<p><center><a target="_blank" href="https://www.datacamp.com/business/demo"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1624651228/schedule_a_demo_bfhlx1.png" /></a></p></p>
<p>87% of data science projects <a target="_blank" href="https://venturebeat.com/2019/07/19/why-do-87-of-data-science-projects-never-make-it-into-production/">never make it into production</a>. Some fail because of a lack of management support, others due to a lack of quality data and proper infrastructure. Organizations looking to reap value from AI and machine learning should be aware of common pitfalls when scoping and managing AI projects. Yet, that is easier said than done. Even those familiar with implementing traditional software projects might still face difficulties implementing AI projects due to their unique nature.</p>
<h2 id="ai-projects-are-considerably-different-from-traditional-software-projects-">AI projects are considerably different from traditional software projects.</h2>
<p>In a traditional software project, a programmer explicitly codes the logic of the program, which takes in an input to produce an output. An example is a banking software that deducts a borrower&#39;s credit score (an output) when they apply for a large loan (an input).</p>
<p>On the other hand, AI projects that leverage machine learning, do not require an explicitly coded logic. Machine learning algorithms systematically learn the pattern between the input and output before predicting an output based on an input. An example is predicting the borrower&#39;s default risk based on their credit score and the sum of loans taken out.</p>
<p>This can introduce considerable inertia when an organization first implements and manages its AI projects. Thus, this post aims to provide some guidelines for your next deployable and profitable AI project.</p>
<h2 id="look-for-viable-simple-and-valuable-ai-projects">Look for Viable, Simple, and Valuable AI Projects</h2>
<p>These three characteristics set the stage for the success of a company’s first AI projects. Here’s why.</p>
<p><i><u>AI projects should be viable</u></i></p>
<p>Before starting new AI projects, organizations should measure their own data maturity before committing resources that could be spent on non-viable projects. The pillars of data maturity range from infrastructure, people, tools, organization, and processes. Useful questions around these pillars could enable organizations to better prioritize which projects are most viable:</p>
<ul>
<li>Does the company have the ability to reliably collect data?</li>
<li>Does the company have the infrastructure to store and clean the data?</li>
<li>Does the team have the prerequisite skills to deliver reliable, accurate model results?</li>
<li>Does the company have the processes, tools, and skills to monitor models in production?</li>
</ul>
<p><i><u>AI projects should be simple</u></i></p>
<p>A technically simple project is more manageable for an organization with relatively low data maturity. A simple project that is fast to deliver helps generate quick wins that build up the experience of the team. This builds up the momentum for future AI projects, spurring a virtuous cycle that builds excitement in the company that generates buy-in for more technically advanced AI projects. As the company progresses in its AI maturity, it can then move on from low-hanging fruit to more complex projects.</p>
<p><i><u>AI projects should be valuable</u></i></p>
<p><a target="_blank" href="https://hbr.org/2019/02/how-to-choose-your-first-ai-project">Harvard Business Review</a> purports that one of the traits of a strong AI project is the value it creates. This can come in three forms - AI can help make better decisions, reduce cost by automating repetitive manual tasks, or be used in new products. Such value derived from successful AI projects can help convince stakeholders to invest in the company’s AI capabilities.</p>
<p>Looking for a simple, valuable, and viable project is not trivial. To find projects that fit the bill, Veljko Krunic, the author of <a target="_blank" href="https://www.manning.com/books/succeeding-with-ai">Succeeding with AI</a>, proposes the following steps.</p>
<ol>
<li>Identify business problems.</li>
<li>Brainstorm AI solutions to business problems.</li>
<li>Estimate the technical viability and complexity of the project.  </li>
<li>Determine business metrics to measure the value of the AI project.</li>
<li>Estimate the business value based on business metrics.</li>
</ol>
<p>Executing these steps is simple in theory but nuanced in practice. In particular,  the choice of business metrics might not be immediately obvious. The next section outlines how business metrics can be selected.</p>
<p>Measure AI Projects with Business Metrics
The business value of an AI project can be measured with appropriate business metrics. Yet, the practice of measuring an AI system’s performance with business metrics can be counterintuitive to many AI practitioners.</p>
<p>Machine learning models are typically measured with evaluation metrics that measure their technical performance on a data set, not business metrics that quantify its value on a business. This is a common practice in academia, where the progress of machine learning algorithms is pegged to quantifiable, objective technical benchmarks. However, AI systems in the real world are tied to tangible results that go beyond accuracy, like revenue, cost, and customer satisfaction.</p>
<p>Thus, there is a need to connect the technical performance of AI systems with business metrics. Often, such links are not immediately clear. Business leaders and AI experts should collaborate and reach a consensus on a business metric that can be used to measure and optimize the performance of an AI system, according to <a target="_blank" href="https://www.cxotalk.com/episode/andrew-ng-explains-enterprise-ai-strategy">Andrew Ng</a>, the co-founder of Google Brain.</p>
<p>In this process, the use of a profit curve can help establish the relationship between business and technical metrics quantitatively, as proposed in the book <a target="_blank" href="https://www.goodreads.com/book/show/17912916-data-science-for-business">Data Science for Business</a>. This translates technical progress to business terms, allowing leaders to answer questions such as ‘how much more revenue is generated with a one percent increase in accuracy?’</p>
<p><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1625684748/manage_ai_projects_1_gb0qja.png" /></center>
