<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>The use of data science and AI in financial services is hardly news. From banks offering loans based on credit scores, insurance companies pricing policies based on risks, to investment funds offering financial advice based on projected risks and returns, data has always been a mainstay for the financial sector. However, to succeed in data science and AI, financial services organizations need to deepen their use of machine learning and AI. In a <a target="_blank" href="https://www.datacamp.com/resources/webinars/scaling-ai-adoption-in-financial-services-1">recent webinar</a>, Shameek Kundu, former Group CDO of Standard Chartered and current Chief Strategy Officer at TruEra, outlined how to accelerate AI adoption in financial services.</p>
<h2 id="ai-application-is-broad-but-shallow">AI Application is broad but shallow</h2>
<p>Shameek explains that there is a broad set of AI and machine learning use cases in financial services today. Here are some notable real-world examples.</p>
<ul>
<li><a target="_blank" href="https://www.axa.com.sg/blog/innovation/machine-learning-for-predictive-underwriting">AXA’s</a> machine learning predictive underwriting streamlines the policy purchase process</li>
<li>Optical Character Recognition (OCR) helps <a target="_blank" href="https://www.citibank.com/tts/sa/conference/cab/meetings/2017/new-york/docs/Machine_Learning_and_Cognitive_Computing.pdf">Citibank</a> expedites their manual document processing</li>
<li>Investigators at the <a target="_blank" href="https://www.straitstimes.com/tech/tech-news/anti-fraud-experts-use-ai-to-predict-cheaters-next-move">OCBC Bank</a> leverages machine learning to detect fraudulent transactions</li>
</ul>
<p>However, that does not mean that financial services organizations are making the most of data science and AI, as deep adoption of these technologies is needed. Temasek reported that practically all financial services <a target="_blank" href="https://www.temasek.com.sg/content/dam/temasek-corporate/news-and-views/Stories/3-ways-ai-transforming-financial-services-today/How%20AI%20Ethics%20and%20Governance%20is%20Heralding%20a%20New%20Era%20for%20Financial%20Services.pdf">use AI in their processes</a> to some extent. Yet, only 13% are truly using AI in a majority of their processes due to industry-wide obstacles.</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1638393895/image2_d2oanj.png" />
Many AI projects remain in their infancy, unable to reach the end goal of full deployment, according to the <a target="_blank" href="https://www.bankofengland.co.uk/-/media/boe/files/report/2019/machine-learning-in-uk-financial-services.pdf?la=en&amp;hash=F8CA6EE7A5A9E0CB182F5D568E033F0EB2D21246">Bank of England Machine Learning Survey</a></center>


<h2>Obstacles for Widespread Adoption of AI</h2>
<h4>Limited data quality and availability</h4>
<p>Increasingly, regulators are scrutinizing how companies are processing data, as evident by the passing of state data privacy laws <a target="_blank" href="https://www.americanbanker.com/news/state-data-privacy-laws-pose-compliance-headaches-for-banks">in the United States</a> and the <a target="_blank" href="https://www.comarch.com/cyber-security/articles/gdpr-implementation-at-insurance-companies-and-banks/">EU</a>. Moreover, cross-border sharing of data remains limited due to geographical variation in the data privacy laws. Such regulations translate to limited data for machine learning models to train and make predictions.</p>

<h4>The immature and fragmented technology landscape</h4>
<p>As the nascent field of machine learning continues to evolve, it experiences growing pains in the form of a relatively immature and fragmented technology landscape.</p>

<p>Today, organizations take around one to three months to deploy a machine learning model to production, according to Algorithima’s survey. The long lead time is a symptom that arises from the lack of mature <a target="_blank" href="https://www.datacamp.com/webinars/practical-guide-to-mlops">machine learning operationalization (MLOps)</a> tools. However, it is only a matter of time before MLOps tooling grows given its <a target="_blank" href="https://www.cognilytica.com/document/infographic-the-rapid-growth-of-mlops/">rapid pace of development</a>.</p>

<h4>The lack of trust in machine learning</h4>
<p>Complex machine learning models are not easy to interpret or to explain their predictions. Lacking an understanding of the black-box models, end-users find it hard to trust their outputs.</p>

<p>A case in point is <a target="_blank" href="https://www.forbes.com/sites/tomtaulli/2021/02/27/ibm-watson-why-is-healthcare-ai-so-tough/?sh=420d6d55375f">IBM Watson</a>. Once touted as the future of healthcare, its black-box model failed to earn trust from its end-users. These doctors rightfully refused to defer life-and-death decisions to an AI black box.</p>

<p>Similarly, financial services regulators and bankers working on high-stake decisions like fraud detection find it difficult to trust black-box models. This is especially so when transparency is integral in preventing discrimination and unfair outcomes, and meeting disclosure obligations. Recognizing this, Federal Reserve Gov. Brainard said in a November 2018 <a target="_blank" href="https://www.federalreserve.gov/newsevents/speech/brainard20181113a.htm">speech</a>, “the challenge of explainability can translate into a higher level of uncertainty about the suitability of an AI approach,” and called for firms in the financial services to remain vigilant in applying black-box models.</p>

<h4>Last Mile Operationalization</h4>
<p>Machine learning projects also experience the last-mile problem – the challenge of getting results to the right people at the right time. For instance, phone app users expecting immediate gratification would not be pleased to learn that a particular recommendation takes ten minutes to load.</p>
<p>Resolving the problem of last-mile operationalization requires not only a maturing MLOps tooling landscape but for machine learning practitioners to pay attention to the users’ experience.</p>

<h4>The Lack of Data Talent</h4>
<p>The full-scale deployment of AI projects takes <a target="_blank" href="https://www.datacamp.com/community/blog/eight-personas">a team of data professionals</a> – including data scientists, analysts, engineers, and machine learning scientists. As such, the <a target="_blank" href="https://www2.deloitte.com/us/en/insights/industry/technology/data-analytics-skills-shortage.html">lack of data talent</a> remains a roadblock for machine learning adoption across industries, including the financial services industry.</p>

