<p><p><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><p></p>
<p>In an age of accelerated digital transformation, learning and development is paramount for forging a resilient workforce that adapts to the changing nature of work. Most recently, MIT Sloan Management Review updated its 2014 <a target="_blank" href="https://sloanreview.mit.edu/article/the-new-elements-of-digital-transformation/">framework on digital transformation</a> to accommodate the people component, with culture and skills underpinning scalable organizational change. Creating a learning culture that sustains continuous learning is foundational for the ability to succeed in the digital age.</p>
<p>This is especially true for equipping organizations with the necessary data skills. With an ongoing data talent shortage, upskilling and reskilling on data science and analytics is top of mind for many executives and leaders today. <a target="_blank" href="https://www2.deloitte.com/us/en/insights/industry/technology/data-analytics-skills-shortage.html">According to Deloitte</a>, the number of jobs posted for analysis skills has consistently surpassed the number of employees available for these roles.</p>
<p>The link between ubiquitous use of data science and business outcomes is widely <a target="_blank" href="https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/catch-them-if-you-can-how-leaders-in-data-and-analytics-have-pulled-ahead">documented</a>. However, In addition to better business outcomes, organizations benefit from higher employee retention rates and satisfaction. A <a target="_blank" href="https://learning.linkedin.com/resources/workplace-learning-report-2018#">LinkedIn learning report</a> stated that 94% of employees believed that investing in training and development would improve their chance to stay at an organization for longer. A <a target="_blank" href="https://www2.deloitte.com/us/en/insights/focus/human-capital-trends/2016/fostering-culture-of-learning-for-employees.html">Deloitte survey</a> suggests that learning programs are a high-priority benefit that organizations can offer to younger employees and one that they expect.</p>
<p>In this article, we discuss the five key steps to take to create a learning culture you can start implementing in your organization today.</p>
<h2 id="five-steps-to-building-a-learning-culture">Five Steps to Building a Learning Culture</h2>
<h3 id="make-learning-an-organizational-value">Make Learning an Organizational Value</h3>
<p>Learning cultures are built throughout the entire organization. The process starts with support at the executive level from CEOs. This <a target="_blank" href="https://www.pwc.be/en/FY20/documents/the-future-of-work-event-BO8-upskilling.pdf">PwC survey</a> shows that 80% of CEOs are worried that the key skills their organizations require will not be available. This survey suggests that many executives rely on upskilling within their organizations rather than hiring outside from other industries and competitors. It also argues that executives must restructure performance evaluation to focus more on continuous feedback related to learning and acquired skills.</p>
<p><a target="_blank" href="https://www.researchgate.net/publication/268328210_Psychological_Safety_Trust_and_Learning_in_Organizations_A_Group-level_Lens">Amy Edmondson</a> suggests that learning environments can be developed through what is called “psychological safety.” Employees must feel safe that they will not face negative consequences for taking risks that lead to high-quality learning outcomes.</p>
<h3 id="engage-middle-managers-and-individual-contributors">Engage Middle Managers and Individual Contributors</h3>
<p>Once leadership has made it known that learning is an organizational value, middle managers and individual contributors are in charge of implementing these strategies. Middle managers must work directly with their direct reports on a learning plan that fits their personal and team’s ambitions, and that are aligned with desired business outcomes.</p>
<p>It is very important that middle managers effectively communicate with their employees both on <a target="_blank" href="https://www.gaapdynamics.com/insights/blog/2019/06/25/five-ways-to-nurture-a-learning-culture-in-your-organization/">why learning is a top priority</a> in the organization and how they can improve on their performance for this objective. In a <a target="_blank" href="https://www.datacamp.com/community/blog/learning-development-data-driven-age">panel</a> DataCamp hosted earlier in the year with L&amp;D leaders from Bloomberg and NatWest Group, Marcus Robertson, Global Curriculum Lead at NatWest pointed out that middle managers need to communicate with both direct reports and leadership, argued that teams can only deliver results if they develop their skills.</p>
<p>Moreover, <a target="_blank" href="https://medium.com/@andrew.sherman_ask_coaching/five-ways-to-create-a-learning-culture-in-your-team-6ed0230cf67a">people learn in unique ways</a>. Some people prefer a more direct approach to feedback while others prefer indirect communication styles. Middle managers understand the people who directly report to them the best. They can use this information to effectively provide feedback on employees’ learning and development in their direct reports.</p>
<h3 id="make-learning-resources-accessible">Make Learning Resources Accessible</h3>
<p>Once the organization is built properly for encouraging and providing proper feedback for learning, it is important to develop high-quality resources for learning, specifically for analytics skills. As intelligent, data-driven organizations scale, they are developing learning resources for their employees to help them upskill and keep up with the changing demands for their roles.</p>
<p>For example, <a target="_blank" href="https://medium.com/airbnb-engineering/how-airbnb-democratizes-data-science-with-data-university-3eccc71e073a">Airbnb</a> created “The Data University” to provide over 30 data-skill-related classes to help their employees develop data awareness, data collection, and visualization skills, and to help them understand how to work with data at scale.</p>
<p>Another example is from <a target="_blank" href="https://www.datacamp.com/community/blog/why-data-culture-matters">Allianz Benelux</a>, which leverages a combination of DataCamp courses and other learning resources to scale its own data analytics Academy.  </p>
<h3 id="develop-personalized-learning-paths">Develop Personalized Learning Paths</h3>
<p>Every employee in an organization has unique learning preferences and reasons for learning analytics skills. As resources are created to empower employees to learn new analytics skills, these unique goals need to be considered to make the learning journey more impactful. <a target="_blank" href="https://www.chieflearningofficer.com/2021/04/21/5-ways-to-foster-a-learning-culture/">This article</a> suggests that personalizing learning and development for employees can lead to <a target="_blank" href="https://www.cnbc.com/2019/02/27/94percent-of-employees-would-stay-at-a-company-for-this-one-reason.html">better retention</a>, better productivity, and better customer outcomes.</p>
<p>Within every data analytics, <a target="_blank" href="https://www.datacamp.com/community/blog/eight-personas">there are eight data personas</a> found in every organization. These range from data consumers and leaders in more non-technical roles to data engineers with significantly more technical backgrounds and requirements. Tailoring learning paths to each of these personas is critical to generating valuable results from learning programs.</p>
<h3 id="experiment-with-different-learning-modalities">Experiment with Different Learning Modalities</h3>
<p>Learning and development curriculum should be created with different approaches, including self-led and more traditional instructor-led training.</p>
<p>This approach, known as <a target="_blank" href="https://www.learnupon.com/blog/what-is-blended-learning/">blended learning</a>, allows organizations to reduce costs, vary learning styles to include methods like webinars and gamification, and significantly increase return on investment. An example of blended learning for data science comes from <a target="_blank" href="https://www.datacamp.com/resources/webinars/thrive-in-a-data-driven-age">Sheil Naik</a> from Bloomberg, who leverages a combination DataCamp and in-house live training code-along on Bloomberg data as part of an upskilling program on Python.</p>
<h2 id="building-a-learning-culture-underpins-data-transformation">Building a Learning Culture Underpins Data Transformation</h2>
<p>Upskilling is the only way for organizations to become data fluent. Creating a learning-driven culture within an organization is a high ROI opportunity that leads to better insights and business outcomes. Young employees expect this culture. It is also the only way to meet the high demand for data analytics skills in our digital, data-driven environment.</p>
<p><p><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><p></p>

https://www.datacamp.com/community/blog/five-steps-to-building-a-learning-culture

Creating a learning culture that sustains continuous learning is foundational for the ability to succeed in the digital age.

