<center><a href="https://www.datacamp.com/jobs/"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1597954137/sue_spotlight_tazxuq.png" /></a></center>

<h2 id="what-was-your-journey-to-joining-datacamp-">What was your journey to joining DataCamp?</h2>
<p>I started off my career as a Software Engineer, got my MBA and dabbled in Investment Banking before transitioning into Product Management at an advertising technology company. As much as I loved solving complex problems for large media publishers with software solutions, I was looking for a change after six years in the industry.</p>
<p>DataCamp really stood out to me because of (1) its meaningful mission of democratizing data fluency, (2) its B2B and B2C business lines, and (3) the team was fantastic and the company was at a pivotal stage of its growth. I knew that I would learn a ton while building a solution that directly benefits our learners’ lives.</p>
<p>I joined DataCamp in 2018 and it’s been everything I’d expected and more. I’d make the same decision again today in a heartbeat.</p>
<h2 id="describe-your-current-team-what-do-you-find-most-interesting-about-your-role-">Describe your current team. What do you find most interesting about your role?</h2>
<p>The Product team at DataCamp consists of inclusive, talented, team-oriented people who are truly passionate about our mission and constantly think about solutions to our users’ problems. I learn so much from them everyday and I love that we are in the middle of an amazing opportunity that we’re primed to seize.
<br><center><a href="https://www.datacamp.com/jobs/"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1598362636/sue2_bdxwih.png" /></a></center></p>
<h2 id="how-has-your-career-grown-since-joining-datacamp-">How has your career grown since joining DataCamp?</h2>
<p>DataCamp embraces transparency and radical candor, which helps to surface wrong assumptions or issues very quickly. It doesn’t matter if you’re a junior employee or a senior executive—anyone is invited to question decisions in order to ensure we’re coming up with the best solutions with the information we have. This in turn helps everyone to learn fast and iterate as needed. I’ve personally grown a lot as a Product leader from discussions resulting from our flat organization.</p>
<p>Also, given the rate of DataCamp’s growth, I’ve had to make decisions and experience situations that I hadn’t experienced before. I sometimes feel that my growth at DataCamp in one year is equivalent to spending three years at another company.</p>
<h2 id="what-are-you-most-excited-for-in-2020-">What are you most excited for in 2020?</h2>
<p>Seeing the realization of exciting initiatives, like Skill Gap Analysis to guide organizations toward company-wide data fluency and Unguided Projects where learners can get more real-world experience solving meaty, open-ended business problems from scratch.</p>
<h2 id="what-do-you-like-best-about-working-for-datacamp-">What do you like best about working for DataCamp?</h2>
<p>The people. We pride ourselves in hiring great people—it&#39;s truly the highest talent + passion per capita I&#39;ve experienced. Everyone cares so much and isn’t afraid to raise difficult questions about whether we’re making the right decision as a team and as a company. The accelerated learning from these discussions has pushed me to become a better Product Leader. Plus, people here are fun and interesting—whether it’s just a casual lunch with a coworker or a team event, I always look forward to them!</p>
<h2 id="what-are-your-favorite-datacamp-cultural-values-and-why-">What are your favorite DataCamp cultural values and why?</h2>
<p>Care for DataCampers and Putting the Team first.</p>
<p>Time and time again, I witness teams putting what’s best for the company/team first over self-interests because they care for their teammates and for where we’re headed together as a company. I love that and it’s this mentality that makes us different from the rest.</p>
<h2 id="what-s-the-weirdest-job-you-ve-ever-had-">What’s the weirdest job you’ve ever had?</h2>
<p>The summer before I started college, I sold massage chairs to shoppers at a local mall. It was mostly commission-based and people don’t generally go to a mall looking to come home with an expensive massage chair. I learned early on that outbound sales is not easy and I’ve had a lot of empathy for Sales teams thanks to that experience!</p>
<h2 id="anything-else-you-d-like-to-share-with-our-learners-">Anything else you’d like to share with our learners?</h2>
<p>DataCamp was founded on the epicenter of active learning by doing. Our unique, hands-on methodology of Assess-Learn-Practice-Apply makes us truly the best place to upskill in data fluency. In this increasingly data-oriented world, you don’t have to be on a Data Scientist track to benefit from what we offer.</p>
<p><em>Would you like to join Sue 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/sue