<p>A <a target="_blank" href="https://www2.deloitte.com/us/en/insights/industry/technology/ai-talent-challenges-shortage.html">Deloitte survey</a> reveals that 23% of the most mature AI adopters reported a significant talent skills gap for AI implementation. Addressing this skill gap requires a <a target="_blank" href="https://www.datacamp.com/community/blog/five-steps-to-building-a-learning-culture">focused reskilling and upskilling strategy for specific talents</a>.</p>

<h2>Conclusion</h2>
<p>The roadblocks listed above reveal that they can be addressed given sufficient time and effort. Addressing such problems would bring the financial services industry one step closer to unlocking its vast potential for AI adoption.</p>

<p>If you are interested in the application of AI in financial services, make sure to tune in to Shameek’s on-demand webinar on “<a target="_blank" href="https://www.datacamp.com/resources/webinars/scaling-ai-adoption-in-financial-services">Scaling AI Adoption in Financial Services</a>”.</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/5-obstacles-standing-in-the-way-of-ai-adoption-in-financial-services

The financial services industry is rife with data and opportunities for generating value with data science and AI. However, increasing AI adoption in financial services requires overcoming five key obstacles. Learn more in this blog post. 

5 obstacles standing in the way of AI adoption in financial services

<ol>
<h2 id="data-science-fun-facts">Data science fun facts</h2>
<p>When you think of data, you typically think of lines of code and numbers. But data science can be used for some truly spectacular things, and you could play a game of Two Truths and a Lie with some of these. In fact, that’s exactly what DataCamp did one week on <a target="_blank" href="https://www.instagram.com/datacamp/">Instagram</a>!</p>

<li>An AI-generated text prediction model was trained to write a <a target="_blank" href="https://www.theverge.com/2017/12/12/16768582/harry-potter-ai-fanfiction">Harry Potter novel</a>.</li>
<li>The earliest case of data visualization being used to influence public policy was around <a target="_blank" href="https://www.datacamp.com/community/blog/florence-nightingale-pioneer-of-data-visualization">getting better sanitary conditions for British soldiers</a>.</li>
<li>AI-powered <a target="_blank" href="https://wyss.harvard.edu/technology/robobees-autonomous-flying-microrobots/">bees</a> are being developed by the Wyss Institute in Boston to be used in crop pollination, climate monitoring, and surveillance, among other things.</li>
<li>The City of Chicago used R to <a target="_blank" href="https://www.datacamp.com/community/blog/three-reasons-why-all-teams-should-learn-r">predict which restaurants were likely to commit violations in sanitation inspections</a>, based on factors such as time since last inspection, the number of nearby sanitation complaints, and the type of facility being inspected. Prioritizing these outlets for review, they were able to discover violators one week earlier on average.</li>
<li>An AI-powered software was created that could predict the results of the Oscars with <a target="_blank" href="https://unanimous.ai/the-swarm-ai-guide-to-the-2021-oscars/">90% accuracy</a>.</li>
<li>Many businesses make heavy use of Python, but Dropbox was built <a target="_blank" href="https://opensource.com/business/13/12/why-python-perfect-startups">almost exclusively on the programming language</a> when it was started up in 2008. So integral was Python to Dropbox that in 2013 they managed to convince Guido van Rossum, Python’s creator, to join their staff. He agreed, on the condition that he <a target="_blank" href="https://realpython.com/world-class-companies-using-python/">wouldn’t be put in a managerial or leadership role</a>.</li>

<h2 id="facts-about-data-science-s-usage">Facts about data science’s usage</h2>
<p>As data changes the world, businesses are recognizing that it’s a force to be reckoned with.</p>

<li>Between the dawn of time and 2003, five exabytes of data had been created at Google. By 2010, this amount of data was being created every two days, and by 2021 it was being created every 40 minutes.</li>
<li>There are approximately 400,000 bytes of data for every grain of sand on earth.</li>
<li>Companies that make use of customer analytics are 23 times more likely to outperform their competitors in customer acquisition (nine times for retention), according to <a target="_blank" href="https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/five-facts-how-customer-analytics-boosts-corporate-performance">McKinsey</a>.</li>

<br><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1638306568/image1_df0rtg.png" /></center><br>


<li>The amount of marketing budget firms allocate towards marketing analytics increased by <a target="_blank" href="https://www.datacamp.com/resources/whitepapers/becoming-a-data-driven-marketer-1">198%</a> in 2020.</li>
<li>In 2019 it was reported that only <a target="_blank" href="https://www.accenture.com/_acnmedia/PDF-115/Accenture-Human-Impact-Data-Literacy-Latest.pdf">27% of organizations</a> were able to make full use of their data to generate actionable insights, with the growing data skills gap cited as a primary reason.</li>
<li><a target="_blank" href="https://www.mckinsey.com/industries/retail/our-insights/the-how-of-transformation">70%</a> of digital transformation initiatives by organizations fail to accomplish their stated goals. That’s where <a target="_blank" href="https://www.datacamp.com/groups/business/data-science-for-managers-free-trial">DataCamp for Business</a> can help!</li>

<h2 id="facts-about-careers-in-data-science">Facts about careers in data science</h2>
<p>With all of the above in mind, it’s no wonder that data science and analytics are highly coveted skills. Those who possess these skills enjoy bright career prospects and an array of positions to choose from.</p>