Five steps to building a learning culture

<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>A good decision-making process finds a balance between decisiveness and due diligence, so conveying information quickly is highly valued in business. This is the essence of visualization—oftentimes in a single graphic, charts quickly show and explain what would otherwise have been thousands of numbers and words. Charts can be created using Business Intelligence tools such as Tableau and Power BI.</p>
<p><center> <img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1631201357/power-bi-vs-tableau-1_jvpny9.png"></center></p>
<p><center> <img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1631201357/power-bi-vs-tableau-2_gkwa8c.png"></center></p>

<h2 id="power-bi-vs-tableau-similarities">Power BI vs Tableau -  Similarities</h2>
<h3 id="they-are-extremely-popular-">They are extremely popular.</h3>
<p>According to Gartner they are the two main leaders in the business intelligence platforms market. Tableau was introduced in 2003 and grew into one of the most popular BI tools, while Power BI was more recently launched by Microsoft in 2011, and added to Office 365 in 2013.</p>
<p><center> <img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1631201357/power-bi-vs-tableau-3_atgfmg.png"></center></p>

<h3 id="they-can-be-used-to-produce-a-variety-of-different-visualizations-">They can be used to produce a variety of different visualizations.</h3>
<p>These can include bar and line charts, treemaps, and geographical maps. On both platforms, you can interact with these visualizations, such as by hovering over them to get more information and applying filters, and they can also be combined to create dashboards.</p>
<p><center> <img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1631201357/power-bi-vs-tableau-4_mcpkdu.png"></center></p>

<h3 id="they-can-connect-to-a-variety-of-data-sources-">They can connect to a variety of data sources.</h3>
<p>These include MS Excel, CSV, and JSON, and the paid version of both platforms gives you access to 50+ additional data connectors such as Google Bigquery, Amazon Redsift, and Salesforce. Visualizations are updated if the underlying data changes.</p>
<h3 id="both-are-user-friendly-with-no-coding-involved-">Both are user-friendly with no coding involved.</h3>
<p>This makes them an ideal first set of tools to learn if you are new to data analytics. If you are a business analyst they are two of the main data visualization tools you will need to work with data. They are by no means the only tools you’ll be working with, as the graph below demonstrates. SQL, R, and Python are also valuable tools, and adding one or more to your skill set is extremely valuable.</p>
<p><center> <img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1631201357/power-bi-vs-tableau-5_xzmwbq.png"></center></p>

<h3 id="thanks-to-recent-updates-the-two-have-become-more-and-more-similar-">Thanks to recent updates, the two have become more and more similar.</h3>
<p>For example, Power BI used to excel in data preparation, as users have always been able to model, clean, and add calculated columns to datasets through Power Query. However, Tableau has recently made its own inroads on this with the launch of Tableau Prep.</p>
<blockquote>
<p>The first question you should ask when choosing between Power BI and Tableau is: do I need to?</p>
</blockquote>
<p>If you are looking to upskill and enhance your value to potential employers, having a working knowledge of either Power BI or Tableau is great. Additionally, both are available here on DataCamp, and once you learn how to use one, learning how to use the other is simple.</p>
<p><center> <img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1631211011/power-bi-vs-tableau-6_gp4fib.png"></center></p>
<p><center> <img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1631201358/power-bi-vs-tableau-7_w3n213.png"></center></p>

<h2 id="power-bi-vs-tableau-the-differences">Power BI vs Tableau—The Differences</h2>
<p>However, there are a few points of difference that should be considered, and if you are considering using them within your business it makes sense to choose one or the other.</p>
<h3 id="power-bi-only-works-on-microsoft-windows-">Power BI only works on Microsoft Windows.</h3>
<p>If you will mostly be using a Mac Computer for your work, Tableau would be more useful.</p>
<h3 id="the-range-of-certifications-available-differs-between-tableau-and-power-bi-">The range of certifications available differs between Tableau and Power BI.</h3>
<p>Tableau offers a wider range of options designed for specific professions. You can find out more about these on the <a target="_blank" href="https://www.tableau.com/learn/certification">Tableau website</a> (some will be retired soon, and there is a new Data Analyst certification going live soon, so be sure to check this), and DataCamp also recently put our <a target="_blank" href="https://www.datacamp.com/community/blog/tableau-student-to-certification">Tableau instructors up to the challenge of taking the exam themselves</a>! Costs range from \$100 to \$250.</p>
<p><a target="_blank" href="https://docs.microsoft.com/en-us/learn/certifications/exams/da-100">Power BI</a> has one exam that covers all the main functionalities of the platform and costs \$165 (although at the time of writing they are still offering it for \$15 to students, people on furlough, and other individuals who have been negatively affected by the COVID-19 pandemic).</p>
<h3 id="tableau-s-exam-is-currently-only-available-in-english-">Tableau’s exam is (currently) only available in English.</h3>
<p>Power BI offers its exam in multiple languages, while Tableau currently only offers it in English—although they have said they will be releasing translated versions in the future.</p>
<h3 id="their-ease-of-integration-will-depend-on-what-kind-of-software-you-use-">Their ease of integration will depend on what kind of software you use.</h3>
<p>As Power BI was developed by Microsoft, it will be easier to integrate into your workspace if you use Office software. Likewise, Tableau was recently acquired by Salesforce so they are worth considering thanks to their native integration. However, both are capable of connecting to Excel and Salesforce, so this is more a question of which would run more smoothly.</p>
<p><center> <img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1631201358/power-bi-vs-tableau-8_hhpu86.png"></center></p>

<h3 id="there-are-different-pricing-levels-">There are different pricing levels.</h3>
<p>This is where the most differences exist between Power BI and Tableau.</p>
<p><center> <img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1631538320/power-bi-vs-tableau-10_audaop.png"></center></p>