Q&A with Sue Lai, VP of Product at DataCamp

Employee Spotlight: Building and Iterating on DataCamp’s Products

<p>How important is data culture to your overall data strategy? On our DataFramed podcast, <a href="https://www.datacamp.com/community/podcast/data-science-mckinsey">Taras Gorishnyy</a> identified the key pillars for data science, AI, and data strategy within an organization:</p>
<ul>
<li>Executive support</li>
<li>Vision for analytics</li>
<li>Robust data foundations</li>
<li>Establishing the impact of analytics early on</li>
<li>A distribution of data skills and a data culture</li>
</ul>

<blockquote>
<p>”Everyone at any level, whether it&#39;s C-level or entry level, should be looking and diving into data the same way you were expected to start using email 20 years ago.&quot; —<a href="https://www.datacamp.com/community/podcast/data-products-dashboards">Tanya Cashorali</a>, Founder at TCB Analytics</p>
</blockquote>
<h2 id="build-data-fluency-across-your-organization">Build data fluency across your organization</h2>
<p>A data-fluent organization is one in which everybody knows how to dive into the data that they need to do their job. For example, our VP of Marketing might not need to write SQL code or any code at all, but to do her job well, she does need to know how to access the analytics dashboards and interact with them. She also needs to know how to ask the right questions of data scientists and how to use the results that they give her. The way we think about this at DataCamp is summarized in the figure below:</p>
<p><center><a href="https://www.datacamp.com/resources/ebooks/definitive-guide-to-machine-learning-for-business-leaders"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1598024504/dataculture_sps31o.png" /></a></center><br></p>
<p>A strong data foundation requires starting with a data engineer, and then hiring data scientists and data analysts. Then, we can bring business analysts into the fold, along with other departments. This process helps everyone become data literate, leading to the company becoming 100% data fluent. Basic understanding of data tools and resources across a company greatly improves the quality of interaction among colleagues, allows teams to make better requests, and empowers everyone to make decisions autonomously.</p>
<p>In order to gauge the state of data fluency across industries, <a href="https://www.datacamp.com/resources/whitepapers/what-300-l-and-d-leaders-have-learned-about-data-fluency">we conducted a survey</a> in which we asked over 300 organizations to what extent they have taken actions to become data fluent:</p>
<p><center><a href="https://www.datacamp.com/resources/ebooks/definitive-guide-to-machine-learning-for-business-leaders"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1598024635/dataculture1_pe5tnn.png" /></a></center><br></p>
<p>On the left, we have the mature companies with mature data fluency competencies, and on the right, immature competencies. Of the mature companies, only 39% had implemented process redesign and culture change with respect to data and only 7% of the immature companies had. There&#39;s a huge amount of improvement to be made here.</p>
<h2 id="involve-your-employees">Involve your employees</h2>
<p>It’s important to prepare your employees for the future of work when crafting your data strategy. This requires ongoing conversations with all of your employees about what type of tasks you think will be automated and what won&#39;t, how your employee base can coexist with the tasks that are automated, and what that human-machine interface actually looks like. For some roles, we will see job automation, but the bigger challenge across the board will be task automation, since certain parts of someone&#39;s job may be automated away. It’s far better to figure out how to upskill and re-skill these employees to transition them to do more high-level, meaningful work.</p>
<h2 id="prioritize-upskilling-employees">Prioritize upskilling employees</h2>
<p>Another question we asked in our survey of 300 organizations was, “What type of business challenges prevent companies from building or improving data fluency?” The majority of organizations flagged their difficulty in hiring top talent, as so much data talent chooses to work in tech these days. This means you won’t be able to hire as many unicorns as you’d like, so you should prioritize upskilling and reskilling everyone in your organization.</p>
<p><center><a href="https://www.datacamp.com/resources/ebooks/definitive-guide-to-machine-learning-for-business-leaders"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1598024506/dataculture2_dqfapk.png" /></a></center><br></p>
<p>To build data skills, many of the organizations we surveyed have considered the following options:</p>
<ul>
<li>Establishing data universities or center of excellence</li>
<li>In-person training</li>
<li>Online learning platforms</li>
<li>Internal campaigns to promote and incentivize learning</li>
<li>Recruiting</li>
<li>Sourcing solutions from vendors</li>
</ul>

