><a target="_blank" href="https://www.datacamp.com/courses/introduction-to-relational-databases-in-sql">Introduction to Relational Databases in SQL</a></li>
<li><a target="_blank" href="https://www.datacamp.com/courses/introduction-to-scala">Introduction to Scala</a></li>
<li><a target="_blank" href="https://www.datacamp.com/courses/introduction-to-data-engineering">Introduction to Data Engineering</a></li>
<li><a target="_blank" href="https://www.datacamp.com/courses/introduction-to-airflow-in-python">Introduction to Airflow in Python</a></li>
<li><a target="_blank" href="https://www.datacamp.com/courses/aws-cloud-concepts">AWS Cloud Concepts</a></li>
<li><a target="_blank" href="https://www.datacamp.com/courses/building-data-engineering-pipelines-in-python">Building Data Engineering Pipelines in Python</a></li>
<li><a target="_blank" href="https://www.datacamp.com/courses/nosql-concepts">NoSQL Concepts</a></li>
<li><a target="_blank" href="https://www.datacamp.com/courses/etl-in-python">ETL in Python</a></li>
<li><a target="_blank" href="https://www.datacamp.com/courses/streaming-concepts">Streaming Concepts</a></li>
<li><a target="_blank" href="https://www.datacamp.com/courses/streaming-data-with-aws-kinesis-and-lambda">Streaming Data with AWS Kinesis and Lambda</a></li>
</ol>


https://www.datacamp.com/community/blog/what-is-data-engineering

 Learn what data engineering is, what is the difference between data science and data engineering, the scope in the field and how to become one.

Learn what data engineering is, compare it with data science, find out the skills required for the role and how to acquire them. 

What is Data Engineering?

https://www.datacamp.com/community/blog/ace-the-data-science-interview

In this episode of DataFramed, we speak to Nick Singh and Kevin Huo on their latest book Ace the Data Science Interview.

Ace the Data Science Interview

<br><center><a target="_blank" href="https://www.datacamp.com/groups/business"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1624651228/schedule_a_demo_bfhlx1.png" /></a></center><br>
<p>In 2021, we witnessed the impact of COVID-19 on accelerating digital transformation. This rapid digitization has led to an explosion in the amount of data organizations are generating. As organizations look to reap the value of this data in 2022, they will adopt new technologies, upskill their workforce, and embrace new ways of work. Here are nine data science trends and predictions to expect in 2022.</p>
<h2 id="-1-organizations-accelerate-culture-transformation-programs">#1 Organizations accelerate culture transformation programs</h2>
<p>Data culture is the collective behaviors and beliefs of people who value, practice and encourage the use of data to improve decision-making. It sets the foundation and mindset for companies to effectively capture value from their ever-growing datasets.</p>
<p>Unfortunately, the <a target="_blank" href="https://c6abb8db-514c-4f5b-b5a1-fc710f1e464e.filesusr.com/ugd/e5361a_d59b4629443945a0b0661d494abb5233.pdf">lack of data culture</a> is a major roadblock in an organization’s roadmap to becoming data-driven.   </p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1642644861/image2_t1r0vh.png" /></center>