<div class="output_wrapper"><table>
<thead>
<tr>
<th>Name of license</th>
<th>Cost (monthly)</th>
<th>Description</th>
<th>Name of license</th>
<th>Cost (monthly, billed annually)</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td>Power BI Desktop</td>
<td>Free</td>
<td>Allows you to use all basic Power BI functions on your local machine</td>
<td>Tableau Public</td>
<td>Free</td>
<td>Can be used to create visualizations connected to Excel, CSV, and JSON files. All visualizations will be publicly viewable</td>
</tr>
<tr>
<td>Power BI Pro</td>
<td>$9.99 (free if you have Office 365)</td>
<td>Provides access to individual users, and enables them to build their own dashboards and connect to a wide range of data sources.</td>
<td>Tableau Explorer</td>
<td>\$35 on Tableau servers, \$42 online</td>
<td>Allows the holder to build dashboards and visualizations based on a dataset provided by colleagues.</td>
</tr>
<tr>
<td>Power BI Premium (per-user)</td>
<td>$20</td>
<td>Similar to Power BI Pro, but has increased storage capacity, allows you to refresh dashboards more often, and offers some additional features.</td>
<td>Tableau Creator</td>
<td>$70</td>
<td>Provides all Explorer permissions, plus the ability to create connections to data sources. <strong>Recommended for individual users.</strong></td>
</tr>
</tbody>
</table></div>
<p>The licenses above are designed for individual data analysts who are creating dashboards. If your company is interested in rolling out Tableau or Power BI, you will also need to consider viewer licenses:</p>
<div class="output_wrapper"><table>
<thead>
<tr>
<th>Name of license</th>
<th>Cost (monthly)</th>
<th>Description</th>
<th>Name of license</th>
<th>Cost (monthly, billed annually)</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td>Power BI Premium (per-capacity)</td>
<td>From $4,995</td>
<td>An enterprise-specific license that enables viewing access to everyone in your organization, (PBI Pro license is still required to edit dashboards).</td>
<td>Tableau Viewer</td>
<td>\$12 per viewer on Tableau servers, \$15 online</td>
<td>An enterprise-specific license that allows the holder to view dashboards created by colleagues and apply filters. Also offers custom enterprise <a target="_blank" href="https://www.tableau.com/about/contact#contact_enterprise-subscriptions">subscription plans.</a></td>
</tr>
</tbody>
</table></div>
<h3 id="which-is-better-power-bi-or-tableau-">Which is better, Power BI or Tableau?</h3>
<p>There is a lot of overlap between Power BI and Tableau, and ultimately both are useful tools to have. For some users, choosing to learn both can be the best option, however, for those who wish to decide between one or the other, there are differences in cost and accessibility that must be considered.  </p>
<p><em>DataCamp offers courses in both <a target="_blank" href="https://www.datacamp.com/courses/introduction-to-tableau">Tableau</a> and <a target="_blank" href="https://www.datacamp.com/courses/introduction-to-power-bi">Power BI</a>. Our courses enable you to practice with each tool’s interface without any installation involved and cover functions such as connecting data and building dashboards.</em></p>
<h3 id="in-summary">In summary</h3>
<div class="output_wrapper"><table>
<thead>
<tr>
<th></th>
<th>Tableau</th>
<th>Power BI</th>
</tr>
</thead>
<tbody>
<tr>
<td>Market leaders?</td>
<td>✅</td>
<td>✅</td>
</tr>
<tr>
<td>Variety of different visualizations?</td>
<td>✅</td>
<td>✅</td>
</tr>
<tr>
<td>Can connect to various data sources?</td>
<td>✅</td>
<td>✅</td>
</tr>
<tr>
<td>User friendly?</td>
<td>✅</td>
<td>✅</td>
</tr>
<tr>
<td>Type of machine it can work on?</td>
<td>Macs and Windows Computers</td>
<td>Windows Computers only</td>
</tr>
<tr>
<td>Certifications?</td>
<td>Multiple</td>
<td>One</td>
</tr>
<tr>
<td>Easier to integrate with...</td>
<td>Salesforce</td>
<td>Microsoft</td>
</tr>
<tr>
<td>Cost</td>
<td>Varies, but Tableau is generally more expensive</td>
<td>Varies, but Tableau is generally more expensive</td>
</tr>
</tbody>
</table></div>
<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/power-bi-vs-tableau

Power BI or Tableau? This comparison will help you decide which of these tools to use for your data science and analytics needs.

Power BI vs Tableau: Which one should you choose?

https://www.datacamp.com/community/blog/beyond-the-language-wars-r-python-for-the-modern-data-scientist

In this episode of DataFramed, we speak with Rick Scavetta and Boyan Angelov about their new book, Python and R for the Modern Data Scientist: The Best of Both Worlds, and how it dawns the start of a new bilingual data science community.  

Beyond the Language Wars: R & Python for the Modern Data Scientist

<p>Personalizing data science learning for your staff is critical to establishing a data fluency. For one thing, different individuals have different learning styles. For another, your business itself is unique, it stands out from the crowd. That’s why your customers choose you. That also means you face a unique set of challenges, one that one-size-fits-all training may not adequately address.</p>
<p>Ensuring that our learning cultivates not only an understanding of the topic at hand but also an application readiness has always been top-of-mind for us, and so in our early days, we focused on producing a large number of courses, developing many tailored to specific practices and industries. Today DataCamp is endowed with over 350 different courses, consisting of videos and interactive, hands-on exercises, delivered by top-notch instructors. This variety of options enables DataCamp to better address skill gaps in organizations. Linked to this, <a target="_blank" href="https://www.datacamp.com/custom-tracks">custom tracks</a> provide a way to select the most relevant courses for ensuring your staff learn the skills you really need.</p>
<p>We have recently made some recent improvements to custom tracks: now you can add assessments and projects to your organization’s tracks. Both of these are designed to test and apply what your employees know, so make for a natural next step after they have completed the courses. Projects, in particular, are designed to simulate real-life situations, and with 90+ of them active at the time of writing there is a high chance that one will more or less mirror the day-to-day application of data science your staff will undertake—something our customer success team will be able to advise upon!</p>
<p>Creating custom tracks is a simple process: you create a draft, give it a name, select the most appropriate courses from the myriad we offer, and publish it to your team. To assist in the process of selecting courses, each Enterprise customer receives a Customer Success Manager who will partner with an expert Learning Solutions Architect to understand your organization’s aims and goals, and will be able to recreate a learning program tailored to these goals.</p>
<p><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1630078515/DataCamp-custom-tracks-in-action_t1oh9m.gif"></center></p>

<p>In short, custom tracks are how you can, in tune with the needs of your business and the learning styles of your staff, create your own learning program so that the training you offer is kept both high-quality and relevant to the work at hand.</p>
<p>Tips for creating custom tracks:</p>
<ul>
<li><p>Include a clear description of who should take this track, why it will benefit your business, and what will be taught. <strong>Do this first</strong>, because it will also provide you with guidance regarding which courses, assessments, and projects to add.</p>
</li>
<li><p>Make sure the title is unique and descriptive. Depending on the size of your organization you may be creating many of these, so this will help to prevent confusion.</p>
</li>
<li><p>Use the Assignments function to make it clear who is to take a track and complete which tasks by when can double completion rates.</p>
</li>
</ul>
<p><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1630069629/custom-track-updates-2021-aug-2_iks5yf.png"></center></p>

<ul>
<li><p>Share it across your intranet.</p>
</li>
<li><p>Celebrate employee success: every individual who completes a DataCamp course and track gets a statement of accomplishment —singing their praises is a great way to motivate others to do the same.</p>
</li>
<li><p>Encourage employees to blow their own trumpet: the aforementioned statement of accomplishment can also be shared on LinkedIn, showing that your organization and DataCamp are contributing to their personal development.</p>
</li>
</ul>
<p><em>If you’re interested in learning more, request a <a target="_blank" href="https://www.datacamp.com/business/demo">demo</a> today to see how DataCamp can spread data fluency within your organization!</em></p>


https://www.datacamp.com/community/blog/custom-track-updates-2021-aug

Know your staff, know your stuff: using custom tracks is great for personalizing upskilling in your business by allowing you to choose the most relevant courses, and you can now add projects and assessments to your tracks. Read on to see what this means for you when you subscribe to DataCamp!