<li>Data science was identified as the skill with the <a target="_blank" href="https://www3.weforum.org/docs/WEF_Future_of_Jobs_2020.pdf">largest skill gap</a>, according to a 2021 report by the World Economic Forum.</li>
<li>In 2020 the number of data science job listings outstripped the number of people searching for such jobs by a factor of <a target="_blank" href="https://quanthub.com/data-scientist-shortage-2020/">3 to 1</a>.</li>
<li>The average salary for a data scientist is <a target="_blank" href="https://searchbusinessanalytics.techtarget.com/feature/Data-scientist-shortage-leaves-organizations-uncertain">$100,000</a> USD according to the Bureau of Labor Statistics, and that of an analyst is <a target="_blank" href="https://www.glassdoor.co.uk/Salaries/us-data-analyst-salary-SRCH_IL.0,2_IN1_KO3,15.htm">$70,000</a> USD.</li>
<li>Many data scientist positions only require a degree in a quantitative subject. If you hold one in the following, you could already be eligible for these positions:</li>

<br><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1638306568/image3_tmv7qn.png" /></center><br>


<li>The UK’s National Health Service has estimated that by 2040 it will require <a target="_blank" href="https://www.datacamp.com/resources/webinars/data-science-is-transforming-healthcare">90%</a> of its staff base to be data literate.</li>
<li>SQL and Python and the <a href="https://insights.stackoverflow.com/survey/2021#technology-most-popular-technologies">third and fourth most popular technologies</a>, respectively, among professional developers.</li>
<li><a target="_blank" href="https://www.infoworld.com/article/3228245/the-80-20-data-science-dilemma.html">80% of a data scientist’s time is spent organizing data</a>.</li>
<li><a target="_blank" href="https://www.datacamp.com/resources/whitepapers/8-rules-for-better-data-storytelling">Effective data storytelling</a> is one of the most powerful skills a data scientist can learn.</li>
<li>The number of data analysts has <a target="_blank" href="https://www.datacamp.com/community/blog/what-skills-and-backgrounds-do-data-scientists-have-in-common">more than doubled</a> since 2010.</li>

<h2 id="data-science-training-and-education-facts">Data science training and education facts</h2>

<li>Bachelor’s degrees in data science were <a target="_blank" href="https://www.discoverdatascience.org/programs/bachelors-in-data-science/">practically non-existent</a> five years ago, according to Discover Data Science. Now over 50 higher education institutions in the US currently offer one.</li>
<li>In 2015 a poll among UK school students and their parents found that a <a target="_blank" href="https://www.ocadogroup.com/media/press-releases/year/2015/python-overtakes-french-most-popular-language-taught-primary-schools">majority of them preferred to learn Python</a> over foreign languages.</li>
<li>Online learning is used by <a target="_blank" href="https://www3.weforum.org/docs/WEF_Future_of_Jobs_2020.pdf">63% of companies</a> to train their employees in new skills and keep their existing ones sharp.</li>
<li><a target="_blank" href="https://resources.new.datacamp.com/resources/static/295ca144a35c0e1852c7a95a6ff10671/Insights_from_Data_Leaders.pdf">99% of organizations</a> are actively investing in data transformation initiatives.</li>
</ol>
<p>Data will change the world, and becoming data-literate will pay dividends for your career. Whether you fancy learning how to use data for number-crunching, storytelling, or making your workflow more efficient, we have it all here on DataCamp! And did you know that we’re running a limited <a target="_blank" href="https://promo.datacamp.com/">$1 monthly subscription promo</a>? Register today to take advantage of this amazing offer!</p>


https://www.datacamp.com/community/blog/interesting-facts-about-data-science

Did you know there are three times as many data scientist job posts on LinkedIn as there are data scientists in the world? Read this and other interesting data science facts here!

Did you know there are 3 times as many data scientist positions as there are available candidates? Read this and other interesting data science facts here!

25 Interesting facts about data science

https://www.datacamp.com/community/blog/providing-financial-inclusion-with-data-science

In this episode of DataFramed, we speak with Vishnu V Ram, VP of Data Science and Engineering at Credit Karma about how data science is being leveraged to increase financial inclusion.

Providing Financial Inclusion with Data Science

<center><a target="_blank" href="https://www.datacamp.com/jobs"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1637796768/careers_qsgeha.png" /></a></center><br>