<p>In 2022, we anticipate chief data officers (CDO) to focus on the key building blocks of data culture, which include proper data governance, data literacy programs, and nurturing a data-driven mindset. These are catalysts for organization-wide data-driven decision-making processes.</p>
<h2 id="-2-organizations-will-scale-data-governance">#2 Organizations will scale data governance</h2>
<p>The growing demand for self-serve analytics fueled the need for compliant, actionable, and high-quality data. Yet, the challenge of measuring and maintaining data quality scales in tandem with the size and complexity of the datasets. As such, companies are adapting their data governance strategies.</p>
<p>One such strategy is to adopt <a target="_blank" href="https://www.montecarlodata.com/what-is-data-observability/">data observability</a> in data pipelines. Simply put, data observability aims to identify, troubleshoot, and resolve data issues in near real-time.</p>
<p>In 2022 and beyond, more companies will scale their data governance programs and adopt new and modern tools to monitor and detect data quality issues.</p>
<h2 id="-3-nlp-ushers-in-a-new-generation-of-low-code-data-tools">#3: NLP ushers in a new generation of low-code data tools</h2>
<p>NLP has grown tremendously in the past few years, thanks to the arms race for ever-larger large language models (LLM) like T5, GPT-3, and Megatron-Turing NLG.</p>
<p><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1642644862/image3_mt06mh.png" />Large language models get larger over the years (<a href="https://huggingface.co/blog/large-language-models">Source</a>)</center></p>
<p>LLMs are pushing the limits of what NLP can do. The most recent models surprised the community with their ability to generate various types of texts (like computer code and guitar tabs) without any explicit training.</p>
<p>Such NLP models have the potential to usher in the age of low-code and no-code tools. Today, <a target="_blank" href="https://blogs.microsoft.com/ai/from-conversation-to-code-microsoft-introduces-its-first-product-features-powered-by-gpt-3/">Microsoft’s Power App</a> allows non-technical users to build apps with natural speech. Such tools will continue to lower the barriers to coding and facilitate the rise of citizen developers and citizen data scientists within organizations.</p>
<h2 id="-4-l-d-becomes-a-fabric-of-company-culture">#4 L&amp;D becomes a fabric of company culture</h2>
<p>Driven by the C-suite’s concern over their <a target="_blank" href="https://www.pwc.com/gx/en/issues/reinventing-the-future/take-on-tomorrow/covid-disparity-evaluation.html">organization’s lack of key skills</a>, companies will continue to invest heavily in learning and development (L&amp;D) programs in 2022. The benefits of such L&amp;D programs are obvious–the World Economic Forum predicted that <a target="_blank" href="https://www.pwc.com/gx/en/issues/upskilling/upskilling-for-shared-prosperity.html">38% of GDP can be gained from upskilling</a> by 2030.</p>
<p>As the workforce continues to cope with the pandemic, we expect companies to allocate their L&amp;D budgets on virtual learning ecosystems that facilitate effective learning and provide communities of practice. Companies looking to improve their employees’ data literacy at scale can leverage existing L&amp;D programs as internal data science skill academies.</p>
<h2 id="data-trend-5-mlops-will-continue-to-mature-within-organizations">Data Trend #5:  MLOps will continue to mature within organizations</h2>
<p>MLOps is a set of practices that combine machine learning, data engineering, and DevOps. It includes standardized processes that automate the machine learning workflow.</p>
<p>Companies can only extract value from machine learning at scale with production-level AI systems. This explains why the demand for MLOps is expected to grow immensely. In fact, the industry is estimated to be valued at <a target="_blank" href="https://www.cognilytica.com/2020/04/02/infographic-the-rapid-growth-of-mlops/">$126.1 billion</a> by 2025.</p>
<p>In the upcoming year, MLOps tools like <a target="_blank" href="https://www.kubeflow.org/">KubeFlow</a> and <a target="_blank" href="https://mlflow.org/">MLFlow</a> will continue to mature. It is only a matter of time before they become a staple for all data science teams.  </p>
<h2 id="data-trend-6-responsible-ai-becomes-more-operationalized">Data Trend #6: Responsible AI becomes more operationalized</h2>
<p>Unfortunately, many existing AI systems today are rife with hidden biases. As such, regulators <a target="_blank" href="https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/what-the-draft-european-union-ai-regulations-mean-for-business">in the EU</a> are planning to hold such AI accountable, and many are expected to follow suit. Companies must ensure that their AI systems remain fair and responsible. Those that fail to do so risk damaging their reputations and are culpable for exacerbating inequality.</p>
<p>That is why companies are increasingly operationalizing principles of Responsible AI to ensure that AI remains fair, interpretable, privacy-preserving, and secure. An example of a framework is PwC’s <a target="_blank" href="https://www.pwc.com/gx/en/issues/data-and-analytics/artificial-intelligence/what-is-responsible-ai.html">Responsible AI Toolkit</a>, which addresses the various dimensions of Responsible AI.</p>
<h2 id="data-trend-7-rise-of-the-data-mesh">Data Trend #7: Rise of the data mesh</h2>
<p>Most data architectures today are in the form of data lakes. That might change soon as a new form of data architecture addresses the weakness of data lakes.</p>
<p>A new alternative, coined by Zhamak Dehghani is called the <a target="_blank" href="https://martinfowler.com/articles/data-monolith-to-mesh.html">data mesh</a>. A data mesh has distributed “data products”–each handled by a cross-functional team of data engineers and product owners. Adopting a data mesh architecture allows companies to <a target="_blank" href="https://www2.deloitte.com/nl/nl/pages/strategy-analytics-and-ma/articles/from-data-mess-to-a-data-mesh.html">deliver data faster and achieve greater business domain agility</a>.</p>
<p>Soon, as the pains of using data lakes become more acute, companies will begin experimenting with data mesh, as did <a target="_blank" href="https://databricks.com/session_eu20/data-mesh-in-practice-how-europes-leading-online-platform-for-fashion-goes-beyond-the-data-lake">Zalando</a> and <a target="_blank" href="https://medium.com/intuit-engineering/intuits-data-mesh-strategy-778e3edaa017">Intuit</a>.</p>
<h2 id="data-trend-8-new-generation-of-tooling-will-improve-the-data-team-s-productivity">Data Trend #8: New generation of tooling will improve the data team’s productivity</h2>
<p>Various data science productivity tools emerged in 2021 and will continue to gain popularity in the years to come. These tools reduce the need for manual work and allow data scientists to perform higher-value tasks.</p>
<p>Such productivity tools include AutoML tools (like <a target="_blank" href="https://docs.h2o.ai/h2o/latest-stable/h2o-docs/automl.html">H2O AutoML</a> and <a target="_blank" href="https://www.automl.org/automl/autopytorch/">Auto PyTorch</a>), which automates the process of machine learning model selection and even hyperparameter tuning.</p>
<p>A multitude of <a target="_blank" href="https://blogs.nvidia.com/blog/2021/06/08/what-is-synthetic-data/">synthetic data generation tools</a> have also been on the rise. Their ability in creating balanced and labeled datasets at scale is particularly attractive for data-hungry companies.</p>
<p>Large data science teams might also find collaboration tools like <a href="https://databricks.com/solutions/data-science">Databricks</a> and <a target="_blank" href="https://www.datacamp.com/workspace">DataCamp Workspace</a> useful. These tools allow data scientists to collaborate asynchronously in data exploration and ML modeling.</p>
<br><center><a target="_blank" href="https://www.datacamp.com/workspace"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1642779150/chrome-capture_krnwqo.gif" /></a></center>
<h2 id="data-trend-9-the-talent-crunch-and-flexible-work-will-broaden-and-improve-the-search-for-data-talent">Data Trend #9: The talent crunch and flexible work will broaden and improve the search for data talent</h2>
<p>As the Great Resignation rages on, the talent crunch becomes ever more severe. This phenomenon prompts organizations to rethink how they recruit and retain data talent.</p>
<p>In particular, we expect companies to prioritize skills over zip codes in their hiring policies, as evident from the <a target="_blank" href="https://www.linkedin.com/business/talent/blog/talent-strategy/global-data-shows-surge-in-remote-work">280% increase in remote job postings</a> on LinkedIn since March 2020. Moreover, as FAANG companies embrace the work-from-home arrangement, we expect other tech companies to follow suit and provide their employees with flexible working options too.</p>
<p>For more on our data trends and predictions for 2022, download the free white paper, or register for our upcoming webinar where we’ll deep-dive into what’s coming in data science in the new year!</p>
<br><center><a target="_blank" href="https://www.datacamp.com/groups/business"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1624651228/schedule_a_demo_bfhlx1.png" /></a></center>