Using custom tracks is great for personalizing upskilling in your business by allowing you to choose relevant courses, and you can now add projects and assessments to your tracks. Read on to see what this means for you when you subscribe to DataCamp!

Custom tracks just got better

<p>Marc Wintjen is a Risk Analytics Architect at Bloomberg with over 20 years of professional experience. He joined us during a webinar to discuss how data literacy helps improve communication while empowering teams throughout the organization. If you’re interested in learning more about Marc, listen to the full webinar <a target="_blank" href="https://www.datacamp.com/resources/webinars/bridging-the-communications-gap-with-data-literacy">here</a>.</p>
<h2 id="the-power-of-data-visualization">The Power of Data Visualization</h2>
<p>Nowadays, organizations generate terabytes of data every day. Once the data is available, the team needs to be able to ask the right questions in order to derive valuable insights from data. Finally, by combining data with a detailed narrative and the right visuals, you will be able to communicate your data insights efficiently and effectively. Marc emphasized how data literacy is the cornerstone of great data visualization, which is achieved when everyone in the organization is empowered to ask the right questions about their data.</p>
<p>Marc defines data literacy as the ability to read, analyze, or argue with data, in order to draw useful data insights for your organization. Data visualization is a key component when communicating data insights across your organization. Selecting the right data visualization tools and visuals will help you break down the complexity behind the data sources and make it easier for others on your team to consume. Data visualization tools also reduce the ramp-up time to understand the context behind the data and invite the reader to discuss the what and the why.</p>
<blockquote>
<p>&quot;Visualizing data breaks down the complexity behind the data sources to make it easier and faster to consume.&quot; - Marc Wintjen, Risk Analytics Architect at Bloomberg</p>
</blockquote>
<h2 id="ask-the-right-questions-for-better-data-insights">Ask the Right Questions for Better Data Insights</h2>
<p>Data visualization is both an art and a science: it is possible to construct a data visualization that is both aesthetically  pleasing, like a work of art, and also provides insights from the underlying statistical evidence captured from the data. The goal is for the audience—or in this case, your team—to quickly digest and interpret the information without any guidance.
To determine the right visualization tools Marc suggests that you need to be able to determine what is the core business question you are trying to answer. Hence, it is important that the team is empowered with enough information to ask the right questions. There are three main techniques to ask the right questions:</p>
<ul>
<li><p><strong>Know Your Data (KYD):</strong> Understand business requirements and what the users will do with the data, including the people, processes, and technology used.</p>
</li>
<li><p><strong>Voice Of the Consumer (VOC):</strong> Understand specific pain points by interviewing your business users and actively listening to their needs.</p>
</li>
<li><p><strong>Always Be Agile (ABA):</strong> The process involves a series of sprints, also called stories, which commonly last 2-3 weeks, and the goal is to understand the what and the why behind each story.</p>
</li>
</ul>
<p>As explained by Marc, Bloomberg uses the Agile methodology, which has become common in the tech industry for application development. The main advantage of the Agile methodology is that it creates an interactive communication line between the business and engineering teams to iteratively deliver value. But all of these techniques help improve your data literacy, regardless of your role within the organization.</p>
<h2 id="select-the-right-visualization-tool-based-on-your-data-type">Select the Right Visualization Tool Based on your Data Type</h2>
<p>Marc argues that to achieve a good level of data literacy in any organization, you need to master the techniques of asking questions and data visualization. Once you understand what the right questions are for your organization, you will be able to implement the right visualization tools. However, you need to keep in mind that good graphics are not just displays to extract information from, but devices to explore information with.</p>
<p>Before defining the right displays and graphs, you need to know what data types you are analyzing. In short, data types are the details of how data is stored and these are commonly well known in different programming languages. Understanding how different data types work will help you create clearer data visualizations much faster. In his book, Practical Data Analysis Using Jupyter Notebook, Marc classifies data types as follows:</p>
<ul>
<li><p><strong>Continuous:</strong> Numeric values like integer, float, or time.</p>
</li>
<li><p><strong>Categorical:</strong> Descriptive values, like a stock ticker, first name, or last name.</p>
</li>
<li><p><strong>Discrete:</strong> Define boundaries around the possible data values, like numbers on a roulette wheel.</p>
</li>
</ul>
<blockquote>
<p>&quot;Good graphics are not just displays to extract information from, but devices to explore information with.&quot; - Marc Wintjen, Risk Analytics Architect at Bloomberg</p>
</blockquote>
<h2 id="steps-to-create-great-data-visualizations">Steps to Create Great Data Visualizations</h2>
<p>Data types and data visualization are core elements of your data model. Marc defines the  data model as the relationships between one or more tailored data sources. To achieve a complete model, Marc describes a set of steps that you should follow when implementing your data model.</p>
<ol>
<li><p><strong>Understand the question:</strong> You must know what questions you are trying to answer with the data, then you can decide which model will best fit your goal.</p>
</li>
<li><p><strong>Identify key parameters:</strong> Identify what data fields will provide the right data insights. In many cases, you will have to conform the data so that each row has to be a consistent data type, to make your analysis faster and easier. There is no need to use every single field of your data source, just the necessary ones.</p>
</li>
<li><p><strong>Visualize your data:</strong> Creating a good chart narrows down to defining what fields you plan on using for analysis dimensions and measures. Dimensions are values with descriptive attributes that are commonly used to identify a person, place, or thing. A measure is any field that can be aggregated using statistical calculations, like max or average.</p>
</li>
<li><p><strong>Reuse your code:</strong> You don&#39;t have to reinvent the wheel every time to create a new chart, app, or dashboard. If you model your data correctly, adding the right trend chart could help you save time and focus on the right tasks.</p>
</li>
</ol>
<p>Data literacy can be a great tool to improve communication across different teams in the organization. To effectively communicate your data insights, your team must acquire a certain level of data skills to create effective data visualization models, which are used to provide context and insight to users that do not have a data science or engineering background.</p>
<p>Data models are the secret sauce to create great graphics that answer the right business questions. If you are interested in learning more, check out the blog on <a target="_blank" href="https://www.datacamp.com/community/blog/storytelling-for-more-impactful-data-science">Storytelling for a More Impactful Data Science</a>.</p>
<p>Lastly, sharing is caring, and even more when it comes to your team. To break down those data analytics silos, you will need to empower others within the organization to read, analyze, and argue with data. If you want to learn more about Marc’s approach to data communication you can listen to the full webinar <a target="_blank" href="https://www.datacamp.com/resources/webinars/bridging-the-communications-gap-with-data-literacy">“Bridging the Communications Gap with Data Literacy”</a>.</p>


https://www.datacamp.com/community/blog/event-recap-bridging-the-communications-gap-with-data-literacy

We recently hosted a webinar with Marc Wintjen, Risk Analytics Architect at Bloomberg, to discuss how data literacy helps improve communication while empowering teams throughout the organization. 

