<h2 id="the-state-of-data-driven-digital-transformation">The state of data-driven digital transformation</h2>
<p>As organizations undergo accelerated digital transformations amid the COVID-19 pandemic, the case for leveraging data, machine learning and analytics has never been more important.</p>
<p>The 2020 McKinsey Global Survey on the <a href="https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/global-survey-the-state-of-ai-in-2020">State of Artificial Intelligence</a> reported that 50% of businesses have adopted artificial intelligence in at least one business function, and 22% of organizations attribute at least 5% of earnings before interest and tax to artificial intelligence initiatives.</p>
<p>However, in order to unlock the value of data and artificial intelligence at scale, organizations must democratize their data and unlock organization-wide data fluency. One of the ways to do so is by providing access to trusted and actionable data across teams and skill levels so that everyone can make data-informed business decisions.</p>
<h2 id="data-infrastructure-at-the-core-of-data-democratization">Data infrastructure at the core of data democratization</h2>
<p>Before achieving data fluency, organizations need to get the foundations right. This starts with understanding how data flows through an organization and then building toward an organized and unified data infrastructure.</p>
<p>In a <a href="https://www.datacamp.com/resources/webinars/data-trends-2021">webinar on 2021 data trends and predictions</a>, Ramnath Vaidyanathan, Vice President of Product Research at DataCamp, described how data typically flows through an organization. This flow requires data engineering tools to take in raw data, ingest and transform them, store them in a centralized location, and produce data insights in the form of dashboards and reports, machine learning outputs, and more.</p>
<p>Scaling access to data within the organization depends heavily on the presence of a solid data infrastructure architecture and the use of the latest data infrastructure tooling, which enables organizations to transform raw data into accessible and valuable data insights.</p>
<p><center><a href="https://www.datacamp.com/resources/webinars/data-trends-2021"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1615906487/dataflowsthruorg_xgcspr.png" /></a><br></p>
<h2 id="the-state-of-data-infrastructure-tooling">The  state of data infrastructure tooling</h2>
<p>The importance of data infrastructure and tooling is evidenced by the rapid growth and evolution of the data infrastructure market. Many solutions are meant to streamline working with a particular element of an organization’s data infrastructure, which has resulted in an increasingly fragmented landscape of tooling that can be challenging to navigate.</p>
<p>The following visual created by Dataiku aptly illustrates how the data infrastructure market has been segmented into numerous granular categories, with an expanding list of competing solutions present within each segment.</p>
<p><center><a href="https://blog.dataiku.com/technoslavia-the-fragmented-world-of-data-infrastructure-in-2020"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1615906493/technoslavia_gxmlfq.png" /></a></p>
<h5 id="source-dataiku-https-blog-dataiku-com-technoslavia-the-fragmented-world-of-data-infrastructure-in-2020-">Source: <a href="https://blog.dataiku.com/technoslavia-the-fragmented-world-of-data-infrastructure-in-2020">Dataiku</a></h5>
<h2 id="the-future-of-data-infrastructure-tooling">The future of data infrastructure tooling</h2>
<p>Late last year, Andreessen Horowitz sought to provide some clarity in this confusing data infrastructure landscape by developing a unified architecture framework. With the growing need to work with large volumes of data and the plethora of available solutions, this framework offers a practical reference when modernizing, mapping, and building an organization’s data stack.</p>
<p><center><a href="https://a16z.com/2020/10/15/the-emerging-architectures-for-modern-data-infrastructure/"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1615906493/andreeson_bjpxzl.png" /></a></p>
<h5 id="source-andreessen-horowitz-https-a16z-com-2020-10-15-the-emerging-architectures-for-modern-data-infrastructure-">Source: <a href="https://a16z.com/2020/10/15/the-emerging-architectures-for-modern-data-infrastructure/">Andreessen Horowitz</a></h5>
<p>As this space and trend evolves, we will see the consolidation and standardization of various tools in the data infrastructure stack. This alongside cloud adoption, data discovery tools, and centralized data governance platforms will allow organizations to provide scalable access to data across teams and functions.</p>
<p>So what does an ideal future data infrastructure deliver? According to <a href="https://www.datacamp.com/resources/webinars/operationalizing-data-within-large-organizations">Maksim Pecherskiy</a>, Data Engineer at the World Bank and former CDO of the city of San Diego, data must be <strong><em>collected, discoverable, reliable, understood, compliant,</em></strong> and <strong><em>actionable</em></strong>. Ultimately, the goal is to make data usable and trustworthy, which in turn drives organization-wide data fluency.</p>
<p><center><a href="https://www.datacamp.com/resources/webinars/data-trends-2021"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1615906507/usedandtrusted_wide_ypurb3.png" /></a></p>
<h2 id="mature-data-infrastructure-includes-cloud-adoption-data-discovery-tools-and-centralized-data-governance-platforms">Mature data infrastructure includes cloud adoption, data discovery tools, and centralized data governance platforms</h2>
<p>As data infrastructure tooling matures around data democratization in 2021 and beyond, so will the adoption and growth of the following technologies:</p>
<h3 id="1-cloud-adoption">1. Cloud Adoption</h3>
<p>The use of cloud technologies is <a href="https://www.oreilly.com/radar/cloud-adoption-in-2020/">becoming the norm</a>, especially in the wake of the accelerated data-driven digital transformation we’ve seen in 2020.</p>
<p>Besides improving data storage and access, cloud solutions also offer numerous user-friendly products for collecting, ingesting, transforming, storing, and creating insights from data.</p>
<p>There are numerous providers in the cloud platform space, and one of the key players is <a href="https://aws.amazon.com/">Amazon Web Services</a> (AWS), which has <a href="https://aws.amazon.com/products/">more than 100 products</a> that enable enterprises to make the most out of their data at high speed, at scale, and at low cost.</p>
<h3 id="2-data-discovery-tools">2. Data Discovery Tools</h3>
<p>Scaling data infrastructure will be crucial as data tables, reports, and models are added to organizational data landscapes. Stakeholders must be readily able to locate reliable, understood, compliant, and actionable data and insights to do their best work.</p>
<p>To tackle this challenge, <a href="https://datacamp.com/community/data-discovery-tools">data discovery tools</a> (also referred to as metadata management tools or data catalogs) can act as versatile search engines for these troves of data within the organization. Metadata essentially refers to a set of data describing the particular data, and is useful for indexing the large variety of data available.</p>
<p>Lyft open-sourced their own data discovery tool <a href="https://www.amundsen.io/">Amundsen</a>, to offer metadata engine solutions that aim to help organizations discover, understand, share, and trust the data they use for their analyses. This capability is paramount for any organization looking to scale its data competencies.</p>
<h3 id="3-centralized-data-governance-platforms">3. Centralized Data Governance Platforms</h3>
<p>As mentioned earlier, creating trust in data is essential for driving data democratization.  This is where data governance plays a major role. With the ever-growing volume and variety of data, centralized oversight of the data quality, consistency, and relevance is crucial.</p>
<p>Centralized data governance platform services offered by companies like <a href="https://www.collibra.com/">Collibra</a> and <a href="https://www.alation.com/solutions/data-governance/">Alation</a> help organizations effectively manage the availability, consistency, and integrity of their data. This then allows all stakeholders to work from a reliable single source of truth.</p>
<p>A maturing data infrastructure is only one of the data trends we foresee for 2021 and beyond. For more, download our white paper on <a href="https://www.datacamp.com/resources/whitepapers/data-trends-and-predictions-2021">Data Trends and Predictions 2021: The Year of Data Fluency</a> or watch our <a href="https://www.datacamp.com/resources/webinars/data-trends-2021">webinar</a>.</p>
<p><center><a href="https://www.datacamp.com/groups/business"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603718763/banner_style_1_cruuf7.png" /></a></p>