https://www.datacamp.com/community/blog/2022-data-trends-and-predictions

2022 data trends and predictions



<p>If you are considering starting a data science career, the sooner you start coding, the better. Learning to code is a critical step for every aspiring data scientist. However, getting started in programming can be daunting, especially if you don’t have previous coding experience.</p>
<p>To choose the right programming language, we must first look at what data scientists do in their daily work. A data scientist is a technical expert who uses mathematical and statistical techniques to manipulate, analyze and extract information from data. There are many domains within the data science realm, from machine learning and deep learning, to network analysis, natural language processing, and geospatial analysis.  To perform their tasks, data scientists rely on the power of computers. Programming is the technique that allows data scientists to interact with and send instructions to computers.  </p>
<p>There are hundreds of programming languages out there, built for diverse purposes. Some of them are better suited for data science, providing high productivity and performance to process large amounts of data. However, this group still comprises a good number of programming languages.</p>
<p>In this article, we will look at some of the top data science programming languages for 2022, and present the strengths and capabilities of each of them.</p>
<ul>
<li>Python</li>
<li>R</li>
<li>SQL</li>
<li>Java</li>
<li>Julia</li>
<li>Scala</li>
<li>C/C++</li>
<li>JavaScript</li>
<li>Swift</li>
<li>Go</li>
<li>MATLAB</li>
<li>SAS</li>
</ul>

<h1 id="python">Python</h1>

<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1642759712/image8_obtmfx.png"></center>
<br>
<p>Ranked first in several programming languages popularity indices, including the <a target="_blank" href="https://www.tiobe.com/tiobe-index/">TIOBE</a> Index and the <a target="_blank" href="https://pypl.github.io/PYPL.html">PYPL</a> Index, the popularity of Python has boomed in recent years. Python is an open-source, general-purpose programming language with broad applicability not only in the data science industry, but also in other domains, like web development and video game development.</p>
<br>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1642759712/image2_m8wk5a.png"></center>
<center>Source: <a target="_blank" href="https://www.tiobe.com/tiobe-index/">TIOBE Index</a></center>