<p>The COVID-19 pandemic has accelerated digital transformation across all industries. As such, organizations are generating data more than ever, and the need for data scientists has never been higher. According to the <a target="_blank" href="https://business.linkedin.com/content/dam/me/business/en-us/talent-solutions/emerging-jobs-report/Emerging_Jobs_Report_U.S._FINAL.pdf">LinkedIn 2020 emerging jobs report</a>, Data Scientist is the number three fastest-growing job on the market (37% annual growth), only trailing other data-intensive jobs such as robotics engineer and artificial intelligence specialist. This growth does not seem to be slowing down, with the <a target="_blank" href="https://www.calu.edu/academics/undergraduate/bachelors/data-science/jobs-career-salaries.aspx">US Bureau of Statistics</a> reporting 11.5 million new data science jobs by 2026 alone.</p>
<p>In this blog post, we take a closer look at what data science is, the types of career opportunities in this space, and some best practices for breaking into data science.</p>
<h2 id="what-exactly-is-data-science">What exactly is data science</h2>
<p>In a nutshell, data science is an interdisciplinary field combining programming, statistics, artificial intelligence, and business subject matter expertise that seeks to extract meaningful insights from data. Data science is responsible for delivering some of the most valuable and visible applications in our lives. Here are a few of the use-cases that data scientists work on regularly:</p>
<ul>
<li><strong>Recommendation engines:</strong> One of the most common data science applications is recommendation engines like which you see in entertainment applications such as Netflix, Spotify, or Youtube.</li>
<li><strong>Time-series forecasting:</strong> Whether stock prices, sales, or energy consumption forecasting, data scientists regularly work with and forecast time-series data.</li>
<li><strong>Supply chain management:</strong> Data scientists are reinventing supply chain management by using data to optimize supply and demand planning, improving shipping processes, and more.</li>
<li><strong>Dashboards and analytics:</strong> Data scientists help decision-makers make better decisions. A key way of doing this is by unearthing never-before-seen insights from data with the use of dashboards.</li>
</ul>
<h2 id="the-different-types-of-data-science-jobs">The different types of data science jobs</h2>
<p>Data science is often used as an umbrella term for any form of data-related work. As such, there’s a spectrum of roles within this space that are all equally exciting and important. Here are the most common job titles you can find in data science and what they all mean:</p>
<ul>
<li><strong>Data Scientist:</strong> Data Scientists investigate, extract, and report meaningful insights unearthed from an organization’s data. They communicate these insights to non-technical stakeholders and understand machine learning workflows and how to tie them back to business applications. Data scientists use <a target="_blank" href="https://www.datacamp.com/tracks/data-scientist-with-python">a wide variety of technical skills</a> to perform their roles and leverage tools like Python, R, SQL, and more.  </li>
<li><strong>Data Analyst:</strong> Data Analysts are responsible for analyzing data and reporting insights from their analysis. They have a deep understanding of the data analysis workflow and report their insights through a combination of coding and non-coding tools such as Python, R, SQL, Tableau, or Power BI.</li>
<li><strong>Machine Learning Scientist:</strong> Machine Learning Scientists are responsible for developing machine learning systems at scale. They derive predictions from data using machine learning models to solve problems like customer churn and customer lifetime value. They are responsible for deploying these models for the organization to use.</li>
</ul>
<p><strong>Did you know?</strong> DataCamp is hiring for various roles, from <a target="_blank" href="https://boards.greenhouse.io/datacamp/jobs/3560880">data scientists</a>, <a target="_blank" href="https://boards.greenhouse.io/datacamp/jobs/2540714">machine learning experts</a> to <a target="_blank" href="https://boards.greenhouse.io/datacamp/jobs/3357355">directors of analytics</a>. If you’re passionate about data science, but feel like your technical skills aren’t fully there, check out other job postings across product, marketing, and content.</p>
<h2 id="how-to-break-into-data-science">How to break into data science</h2>
<p>While there is no one-size-fits-all approach to breaking into data science, here are a few pointers that can push you in the right direction.</p>
<ul>
<li><strong>Education:</strong> Most data science roles require a bachelor’s degree to break into. More importantly, it’s important to showcase that you have the necessary skills to work with programming languages, statistics, and machine learning. Options range here from traditional education to online learning providers and bootcamps.</li>
<li><strong>Showcase a portfolio of projects:</strong> One of the best ways to showcase your knowledge and skills is through a portfolio of projects. The best portfolios contain passion-driven work on datasets that genuinely excite you, not on widely known datasets like Titanic or MNIST. Check out <a target="_blank" href="https://www.datacamp.com/workspace">DataCamp Workspace’s publications</a> to see different types of portfolio projects.</li>
<li><strong>Consider getting certified:</strong> Certifications are a great way to supplement your portfolio and prove your skills. Here is a list of <a target="_blank" href="https://www.datacamp.com/community/blog/top-5-data-science-certifications">data science certifications</a> you can use to prove your case.</li>
<li><strong>Get experience:</strong> A great way to convince hiring managers of your skills is by proving that you’ve already done the work. You can do this through internships, graduate schemes, temporary job placements, freelance work, and more.  </li>
<li><strong>Double down on your non-technical skills:</strong> Graduates of liberal arts not only bring great soft skills to the table, but they also possess critical thinking and decision-making skills. Degrees in Economics, Psychology, Sociology, or even Journalism, all contribute to being successful in a multidisciplinary such as data science. Moreover, In the age of ethical AI,  non-technical skills such as empathy, active listening, and creative thinking are huge assets.</li>
</ul>
<h2 id="the-future-of-data-science">The future of data science</h2>
<p>Throughout this article, we’ve seen how data science is becoming more and more integral for organizations and how that’s fueling its growth. This will only intensify even more. There is reason to believe that the demand for the title “data scientist” may decrease over time, but the need for data skills will not, as data culture and upskilling are top of mind for organizations to remain competitive in the data-driven age.</p>
<p>If you want to be part of the data revolution, <a target="_blank" href="https://www.datacamp.com/jobs">consider joining DataCamp</a>, or start learning today and join our community of 9 million+ learners.</p>
<br><center><a target="_blank" href="https://www.datacamp.com/jobs"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1637796768/careers_qsgeha.png" /></a></center>


https://www.datacamp.com/community/blog/how-to-break-into-data-science

Looking to break into data science? Find out how to get started on your data science journey and what types of opportunities are available today. 

How to break into data science

<h2 id="what-will-i-learn-">What will I learn?</h2>
<p>Experience is the best teacher: case study courses on DataCamp focus on a particular problem and test your ability to apply your knowledge to find the solution. In this new Tableau course, you’ll learn one of the most important soft skills a data analyst can learn: adaptability, as the issues businesses face which analysts address with actionable insights can be extremely variable. Furthermore, you’ll also gain a deeper understanding of how Tableau can be used not only to report information but also to discover actionable business insights.</p>
<h2 id="why-should-i-take-this-course-">Why should I take this course?</h2>
<p>Customer churn, or the percentage of customers in a given period who do not renew, is a costly problem. According to Bain &amp; Company, <a target="_blank" href="https://media.bain.com/Images/BB_Prescription_cutting_costs.pdf">increasing customer retention by 5% can lead to a 25% increase in profits</a>. There are several reasons behind this figure: returning customers don’t require as much marketing expenditure, they have a tendency to <a target="_blank" href="https://media.bain.com/Images/Value_online_customer_loyalty_you_capture.pdf">spend more money</a> on your products after six months of buying from you, and they are more likely to drive word-of-mouth marketing (which is both free <a target="_blank" href="https://www.nielsen.com/us/en/insights/article/2012/consumer-trust-in-online-social-and-mobile-advertising-grows/">and the most trusted form of marketing according to Nielsen</a>).</p>
<p>Reducing the opportunity cost of customer churn is one of the roles analysts, marketers, and managers play in guiding data-driven decision-making. Tableau is one of the most widely used tools for analyzing and visualizing data thanks to its user-friendly interface. As you’ll discover in this course, it can be applied to answering business questions such as this.</p>
<p>Taking this course will help you gain a better understanding of Tableau by applying the skills you learned in the prerequisite courses (see below) and will also increase your employability by demonstrating your Tableau skills.</p>
<h2 id="what-are-the-prerequisites-for-this-course-">What are the prerequisites for this course?</h2>
<p>There are three courses we recommend you take before starting this course:</p>
<ul>
<li><a target="_blank" href="https://www.datacamp.com/courses/introduction-to-tableau">Introduction to Tableau</a> for getting used to the Tableau interface, its different features, and building visualizations.</li>
<li><a target="_blank" href="https://www.datacamp.com/courses/analyzing-data-in-tableau">Analyzing Data in Tableau</a> for creating calculated fields.</li>
<li><a target="_blank" href="https://www.datacamp.com/courses/creating-dashboards-in-tableau">Creating Dashboards in Tableau</a> for the steps you need to take to create interactive dashboards and later stories.</li>
</ul>
<h2 id="-what-s-in-this-course-https-www-datacamp-com-courses-case-study-analyzing-customer-churn-in-tableau-"><a target="_blank" href="https://www.datacamp.com/courses/case-study-analyzing-customer-churn-in-tableau">What’s in this course?</a></h2>
<h4 id="chapter-1">Chapter 1</h4>
<iframe width="560" height="315" src="https://www.youtube.com/embed/ODluDEexg9o" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