https://www.datacamp.com/community/blog/data-infrastructure-tools

Unlocking the value of data in an organization starts with having the right data infrastructure and tooling foundations. Here’s a look at the current state and future trends of data infrastructure.

What Mature Data Infrastructure Looks Like | DataCamp

What Mature Data Infrastructure Looks Like

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 Changing Lives Around the World with DataCamp Donates | DataCamp

 Changing Lives Around the World with DataCamp Donates

<p>The most successful organizations today rely heavily on data to make decisions. Many of these data-fluent organizations generate millions of rows of data every day that are used and analyzed by a wide range of teams. One of the key challenges to equipping teams with the ability to access and work with data is ensuring that data is <a href="https://www.datacamp.com/resources/webinars/operationalizing-data-within-large-organizations">collected, trusted, reliable, actionable, and discoverable</a>.</p>
<p>To navigate these complex data landscapes, many organizations have developed and open-sourced in-house technologies, called data discovery tools.</p>
<p>In a <a href="https://www.datacamp.com/resources/webinars/the-path-to-data-fluency">recent webinar</a>, Ramnath Vaidyanathan, VP of Product Research at DataCamp describes how organizations can go from <strong><em>data reactive</em></strong>, where no one has the skills or access to work with data, to <strong><em>data fluent</em></strong>, where everyone has access and skills to work with data and make data-driven decisions. Organizations can increase their data maturity through investments in the <a href="https://www.datacamp.com/community/blog/iptop">IPTOP framework</a>: infrastructure, people, tools, organization, and processes. As organizations scale access to data through investments in data infrastructure, they will need to provide easy data discoverability with data discovery tools.</p>
<blockquote>
<p>Data is not useful if people cannot access it, if they don’t understand the context of the data collected, and if they cannot find what they need. This is why data discovery tools are really critical.
Ramnath Vaidyanathan, VP of Product Research at DataCamp</p>
</blockquote>
<p><center><a href="https://www.datacamp.com/webinars/the-path-to-data-fluency"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1615904291/Screen_Shot_2021-03-16_at_10.18.07_AM_mp6zpm.png" /></a><br></p>
<h2 id="how-data-fluent-companies-use-data-discovery-tools">How data-fluent companies use data discovery tools</h2>
<p>Many data-driven companies have implemented data discovery tools in their data infrastructure. Let’s examine the motivations and drivers behind notable data discovery tools like <a href="https://eng.uber.com/metadata-insights-databook/">Uber Databook</a> and <a href="https://www.amundsen.io/">Lyft Amundsen</a>.</p>
<p><center><a href="https://www.datacamp.com/webinars/the-path-to-data-fluency"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1615904178/datadiscoveryengines_pi2uj7_oqvtm7.png" /></a><br></p>
<h3 id="data-discovery-challenges-to-consider">Data discovery challenges to consider</h3>
<p>A key data discovery challenge is productivity and compliance. <a href="https://eng.lyft.com/amundsen-lyfts-data-discovery-metadata-engine-62d27254fbb9">Lyft outlined</a> that it faced these challenges as the size of its data grew exponentially and was projected to continue to grow at a similar rate for at least the next 10 years. When developing Amundsen, they noted that about 25% of an analyst’s time was spent on data discovery. They also saw increasingly more stringent compliance demands in the countries they serviced.</p>
<p><a href="https://eng.uber.com/metadata-insights-databook/">Uber expressed similar concerns</a> as it began to expand beyond its primary function as a ride-sharing app to services like Uber Eats, Uber Freight, and Jump Bikes. When developing Databook, Uber had over 15 million trips a day and 18,000 employees across different teams. Another challenge was that its data <a href="https://eng.uber.com/databook/">existed in many different forms</a> including <a href="https://hive.apache.org/">Hive</a>, <a href="https://prestodb.io/">Presto</a>, and <a href="https://www.vertica.com/">Vertica</a>. Analysts needed to be able to efficiently access and understand the various data sources in order to achieve data fluency.</p>
<p><a href="https://engineering.fb.com/2020/10/09/data-infrastructure/nemo/">Facebook built its Nemo data discovery platform</a> to address the need for high-quality, trustworthy data as data size and complexity increased across roles and geographic locations. Due to the privacy concerns associated with the data they analyze, Facebook also faced significant compliance concerns that had to be addressed when offering tables to specific analysts.</p>
<p>John Bodley from Airbnb said <a href="https://www.youtube.com/watch?v=gayXC2FDSiA">in a talk at a 2017 conference</a>, “As Airbnb grows, so do the challenges around the volume, complexity, and obscurity of data.” This means that employees often found data to be “siloed, inaccessible, and lacking context.” As a result, employees were making duplicate tables and did not use data if they didn’t trust it. Airbnb developed <a href="https://medium.com/airbnb-engineering/democratizing-data-at-airbnb-852d76c51770">Dataportal</a> to help make the data discovery process significantly more efficient by removing these barriers to productive data analysis.</p>
<p>These challenges are widespread, industry-agnostic, and apply to any organization looking to scale data-driven decision making. The need for easily discoverable data will become paramount as organizations look to become data fluent.</p>
<h2 id="the-four-key-goals-of-data-discovery-tools">The four key goals of data discovery tools</h2>
<p>Now that we understand data discovery challenges, let’s look at how various data discovery tools address them. <a href="https://eng.uber.com/databook/">Uber</a> breaks down the goals of a  successful data discovery platform into four components:</p>
<ol>
<li><strong><em>Extensibility</em></strong>: The ability to easily add metadata, storage, and entities to tables</li>
<li><strong><em>Accessibility</em></strong>: The ability to programmatically access all metadata</li>
<li><strong><em>Scalability</em></strong>: The ability to support a lot of concurrent read requests</li>
<li><strong><em>Power</em></strong>: The ability to support read and write requests across multiple data centers</li>
</ol>
<p>Attaining these data discovery goals increase as organizations mature their data competencies. As the scale of their data increases, each of these components becomes increasingly more important.</p>
<h2 id="lyft-and-airbnb-provide-detailed-metadata">Lyft and Airbnb provide detailed metadata</h2>
<p><a href="https://eng.lyft.com/amundsen-lyfts-data-discovery-metadata-engine-62d27254fbb9">Lyft’s engineering team</a> argues that “metadata is the holy grail of future applications.” They break metadata down into two subcategories. The first is a describing set of data made up of application context <em>(i.e., what humans need to know about the data to operate with it)</em>, behavior <em>(i.e., who owns a particular dataset and its common usage patterns)</em>, and change <em>(i.e., how the dataset is changing over time)</em>. The second component is the data being described which is any data stored in the organization in any format such as data stores, dashboards, datastreams, and more.</p>
<p>When developing <a href="https://medium.com/airbnb-engineering/democratizing-data-at-airbnb-852d76c51770">Dataportal</a>, Airbnb’s data discovery platform, metadata was an integral part of the platform’s added value to the data pipeline. Before Dataportal was introduced, many employees did not trust the data they were using due to a lack of context and metadata. Airbnb argues that “the understanding of the entire data ecosystem, from the production of an event log to its consumption in visualization, provides more value than the sum of its parts.”</p>
<p><center><a href="https://medium.com/airbnb-engineering/democratizing-data-at-airbnb-852d76c51770"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1615904101/airbnbimage_irgwjr.png" /></a><br></p>
<h5 id="source-airbnb-https-medium-com-airbnb-engineering-democratizing-data-at-airbnb-852d76c51770-">Source: <a href="https://medium.com/airbnb-engineering/democratizing-data-at-airbnb-852d76c51770">Airbnb</a></h5>
<h2 id="ensure-high-quality-search-results-with-data-discovery">Ensure high-quality search results with data discovery</h2>
<p>Once this context is provided, data discovery requires effective search. No matter how trustworthy and easy-to-understand each table is, data discovery platforms are only useful if analysts can find the data they need quickly.     </p>
<p><a href="https://www.amundsen.io/">Lyft Amundsen’s</a> landing page allows users to search for tables using natural language queries and provides visibility into the most popular tables used in the organization for quick access. Furthermore, analysts have the option to leave feedback on the table to tune future search results.</p>
<p>Facebook’s data discovery solution, <a href="https://engineering.fb.com/2020/10/09/data-infrastructure/nemo/">Nemo</a>, allows analysts to filter on usage, privacy restrictions, and recency of data while also leveraging Facebook’s competency in natural language processing by allowing users to type questions into the search bar to get relevant tables.  </p>
<p>Uber Databook allows similar search filtering capabilities by allowing analysts to filter on dimensions such as name, owner, column, and nested columns. Uber leverages <a href="https://en.wikipedia.org/wiki/Elasticsearch">Elasticsearch</a> to deliver search results quickly and effectively.</p>
<h2 id="arriving-at-data-democratization-through-data-discovery">Arriving at data democratization through data discovery</h2>
<p>Scaling data fluency and data discoverability go hand in hand, as analysts need to have easy access to data to make data-informed decisions.</p>
<p><center><a href="https://www.datacamp.com/webinars/the-path-to-data-fluency"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1615904005/Screen_Shot_2021-03-16_at_10.13.21_AM_romy3u.png" /></a><br></p>
<p>Organizations with low data maturity should focus on building up data skills and culture and scaling data infrastructure. However, as organizations progress in their data maturity journey and expand data access, enabling extensible and scalable data discovery will be integral to becoming a data-driven organization. For an in-depth discussion on all the levers to grow organizational data maturity, watch our webinar on <a href="https://www.datacamp.com/webinars/the-path-to-data-fluency">the path to data fluency</a>.</p>


https://www.datacamp.com/community/blog/data-discovery-tools

Understand how top-performing companies solve data discovery challenges to drive data fluency at scale.