<p>Any data science tasks you can think of can be done with Python. This is mainly thanks to its rich ecosystem of libraries. With thousands of powerful packages backed by its huge community of users, Python can perform all kinds of operations, from data preprocessing, visualization, and statistical analysis, to the deployment of machine learning and deep learning models. Here are some of the most used libraries for data science and machine learning purposes:</p>
<p><strong>NumPy</strong>: is a popular package that offers an extensive collection of advanced mathematical functions. Many packages are based on Numpy objects, like the famous NumPy arrays.</p>
<p><strong>pandas</strong>: is a key library in data science, used for performing all kinds of manipulation of databases, also called DataFrames.</p>
<p><strong>Matplotlib</strong>: the standard Python library for data visualization.</p>
<p><strong>scikit-learn</strong>: built on top of NumPy and SciPy, it has become the most popular Python library for developing machine learning algorithms.</p>
<p><strong>TensorFlow</strong>: developed by Google, it is a powerful computational framework for developing machine learning and deep learning algorithms.</p>
<p><strong>Keras</strong>: an open-source library designed to train neural networks with high performance.</p>
<p>Due to its simple and readable syntax, Python is often referred to as one of the easiest programming languages to learn and use for beginners. If you are new in data science and don’t know which language to learn first, Python is one of the best options.</p>
<p>If you want to be a Python expert, DataCamp is here to help. Check out the <a target="_blank" href="https://www.datacamp.com/learn/python">Python courses</a> in our catalog and start your training to become a successful data scientist.</p>
<h1 id="R">R</h1>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1642759712/image10_zoxunl.png"></center>

<p>Not yet as highly trending as Python according to the popularity indices, R is a top option for aspiring data scientists. Frequently portrayed in data science forums as the main competitor of Python, learning one of these two languages is a critical step to break into the field.</p>
<p>R is an open-source, domain-specific language, explicitly designed for data science. Very popular in finance and academia, R is a perfect language for data manipulation, processing and visualization, as well as statistical computing and machine learning.</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1642759712/image9_ezal9o.png"></center>
<center>Source: <a target="_blank" href="https://www.kdnuggets.com/2019/05/poll-top-data-science-machine-learning-platforms.html">KDnuggets</a></center>


<p>Like Python, R has a large community of users and a vast collection of specialized libraries for data analysis. Some of the most notable ones belong to Tidyverse family, a collection of data science packages. It includes dplyr, for data manipulation, and the powerful ggplot2, the standard library for data visualization in R. As for machine learning tasks, libraries like caret will make your life much easier when developing your algorithms.</p>
<p>Although it is possible to work with R directly on the command line, it is common to use Rstudio, a powerful third-party interface that integrates various capabilities, such as data editor, data viewer, and debugger.</p>
<p>Whether you are new to data science or want to add new languages to your arsenal, learning R is a perfect choice. Check out our rich catalog of <a target="_blank" href="https://www.datacamp.com/learn/r-programming">R courses</a> to start sharpening your skills.</p>
<h1 id="sql">SQL</h1>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1642759713/image17_jc0qta.png"></center>

<p>Much of the world&#39;s data is stored in databases. SQL (Structured Query Language) is a domain-specific language that allows programmers to communicate with, edit and extract data from databases. Having a working knowledge of databases and SQL is a must if you want to become a data scientist.</p>
<p>Knowing SQL will enable you to work with different relational databases, including popular systems like SQLite, MySQL, and PostgreSQL. Despite the tiny differences between these relational databases, the syntax for basic queries is pretty similar, which makes SQL a very versatile language.</p>
<p>Whether you choose Python or R to start your data science journey, you should also consider learning SQL. Due to its declarative, simple syntax, SQL is very easy to learn compared to other languages, and it will help you a lot along the way.</p>
<p>Want to get started in SQL? Have a look at the different <a target="_blank" href="https://www.datacamp.com/learn/sql">SQL courses</a> and <a target="_blank" href="https://www.datacamp.com/tracks/sql-fundamentals">skill tracks</a> offered by DataCamp and get ready to become a query master.</p>
<h1 id="Java">Java</h1>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1642759713/image16_c7j66k.png"></center>

<p>Ranked #2 in the PYPL Index #3 in the TIOBE Index, Java is one of the most popular programming languages ​​in the world. It’s an open-source, object-oriented language, known for its first-class performance and efficiency. Endless technologies, software applications and websites rely on the Java ecosystem.</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1642759713/image15_oykldb.png"></center>
<center>Source: <a target="_blank" href="https://pypl.github.io/PYPL.html">PYPL Index</a></center>