<p><center><a href="https://www.datacamp.com/resources/ebooks/definitive-guide-to-machine-learning-for-business-leaders"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1598024507/dataculture3_p3dveu.png" /></a></center><br></p>
<p>In polls we’ve conducted on DataCamp webinars, over 50% of respondents have said that less than 50% of their data work was actually used to inform decision making. This leakage is due to culture!</p>
<h2 id="call-to-action">Call to action</h2>
<p>List three to five outputs of data work in your organization and audit them with respect to how much your organization actually uses them to inform decision making. Too often, organizations hire a lot of great data analysts and data scientists to build dashboards and machine learning models, only to see them not be utilized in the intended way due to cultural challenges. All this good work can be wasted, resulting in a poor return on investment.</p>
<p>Find out more about best practices to building your data culture in <a href="https://www.datacamp.com/resources/ebooks/definitive-guide-to-machine-learning-for-business-leaders">The Definitive Guide to Machine Learning for Business Leaders</a>.</p>
<p><center><a href="https://www.datacamp.com/resources/ebooks/definitive-guide-to-machine-learning-for-business-leaders"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1596642506/eBook_CTA_banner_xwtyv2.png" /></a></center><br></p>


https://www.datacamp.com/community/blog/data-culture

Having a strong data culture is about equipping everyone with the skills they need to leverage data to perform their job well. 

Why Data Culture Is a Key Component of Data Strategy

https://www.datacamp.com/community/blog/how-to-learn-sql

Here’s a comprehensive guide on how to go from a SQL beginner to a seasoned pro.

How to Learn SQL

<center><a href="https://www.eventbrite.com/e/data-science-and-machine-learning-at-scale-tickets-118881895951"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1598984181/Live_Code-Along-_Data_Science_and_Machine_Learning_at_Scale_pe07xz.png" /></a></center>

<br><p>In this live code-along session, Hugo Bowne-Anderson, Data Scientist and Head of Evangelism and Marketing at Coiled, will teach you everything you wanted to know about scaling your data science work to larger datasets and larger models, while staying in the comfort of the PyData ecosystem (numpy, pandas, scikit-learn, Jupyter notebooks). This live code along session is a collaboration between <a href="https://www.datacamp.com">DataCamp</a> and <a href="https://coiled.io/">Coiled</a>.</p>
<p><ceneter><a href="https://www.eventbrite.com/e/data-science-and-machine-learning-at-scale-tickets-118881895951"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1598981428/https_3A_2F_2Fcdn.evbuc_lz8itj.png" /></a></center></p>
<p>You&#39;ll come away knowing:</p>
<ul>
<li>How to reason about when you need to scale your data and machine learning work and when to not;</li>
<li>How to leverage distribute computation on your local workstation (such as you laptop) to analyze larger datasets and build larger, more complex models;</li>
<li>How to harness the power of clusters to support larger-than-memory computation, all from the comfort of your own laptop;</li>
<li>How to do all of this while writing code similar to the numpy, pandas, and/or sckit-learn code you already write.</li>
</ul>
<h3 id="when">When</h3>
<p>Tuesday, September 15th, 2020, 4 PM EDT</p>
<h3 id="where">Where</h3>
<p>Live on our Facebook page. <a href="https://www.eventbrite.com/e/data-science-and-machine-learning-at-scale-tickets-118881895951">Register</a> to be reminded!</p>
<h3 id="instructions">Instructions</h3>
<p>Follow the steps found in this <a href="https://github.com/coiled/data-science-at-scale">Github repository</a> if you wish to code along with Hugo! All relevant materials will be in the repository by the end of the day, Monday, September 14th, so make sure to check it out again the morning of the live coding session.</p>
<h3 id="prerequisites">Prerequisites</h3>
<p>Not a lot. It would help if you knew:</p>
<ul>
<li>programming fundamentals and the basics of the Python programming language (e.g., variables, for loops);</li>
<li>a bit about <code>pandas</code>, <code>numpy</code>, and <code>scikit-learn</code> (although not strictly necessary);</li>
<li>a bit about Jupyter Notebooks;</li>
<li>your way around the terminal/shell.</li>
</ul>
<h5 id="however-hugo-finds-that-the-most-important-and-beneficial-prerequisite-is-a-will-to-learn-new-things-so-if-you-have-this-quality-you-ll-definitely-get-something-out-of-this-code-along-session-">However, Hugo finds that the most important and beneficial prerequisite is a will to learn new things, so if you have this quality, you&#39;ll definitely get something out of this code-along session.</h5>
<p>If you&#39;d like to just watch and not code along, you&#39;ll also have a great time, and these notebooks will be downloadable afterward.</p>
<p>If you are going to code along and use the <a href="https://www.anaconda.com/download/">Anaconda distribution</a> of Python 3 (see below), Hugo asks that you install it before the session.</p>
<p><strong>Note:</strong> Live submissions to <a href="https://www.kaggle.com/">Kaggle</a> may happen during the event, so if you want to do that, make sure to register for an account before the session.</p>
<p>If you have any thoughts, comments, or questions, feel free to reach out to Hugo on Twitter: <a href="https://twitter.com/hugobowne">@hugobowne</a>.</p>