<p>In this first chapter, you&#39;ll start exploring the new dataset and revisit creating calculated fields in Tableau to better understand why customers are churning.</p>
<h4 id="chapter-2">Chapter 2</h4>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1637267307/casestudycourse1_qjnjaw.png" /></center><br>

<p>You&#39;ll continue by leveraging the Tableau knowledge you gained from the prerequisite courses to investigate further why customers are leaving Databel.</p>
<h4 id="chapter-3">Chapter 3</h4>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1637267306/casestudy2_ijvq0k.png" /></center><br>

<p>In the last chapter, you&#39;ll create Tableau interactive dashboards and arrange them into stories, so that you can share your results with stakeholders.</p>
<h2 id="additional-tips">Additional tips</h2>
<ul>
<li>This course’s instructions will recommend you use your own workbook as much as you can rather than loading solution workbooks from the course itself—although, you can do so to continue where you left off if you can’™ do the case study in one go.</li>
<li>There can be different ways to go about each exercise, but the aim of each is to build a visualization/calculation that enables you to easily answer the question at the end. Don’t worry too much if you’re not following the instructions exactly, or if DataCamp’s workbooks look different from yours.</li>
</ul>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1637267307/casestudycourse1_qjnjaw.png" /></center><br>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1637267307/Screenshot_2021-11-17_at_16.56.25_utgxqo.png" /></center><br>

<p>In this example, DataCamp and Shaun came up with different visualizations but both answer the question correctly.</p>
<ul>
<li>That being said, after you’ve answered the question correctly, comparing how you got your answer with DataCamp’s solution can be a great way to broaden your understanding of what you can do in Tableau.</li>
<li>You’ll be able to add your visualizations to your Tableau Public profile (if you have one) in the final, optional, exercise of the course so that you can preserve and present what you’ve created.</li>
</ul>
<h2 id="important-information-for-current-tableau-learners-on-datacamp">Important information for current Tableau learners on DataCamp</h2>
<p>This course will be added to our Tableau Fundamentals Track, and therefore we’ll be launching a new version of this track which includes this course. If you’re <strong>currently enrolled in the track</strong> you’ll be given the option of switching to the new version, but you can stick with the current version and complete it if you’d prefer. If you have <strong>already completed it</strong>, you’ll be given a similar set of options: switch to the new track and “re-earn” your statement of accomplishment by completing this course alone, or retain your current one. Anyone who does switch to the new version will also have 24 hours if they would like to change their minds.</p>
<p>You can read more about <a target="_blank" href="https://support.datacamp.com/hc/en-us/articles/360034184033">how to switch tracks</a> and <a target="_blank" href="https://support.datacamp.com/hc/en-us/articles/360004615593-How-does-DataCamp-keep-Career-and-Skill-Tracks-up-to-date-">what happens when DataCamp switches tracks</a> through our Help Center.</p>
<p>In any case, we’d highly recommend taking this course for the reasons stated in this piece!</p>
<p>Ready to take the next step towards creating beautiful and interactive data visualizations? Get started in <a target="_blank" href="https://www.datacamp.com/courses/case-study-analyzing-customer-churn-in-tableau">this course</a> today!</p>


https://www.datacamp.com/community/blog/analyzing-customer-churn-in-tableau

Are you feeling ready to apply your Tableau skills? Find out how in our new course, Analyzing Customer Churn in Tableau, you’ll grow business-critical data analysis and visualization skills to increase a telecom company’s customer retention, as well as additional pieces of advice.

Find out how in our new course, Analyzing Customer Churn in Tableau, you’ll grow business-critical data analysis and visualization skills to increase a telecom company’s customer retention, as well as additional pieces of advice.