How Data Discovery Tools Enable Data Democratization | DataCamp

How Data Discovery Tools Enable Data Democratization

<p><center><a href="mailto:donate@datacamp.com"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1616591654/zeeshan_usmani_ith4xe.png" /></a><br></p>
<p><em>Join Zeeshan Usmani for a live panel discussion this Friday, March 26 at 12:00 PM ET: “Changing Lives Around the World with DataCamp Donates.” He will join a diverse group of six influencers who live at the intersection of education, data science, and technology to shed light on how DataCamp Donates partners are providing free training for disadvantaged communities across the globe. You can watch the discussion on <a href="https://fb.me/e/1aevu5usu">Facebook</a>, <a href="https://youtu.be/75KN2vENEMQ">YouTube</a>, and <a href="https://www.linkedin.com/events/panel-changinglivesaroundthewor6778015864181538816/">LinkedIn</a>.</em></p>
<p>I’ve been following DataCamp ever since it launched in 2014. Their innovative technology was lightyears ahead of any other online learning platform and still is. I was a big fan then, and am an even bigger fan now. And that’s not because they still offer the best data education solution for individuals and businesses. It’s because they are radically changing the global data landscape through DataCamp Donates. This initiative is making in-demand skills available for free to thousands of students and unemployed people around the world, and companies everywhere should take note.</p>
<p>Countless people have asked me online and in person, “What’s the best way to learn data science?” My answer has always been the same: Use DataCamp.</p>
<p>To be specific, I recommend the <a href="https://datacamp.com/tracks/personalized-data-scientist-with-python">Data Scientist in Python</a> career track to beginners. It’s always been great, but now it’s improved with guided projects incorporated alongside the courses. I’m heartened to see how their course catalog has blossomed to include even more offerings on both sides of the skill spectrum. DataCamp’s new <a href="https://datacamp.com/courses?technologies=Theory">For Everyone courses</a> are particularly crucial for individuals and businesses just dipping their toes into the world of data, no coding required. Amazingly, all these courses are available for free right now when you make a free account on DataCamp. I think everyone in the world would benefit from taking them.</p>
<p>DataCamp is the best way to learn data science at your own pace. That’s never been in question. However, that’s not the reason I offered to write this guest post on DataCamp’s blog. I want to highlight the incredible opportunities DataCamp is affording disadvantaged communities worldwide with DataCamp Donates. When I saw firsthand how easy it was for student-led groups to attain free Premium one-year DataCamp subscriptions for aspiring scientists, analysts, and engineers in their network, I knew this was a movement I wanted to help spread around the world.</p>
<p>Many companies talk a lot about how they’re democratizing tech skills. When it comes to data skills, DataCamp is the only one truly doing it. They are practicing a new radical form of corporate philanthropy with DataCamp Donates by <a href="https://www.datacamp.com/community/blog/datacamp-donates">committing to donate 25,000 subscriptions around the world</a>. Nathaniel Taylor-Leach, the Community Partnerships Manager heading up the initiative, understands the specific needs and challenges facing schools and nonprofits in today’s harsh economic climate. He works with them to support their networks of committed learners who are hungry for the skills, experience, and confidence they need to get the jobs they want. I look forward to working with him to bring DataCamp Donates to more schools and nonprofits in the United States, Middle East, and beyond.</p>
<h4 id="-dr-zeeshan-ul-hassan-usmani-"><em>Dr. Zeeshan-ul-hassan Usmani</em></h4>
<p><em>Dr. Usmani is a Fulbright Scholar and Eisenhower Fellow. He holds a PhD and MS in Computer Science from the Florida Institute of Technology. His work has been mentioned in The Wall Street Journal, AOL News, Wired Magazine, NPR, MIT’s Technology Review, Florida Today, The Economist, Brown Journal of World Affairs, Journal of Defense Modeling and Simulation and CNN. He has authored dozens of research papers, articles, and several books. His research strengths include artificial intelligence, data sciences and predictive analytics. He was a Visiting Scholar at Brown University and an Industry Professor at Coventry University. He divides his time between Princeton, NJ, Colombo (Sri Lanka), Dubai (UAE) and Islamabad (Pakistan).</em></p>


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Why Zeeshan Usmani, Pakistani data science and AI expert, recommends DataCamp as the best way to learn data science.