Event Recap: Bridging the Communications Gap with Data Literacy

<p>Financial analysts work in heavily quantitative and data-oriented roles. In a <a target="_blank" href="https://www.mckinsey.com/business-functions/organization/our-insights/beyond-hiring-how-companies-are-reskilling-to-address-talent-gaps">McKinsey survey</a>, 87% of respondents said they were either currently facing or would be facing skill gaps within the next 5 years with 43% of them saying the largest gaps would be in data roles. These roles are increasingly important in Finance as <a target="_blank" href="https://www.gartner.com/smarterwithgartner/fill-finances-skill-gaps/">Gartner</a> predicts over 80% of financial service organizations will increase their use of advanced analytics in 2021.</p>
<p>Finance teams should be learning “modern” data tools like R, Python, and SQL or business intelligence to gain previously hidden insights. In addition to generating valuable insights, allowing analysts to learn new data skills is very appealing to job seekers according to 64% of participants in a <a target="_blank" href="https://www.fm-magazine.com/news/2016/apr/data-analysis-skills-in-finance-201614245.html">Robert Half survey</a>.</p>
<p>In this article, we will look at 4 data science use cases financial analysts can start leveraging today to supplement their subject matter expertise.</p>
<h2 id="four-ways-financial-analysts-can-start-with-data-skills">Four ways Financial Analysts Can Start with Data Skills</h2>
<h3 id="analyze-data-quickly-and-communicate-insights">Analyze Data Quickly and Communicate Insights</h3>
<p>A key component of a Financial Analyst’s responsibilities is their ability to analyze and draw insights from financial data. In addition to their skills with Excel, they can make the analysis process more efficient and effective by leveraging tools like SQL alongside BI tools and open source programming languages like R and Python.  </p>
<p>These tools are especially useful in analyses involving larger datasets. SQL is a programming language used to communicate with databases to retrieve and organize data. It allows analysts to retrieve just the information they are interested in, leading to faster analysis workflows. For example, if an analyst was building a model to project cash flows for a specific department, they could filter the database, with all the organization&#39;s cash flows, for just the department they would like to analyze. Quickly accessing just the data needed for analysis will lead to significantly more efficient workflows.</p>
<p>Once the data are collected, insights can be quickly made through SQL by calculating summary statistics. For example, the average and standard deviation of cash flows in each month can be calculated quickly using grouped calculations in SQL.</p>
<p>Communicating insights from analyses to stakeholders is a critical component of adding value. Discovering how to improve cash flows is valuable only if effectively communicated to decision-makers in a way that causes them to take action. These insights can be accurately described through BI tools (such as <a target="_blank" href="https://www.datacamp.com/courses/introduction-to-power-bi">powerBI</a> and <a target="_blank" href="https://www.datacamp.com/tracks/tableau-fundamentals">Tableau</a>) or open-source libraries in Python and R such as <a target="_blank" href="https://plotly.com/dash/">Dash</a> and <a target="_blank" href="https://www.datacamp.com/courses/building-dashboards-with-shinydashboard">Shiny</a>. These tools allow analysts to quickly display their findings through interactive dashboards.</p>
<p><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1630090647/how-financial-analysts-can-start-leveraging-data-skills-1_van0yn.png"></center></p>

<p><center>Source: <a target="_blank" href="https://simplywall.st">Simply Wall Street</a></center></p>
<h3 id="make-better-decisions-by-forecasting-time-series-data">Make Better Decisions by Forecasting Time Series Data</h3>
<p>Another valuable use case in data science for financial analysts is analyzing time series data. Making accurate predictions is a common task for financial analysts. They can predict future stock price movement, customer churn, cash flows, or customer lifetime value. These predictions need to be reliable and accurate to add value to decision-making processes.</p>
<p>Historical data can be leveraged in prediction tasks to make more realistic and confident predictions. Seasonality is critical in many forecasting tasks. During the year, business activities fluctuate. This is especially true when disrupting events like covid-19 affected most organizations’ operations. Understanding these seasonal fluctuations can lead to significantly better predictions. There are many <a target="_blank" href="https://www.datacamp.com/tracks/time-series-with-r">open-source tools in R</a> and <a target="_blank" href="https://www.datacamp.com/tracks/time-series-with-python">Python</a> which streamline time-series analysis. These allow for a more dynamic approach to prediction tasks.</p>
<h3 id="automate-business-processes-with-programming-skills">Automate Business Processes with Programming Skills</h3>
<p>A valuable application of programming skills in any field is the ability to automate repeated tasks. Scripts in R or Python can run repeated tasks every day to save analysts time and allow them to work on other responsibilities.</p>
<p>One repeated task is an analyst putting together a list of the best-performing stocks for a given industry from the previous day. An analyst can create a script to run every night after the markets close. This script may scrape the necessary data by accessing the organization’s data api, use python data types and tools such as <a target="_blank" href="https://pandas.pydata.org/">pandas</a> DataFrames and a <a target="_blank" href="https://jupyter.org/">Jupyter notebook</a> to effectively clean this data (for easier and more use), and finally generate an excel spreadsheet file in the format that the analyst always inputted the data manually in the past.</p>
<p>The next morning, the analyst can begin working on the subsequent tasks, such as writing a report about this data. Over a few weeks, the amount of work this analyst can complete because they don&#39;t have to make this spreadsheet manually every morning will be significant.</p>
<h3 id="predict-outcomes-with-machine-learning">Predict Outcomes with Machine Learning</h3>
<p>Machine learning plays an integral role in financial analysis. Supervised and unsupervised learning both can be leveraged in finance to improve the efficiency and effectiveness of tasks like document analysis and digitization of physical documents.</p>
<p>There are many applications of supervised learning (training machine learning models with “correct” labels to perform the task without given labels in the future) that can be used for financial tasks. Some of these tasks include converting physical documents into digital documents, fraud detection, optimal portfolio management, and future stock price predictions. All of  these tasks are very important for financial analysts to perform accurately. Analysts can leverage machine learning with the large amounts of data available to them to more effectively complete these tasks. Other applications of machine learning in finance can be found <a target="_blank" href="https://www.datacamp.com/resources/whitepapers/digital-transformation-in-finance">here</a>.</p>
<p>Unsupervised learning (training models without any predefined “correct” labels) can also be leveraged in these types of tasks also. One model that can be built may organize newly digitized documents in an appropriate file structure for easy access.</p>
<p>By building these models to handle basic document processing and analysis tasks, an analyst can spend more time working on creating actionable insights from all of this available data that is typically underutilized because of its inaccessibility.</p>
<p><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1630090905/how-financial-analysts-can-start-leveraging-data-skills-2_poo6tw.png"></center></p>