Figure 1: An illustration of a profit curve that links the accuracy of an AI system with the resulting profit</p>
<h2 id="communicate-the-value-of-an-ai-project-with-business-outcomes">Communicate the Value of An AI Project with Business Outcomes</h2>
<p>Communication between machine learning practitioners typically involves technical jargon around machine learning models, and rightfully so. However, that does not make sense to a non-technical audience, which cares much more about the business impact of a model rather than a 1% incremental improvement in accuracy.</p>
<p>An AI practitioner can avoid such a trap by keeping in mind the target audience when discussing updates around AI projects. Particularly, they should be mindful that the improvement of business metrics has a higher priority than that of technical metrics for business stakeholders. They can also benefit from <a target="_blank" href="https://www.datacamp.com/community/blog/storytelling-for-more-impactful-data-science">data storytelling techniques</a> in conveying how AI projects can help with business metrics—whether it is an improvement in customer experience, an increase in retention rates, or a decrease in cost.</p>
<h2 id="implement-ai-systems-and-machine-learning-ml-pipelines-go-hand-in-hand">Implement AI Systems and Machine Learning (ML) Pipelines Go Hand-in-Hand</h2>
<p>We have seen how an AI system is different from a traditional software system. In particular, an AI system does not require a programmer to explicitly code its logic since it is able to learn patterns from data. For this to happen, one needs to first build the pipeline that takes in the necessary data, transforms it, and feeds it to the AI system.</p>
<p>As such, AI systems consist of not only the machine learning algorithm but a <a target="_blank" href="https://ml-ops.org/">machine learning (ML) pipeline</a>. This pipeline describes the flow of data from start to finish, including how it is obtained, transformed, used, presented, and monitored.</p>
<p>Machine learning pipelines are a part of a larger software system. The procedures of <a target="_blank" href="https://www.datacamp.com/courses/designing-machine-learning-workflows-in-python">developing a machine learning pipeline</a> and a typical software system are largely similar. Among other things, the need for competent data engineers, the use of software development processes similar to DevOps, and the importance of security are common themes in developing both AI and software projects.</p>
<p>Here is an example of an ML pipeline of a computer vision system that detects anomalies in a phone manufacturing process. This simplified ML pipeline detects phone defects using photos and facilitates the work of a quality assurance team.</p>
<p><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1625684748/manage_ai_projects_2_o7jm6p.png" /></center>
Figure 2: A simplified ML pipeline of a phone manufacturer</p>
<p>From this example, we can tell that the design of a machine learning pipeline is highly dependent on the data, the algorithm, and the business use case. There is rarely a one-size-fits-all machine learning pipeline.</p>
<p>A complete pipeline also includes data collection, feature extraction, data verification, results monitoring, among others, making it vast and highly complex, according to <a target="_blank" href="https://papers.nips.cc/paper/2015/file/86df7dcfd896fcaf2674f757a2463eba-Paper.pdf">Google’s highly influential paper</a>. This is why a Machine Learning Engineering Team is usually dedicated to building and maintaining the pipeline.</p>
<h2 id="build-robust-ml-systems-from-the-get-go-to-reduce-costly-changes">Build robust ML systems from the get-go to reduce costly changes</h2>
<p>The deployment of machine learning systems might not go as expected, potentially due to a change in the data, a weakness in the model, or a defect in the pipeline. When this happens, practitioners wanting to improve the system may propose drastic changes to the existing pipeline, only to discover to their dismay that machine learning pipelines quickly become resistant to change once they are implemented.</p>
<p>Veljko Krunic calls this process the “ossification of ML pipelines.” The inertia to change comes from the challenges of modifying the code of such a technically complicated project. Business teams who foresee the ripple effect of pipeline changes on business operations may also object to the changes. In the example in Figure 2 above, if the algorithm’s capacity to automatically flag anomalous phones reduces drastically, the replacement headcount required for manual phone inspection might take weeks to be filled.</p>
<p>Making drastic changes to ossified AI projects is costly. A revamp might be necessary when an unscalable proof-of-concept machine learning pipeline is implemented for production. That said, the ossification of ML pipelines is oftentimes unavoidable.</p>
<p>As such, companies aiming to manage AI projects should aim to implement the right pipeline from the get-go. Machine Learning Model Operationalization Management (MLOps) is a burgeoning field that helps organizations achieve such a goal. MLOps tools and practices provide an end-to-end machine learning development process to design, build and manage reproducible, testable, and iterative ML-powered software. This enables data teams to implement continuous integration and continuous delivery for their machine learning pipelines, monitor data drifts, and maintain the explainability of their pipelines.</p>
<h2 id="build-your-team-s-capability">Build Your Team’s Capability</h2>
<p>Managing an AI project is no easy feat. An organization more than just the right technical talent to build AI projects. It also needs a pervasive data culture and strong data literacy to catalyze AI adoption. Much like implementing AI projects takes time, programs to teach data literacy are long-drawn.</p>
<p>That is where <a target="_blank" href="https://www.datacamp.com/groups/business">DataCamp for Business</a> can help by providing an interactive learning platform for companies that need to upskill and reskill their people on data skills. With topics ranging from data engineering to machine learning, over 1,600 companies trust DataCamp for Business to upskill their talent.</p>
<p><center><a target="_blank" href="https://www.datacamp.com/business/demo"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1624651228/schedule_a_demo_bfhlx1.png" /></a></p></p>