DataCamp Makes In-Demand Data Skills Available to Everyone

<h2 id="the-state-of-data-science-and-machine-learning-in-healthcare">The state of data science and machine learning in healthcare</h2>
<p>As healthcare continually advances through digitalization and digital transformation, it’s become one of the best equipped industries to maximize usage of data science and machine learning. Since 2015, venture capital investments in AI companies in healthcare have seen a 22X increase in Europe alone (<a href="https://eithealth.eu/wp-content/uploads/2020/03/EIT-Health-and-McKinsey_Transforming-Healthcare-with-AI.pdf">McKinsey</a>).</p>
<p><center><a href="https://eithealth.eu/wp-content/uploads/2020/03/EIT-Health-and-McKinsey_Transforming-Healthcare-with-AI.pdf"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1616079609/healthcaregraph_joljln.png" /></a><br></p>
<p>Data science and machine learning are transforming healthcare across several verticals, from patient care to pharmaceuticals and more. But scaling the impact of data science in healthcare requires careful consideration of many challenges, including compliance, data governance and oversight, data culture, and the availability of data skills.</p>
<h2 id="the-opportunity-for-data-science-and-machine-learning-in-healthcare">The opportunity for data science and machine learning in healthcare</h2>
<p>Today, the healthcare space is ripe for machine learning and data science due to the high volume of healthcare data and the many applicable use cases for public health outcomes. According to <a href="https://www.statista.com/statistics/1037970/global-healthcare-data-volume/">Statista</a>, the amount of data generated yearly in the global healthcare industry is around 2,314 exabytes (1 exabyte = 1B gigabyte), marking a 15X increase in the amount of data generated in global healthcare since 2013.</p>
<p>There are countless benefits for society in terms of improved population health outcomes. <a href="https://www.medtecheurope.org/wp-content/uploads/2020/10/mte-ai_impact-in-healthcare_oct2020_report.pdf">Deloitte</a> estimates that efficiencies gained through data science and machine learning use cases can save between 380,000 to 403,000 lives in Europe alone. Data science and machine learning can be integrated throughout the patient journey from prevention and early detection, to diagnosis, to treatment and care management.</p>
<p>For example, individuals can leverage wearables and <a href="https://www.babylonhealth.com/product/monitor">personalized applications</a> for early disease detection and prevention, or experience lower wait times with <a href="https://alixir.ai/">deep learning powered medical imagery analysis</a>. The research and development space is also gearing up to create massive gains in health outcomes, from <a href="https://postera.ai/about/">automated drug discovery</a> to DeepMind’s progress on protein folding with its <a href="https://deepmind.com/blog/article/AlphaFold-Using-AI-for-scientific-discovery">AlphaFold</a> algorithm.</p>
<p>Healthcare providers across verticals can also realize massive gains in terms of cost savings and improved efficiencies. For instance, operationalizing data science and machine learning can save the European healthcare system between 170.9 to 212.4 billion euros a year (<a href="https://www.medtecheurope.org/wp-content/uploads/2020/10/mte-ai_impact-in-healthcare_oct2020_report.pdf">Deloitte</a>). Efficiency gains can run the gamut from pharmaceutical companies improving supply chain flows, to insurance providers optimizing commercial spend by predicting customer churn, to increased productivity through workflow automation across several verticals.</p>
<h2 id="data-science-and-machine-learning-use-cases-in-healthcare">Data science and machine learning use cases in healthcare</h2>
<h3 id="patient-care">Patient Care</h3>
<p>Data science and machine learning use cases can improve health outcomes for individuals and automate time-consuming administrative tasks for healthcare professionals.</p>
<p><strong><em>Appointment management</em></strong>: With the use of machine learning and rule-based artificial intelligence, healthcare providers can optimize for patient outcomes and alleviate resource mismanagement with automated appointment management.</p>
<p><strong><em>Early diagnostics and prevention</em></strong>: <a href="https://www.babylonhealth.com/product/monitor">Health monitoring applications</a> and <a href="https://store.alivecor.com/products/kardiamobile">wearables</a> leverage machine learning and descriptive analytics to provide important insights over various aspects of an individual’s health. These tools can empower individuals to make data-driven decisions around their health, and diagnose potential diseases early on.</p>
<p><strong><em>Patient triage</em></strong>: By using machine-learning powered <a href="https://www.mediktor.com/en">symptom checker applications</a>, healthcare providers can triage patients based on need and urgency. This can lead to massive decreases in wait times for patients and massive efficiencies gained for healthcare providers.</p>
<p><strong><em>Medical imaging and diagnostics</em></strong>: Arguably one of the most important use-cases for data science and machine learning in healthcare (<a href="https://eithealth.eu/wp-content/uploads/2020/03/EIT-Health-and-McKinsey_Transforming-Healthcare-with-AI.pdf">McKinsey</a>), medical imaging and diagnostics promises a wide range of gained efficiencies and improved health outcomes across the board. With the use of deep learning, healthcare providers can <a href="https://arterys.com/">automate workflows</a> and deliver value to patients faster.</p>
<h3 id="business-processes-and-management">Business Processes and Management</h3>
<p>As in any industry, healthcare applications for data science and machine learning span a range of use cases for improved operational efficiency and customer experience.</p>
<p><strong><em>Robotic process automation</em></strong>: Using a combination of machine-learning and rule-based artificial intelligence, healthcare providers across verticals can streamline workflows and digitalize processes.</p>
<p><strong><em>Customer churn</em></strong>: Insurance providers can use machine learning to predict which customers will churn, which can help them retain customers and optimize marketing spend.</p>
<p><strong><em>Chatbots</em></strong>: With the use of chatbots, healthcare providers from hospitals to insurance agencies can deliver better customer service and faster time to value for healthcare consumers.</p>
<p><strong><em>Business Intelligence</em></strong>: Business intelligence combines business analytics, data manipulation, and visualization to help organizations make more data-driven decisions. By leveraging data insights, healthcare providers can gain more visibility into financial operations, automate compliance reporting, and much more (<a href="https://www.villanovau.com/resources/bi/business-intelligence-in-healthcare/#:~:text=By%20providing%20a%20foundation%20for,monitor%20and%20forecast%20patient%20diagnoses.&amp;text=With%20business%20intelligence%2C%20providers%20can,costs%20and%20improve%20operational%20efficiency.">Villanova University</a>).  </p>
<h3 id="pharmaceuticals">Pharmaceuticals</h3>