<p><center>Source: <a target="_blank" href="https://www.bloomberg.com/professional/solution/bloomberg-terminal/">Bloomberg Terminal</a></center></p>
<h2 id="upskilling-is-the-only-way-moving-forward">Upskilling is the Only Way Moving Forward</h2>
<p>As data science is integrated more into the financial industry, the only way for financial analysts to continue to be successful and fully produce their potential value to their organizations is to acquire data skills. As seen in the previous four use-cases, data science skills can greatly improve productivity and quality of work through automation, organization, and data visualizations.</p>
<p>A <a target="_blank" href="https://www.six-group.com/en/newsroom/media-releases/2020/20201209-industry-pulse.html">recent survey</a> showed that 90% of financial firms expect to increase their data consumption within the next year and 52% of them believe meaningfully analyzing this data is a top strategic priority. Given the data talent skills gap, the only way to succeed with these expectations is for financial analysts to upskill.  </p>


https://www.datacamp.com/community/blog/how-financial-analysts-can-start-leveraging-data-skills

In this article, we will look at 4 data science use cases financial analysts can start leveraging today to supplement their subject matter expertise. 

How Financial Analysts can start leveraging data skills



<p>It may seem unusual that someone can be more experienced in Python and SQL than typing on a physical keyboard, but that’s exactly how some people started off their careers in data science. Felix Ayoola, a learner and partner of ours in Nigeria who <a target="_blank" href="https://www.datacamp.com/donates/stories/felix-ayoola">previously</a> commended the <a target="_blank" href="https://www.datacamp.com/mobile">DataCamp for Mobile app</a>, continues to sing its praises: “A lot of people here who were gifted access to data science education through DataCamp Donates started off learning on their phones—in fact, much of the tech community as a whole here have similar stories of using a mobile phone as a crucial first step in their learning journey. There’s a data scientist friend of mine who was gifted a laptop after she had spent years learning on a phone—this proved to her family that a laptop would be a worthy investment. There was also someone I know who learned coding exclusively from his phone and started up his own recruitment business. Through the earnings, he was able to afford a laptop.”</p>
<p>Personal finances are the main driver of this. “Very few people in Nigeria can afford both a phone and a computer,” Felix continues, “and because a good-quality phone can cost half the price of a second-hand old laptop, we often start off there. Additionally, broadband is very expensive here, much more so than data. A quirk of Nigerian cellular services is that you can get some data packages very cheaply but they can only be used at night. As a result, many of us get one of these and do any data-intensive activities at night—this can include DataCamp courses.”</p>
<blockquote>
<p>“Because of the challenges associated with accessing a PC in my country, DataCamp enhancing the mobile phone learning experience is a great way to broaden access to data science education.”</p>
</blockquote>
<p>DataCamp for Mobile was initially born to supplement learning on the PC, as a quick and easy way of getting some practice in while learners have five minutes to spare. However, as DataCamp usage has picked up throughout the developing world, the dynamics of mobile usage have shifted, too:</p>
<p><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1630070399/datacamp-mobile-blog_fqbkz1.png"></center></p>

<p>Furthermore, in line with Felix Ayoola’s anecdote, Nigeria takes the lion’s share of DataCamp Donates learners accessing DataCamp content through their cell phones, with almost 40% coming from this country alone. 20% of DataCamp Donates learners worldwide access our content solely through mobile devices, and we are hoping to expand this figure!</p>
<p>Understanding how this happened goes hand in hand with understanding the proliferation of cell phone usage in the developing world, including the reasons behind this and the effects.</p>
<hr>
<p>Once just a little device for talking to people, now the cell phone is a mini-computer that can do all sorts of amazing things thanks to built-in features and a plethora of apps—anyone who remembers early iPhone adverts telling us “there’s an app for that” will remember how revolutionary this was.</p>
<p>But moreover, smartphones and the contracts they come with are cheap. At least, some brands are. The crux of it all is, you can get a lot for little thanks to them. Nowhere is this made more apparent than in the developing world, where their usage is skyrocketing. Nowadays you’d be hard-pressed to find a country in which fewer than 70% of people own or share a cell phone. PC ownership, however, remains low.</p>
<p><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1630078213/datacamp-mobile-blog-4_ufmw4n.png"></center></p>

<p><center><a target="_blank" href="https://www.pewresearch.org/internet/2019/03/07/use-of-smartphones-and-social-media-is-common-across-most-emerging-economies/">Source</a></center></p>
<p>And this isn’t solely the result of developing countries having expanding middle classes: even on the lowest incomes, people find themselves able to afford access, at the least, to a cell phone. This can be through sharing a device like the widespread existence of <a target="_blank" href="https://timesofindia.indiatimes.com/blogs/voices/one-device-households/">one-device households</a> in India or through allocating a huge portion of this income to their phone bill (for example, the World Bank reported that for the median African cell phone user this portion is <a target="_blank" href="https://documents1.worldbank.org/curated/en/896971468194972881/pdf/102725-PUB-Replacement-PUBLIC.pdf">13%</a>).</p>
<p>Cell phones have changed lives for the better, beyond the happiness of being able to contact loved ones or share one’s life on social media. M-Pesa, an app used for transferring money and receiving loans, is used by at least 96% of households in Kenya and has been credited with improving the financial situation of <a target="_blank" href="https://www.usglc.org/blog/the-technology-thats-making-a-difference-in-the-developing-world/">1 in 10</a> Kenyan families living in extreme poverty. Cell phones can also be invaluable when hardship strikes: 90% of Syrian refugee households in Lebanon own a cell phone, using them to access information, transfer money, stay in touch with friends and family, and translate foreign languages into their own. Today <a target="_blank" href="https://journals.sagepub.com/doi/full/10.1177/2056305118764432">communal charging stations</a> are a near-quintessential facility in refugee camps.</p>
<p>Mobile technologies have been able to answer some of the more unique challenges in education in the developing world. <a target="_blank" href="https://en.unesco.org/news/unesco-study-shows-effectiveness-mobile-phones-promoting-reading-and-literacy-developing-0">Illiteracy</a> is being combatted through free learn-to-read apps and voice-to-text technology, which has been especially useful for visually impaired individuals. Also, practice makes perfect: the thought that a collection of printed books might be more expensive than a mobile phone with access to a <a target="_blank" href="https://www.geopoll.com/blog/teaching-through-a-phone-mobile-and-education-in-the-developing-world/">digital library</a> would have been unthinkable years ago, but such have the times changed.</p>