New Tableau Case Study: How to Analyze Customer Churn

<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1637177872/competitionwinners_qzch29.png" /></center><br>

<p>Taking part in a <a target="_blank" href="https://app.datacamp.com/learn/competitions">data science competition</a> can be challenging. Not only is there completing the task itself to think about, but there’s also the extra question of how you can impress the judges and crowds enough for your publication to win. Additionally, since your publication will appear on your user profile, you’ll want to ensure it will impress anybody who may see it afterward—including potential employers.</p>
<p>That’s why we reached out to some of the recent Workspace competition’s winners to get their advice on how you can repeat their achievements—from ways to approach the exercises and make them easier for yourself, to sprucing up your entry at the end.</p>
<ol>
<li><p><strong>Read the judging criteria.</strong> For example, creativity (i.e., &quot;How &#39;out of the box&#39; the analysis conducted is&quot;) was one of the highest weighted criterion in the Workspace competition, and the &quot;analysis&quot; criterion also mentioned that &quot;Visualisation tools and techniques utilized&quot; carry some importance; hence, I made a conscious effort to use creative visualizations, whenever applicable.</p>
</li>
<li><p><strong>Templates are your friend.</strong> Find an existing DataCamp course, project, or workspace template and use it as a basis to build your solution upon. More or less, there are standard processes in EDA and you just have to adapt them to the specific problem.</p>
</li>
<li><p><strong>Talk to your readers</strong>, since this is an activity that will be published on DataCamp, that you&#39;ll feature in your portfolio, and that you&#39;ll share with colleagues and business professionals.</p>
</li>
<li><p><strong>Don&#39;t hesitate to try stuff out.</strong> Your first idea will likely not be your final idea, so use this opportunity to test something you&#39;ve been wanting to test, implement something you&#39;ve been wanting to learn, etc.</p>
</li>
<li><p><strong>Think outside the box.</strong> It’s easier said than done, but it can be a good idea to take the competition’s instructions a bit further. There are sometimes clues within them: in my case, the description asked to find &quot;what kind of people get which kind of loan”. This spelled &quot;clustering&quot; to me: to divide the loaners in groups and find the group characteristics. With hundreds of submissions, you’ll want to stand out.</p>
</li>
<li><p><strong>Take time on your visualizations.</strong> Allocate time to perfect the presentation elements of your publication (graphs, outline, final render on the DataCamp publication system, etc.).</p>
</li>
<li><p><strong>Have fun with it:</strong> Find an aspect of the problem that you&#39;ll enjoy doing the analysis on. For example, I love applying methods on (unstructured) text-based data, hence I often have a tendency to choose independent variables that are text-based, and then work with those in my modeling.</p>
</li>
<li><p><strong>Think about what your research objectives might be.</strong> Then, think about what the dependent variables and independent variables may likely be. There&#39;s often a choice to be made here, contingent on the research objective you decided to pursue. The best kinds of objectives are when your own curiosity is fully engaged, which will help drive your analysis and make things interesting.</p>
</li>
<li><p><strong>Remember the overall goal of data science.</strong> Every step in the analysis, each decision you take, should be with the business problem in mind.</p>
</li>
<li><p><strong>Don&#39;t submit at the last minute</strong>—in case there are any issues with publishing. After all that hard work, you wouldn&#39;t want to miss the deadline due to a technicality.</p>
</li>
</ol>
<p><i>Many congratulations to all four of them on their achievements, and if you’re feeling inspired check out our <a target="_blank" href="https://app.datacamp.com/learn/competitions">current and upcoming competitions</a>!</i></p>


https://www.datacamp.com/community/blog/datacamp-competitions-winners-tips

With our new weekly competitions up and running, the winners of our recent Workspace Competition give some bite-size tips on how you can excel at these yourself.

DataCamp Competitions—10 tips from the Workspace Competition’s winners

https://www.datacamp.com/community/blog/the-data-storytelling-skills-data-teams-need

In this episode of DataFramed, we discuss the role of data storytelling when driving change with analytics, and the importance of the analyst role within a data-driven organization.