<p>With AI-powered drug discovery and improved supply chain management, pharmaceutical companies can leverage data science and machine learning to provide more value for individuals.</p>
<p><strong><em>Drug discovery</em></strong>: According to the <a href="https://aiindex.stanford.edu/wp-content/uploads/2021/03/2021-AI-Index-Report_Master.pdf">AI Index Report 2021</a>, AI-powered drug discovery startups received the most private AI investments across all industries. The promises of ML-based drug discovery are <a href="https://www.ft.com/content/fe55190e-42bf-11ea-a43a-c4b328d9061c">starting to bear fruit</a>, and may result in massive gains in population health outcomes.</p>
<p><strong><em>Supply chain planning</em></strong>: The use of data science and machine learning in supply chain planning can reduce time to production, reduce wait times for vaccine deliveries, and optimize supply chain spend for pharmaceutical companies.</p>
<p><strong><em>Forecasting excellence</em></strong>: Using simple to complex forecasting tools, pharmaceutical companies can leverage population health data to forecast supply and demand for certain medications, as well as optimize business processes across the board for finance, marketing, sales, and more.</p>
<p><strong><em>Improving clinical trial processes</em></strong>: The use of data science and wearables can reduce risks for patients by <a href="https://www.berghealth.com/">automatically monitoring and flagging</a> any adverse effects during clinical trials. Moreover, machine-learning powered applications can speed up clinical trials by assessing patient eligibility, pre-screening, and randomization.</p>
<h2 id="challenges-and-risks-to-operationalizing-data-science-and-machine-learning-in-healthcare">Challenges and risks to operationalizing data science and machine learning in healthcare</h2>
<h3 id="data-quality-and-infrastructure">Data Quality and Infrastructure</h3>
<p>A significant challenge for any organization trying to operationalize and scale data science and machine learning is enabling a modern, secure, centralized, and discoverable data infrastructure (<a href="https://www.datacamp.com/resources/webinars/webinar-scaling-data-science-at-your-organization-pt-2">DataCamp</a>). The issue is especially true for healthcare organizations where data is still being digitized, and building large datasets is hindered by a lack of data interoperability and misalignment on quality between healthcare providers (<a href="https://eithealth.eu/wp-content/uploads/2020/03/EIT-Health-and-McKinsey_Transforming-Healthcare-with-AI.pdf">McKinsey</a>).</p>
<h3 id="compliance-and-governance">Compliance and governance</h3>
<p>Since healthcare organizations collect highly valuable and sensitive data, governance and compliance are central aspects of operationalizing data science and machine learning in healthcare (<a href="https://www.collibra.com/blog/what-is-data-governance-in-healthcare">Collibra</a>).  While regulations differ between regions (<a href="https://eithealth.eu/wp-content/uploads/2020/03/EIT-Health-and-McKinsey_Transforming-Healthcare-with-AI.pdf">McKinsey</a>), the healthcare industry contains one of the most complex regulatory landscapes. For example, the Health Insurance Portability and Accountability Act (HIPAA) in the United States dictates national standards for protecting and managing patients’ health data.</p>
<p>These types of health-specific regulations are only one aspect of the complex regulatory landscape for the healthcare industry. Data protection laws such as the General Data Protection Regulation (GDPR) in the European Union and the California Consumer Protection Act (CCPA) create additional complexity for healthcare organizations when building and linking disparate datasets for data science and machine learning applications. As such, creating strong data governance and compliance is essential to operationalizing data science and machine learning in healthcare.</p>
<h3 id="skills">Skills</h3>
<p>Enabling data infrastructure and reaching consensus on regulatory frameworks are essential to operationalizing data science and machine learning in healthcare. But the most significant threat to the adoption of these technologies in healthcare is the data skills gap.  In fact, according to research by <a href="https://thedataliteracyproject.org/files/documents/Qlik%20-%20The_Data_Literacy_Index_October_2018.pdf">Qlik</a>, when ranking the data literacy of various industries, the healthcare industry is the least performing one. The lack of data skills within healthcare organizations hinders the utilization of data science and machine learning across verticals and limits the ability of organizations to create buy-in around data initiatives.</p>
<p>For example, frontline health workers need to have basic AI literacy to be able to understand and interact with machine-learning based systems and applications (<a href="https://eithealth.eu/wp-content/uploads/2020/03/EIT-Health-and-McKinsey_Transforming-Healthcare-with-AI.pdf">McKinsey</a>). Managers and leaders at pharmaceutical or insurance companies need to understand what’s possible with data science and machine learning, so they can drive data initiatives and contribute to growing a data-driven culture (<a href="https://www.datacamp.com/resources/webinars/path-to-data-fluency">DataCamp</a>).</p>
<p>Merely hiring skilled data workers isn’t enough. There’s a shortage of talent that understands the complexities of healthcare and a shortage of data science talent in general (<a href="https://www.forbes.com/sites/louiscolumbus/2017/05/13/ibm-predicts-demand-for-data-scientists-will-soar-28-by-2020/">Forbes</a>).</p>
<blockquote>
<p>A lack of data literacy is the single largest enemy we are fighting. As the world becomes increasingly driven by data, it’s an even bigger challenge. Everyone has to understand the basics, and we have to be able to convey that in a way that is intuitive and fun</p>
<h5 id="-bill-zhang-chief-data-and-analytics-officer-aig-japan-https-go-thoughtspot-com-white-paper-hbr-new-decision-makers-thank-you-html-"><a href="https://go.thoughtspot.com/white-paper-hbr-new-decision-makers-thank-you.html">Bill Zhang, Chief Data and Analytics Officer, AIG Japan</a></h5>
</blockquote>
<h2 id="how-data-training-solves-the-healthcare-industry-s-data-challenges">How data training solves the healthcare industry’s data challenges</h2>
<p>Addressing the data skills gap is foundational to operationalizing data science and machine learning in healthcare. According to the <a href="https://eithealth.eu/wp-content/uploads/2020/03/EIT-Health-and-McKinsey_Transforming-Healthcare-with-AI.pdf">World Economic Forum</a>, digital and data training in healthcare and pharmaceuticals can boost global GDP by more than 400 billion dollars by 2030. Creating a continuous learning culture is imperative to build the next generation of leaders in healthcare (<a href="https://eithealth.eu/wp-content/uploads/2020/03/EIT-Health-and-McKinsey_Transforming-Healthcare-with-AI.pdf">McKinsey</a>), who will combine data and biomedical sciences to scale and operationalize the value of data science and machine learning in healthcare.</p>
<p><center><a href="https://www.datacamp.com/groups/business"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603718763/banner_style_1_cruuf7.png" /></a></p>