<p>Although Java is a preferred choice when developing websites or building applications from scratch, in recent years, Java has gained a prominent role in the data science industry. This is mainly because of the Java Virtual Machines, which provide a solid and efficient framework for popular big data tools, such as Hadoop, Spark, and Scala.</p>
<p>Due to its high performance, Java is a suitable language for developing ETL jobs and performing data tasks that require big storage and complex processing requirements, like machine learning algorithms.</p>
<h1 id="julia">Julia</h1>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1642759712/image4_omnnyl.png"></center>

<p>Julia can be considered a data science rising star. Despite being one of the youngest languages on this list, (it was released in 2011) Julia has already impressed the world of numerical computing. Sometimes referred to as the inheritor of Python, Julia is a highly effective tool compared to other languages used for data analysis.</p>
<p>Although it has gained notoriety thanks to its early adoption by several major organizations, including many in the financial industry, Julia still lacks the maturity to compete with top data science languages. It still has a small community and doesn&#39;t have as many libraries as its main competitors, Python or R.</p>
<p>Julia’s main downside is its youth, but there are numerous reasons to keep an eye on it. Let’s see how it evolves in the coming years.</p>
<h1 id="scala">Scala</h1>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1642759712/image6_ks3ls8.png"></center>

<p>Although it’s not very common to see Scala in the top rankings of programming languages, (it holds the #18 position in the PYPL Index and #33 in TIOBE) speaking about this programming language is mandatory in the context of data science.</p>
<p>Scala has recently become one of the best languages for machine learning and big data. Released in 2004, Scala is a multi-paradigmatic language explicitly designed to be a clearer and less wordy alternative to Java.</p>
<p>Scala also runs on the Java Virtual Machine, thereby allowing interoperability with Java and making it a perfect language for distributed big data projects. For example, the Apache Spark cluster computing framework is written in Scala.</p>
<h1 id="#C/C++">#C/C++</h1>
<center><div class="row">
<div class="column"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1642759713/image14_qdxjsy.png"></div>
<div class="column"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1642759712/image3_c5hyii.png"></div></div></center>      

<p>Considered two of the most optimized languages, being familiar with C and its close relative C++, can be very useful when it comes to addressing computationally intensive data science jobs.</p>
<p>C and C++ are comparatively faster than other programming languages, making them well-suited candidates for developing big data and machine learning applications. It isn’t a coincidence that some of the core components of popular machine learning libraries, including PyTorch and TensorFlow, are written in C++.</p>
<p>Due to their low-level nature, C and C++ are among the most complicated languages to learn. Therefore, although they may not be the first choices when embarking into the world of data science,  once you get a solid understanding of the fundamentals of programming, mastering them is a smart move that can make a great difference to your resume.</p>
<h1 id="javascript">JavaScript</h1>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1642759712/image11_h2oqqk.png"></center>

<p>JavaScript is on top of the preferred programming languages, according to the <a href="https://insights.stackoverflow.com/survey/2021">Stack Overflow Developer Survey 2021</a>. JavaScript is a multiparadigm, versatile language, widely known for its capacity to build rich and interactive web pages.</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1642759712/image5_hrf2tr.png"></center>
<center>Source: <a target="_blank" href="https://insights.stackoverflow.com/survey/2021">Stack Overflow</a></center>

<p>Although the majority of JavaScript users work in the web development sector, in recent years the language has gained notoriety in the data science industry. Today, JavaScript supports popular libraries for machine learning and deep learning, such as TensorFlow and Keras, as well as incredibly powerful visualization tools, like D3.</p>
<p>Thanks to the support of popular libraries for machine learning, and due to its broad popularity amongst web developers, it’s a smooth entry option for all front-end and back-end programmers who want to break into data science.</p>
<h1 id="swift">Swift</h1>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1642759712/image7_xgg8mn.jpg"></center>

<p>One of the downsides of Python and R is that neither of them were built with mobile devices in mind. In the coming years, we can expect an even bigger advancement of mobile, wearables and the IoT (Internet of Things). Swift was developed by Apple to make it easier to create apps and, with that, grow its app ecosystem and increase customer retention. Soon after its release in 2014, Apple and Google started working together to make it a key tool in the interplay between mobile and machine learning.</p>
<p>Swift is now compatible with TensorFlow and is interoperable with Python. An additional advantage of Swift is that it is no longer limited to the iOS ecosystem and it has turned open-source to work on Linux.</p>
<p>For these reasons, if you are a mobile developer and feel curious about data science, Swift is what you’re looking for.</p>
<h1 id="go">Go</h1>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1642759713/image13_up9uzk.png"></center>

<p>Go (or GoLang) is a language with increasing popularity, especially for machine learning projects. Google introduced it in 2009 with C-like syntax and layouts. According to many developers, Go is the 21st-century version of C.</p>
<p>More than a decade after its launch, Go is becoming extremely popular due to its flexible and easy-to-understand language. In the context of data science, Go can be a good ally for machine learning tasks. Despite its prospects, the data science community of Go is still very small.</p>
<h1 id="matlab">MATLAB</h1>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1642759712/image1_dwh0d6.png"></center>