https://www.datacamp.com/community/blog/data-science-machine-learning-at-scale

Learn how to scale your data science and machine learning work to larger datasets and larger models, using the Python tools and APIs you love.

DataCamp/Coiled Live Coding: Data Science and Machine Learning at Scale

<p><center><a href="https://datacamp.com/freeweek"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1598634928/FreeWeek-Sep-social_znw5vl.png" /></a></center></p>
<p><center><a href="https://datacamp.com/freeweek"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1598634744/Free_Week_CTA_banner_jqjp39.png" /></a></center><br></p>
<p>You read that headline correctly. Once again, we’re giving away access to DataCamp, the world’s premier online learning platform for data science, data engineering, and analytics. From now through September 9, you can take as many courses as you want—and we won’t even ask for your credit card. All you need to get started is an email address.</p>
<p>During our last Free Week, a record <a href="https://www.datacamp.com/community/blog/free-week-thank-you">135,000 new learners from all over the world joined us</a>. But so much has changed in the past three months. More people than ever are in need of new skills to secure careers. Equally important is the continued need to learn safely and effectively from home—but many don’t have the resources to invest in education right now.</p>
<p>That’s why we’ve removed all the barriers to entry once again with our Free Week. Whether you’re just dipping your toes into the world of data or diving in headfirst, you have until September 9 to build new data skills on DataCamp.</p>
<p><center><a href="https://datacamp.com/freeweek"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1598635973/assess-learn-practice-apply_gynuee.png" /></a></center><br></p>
<p>Take advantage of this limited time offer to maximize your potential with DataCamp’s courses, projects, and assessments. You’ll even have access to our newest courses, like <a href="https://www.datacamp.com/courses/introduction-to-power-bi">Introduction to Power BI</a> and <a href="https://www.datacamp.com/courses/introduction-to-airflow-in-python">Introduction to Airflow in Python</a>. If you start now, you may even be able to complete some of our most popular skill tracks, like <a href="https://www.datacamp.com/tracks/r-programming">R Programming</a> or <a href="https://www.datacamp.com/tracks/python-fundamentals">Python Fundamentals</a>. And don’t worry—you’ll still get a Statement of Accomplishment for every course you complete during Free Week.</p>
<p>We hope you’ll join us in our mission to democratize data science education by doing your part for <em>yourself</em>. Share the news about our Free Week with your colleagues, friends, and families. If this is your first time back since the first Free Week, welcome back. You can pick up right where you left off. Help us crack 200,000 new learners the second time around—sign up at <a href="https://datacamp.com/freeweek">datacamp.com/freeweek</a> right now!</p>
<p><center><a href="https://datacamp.com/freeweek"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1598634744/Free_Week_CTA_banner_jqjp39.png" /></a></center><br></p>


https://www.datacamp.com/community/blog/free-week-2

All of DataCamp is completely free to everyone—again. Because we believe that everyone deserves the chance to become data literate safely at home.

Announcing Free Week Redux

<h2 id="focus-on-solid-data-foundations-and-tooling">Focus on solid data foundations and tooling</h2>
<p>Having good quality data is a huge challenge in itself. We recommend companies that want to leverage machine learning, artificial intelligence, and data science to consider Monica Rogati’s <em><a href="https://hackernoon.com/the-ai-hierarchy-of-needs-18f111fcc007">AI Hierarchy of Needs</a></em>, which has machine learning close to the top as one of the final pieces of the puzzle.</p>
<br><center><a href="https://www.datacamp.com/resources/ebooks/definitive-guide-to-machine-learning-for-business-leaders"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1597674428/aihierarchy_ahlqzo.png" /></a></center>
<p>Source: <a href="https://hackernoon.com/the-ai-hierarchy-of-needs-18f111fcc007">Hackernoon</a></p>