https://www.datacamp.com/community/blog/data-science-in-healthcare

The integrated use of data science and machine learning in healthcare has many applications for improving patient care, business processes and operations, and pharmaceuticals. But the healthcare industry faces considerable challenges in data quality and infrastructure, compliance and governance, and upskilling.

The integrated use of data science and machine learning in healthcare has many applications for improving patient care, business processes and operations, and pharmaceuticals. 

How Data Science is Transforming Healthcare | DataCamp

How Data Science is Transforming Healthcare

<p>One of the first DataCamp Donates partners is LaunchCode, a nonprofit offering free tech education and job placement opportunities, headquartered in St. Louis, MO. DataCamp Donates is our initiative to donate Premium DataCamp subscriptions to those who need a leg up to start a career in STEM in today’s harsh economy. Eight months later, we’ve donated over 21,000 subscriptions all over the globe, and we are witnessing how DataCamp has equipped real people in local communities to achieve their career goals.</p>
<p>“DataCamp fits directly into the mission we have here at LaunchCode—to provide free education for anyone looking to learn how to program regardless of what experience you have prior to taking a LaunchCode course,” says John Woolbright, Community Engagement Manager at LaunchCode. “When we started our partnership, we had next to no support for people who wanted to learn Data Science in Kansas City. Since then, I have been able to transition our students from web development into data science to get a more broad learning experience.”</p>
<h2 id="achieving-high-engagement-and-adoption-on-datacamp">Achieving high engagement and adoption on DataCamp</h2>
<p>The partnership was an instant hit with many of their learners. “I was excited to have a new avenue for personal and professional growth,” says Forest Roberts.</p>
<p>Other learners like Jen Murphy locked onto specific courses. “Introduction to Data Visualization with Seaborn explains concepts with clear videos. The hands-on learning is built in such a way that each question required more of me as a learner. By the last step, I was providing almost all of the required code on my own.”</p>
<p>The excitement generated by becoming a DataCamp Donates partner has led to extremely high adoption and engagement levels. “Our students have truly enjoyed DataCamp and find that time spent with the software is exceptionally valuable,” says John Woolbright. The numbers don’t lie—LaunchCode learners have completed an average of four DataCamp courses each (in addition to LaunchCode’s own computer science and programming courses).</p>
<h2 id="datacamp-builds-skills-for-job-readiness">DataCamp builds skills for job readiness</h2>
<p><center><a href="https://www.launchcode.org"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1615918722/Megan_Cullins_iuxzhf.png" /></a><br></p>
<p>Some members of the LaunchCode community have even landed jobs with skills gained from DataCamp Donates partnership. Megan Cullins was in her ninth year working at an office furniture company when she first started learning with LaunchCode and DataCamp. She completed 50 DataCamp courses and accumulated 322,000 XP to expand her Python skills. “After a couple of months of learning, I was confident enough to interview and eventually landed a job at a data analytics company! I didn&#39;t know any Python before this class, but because of DataCamp I was able to honestly tell an interviewer that I did have experience, which helped me get a job with their company.”</p>
<p>John wasn’t surprised Megan was able to secure a position. It’s the speed at which she and others achieved job readiness that was so impressive. “We started by rolling out 25 seats for Kansas City in September. Onboarding students was a breeze. By the end of the year, four of the top DataCamp learners got jobs in data science with no previous experience—just three months of learning on DataCamp.”</p>
<p>The correlation between time spent learning on DataCamp and job readiness is not lost on Megan. “I feel that my access to DataCamp absolutely helped me get a job,” she says. “The lessons I learned on DataCamp are applied every day on my job, and I love it!”</p>
<p><em>To learn more about DataCamp Donates, read our recent <a href="https://www.datacamp.com/community/blog/datacamp-donates-50-partners">blog post</a> announcing 20,000 licenses donated to nonprofits as well as the <a href="https://support.datacamp.com/hc/en-us/articles/360051596574">DataCamp Donates FAQ</a>. To apply on behalf of an organization, email <a href="mailto:donate@datacamp.com">donate@datacamp.com</a>. If you live in the greater Kansas City and St. Louis areas and would like free data science education or you want to support LaunchCode’s efforts directly, visit <a href="https://www.launchcode.org">www.launchcode.org</a>.</em></p>