<p>MATLAB is a language mainly designed for numerical computing. Broadly adopted in academia and scientific research since its launch in 1984, MATLAB provides powerful tools to carry out advanced mathematical and statistical operations, making it a great candidate for data science.</p>
<p>However, MATLAB has an important downside: it is proprietary. Depending on the case (academic, personal or business use), you may have to pay a large amount of money to get a license, making it less attractive than other programming languages that can be used for free.</p>
<h1 id="sas">SAS</h1>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1642759712/image12_lqtj5r.jpg"></center>

<p>SAS (Statistical Analytical System) is a software environment designed for business intelligence and advanced numerical computing. SAS has been around for a long time, and it’s widely adopted across major firms in many sectors, creating a big market for SAS developers.</p>
<p>However, SAS is steadily losing popularity against other data science programming languages like Python and R. This is mainly because, as occurred with MATLAB, you need a license to use SAS. This creates a barrier to entry for new users and companies, who will feel prone to use free, open-source languages.</p>
<h1 id="conclusion">Conclusion</h1>
<p>We hope this post will help you navigate the rich and diverse landscape of data science programming languages.
There is no single language that is best in absolute terms to solve all the problems and situations that may arise during your work as a data scientist. However, if you are a newcomer in data science, our recommendation is to start by picking either Python or R. You can enroll in our free <a target="_blank" href="https://www.datacamp.com/courses/intro-to-python-for-data-science">Introduction to Python Tutorial</a> and <a target="_blank" href="https://www.datacamp.com/courses/free-introduction-to-r?utm_source=blog_launch_new_intro&amp;utm_medium=blog&amp;utm_campaign=launch_new_intro">Introduction to R Tutorial</a> to see which one you like the most. From there, the key to success is patience and practice. To get hands-on programming experience, we recently launched <a target="_blank" href="https://www.datacamp.com/workspace">DataCamp Workspace</a>, an online environment to write code, apply your skills  and create your data science portfolio.</p>
<p>Once you feel confident with your chosen language, you could level up with solid SQL training. Fortunately, DataCamp offers a good number of <a target="_blank" href="https://www.datacamp.com/learn/sql">SQL courses</a>.</p>
<p>From there, the sky&#39;s the limit. Becoming knowledgeable in multiple programming languages is an asset, and moving between languages according to the needs of your organization will help you become a versatile data scientist and develop a more successful career.</p>


https://www.datacamp.com/community/blog/blog-top-programming-languages-for-data-scientists-in-2022

Thinking about breaking into data science but don’t know which programming language to choose? Here’s all you need to know about the programming languages that will lead the data science sector in 2022.

Discover top programming languages to add to your skill set as a data scientist.  Learn the pros and cons of the languages and which ones to keep an eye out for. 

Top programming languages for data scientists in 2022

<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1642616851/DC_for_Classrooms_logo_1_ejektm.png" /></center><br>