The Data Storytelling Skills Data Teams Need

<p>A great data science podcast is one that informs, entertains, and presents you with new ideas related to data science. The ethics of AI, tips on finding a data science job, how data is transforming all industries from finance to healthcare, and the latest ideas from data leaders are just a snippet of what’s out there.  With so many on offer, it can be hard to know what to listen to first, so we’ve compiled this segmented list of our favorite data science podcasts. From podcasts that discuss a broad range of data science talking points to ones that focus on topics like visualization or how-to build a career in data science, these monologues and discussions will leave you better-informed in data science than you were before you hit the “play” button.</p>
<h1 id="table-of-contents">Table of Contents</h1>
<ul>
<li>General data-driven podcasts</li>
<li>Data science career advice podcasts</li>
<li>Data visualization podcasts</li>
<li>Data science ethics podcasts</li>
<li>Technology-specific data science podcasts</li>
<li>Applied-data-science podcasts</li>
<li>Fun data science podcasts</li>
</ul>
<h2 id="general-data-driven-podcasts">General data-driven podcasts</h2>
<ol>
<li><a href="https://sanyambhutani.com/tag/chaitimedatascience/">Chai Time Data Science</a>—Hosted by deep learning engineer Sanyam Bhutani, this data science podcast primarily features interviews with Kagglers, data science researchers, and other prominent data scientists.</li>
<li><a href="https://datascienceathome.com/">Data Science At Home</a>—Hosted by Dr Francesco Gadelatta, the founder of Amethix Technologies, this data science podcast focuses on AI, machine learning, and algorithms.</li>
<li><a href="https://www.datafuturology.com/about">Data Futurology</a>— ”People don’t want to guess. They want to hear from others who’ve tried and tested how to get it right.” Through this data science podcast, Data Futurology delivers hours of thought leadership and actionable insights.</li>
<li><a href="https://www.listennotes.com/podcasts/data-science-now-closter-gBld0MnUkAR/">Data Science Now</a>—Aiming to educate, the episodes of this podcast cover specific topics such as the history of data science, getting a data science job, and examples of its practical application.</li>
<li><a href="https://www.datascience.salon/">Data Science Salon</a>—With speakers and attendees from prominent, well-known brands (think Warner Brothers, Google, Netflix, and IBM) Data Science Salon presents an eclectic, well-informed mix of thought leadership.</li>
<li><a href="https://dataskeptic.com/">Data Skeptic</a>—With over 200 episodes, this is one of the best-known data science podcasts, covering a huge variety of topics and releasing episodes almost every week.</li>
<li><a href="https://www.datacamp.com/community/podcast">DataFramed</a>—DataCamp’s own in-house data science podcast, our data evangelist Adel Nehme discusses how organizations can build data cultures and scale their data literacy with professionals from companies such as <a href="https://www.datacamp.com/community/podcast/the-past-and-present-of-data-science">Viacom</a> and <a href="https://www.datacamp.com/community/podcast/the-path-to-building-data-cultures">Allianz</a>.</li>
<li><a href="https://www.analyticsvidhya.com/blog/2018/06/launching-datahack-radio-analytics-vidhyas-exclusive-podcast-series/">DataHack Radio</a>—Hosted by the popular blog Analytics Vidhya, each episode features a machine learning expert being interviewed in order to keep up with recent developments in the field.</li>
<li><a href="https://wandb.ai/fully-connected/gradient-dissent">Gradient Dissent</a>—This data science podcast features deep-learning experts discussing how they implement this at companies such as Facebook, Google, and Lyft.</li>
<li><a href="https://hdsr.mitpress.mit.edu/podcast">Harvard Data Science Review Podcast</a>—This data science podcast approaches the practical applications of data science, with episodes being described as “case studies” into how data influences the modern world.</li>
<li><a href="https://www.bbc.co.uk/programmes/p02nrss1/episodes/downloads">More or Less: Behind the Stats</a>—This BBC data science podcast focuses on statistics. Recent episodes have included the numbers behind Squid Game and interviews with the most recent Nobel Prize for Economics winners.</li>
<li><a href="https://www.oreilly.com/radar/topics/oreilly-data-show-podcast/">O’Reilly Data Show Podcasts</a>—This data science podcast has a focus on emerging data technologies.</li>
<li><a href="https://changelog.com/practicalai">Practical AI</a>—True to its name, this data science podcast describes itself as “keeping up with the latest advances of AI while keeping one foot in the real world”.</li>
<li><a href="https://www.dataengineeringpodcast.com/">The Data Engineering Podcast</a>—Written and hosted by a data engineer, this data science podcast gets into the technicalities of data management.</li>
<li><a href="https://gradientflow.com/podcast/">The Data Exchange</a>—Hosted by Ben Lorica, the chair of the NLP Summit and the O’Reilly Data Show’s founder, the weekly episodes of this data science podcast focus on machine learning and AI.</li>
<li><a href="https://twimlai.com/">The TWIML AI Podcast</a>—Standing for “This week in machine learning”, this data science podcast brings together some of the leading experts in AI and machine learning.</li>
<li><a href="https://towardsdatascience.com/podcast/home">Towards Data Science</a>—Sponsored by the popular Medium blog, TDS discusses a wide variety of artificial intelligence topics, from AI governance to AI research.</li>
</ol>
<h2 id="data-science-career-advice-podcasts">Data science career advice podcasts</h2>
<ol>
<li><a href="https://www.becomingadatascientist.com/category/podcast/">Becoming a Data Scientist</a>—As the name suggests, this data science podcast focuses on Renee Teate’s data science career journey and the challenges he faced along the way.</li>
<li><a href="https://datacast.simplecast.com/">Data Cast</a>—Interviewing prominent data scientists, each episode focuses on the career lessons each learned before they landed their current data science job.</li>
<li><a href="https://podtail.com/en/podcast/dataxpresso/">DataXpresso</a>—This data science podcast describes itself as being for data professionals who want to build their careers around data analytics and technology.</li>
<li><a href="https://nssdeviations.com/">Not so Standard Deviations</a>—Roger Peng and Hillary Weaver spend each episode discussing data science news and relating it to their own experiences of working with data.</li>
<li><a href="https://theartistsofdatascience.fireside.fm/">The Artists of Data Science</a>—Describing itself as “the ONLY self-development podcast for data scientists”, ADS delivers a mix of professional and personal development episodes on top of discussions about the latest developments in the field.</li>
<li><a href="https://podcasts.apple.com/us/podcast/the-data-life-podcast/id1453716761">The Data Life</a>—This data science podcast covers all the aspects of data science jobs, from the technicalities to the soft-skill aspects of working in this field.</li>