https://www.datacamp.com/community/blog/dcdonates-launchcode

Our partnership with LaunchCode is getting real people real jobs.

How a Nonprofit Leveraged DataCamp to Get Learners Employed, Fast | DataCamp

How a Nonprofit Leveraged DataCamp to Get Learners Employed, Fast

<p>From powering self-driving cars to conquering the game of Go, organizations have dominated headlines by making previously impossible tasks possible with data science. It is therefore easy to overlook the hidden data science revolution, which is that companies are increasingly making what is currently possible widespread. In other words, more companies are striving for stronger organizational data fluency.
An organization’s path to data fluency depends on five key pillars, according to Ramnath Vaidyanathan, the VP of Product Research at DataCamp: Infrastructure, People, Tools, Organization and Processes (IPTOP). In a <a href="https://www.datacamp.com/resources/webinars/the-path-to-data-fluency">webinar last month</a>, he shared a data fluency framework that any organization can use to advance in data maturity from data reactive to data scaling, data progressive, and data fluent.</p>
<h2 id="to-move-from-data-reactivity-build-a-data-culture">To Move from Data Reactivity, Build a Data Culture</h2>
<p>A data reactive company is at the lowest rung of the data maturity ladder. Such a company rarely accesses, uses, and presents data, contributing to the absence of a data culture. It uses disparate legacy tools to collect data in silos and perform ad-hoc data processes. Further, it does not invest in a data upskilling strategy to build data talent internally and in data infrastructure.
A company can progress in its organizational data literacy from data reactive to data scaling by building its data culture and data infrastructure. To inseminate such a data culture, the company should strongly consider pursuing low-hanging fruits that allow stakeholders to see the value of data literacy. These include proof-of-concept dashboards and analytics projects like customer churn modelling. The company can also begin developing its data architecture and data collection processes.</p>
<h2 id="depart-from-data-scaling-with-the-proper-infrastructure-and-organizational-model">Depart from Data Scaling with the Proper Infrastructure and Organizational Model</h2>
<p>Unlike a data reactive company, a data scaling company has some form of data strategy and culture. But its shortcomings are still apparent. It has very few employees with the necessary skills and access to analyze, report, and present data confidently, resulting in a weak data culture. Moreover, it lacks centralized data storage and teams, resulting in highly siloed datasets and data processes.</p>
<p>Considering these shortcomings, a data scaling company must improve its organizational data literacy. To do so, it must enable data access by working on its data infrastructure and by building centralized data storage. Such data infrastructure allows high-quality data to be stored in the cloud and easily accessed by all stakeholders. With appropriate data storage and access, employees are empowered to develop data quality standards. For example, DataCamp stores its data in Amazon Redshift, a cloud-based centralized data warehouse, and manages data pipelines using Apache Airflow. This marks a departure from siloed data that is difficult to reconcile and paves the way to a single source of truth.</p>
<p><center><a href="https://www.datacamp.com/resources/webinars/the-path-to-data-fluency"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1615985155/fivetrain_whyweo.png" /></a></p>
<h5 id="a-simplified-view-of-datacamp-s-centralized-data-storage">A simplified view of DataCamp’s centralized data storage</h5>
<p>A data scaling company must also develop a clear organizational model for data scientists as part of its data maturation. Some companies choose to develop a centralized model characterized by a data science team, which acts as a <a href="https://hbr.org/2019/01/how-to-set-up-an-ai-center-of-excellence">center of excellence</a>, that handles requests from various functions like finance, marketing and engineering. Others decide to adopt a decentralized model, where data scientists are embedded within functions and handle the data needs of that function exclusively. A company should adopt the model that best fits its data and business needs.</p>
<p><center><a href="https://www.datacamp.com/groups/business"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1615985029/Model_jgxxzz.png" /></a><br></p>
<h2 id="transition-from-data-progressive-with-data-tools-and-processes">Transition from Data Progressive with Data Tools and Processes</h2>
<p>The next milestone in the company’s path of data fluency is the state of data progressive. In a data progressive company, every team has at least one data fluent employee who can analyze, report, and present their data. However, there is still room for improvement in terms of data quality and discoverability as a result of its maturing data infrastructure. Also, while the company’s employees have some level of data literacy, its data is almost exclusively used for reporting and is therefore underutilized. The value of data is further suppressed by the lack of data democratization contributed by the difficulty in accessing data tools and data processes.</p>
<p>To advance from the state of data progressiveness to data fluency, an organization should consider developing its data infrastructure, people, tools, organization, and processes simultaneously in accordance with the IPTOP data fluency framework.</p>
<p>To transition to data fluency, a company can reinforce its data culture by developing and investing in appropriate upskilling strategies for its people. This involves continuous data education that caters to the <a href="https://www.datacamp.com/community/blog/eight-personas">different data personas</a> and the business goals of the company. For instance, <a href="https://hbr.org/2016/10/atts-talent-overhaul">AT&amp;T&#39;s intensive talent overhaul</a> saw it investing $1B to upskill 140,000 employees over 10 years. Companies stand to benefit from a stronger data culture by incentivizing data-driven behaviors and rewarding data evangelists who actively promote the company’s data culture.</p>
<p>A company can also build internal data products that operationalize data processes as part of its data literacy program. These data products can be used to improve data quality standards and monitor business goals. For instance, DataCamp has <a href="https://www.datacamp.com/open-source">internal frameworks</a> that allow stakeholders to create presentation-ready visualizations of business metrics with minimal code.</p>
<p><center><a href="https://www.datacamp.com/resources/webinars/the-path-to-data-fluency"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1615985174/image3_yiwxjc.png" /></a></p>
<h5 id="datacamp-dashboards-developed-easily-with-internal-frameworks">DataCamp dashboards developed easily with internal frameworks</h5>
<p>As a company progresses in organizational data literacy, it tends to move toward a hybrid model, where data scientists belong not only in the centralized data team but also in a functional team. This allows the data science team to build and share data tools that align with functional business goals.</p>
<p>Achieving data fluency requires organizations to make data an integral part of their processes. Such processes reduce the friction of extracting value from data and democratize data within the organization. For instance, <a href="https://medium.com/airbnb-engineering/data-quality-at-airbnb-e582465f3ef7">AirBnb’s Data Quality Initiative</a> defines and enforces the quality of datasets across the company, which in turn builds stakeholder trust in the data and strengthens the data culture. Netflix makes it seamless for stakeholders to create effective notebooks <a href="https://netflixtechblog.com/notebook-innovation-591ee3221233">by customizing notebook templates to different target personas</a>.</p>
<h2 id="become-data-fluent-with-the-iptop-framework">Become Data Fluent with the IPTOP Framework</h2>
<p>Finally, data fluency is a company’s ideal state of data maturity. In a data-fluent company, every employee has the skills to access and understand the data they need to do their jobs. They are empowered by reliable data infrastructure and tools that make data-driven decision-making seamless. Better yet, the company has data processes and organization models that enable cross-departmental collaboration to scale efficiently.</p>
<p>Clearly, an organization’s path to data fluency is not linear. It requires a clear data strategy and a strong data literacy framework. The chart below summarizes the steps an organization should take to move its data literacy from data reactive to data fluent using the IPTOP framework.</p>
<p><center><a href="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1615989330/blueprint_data_fluency_przpge.png"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1615989330/blueprint_data_fluency_przpge.png" /></a></p>
<p>For more on the path to scaling data maturity, <a href="https://www.datacamp.com/resources/webinars/the-path-to-data-fluency">tune into our webinar</a>.</p>