<p>In line with DataCamp’s mission to democratize data, we have enhanced the learning experience on mobile. We are working on making the app experience faster, which will have a big effect when running code in the app on a cellular connection, and negate the effect of slow internet. We are exploring translating the app into Hindi and Spanish since these are two of the most commonly used languages in the developing world. We have also expanded the amount of content available through the app, from offering entire courses to short lessons complete with coding exercises so that one can gain a sizable amount of data science education without ever needing to touch a PC.</p>
<p>Individuals from one of DataCamp Donates’ partners, <a target="_blank" href="https://www.datacamp.com/donates/stories/gdsc-dha-suffa-university">DSC Suffa</a>, recently gave the following testimonials regarding Mobile:</p>
<p>“Mobile was my first experience with DataCamp. It was a very smooth and easy process with hands-on practice provided. The app is an amazing feature that allows me to complete my learning from anywhere, with a practice platform that runs easily on it too. The points I earned after watching completing exercises were a constant push and motivator to do more and excel.
Thanks a lot DataCamp for this amazing opportunity!”
-Syed Husnain Haider Bukhari</p>
<p>“Just like DataCamp&#39;s desktop portal, the mobile app is equally fast, efficient, responsive, and interactive. The interface is quite engaging and user-friendly which makes navigation very easy. Also, the learning notifications and performance tracking mechanism play an instrumental role in keeping me motivated and thus help me do at least a few exercises regularly. The only thing I&#39;d like to be included in the app is a view of the leaderboard. Other than this, the app is doing excellent and the developers at DataCamp deserve great appreciation for it.”
-Omar Hanif</p>
<p>“It’s a great app, portable, easy to use, and convenient. Plus it has a built-in shell and code editor so you don’t have to worry about the coding stuff if you are away from your system. It has all the features of its desktop site. I would highly recommend that others give it a try.”
-Ali Raza Ali Qadri</p>
<p><em>DataCamp Mobile is free to install and available on the <a target="_blank" href="https://apps.apple.com/us/app/datacamp-learn-data-science/id1263413087">Apple Store</a> and <a target="_blank" href="https://play.google.com/store/apps/details?id=com.datacamp&amp;hl=en_US&amp;gl=US">Google Play</a>. We also have an app-exclusive feature known as lessons, which consist of a short video followed by 3-6 coding challenges. There are typically fifteen lessons per course, and Mobile allows you to take one lesson for free every day—perfect for keeping your mind sharp!</em></p>
<p><em>A huge thanks to Felix Ayoola, who inspired the writing of this article. He works as a freelance designer, and if you would like to get in touch with him you can do so through <a target="_blank" href="https://www.linkedin.com/in/ayoolafelix/?originalSubdomain=ng">LinkedIn</a>.</em></p>
<p><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/c_scale,w_292/v1630001650/a-cellular-story-6_ot0gje.png"></center></p>


https://www.datacamp.com/community/blog/a-cellular-story

Because democratizing data is our mission, it’s not often we do it accidentally. Here we discuss how cell phones have radically improved life in the developing world, and how a feature we designed for five minutes on the subway grew into something much larger and massively broadened access to data science education.

Here we discuss how cell phones have radically improved life in the developing world, and how a feature we designed for five minutes on the subway grew into something much larger and massively broadened access to data science education.

A Cellular Story: DataCamp for Mobile in the developing world

<p><center><a target="_blank" href="https://www.datacamp.com/certification"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1629916991/how-can-i-get-certified-1_od8wwh.png"></center></p>

<p>Data science certifications come in various forms. Some of them can be done part-time at low cost, while others will be more expensive and take up more of your time. Certifications can also vary based on what else they provide: some are certification-only, some come with a training period beforehand, and some will assist you in the process of landing a job afterward.</p>
<p>Ultimately deciding which certification you should pursue should be based on these four things:</p>
<p><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1629916991/how-can-i-get-certified-2_avxhek.png"></center></p>

<p>There are a number of different organizations that offer data science certificates, but we’ll focus on four different types of providers:</p>
<ol>
<li>Higher education</li>
<li>Certification only providers</li>
<li>Certification and learning platforms</li>
<li>Bootcamps</li>
</ol>
<h3 id="higher-education-">Higher education:</h3>
<p>The most traditional option, going to university or college, is definitely a good one if you’re intent on data science as a career path, with more and more institutions offering data science courses. However, it is worth pointing out that <strong>you don’t necessarily need to study data science itself</strong> in order to land a job in the field. In many cases, job listings will ask for a “quantitative degree”, which can cover a broad range of numerical subjects—statistics, mathematics, economics, etc. Augmenting such a degree with an additional certification in data science can be a way to prove your interest to employers while at the same time keeping your options in other career fields open.</p>
<h3 id="certification-only-providers-">Certification only providers:</h3>
<p>There are organizations that specifically tailor themselves to getting you certified in the form of <strong>chartership</strong> (Adasci in India and DASCA in the USA), so if you apply it will be assumed that you know all the underlying information already. Additionally, since they are staffed by researchers and industry experts their exams are constantly updated, so make sure you stay on top of data science news and trends!</p>
<h3 id="online-learning-platforms-">Online learning platforms:</h3>
<p>There are also websites whose certification services will encompass a quiz at the least—the sky’s the limit from there on out. We’d be remiss if we didn’t mention DataCamp—<a target="_blank" href="https://www.datacamp.com/certification">our new certification service</a> consists of various exam-style questions, assignments, and a live presentation delivered to experts and non-technical audiences, to ensure you are fully prepared for the challenges you might face in a data science career. Afterward, you will have access to our <a target="_blank" href="https://www.datacamp.com/careerservices">careers service</a>, where advisors with industry links will be able to assist you in your job search.</p>
<p><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1629987487/how-can-i-get-certified-3_hplosp.png"></center></p>