<p>This hierarchy illustrates that before machine learning can happen, you need solid data foundations and tools for extracting, loading, and transforming data (ETL), as well as tools for cleaning and aggregating data from disparate sources.</p>

<p>This requires strong <a href="https://www.datacamp.com/community/blog/the-path-to-becoming-a-data-engineer">data engineering</a> practices—you’ll need to leverage databases, understand how to process data correctly, schedule your workflows, and make use of cloud computing.</p>

<p>So before you hire your first machine learning engineer, you should first set up your data engineering, data science, and data analysis functions.</p>

<h2>Beware of bias in your data and algorithms</h2>
<p>Machine learning can only be as good as the data you feed it. If your data is biased, your model will be too. For example, <a href="https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G">Amazon built a ML recruiting tool</a> to predict the success of applicants based on resumes with ten years’ worth of training data that favored males due to historic male dominance across the tech industry—which caused the ML tool to also be biased against women.</p>

<p>This is why data ethics has emerged as such an important topic in recent years. As more and more data is generated, the impact of how that data is used also scales dramatically. This requires principled consideration and monitoring. As <a href="https://mlconf.com/blog/interview-with-cassie-kozyrkov-chief-decision-scientist-at-google-by-reshama-shaikh-program-committee-member/">Cassie Kozyrkov</a>, Google&#39;s Chief Decision Scientist, has analogized, a teacher is only as good as the books they’re using to teach the students. If the books are biased, their lessons will be too.</p>

</h2>Keep tabs on your model and improve it</h2>
<p>Remember that the job of machine learning doesn’t end when your model is in production, making predictions, or performing classifications. Models that are deployed and doing work still need to be monitored and maintained.</p>

<p>If you have a model predicting credit card fraud based on transaction data, you get useful information every time your model makes a prediction and you act on it. On top of this, the activity you’re trying to monitor and predict—in this case, credit card fraud—may be dynamic and change over time. This process, where data that’s generated is constantly in flux, is called <em>data drift</em>—and it proves how essential it is to regularly update your model.</p>

<br><center><a href="https://www.datacamp.com/resources/ebooks/definitive-guide-to-machine-learning-for-business-leaders"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1597674553/datadrift_erjrtz.png" /></a></center>
<p>Source: <a href="https://databricks.com/blog/2019/09/18/productionizing-machine-learning-from-deployment-to-drift-detection.html">DataBricks</a></p>

<br><center><a href="https://www.datacamp.com/resources/ebooks/definitive-guide-to-machine-learning-for-business-leaders"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1596642506/eBook_CTA_banner_xwtyv2.png" /></a></center>


https://www.datacamp.com/community/blog/data-foundations-and-ethics

Having good quality data requires strong data foundations, along with a commitment to monitoring models and removing bias.

How to Ethically Use Machine Learning to Drive Decisions

<p>It’s been less than a month since we first announced the details of our <a href="https://www.datacamp.com/community/blog/free-datacamp-subscriptions">free subscription giveaway</a>, and in that time, we’ve substantially expanded our circle  of nonprofit partners. Our employees are thrilled that organizations around the world are taking advantage of a free year of DataCamp.</p>
<p>We still have many more subscriptions to donate. Across the world, many disadvantaged communities exist in massive technology gaps where there is less access to tech education than the technologies themselves.  If you belong to such a community, we want to help you achieve data literacy as a stepping stone to the career of your dreams. We believe that <a href="https://www.datacamp.com/community/blog/datacamp-classroom">data education is a basic human right</a>, and we invite you to join us!</p>
<p>Please contact us at <a href="mailto:giveaway@datacamp.com">giveaway@datacamp.com</a> with referrals to nonprofit organizations that are best suited to help your local community of aspiring data scientists, data analysts, and data engineers. If you work for such an organization, reach out to us to explore a partnership. We have a limited number of licenses to give away, and they’re going fast. Please read our <a href="https://support.datacamp.com/hc/en-us/articles/360051596574">FAQ</a> for more details.</p>
<h2 id="our-newest-partners">Our newest partners</h2>
<p>Here are the stellar organizations that have recently joined us: <br></p>
<center><a href="http://www.becode.org"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/c_scale,w_115/v1598377503/BeCode_zbqzh7.png" /></a></center>