<p>DataCamp is proud to announce our partnership with Tableau to make world-class data science software and education widely available to the teachers and students of the world. Every teacher with both a DataCamp for Classrooms account and a Tableau for Teaching license <a target="_blank" href="https://forms.gle/QLzKuEpFDRTnPkU26">can apply</a> to gain free access to DataCamp’s interactive Tableau courses for themselves and their students. We see this as an important opportunity to bring STEM education fully into the 21st century with a partner just as dedicated as us to democratizing data science.</p>
<p>Data is being consumed at an astronomical rate but are all students given the same opportunity to see and understand data? Do instructors have the resources easily and readily accessible to prepare the next generation of students? The answer, sadly, is a resounding no.</p>
<p>“Tableau for Teaching is excited about the partnership with DataCamp,” says Jessica Lyons, Senior Manager of <a target="_blank" href="https://www.tableau.com/academic/teaching">Tableau for Teaching</a>. “The mission of arming students and instructors with the resources needed to create a data-literate world aligned perfectly with DataCamp’s goals."</p>
<p>The feeling is mutual. At DataCamp, we see Tableau as an ideal platform to get high school and college students excited about data. No other data visualization tool caters to novices and experts alike as deftly as Tableau does. Teachers armed with these resources will empower their students to better understand and analyze the data in their lives, preparing them for tomorrow’s data-driven industries. With just some basic knowledge of Excel/Spreadsheets and a good data set, young people can create powerful and inviting visualizations of the data that matters to them most.</p>
<h2>How do teachers apply?</h2>
<p>It’s a simple three-step process for qualified teachers to apply for Tableau access on DataCamp:</p>
<ol>
<li><a target="_blank" href="https://www.datacamp.com/groups/classrooms">Apply and get approved for DataCamp for Classrooms</a></li>
<li><a target="_blank" href="https://www.tableau.com/academic/teaching#form">Apply and get approved for Tableau for Teaching</a></li>
<li>Fill out the <a target="_blank" href="https://forms.gle/QLzKuEpFDRTnPkU26">official DataCamp + Tableau application form</a></li>
</ol>
<p>DataCamp and Tableau representatives will review all applicants within a week of the submission date to verify their status in both programs. Once verified, you&#39;ll receive a confirmation email once Tableau training has been added to your DataCamp group. All the users you invite into that DataCamp for Classrooms account will get immediate, free access to our Tableau courses.</p>
<h2>What do students and teachers get with DataCamp + Tableau?</h2>
<p>DataCamp for Classrooms offers free unlimited Professional licenses for you and your students to learn on DataCamp. That means access to over 350 courses in R, Python, SQL, Git, Scala, Spreadsheets, Excel, as well all of our projects, practice modules, the mobile app, and more. And teachers get a group dashboard with all the functionality they need to run a successful classroom: the ability to create teams, give assignments, and monitor progress. College/university teachers from around the world and high school teachers in the US and UK are eligible to apply.</p>
<p>By also becoming a recipient of Tableau for Teaching and <a target="_blank" href="https://forms.gle/QLzKuEpFDRTnPkU26">completing this form</a>, teachers will gain access to our six outstanding courses on Tableau, <a target="_blank" href="https://app.datacamp.com/learn/courses/introduction-to-tableau">including Introduction to Tableau</a>, <a target="_blank" href="https://app.datacamp.com/learn/courses/connecting-data-in-tableau">Connecting Data in Tableau</a>, and <a target="_blank" href="https://app.datacamp.com/learn/courses/analyzing-data-in-tableau">Analyzing Data in Tableau</a>—everything you and your students need to go from novices to experts. Just like all our other courses, they can run entirely in-browser (no additional software required), or users can download the datasets and run our exercises on their desktop-based Tableau clients. Previously reserved for Premium and Enterprise DataCamp customers, these in-demand courses will completely change how educators around the world teach students Tableau.</p>
<p>If you’re a teacher, apply to these revolutionary edtech programs today (students, please refer this offer to your STEM teachers)! This is the only way to learn to become a Tableau whiz at your own pace for free.</p>
<p>To apply for free Tableau courses through DataCamp for Classrooms, <a target="_blank" href="https://forms.gle/QLzKuEpFDRTnPkU26">click here</a>.</p>
<p>To apply for a free DataCamp for Classrooms account, <a target="_blank" href="https://www.datacamp.com/groups/classrooms">click here</a>.</p>
<p>To apply for a free Tableau for Teaching license, <a target="_blank" href="https://www.tableau.com/academic/teaching#form">click here</a>.</p>


https://www.datacamp.com/community/blog/datacamp-and-tableau-partner-to-teach-young-people-data-visualization

DataCamp has partnered with Tableau for Teaching to provide world-class courses on Tableau to teachers and students around the globe for free. Find out how you can help improve your students’ data literacy with DataCamp for Classrooms.

DataCamp and Tableau Partner to Teach Young People Data Visualization

<p>A third of organizations polled in the CIPD’s <a target="_blank" href="https://www.cipd.co.uk/knowledge/strategy/development/learning-skills-work#gref">learning and skills at work report</a> saw their training budgets slashed in 2021. Smaller budgets, coupled with more people working from home than ever before, meant that team managers and L&amp;D professionals had to do more with less. This resulted in a drastic shift to digital training. Analysis by the CIPD has shown that organizations that embraced this change are reaping the rewards, with an improved culture and increased demand for learning from their workforce.</p>
<p>But it wasn’t all positive. The report also revealed that 77% of team managers and L&amp;D professionals had no way of assessing the impact of their learning program. The most commonly self-reported method for evaluating training programs was “participant satisfaction surveys”—subjective reporting that’s difficult to link to ROI and your organization’s business goals.</p><br>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1640192579/Marketing/Blog/L_D_reporting_-_DataCamp.jpg" />CIPD: <a target="_blank" href="https://www.cipd.co.uk/knowledge/strategy/development/learning-skills-work#gref">Barriers to the evaluation of learning and development program and initiatives (%)</a></center>