<blockquote>
<p>&quot;We had a whole conversation around my resume. Working with the DataCamp career services team really helped me position myself for success during my job search.&quot;</p>
</blockquote>
<p><center>-<a target="_blank" href="https://www.datacamp.com/community/blog/certified-learners-landing-dream-jobs">Peter L’Oiseau</a>, Sports Analyst for the Canadian Olympics Team</center></p>
<p>Other options include Coursera, who have partnered with IBM, Google, and the Indian Institute of Technology to create a variety of certification tracks suited to various data science professions; Udacity, who have partnerships with major companies including Kaggle, IBM, and Starbucks; and Google who have a Data Analytics Professional certification.</p>
<h3 id="bootcamps-">Bootcamps:</h3>
<p>Bootcamps can be a great option if price is less of a concern than time. They generally take around two months to complete and cost upwards of $3,000. Some have excellent industry links and people who graduate from them can expect to get a job within 90 days.</p>
<blockquote>
<p>&quot;I enrolled in a full-stack web development bootcamp called Le Wagon where I was able to hone my skills through three months of intensive learning. After this, I went out and looked for employment within web development, and that was when I came across DataCamp.&quot;</p>
</blockquote>
<p><center>-<a target="_blank" href="https://www.datacamp.com/community/blog/employee-spotlight-jeremaia">Jeremaia Harrison</a>, Full Stack Web Developer at DataCamp</center></p>
<p>The most important thing to bear in mind when choosing a method is substance over style. A good certification should be reinforced with teaching by supportive experts, theory that is up-to-date, and practice exercises that reflect real-life scenarios. Learn in a way that challenges you but at the same time helps you retain everything, and you’ll know you’ll have found the right method.</p>
<p>Below are some examples of providers to help further guide your decision:</p>
<h4 id="online-certifications">Online certifications</h4>
<div class="output_wrapper"><table>
<thead>
<tr>
<th>Organization</th>
<th>Types of certification</th>
<th>Cost ($)</th>
<th>Time commitment</th>
</tr>
</thead>
<tbody>
<tr>
<td>DataCamp</td>
<td>Data Science Certification</td>
<td>29/month</td>
<td>Own pace</td>
</tr>
<tr>
<td>Google</td>
<td>Data Analytics Professional Certification</td>
<td>40/month</td>
<td>10hrs/week over 6 months</td>
</tr>
<tr>
<td>Udacity</td>
<td>Data Science Nanodegree program</td>
<td>1.4K</td>
<td>10 hrs/week over 4 months</td>
</tr>
<tr>
<td>Coursera</td>
<td>IBM Data Scientist</td>
<td>40-80/month</td>
<td>4 hrs/week, 11 months</td>
</tr>
<tr>
<td></td>
<td>IBM Data Analyst</td>
<td>40-80/month</td>
<td>3 hrs/week, 11 months</td>
</tr>
<tr>
<td></td>
<td>Google Data Analytics</td>
<td>40-80/month</td>
<td>10 hrs/week, 6 months</td>
</tr>
<tr>
<td></td>
<td>IIT Data Science &amp; Machine learning cert</td>
<td>1.5K</td>
<td>6-8 hrs/week, 6 months</td>
</tr>
<tr>
<td>edX</td>
<td>Harvard Data Science Professional</td>
<td>1K</td>
<td>2-3 hrs/week, 1.5 years</td>
</tr>
<tr>
<td></td>
<td>MIT Statistics &amp; Data Science (MicoMasters)</td>
<td>1.5K</td>
<td>10-14 hrs/week, 1 year</td>
</tr>
</tbody>
</table></div>
<h4 id="universities">Universities</h4>
<div class="output_wrapper"><table>
<thead>
<tr>
<th>Organization</th>
<th>Types of certification</th>
<th>Cost ($)</th>
<th>Time commitment</th>
</tr>
</thead>
<tbody>
<tr>
<td>Harvard</td>
<td>Harvard Data Science Professional</td>
<td>800</td>
<td>2-3hrs/week over 1.5 years</td>
</tr>
<tr>
<td></td>
<td>Data Science Graduate Certificate</td>
<td>12K</td>
<td>1.5 years</td>
</tr>
<tr>
<td>University of Toronto</td>
<td>Data Science certificate</td>
<td>4K or 1K/course</td>
<td>160 hrs</td>
</tr>
<tr>
<td>Indian Institute of Technology (IIT) Roorkee</td>
<td>Data Science and Machine Learning Certificate</td>
<td>1.5K</td>
<td>8hrs/week over 6 months</td>
</tr>
</tbody>
</table></div>
<h4 id="certification-providers">Certification Providers</h4>
<div class="output_wrapper"><table>
<thead>
<tr>
<th>Organization</th>
<th>Types of certification</th>
<th>Cost ($)</th>
<th>Time commitment</th>
</tr>
</thead>
<tbody>
<tr>
<td>Certified Analytics Professionals (CAP)</td>
<td>Associate certified analytics professional</td>
<td>700-1000</td>
<td>16-20 hours</td>
</tr>
<tr>
<td></td>
<td>Certified analytics professional</td>
<td>800-1100</td>
<td>18-25 hours</td>
</tr>
<tr>
<td>Data science council of America (DASCA)</td>
<td>Big data engineering</td>
<td>500- 600</td>
<td>Self-paced depending on candidates experience</td>
</tr>
<tr>
<td></td>
<td>Big data analytics</td>
<td>585-620</td>
<td>Self-paced depending on candidates experience</td>
</tr>
<tr>
<td></td>
<td>Data scientist</td>
<td>650-950</td>
<td>Self-paced depending on candidates experience</td>
</tr>
<tr>
<td>The Open Group</td>
<td>Three levels of certification: DS, Master Certified DS and Distinguished DS</td>
<td>150-400</td>
<td></td>
</tr>
<tr>
<td>Association of data scientists (Adasci)</td>
<td>Chartered Data Scientist</td>
<td>250</td>
<td>3 hour exam</td>
</tr>
</tbody>
</table></div>
<h4 id="bootcamps">Bootcamps</h4>
<div class="output_wrapper"><table>
<thead>
<tr>
<th>Organization</th>
<th>Types of certification</th>
<th>Cost ($)</th>
<th>Time commitment</th>
</tr>
</thead>
<tbody>
<tr>
<td>Le Wagon</td>
<td>Data Science course in London</td>
<td>10K</td>
<td>9 wks (full time)</td>
</tr>
<tr>
<td>General assembly</td>
<td>Data Science immersive online</td>
<td>16K</td>
<td>2.5 months (full time)</td>
</tr>
<tr>
<td></td>
<td>Data Analytics course (beginner)</td>
<td>5K</td>
<td>40 hours</td>
</tr>
<tr>
<td></td>
<td>Data Science course (working professionals)</td>
<td>5K</td>
<td>60 hours</td>
</tr>
<tr>
<td>BrainStation</td>
<td>Data Science Bootcamp online</td>
<td>15K</td>
<td>2.5 months</td>
</tr>
<tr>
<td></td>
<td>Data Science Course online</td>
<td>3.2K</td>
<td>1.5 month</td>
</tr>
<tr>
<td>Thinkful</td>
<td>Data Science Bootcamp</td>
<td>18.5K</td>
<td>5-6 months</td>
</tr>
</tbody>
</table></div>
<p>(Disclaimer: while the prices and other details listed were correct at the time of writing, they may have changed since then)</p>
<p><em>Interested in getting certified through DataCamp? Click <a target="_blank" href="https://www.datacamp.com/certification">here</a> to find out more.</em></p>
<p><center><a target="_blank" href="https://www.datacamp.com/certification"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1629916991/how-can-i-get-certified-1_od8wwh.png"></center></p>


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