<p><a href="http://www.becode.org">BeCode</a> is a Belgian network of inclusive coding bootcamps offering the opportunity for motivated people far away from employment (refugees, school dropouts, long-time unemployed) to start a promising career in the digital workforce.  They aim to help bridge the gap between motivated job seekers and the employer market, by using the shortage of digital skills as a lever for a more inclusive workforce. They currently train people in Web Development, AI/Data Science and Cybersecurity (DevSecOps).</p>
<center><a href="https://connectionstosuccess.org/"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/c_scale,w_150/v1598377362/Connections_to_Success_okmvdy.png" /></a></center>

<p><a href="https://connectionstosuccess.org/">Connections to Success</a> inspires families to realize their dreams and achieve economic independence by providing hope, resources, and a plan. They provide a comprehensive network of services and support to help people living in poverty become economically self-sufficient. Their model, with lifelong engagement at its core, integrates workforce readiness training, life coaching, healthy parenting/relationship training, education/skills training and employment services to help participants set and reach their goals.</p>
<center><a href="http://www.datasciencenigeria.org"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/c_scale,w_178/v1598377365/Data_Science_Nigeria_pbr8hn.png" /></a></center>

<p><a href="http://www.datasciencenigeria.org">Data Science Nigeria</a> (DSN) is a registered non-profit organization with a vision to build a world-class Artificial Intelligence knowledge, research and innovation ecosystem that delivers high impact and transformational research, business use applications, AI-first start-ups, employability and social good use cases; such that in 10 years, Nigeria will become one of the top 10 AI talent/knowledge destinations with 20% GDP multiplier impact.</p>
<center><a href="http://www.masassociation.org/"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/c_limit,w_216/v1598377367/MASA_Logo_-_MASA_Corporate_Relation_tgh9mo.jpg" /></a></center>
The <a href="http://www.masassociation.org/">Malaysian Actuarial Student Association</a> (MASA) strives to unite actuarial science students across Malaysia and empower them to be technically capable, equipped with industrial awareness and actively involved in the actuarial industry. They provide educational, industrial, and networking support to their student members.

<center><a href="https://www.pidonline.org/"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/c_scale,f_auto,q_auto:best,w_127/v1598386170/partnersindevelopment_bdaafq.png" /></a></center><br>

The mission of <a href="https://www.pidonline.org/">Partners in Development</a> (PID) is to provide tools so individuals born into extreme poverty can have a way out. By providing education, economic advancement, adequate housing, and primary medical care, they help change the lives of families and the future of their children. After 30 years of development history, they know that there are many talented people with no way to learn or compete in the Global Market. They are happy to partner with DataCamp in bringing both education and economic development to their programs in Haiti, Guatemala, Mississippi and Peru.

<center><a href="https://www.10academy.org/"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/c_scale,w_179/v1598377369/10_Academy_FA-02_-_transparent_background_jmqw9d.png" /></a></center>
<a href="https://www.10academy.org/">10 Academy</a> enables exceptionally talented young Africans to have an outsized impact on the world. 10 Academy does this by collecting and certifying information on the advanced digital skill and career potential of platform users, and using this information to place talented young Africans into jobs, at scale.

<h3 id="our-continuing-partners">And a huge “thank you” to our continuing partners:</h3>

<style>
  .custom-partners {
    list-style: none;
    margin-left: auto;
    margin-right: auto;
    padding: 0;
    max-width: 768px;
  }

  .custom-partners li {
    box-sizing: border-box;
    margin-top: 24px;
  }

  .custom-partners a {
    display: flex;
    align-items: center;
    text-align: center;
    height: 100%;
  }

  .custom-partners img {
    width: 100%;
  }

  @media screen and (min-width: 768px) {
    .custom-partners {
      display: flex;
      flex-wrap: wrap;
    }

    .custom-partners li {
      padding: 0 24px;
      width: 50%;
    }
  }

  @media screen and (min-width: 1200px) {
    .custom-partners li {
      width: 33.3333%;
    }
  }
</style>
<ul class="custom-partners">
<li><a href="https://perscholas.org/"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1595856035/perscholasLogo_nmwzii.png" /></a></li>

<li><a href="https://www.blackgirlscode.com/"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1598467384/bgc_brand_presence_logos_primary_small_black_vfinal_baqoof.jpg" /></a></li>

<li><a href="https://www.launchcode.org/"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1595601205/BOGO_LaunchCode_gts0z8.png" /></a></li>

<li><a href="https://www.blossomacademy.co/"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1596129149/Academy_Logo-_Transparent_du3gpr.png" /></a></li>