<br><p>As shown in the image above, there were many barriers to better reporting—competing priorities and a lack of time being top of that list. At DataCamp, we believe it should be simple to report on the business impact of your learning program. That’s why all <a target="_blank" href="https://www.datacamp.com/groups/business">DataCamp for Business groups</a> now have more ways than ever before to access insights you can share with stakeholders quickly.</p>
<h1 id="how-to-level-up-your-reporting-in-2022-">How to level up your reporting in 2022:</h1>
<h2 id="1-create-professional-one-click-reports-with-custom-reports">1. Create professional one-click reports with Custom Reports</h2>
<p>We get it. Reporting on the impact of your learning programs takes time. That’s why we created Custom Reports. They’re pre-made reports that DataCamp admins can use to demonstrate your learning ROI and reveal measurable insights, including everything from a weekly status report to your most engaged learners or most popular courses—with new reports added all the time.</p>
<p>Create professional reports from our pre-made templates
Get detailed insights into your learning program
Schedule regular, automated reports, so you never miss a deadline</p>
<p>Ready to improve your reporting? <a target="_blank" href="https://support.datacamp.com/hc/en-us/articles/4412251811223-DataCamp-Custom-Reports-An-Overview">Learn how to get started on our help center</a>.</p>
<br><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1640192361/Marketing/Blog/DataCamp-for-Business-Reporting-with-Custom-Reports.gif" /></center>

<h2 id="2-plug-and-play-connect-your-learning-data-to-tableau-and-power-bi">2. Plug and play—connect your learning data to Tableau and Power BI</h2>
<p>With our 2021 update to Data Connector, you can now create reports and dashboards directly in Tableau or Power BI using DataCamp’s built-in Athena connection. The Athena plug-in makes it easier to view, query, and visualize learning insights directly in your platform of choice.</p>
<p>Ready to analyze and visualize your data in Tableau or Power BI? Invite your engineering team to <a target="_blank" href="https://enterprise-docs.datacamp.com/data-connector/using-the-data-connector/analyzing-data">follow these steps</a>.</p>
<br><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1640192282/Marketing/Blog/DataCamp-for-Business-Reporting-with-Data-Connector.gif" /></center>

<h2 id="3-build-custom-reports-in-python-or-r-with-workspace">3. Build custom reports in Python or R with Workspace</h2>
<p>If your organization has enabled the Workspace editor, you can build your reports from scratch in Python or R, or choose from one of our ready-to-use Python Custom Reports.</p>
<ul>
<li>Analyze your organization’s learning data from the comfort of your browser</li>
<li>Share your insights while staying in control of who gets to see them</li>
<li>Comment directly on your Workspace reports, just like in Google Docs</li>
</ul>
<p>Want to build your own Custom Reports? <a target="_blank" href="https://app.datacamp.com/workspace/">Learn more</a>.</p>
<br><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1640192284/Marketing/Blog/DataCamp-for-Business-Reporting-with-workspace.gif" /></center>

<br><p>Let’s make 2022 the year that you go beyond employee satisfaction surveys and level up how you report on the difference you’re making to your workforce. <a target="_blank" href="https://www.datacamp.com/groups/business">Learn more about DataCamp for Business</a> or if you’re an existing customer, log in to try these new features.</p>
<h2 id="you-might-also-like-">You might also like:</h2>
<h4 id="white-paper-what-300-l-d-leaders-have-learned-about-building-data-fluency">White Paper: What 300+ L&amp;D Leaders Have Learned About Building Data Fluency</h4>
<p>Two of the most important actions required to deliver organization-wide data literacy are strong executive support and demonstrating value across multiple teams.</p>
<h5 id="-download-the-white-paper-https-www-datacamp-com-resources-whitepapers-what-300-l-and-d-leaders-have-learned-about-data-fluency-"><a target="_blank" href="https://www.datacamp.com/resources/whitepapers/what-300-l-and-d-leaders-have-learned-about-data-fluency">Download the white paper</a></h5>
<h4 id="customer-success-story-autodesk-bold-">Customer Success Story: Autodesk<bold></h4>
<p>Find out how this Fortune 1000 software company stayed ahead of the competition.</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1640194048/Marketing/Blog/Autodesk_and_DataCamp_-_customer_success_story.png" /></center>

<h5 id="-read-the-case-study-on-datacamp-https-www-datacamp-com-resources-case-studies-autodesk-case-study-"><a target="_blank" href="https://www.datacamp.com/resources/case-studies/autodesk-case-study">Read the case study on DataCamp</a>.</h5>


https://www.datacamp.com/community/blog/3-ways-to-level-up-your-team-reporting-in-2022

77% of learning professionals don’t know how to assess the impact of learning programs. That’s why, in 2022, DataCamp is making it easier for admins to report on the difference you’re making, using Custom Reports and our new Power BI and Tableau connectors. 

DataCamp is making it easier for admins to report on the difference you’re making, using Custom Reports and our new Power BI and Tableau connectors. 

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