https://www.datacamp.com/community/blog/achieving-organizational-data-fluency

The blueprint for ramping your organization’s data maturity from data reactive to data scaling, data progressive, and data fluent.

Achieving Organizational Data Fluency with IPTOP | DataCamp

Achieving Organizational Data Fluency with IPTOP

<p><center><a href="https://www.datacamp.com/jobs/"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1614871377/Raushon_tnvnpe.png" /></a></p>
<h2 id="tell-us-about-your-path-to-joining-datacamp-">Tell us about your path to joining DataCamp.</h2>
<p>After graduating university, I started working in tech as a Project Manager, but it wasn&#39;t long before I became fascinated with product development and the idea of designing and building products people love. I transitioned into my first Product role over five years ago and that’s when I realized that I was made for this job. Since then, I’ve worked as a Product Manager in various industries, with my previous role being in Fintech. Having worked in Fintech for a few years, I was keen to work in a less regulated environment where I could be more creative and contribute towards something more meaningful, and DataCamp seemed like the right place for that.</p>
<h2 id="how-was-your-onboarding-experience-at-datacamp-">How was your onboarding experience at DataCamp?</h2>
<p>I would describe my onboarding experience at DataCamp as &quot;do what you think is best.&quot; What I mean by this is that I was given the freedom to figure out what would help me to settle in quickly and do just that. This definitely pushed me to be self-sufficient from day one, which in hindsight is a blessing in disguise as after just two months, I already feel like I’ve been here a long time.</p>
<h2 id="what-excites-you-about-datacamp-">What excites you about DataCamp?</h2>
<p>Collaborating with so many talented people.</p>
<h2 id="what-s-your-home-office-setup-">What&#39;s your home office setup?</h2>
<p>Surely you would think that after a year of working from home, I’d have invested in a proper office setup? But no, I currently sit at my vanity on the most uncomfortable chair for hours everyday! And only because I don&#39;t want to ruin the aesthetics of my home with an office chair.</p>
<h2 id="what-do-you-like-to-do-in-your-spare-time-">What do you like to do in your spare time?</h2>
<p>I am a huge gym lover. Pre-lockdown, when gyms were still open, I would go to the gym five times a week. Otherwise, I always have my head in a book (I carry a book with me wherever I go) or am exploring the outdoors, wandering around in nature or hiking. I also love playing the piano but that&#39;s when the people in my house aren&#39;t tired of the noise I apparently make.</p>
<h2 id="what-s-something-interesting-you-ve-learned-recently-">What’s something interesting you’ve learned recently?</h2>
<p>Making sushi from scratch.</p>
<h2 id="how-do-you-feel-about-datacamp-s-evolution-and-positioning-in-the-market-">How do you feel about DataCamp’s evolution and positioning in the market?</h2>
<p>DataCamp’s evolution is impressive to say the least. There aren’t many companies out there that position themselves as having a mission to fight data illiteracy. The platform is dedicated to providing people with hands-on learning opportunities at a cost that can be easily justified. DataCamp has also done a great job at differentiating itself from its competitors through its continuous focus on innovation, and <a href="https://www.datacamp.com/community/blog/datacamp-2-0">DataCamp 2.0</a> is the prime example of this differentiation.</p>
<h2 id="what-would-you-tell-others-who-are-considering-working-at-datacamp-">What would you tell others who are considering working at DataCamp?</h2>
<p>If you want to work for a company that will push you to new levels of success, DataCamp is definitely the right place. The company really wants its employees to thrive as it continuously focuses on creating an environment that is both empowering and positive. Ultimately, DataCamp employees are not all that dissimilar. We all want to come to work, do good work, and feel valued for it. We also want to know that we’re working toward something more, and at DataCamp this is exactly how you end up feeling, regardless of how long you’ve been with the company.</p>
<p><em>Would you like to join Raushon at DataCamp? We’re currently hiring in New York; London; Leuven, Belgium; and remote locations. Check out our <a href="https://www.datacamp.com/jobs/">open roles</a>.</em></p>


https://www.datacamp.com/community/blog/raushon

Q&A with Raushon Uddin, Senior Product Manager at DataCamp

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