<li><a href="https://www.widsconference.org/podcast.html">Women in Data Science</a>—Hosted exclusively by female data scientists, many of this data science podcast’s episodes focus heavily on how women can succeed at careers in the field and what lessons the guests learned on their journeys.</li>
</ol>
<h2 id="data-visualization-podcasts">Data visualization podcasts</h2>
<ol>
<li><a href="https://dataviztoday.com/">Data Viz Today</a>—Hosted by a former Pentagon analyst, this is a data science podcast that focuses on the end-stage: bringing your datasets to life through charts.</li>
<li><a href="https://policyviz.com/podcast/">The Policy Viz Podcast</a>—Founded by an economist who had felt modern education was not adequately teaching people how to communicate their research findings, this podcast brings in a mix of researchers and designers to discuss the different ways of doing this—chief among them is data visualization.</li>
</ol>
<h2 id="data-science-ethics-podcasts">Data science ethics podcasts</h2>
<ol>
<li><a href="https://datascienceethics.com/about/">Data Science Ethics</a>—This data science podcast focuses on all the ethical considerations we must remember when applying data, from amoral data gathering to the unintended consequences of improperly trained algorithms.</li>
</ol>
<h2 id="technology-specific-data-science-podcasts">Technology-specific data science podcasts</h2>
<ol>
<li><a href="https://pythonbytes.fm/">Python Bytes</a>—This data science podcast has a focus on keeping its viewers up to date with the latest developments in Python.</li>
<li><a href="https://talkpython.fm/">Talk Python to Me</a>—Created by the same people as Python Bytes, this podcast has a similar focus on Python though each episode is geared towards interviews with industry experts.</li>
<li><a href="https://r-podcast.org/">The R-Podcast</a>—With a focus on R, this podcast uses publicly available datasets to demonstrate to a novice audience what the programming language is capable of.</li>
</ol>
<h2 id="applied-data-science-podcasts">Applied-data-science podcasts</h2>
<p>In this section, we bring you some podcasts that focus on fields such as healthcare, but due to the widespread adoption of data science in these sectors have a few episodes that discuss this.</p>
<ol>
<li><a href="https://adversariallearning.com/">Adversarial Learning</a>—&quot;This is our podcast about data, data science, science, Shingy, and whatever else we feel like talking about. Please listen to it.”</li>
<li><a href="https://casualinfer.libsyn.com/">Causal Inference</a>—Sponsored by the American Journal of Epidemiology, this podcast also discusses statistics, causal inference, and public health. This makes it a good resource for anyone looking to apply their data science skills to this particular field.</li>
<li><a href="https://www.cyentia.com/podcast/">Cyentia</a>—Hosted by the Cyentia Research Institute, this firm specializes in cybersecurity and uses their podcast to deliver their research, often featuring data analytics.</li>
</ol>
<h2 id="fun-data-science-podcasts">Fun data science podcasts</h2>
<ol>
<li><a href="https://datacrunchcorp.com/data-crunch-podcast/">Data Crunch</a>—With a bombastic approach to data analytics (“trudging through the data swamp”, “getting on the insight train”) this tech consultancy firm delivers a data science podcast that has just as wild an approach.</li>
<li><a href="https://analyticshour.io/">Digital Analytics Power Hour</a>—Hosted by three data scientists, this data science podcast has an edgier image than many of the entries on this list, described as the brainchild of a post-conference bar conversation and with each host described by their job title, location, and drink of choice.</li>
<li><a href="https://www.learningmachines101.com/faq/">Learning Machines 101</a>—Terminator references aplenty, this data science podcast aims to demystify artificial intelligence to a general audience.</li>
<li><a href="https://banana-data.buzzsprout.com/">The Banana Data Podcast</a>—With the tagline “Humanize your data”, this data science podcast sticks to the theme of making data science accessible to all, with titles such as “In English Please” and “The Messiness of Data”.</li>
<li><a href="https://leapica.com/podcast/">The Present Beyond Measure Show</a>—Hosted by analytics professional Lea Pica, this data science podcast describes itself as focusing on the intersection between data analytics, visualization, and storytelling. There’s no shortage of panache on show here, with the host describing himself as “your slide Sherpa, your Viz Vizier”.</li>
</ol>
<p>Looking for somewhere to start? DataCamp’s podcast, DataFramed, is available to listen to on <a href="https://open.spotify.com/show/02yJXEJAJiQ0Vm2AO9Xj6X">Spotify</a>, <a href="https://podcasts.apple.com/us/podcast/dataframed/id1336150688">Apple</a>, and Google Podcasts.</p>
<p><div class="jsx-2360076205 PodcastStoreBadge"><a href="https://podcasts.apple.com/au/podcast/dataframed/id1336150688" target="_blank" rel="noopener noreferrer" class="jsx-2360076205" data-ol-has-click-handler=""><svg height="40" viewBox="0 0 164.8566 40" width="164.857" xmlns="http://www.w3.org/2000/svg"><linearGradient id="a" gradientUnits="userSpaceOnUse" x1="20.098" x2="20.098" y1="7.418" y2="32.581"><stop offset="0" stop-color="#f452ff"></stop><stop offset="1" stop-color="#832bc1"></stop></linearGradient><path d="M155.327 0H9.535c-.367 0-.73 0-1.095.002-.306.002-.61.008-.919.013A13.22 13.22 0 0 0 5.517.19a6.665 6.665 0 0 0-1.9.627A6.435 6.435 0 0 0 .193 5.521c-.12.663-.162 1.332-.179 2.002-.01.307-.01.615-.015.921V31.56c.005.31.006.61.015.922.017.67.059 1.34.18 2.001.116.67.313 1.298.624 1.905.304.595.7 1.144 1.179 1.614.473.477 1.02.875 1.618 1.179a6.7 6.7 0 0 0 1.901.63c.665.12 1.339.159 2.004.177.31.007.613.011.919.011.366.002.728.002 1.095.002h145.792c.36 0 .725 0 1.084-.002.305 0 .618-.004.922-.01.67-.02 1.342-.058 2-.178a6.804 6.804 0 0 0 1.909-.63A6.277 6.277 0 0 0 162.858 38a6.395 6.395 0 0 0 1.181-1.614 6.604 6.604 0 0 0 .62-1.905c.123-.662.162-1.332.185-2.002.004-.31.004-.61.004-.921.008-.364.008-.725.008-1.094V9.536c0-.366 0-.73-.008-1.092 0-.306 0-.614-.004-.92-.024-.67-.063-1.34-.186-2.003a6.618 6.618 0 0 0-.619-1.903 6.448 6.448 0 0 0-2.798-2.8 6.767 6.767 0 0 0-1.909-.627c-.658-.119-1.33-.16-2-.177-.304-.004-.617-.01-.922-.012-.36-.002-.724-.002-1.084-.002z" fill="#a6a6a6"></path>&lt;path d=&quot;M8.445 39.125c-.305 0-.602-.004-.904-.01-.56-.016-1.223-.048-1.87-.164a5.883 5.883 0 0 1-1.656-.548 5.407 5.407 0 0 1-1.397-1.016 5.32 5.32 0 0 1-1.02-1.397 5.723 5.723 0 0 1-.544-1.657c-.122-.673-.153-1.355-.166-1.875-.007-.21-.015-.913-.015-.913v-23.1s.009-.692.015-.895c.013-.524.044-1.206.165-1.872a5.758 5.758 0 0 1 .544-1.662 5.568 5.568 0 0 1 4.07-2.964C6.341.93 7.023.9 7.543.886l.902-.0</p>


https://www.datacamp.com/community/blog/data-science-podcasts-ultimate-list

We’ve hand-picked the ultimate list of top data science podcasts for you to gain a deeper understanding of data science and analytics. Add them to your playlist now!

We’ve hand-picked the ultimate list of top data science podcasts for you to gain a deeper understanding of data science and analytics. 

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