<li><a href="https://mydatau.org/"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1595601205/BOGO_DataU_i2j4rf.png" /></a></li>

<li><a href="https://codelouisville.org/"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1595602237/BOGO_CodeLou_rniwjs.png" /></a></li>

<li><a href="https://www.boldrimpact.com/"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1596133516/1596133132310_Copy_of_BOLDR_Logo_Final_GDrive_c0uz83.png" /></a></li>

<li><a href="https://teachforthephilippines.org/"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1596133523/TFP_Circular_Logo_-_Red_zblbmn.png" /></a></li>

<li><a href="https://wi-tech.org/"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1596133646/01_Official_WiTech_Logo_MAIN_vfq1us.png" /></a></li>
</ul>

https://www.datacamp.com/community/blog/free-datacamp-subscriptions-update-1

We’ve added five fantastic organizations to our growing list of partners—they’re helping us to democratize data science education.

Join Our Movement to Democratize Data Education

<p>When building a ML model, you must designate an <em>evaluation metric</em>, which tells the algorithm what you’re optimizing for. One commonly used evaluation metric is accuracy, that is, what percentage of your data your model makes the correct prediction for. This may seem like a great choice: who would want a model that <em>isn’t</em> the most accurate?</p>
<p>Actually, there are many cases where you wouldn&#39;t want to optimize for accuracy—the most prevalent being when your data has <em>imbalanced classes</em>. Say you’re building a spam filter to classify emails as spam or not, and only 1% of emails are actually spam (this is what is meant by imbalanced classes: 1% of the data is spam, 99% is not). Then a model that classifies all emails as non-spam has an accuracy of 99%, which sounds great, but is a meaningless model.</p>
<p>There are alternative metrics that account for such class imbalances. It is key that you speak with your data scientists about what they’re optimizing for and how it relates to your business question. A good place to start these discussions is not by focusing on a single metric but by looking at what’s called the <em><a href="https://en.wikipedia.org/wiki/Confusion_matrix">confusion matrix</a></em> of the model, which contains the following numbers:</p>
<ul>
<li>False negatives (e.g., real spam incorrectly classified as non-spam)</li>
<li>False positives (non-spam incorrectly classified as spam)</li>
<li>True negatives (non-spam correctly classified)</li>
<li>True positives (spam correctly classified)</li>
</ul>

<p><center><a href="https://www.datacamp.com/resources/ebooks/definitive-guide-to-machine-learning-for-business-leaders"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1597671791/confusionmatrix_ncbvns.png" /></a></center>
Source: <a href="https://glassboxmedicine.com/2019/02/17/measuring-performance-the-confusion-matrix/">Glass Box Medicine</a></p>
<p>A lot of attention is currently focused on the importance of the data you feed your ML models and how it relates to your evaluation metric. <a href="https://www.theguardian.com/technology/2018/feb/02/youtube-algorithm-election-clinton-trump-guillaume-chaslot">YouTube</a> had to learn this the hard way: When they optimized for revenue based on view time (how long people stay glued to videos), this had the negative effect of recommending more violent and incendiary content, along with more conspiracy videos and fake news.</p>
<p>An interesting lesson here is that optimizing for revenue—since viewing time is correlated with the number of ads YouTube can serve you, and thus, revenue—may not be aligned with other goals, such as showing truthful content. This is an algorithmic version of Goodhart’s Law, which states: “When a measure becomes a target, it ceases to be a good measure.&quot;</p>
<p>The most well-known example is a Soviet nail factory, in which the workers were first given a target of a number of nails and produced many small nails. To counter this, the target was altered to the total weight of the nails, so they then made a few giant nails. But algorithms also fall prey to Goodhart’s law, as we’ve seen with the YouTube recommendation system.</p>
<p>Find out more about best practices for machine learning in <a href="https://www.datacamp.com/resources/ebooks/definitive-guide-to-machine-learning-for-business-leaders">The Definitive Guide to Machine Learning for Business Leaders</a>.</p>
<p><center><a href="https://www.datacamp.com/resources/ebooks/definitive-guide-to-machine-learning-for-business-leaders"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1596642506/eBook_CTA_banner_xwtyv2.png" /></a></center><br></p>


https://www.datacamp.com/community/blog/evaluation-metric

Optimizing for accuracy can actually cause more harm than good. You must choose your evaluation metric with care.

community

datacamp

Official Blog