https://www.datacamp.com/community/blog/how-to-manage-ai-projects-effectively

As AI continuously evolves, managing data skills is a key element if you want to succeed in the fast-changing AI landscape. This blog post compiles the best management practices for large-scale AI projects.

How To Manage AI Projects Effectively

<p>If you would like to learn more, you can watch the full webinar <a target="_blank" href="https://www.datacamp.com/resources/webinars/developing-an-ai-literate-nation">here</a>.</p>
<p>In June of 2017, AI Singapore was launched with the mission to build AI capabilities in Singapore, create social and economic impact through AI, <a target="_blank" href="https://www.datacamp.com/community/blog/AI-Singapore-2021">grow AI talent</a>, and create a vibrant AI ecosystem. Building an AI fluent nation involves developing the tools to empower citizens to become AI aware, AI literate, and AI experts. During the webinar Laurence Liew, Director of AI Innovation, and Koo Sengmeng, Senior Deputy Director of AI Innovation at AI Singapore, reflected on how AI Singapore has successfully rolled out national upskilling programs, covering various strata of Singaporean society. During the discussion, three broad themes emerged:</p>
<h2 id="the-pillars-of-building-a-vibrant-national-ai-ecosystem">The pillars of building a vibrant national AI ecosystem</h2>
<p>To achieve its mission in building a vibrant AI ecosystem, AI Singapore based its core activities on five main pillars</p>
<br><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1625683395/event_recap_ai_singapore_laul14.png" /></center>

<h4 id="talent-development-is-key-when-building-an-ai-fluent-nation">Talent development is key when building an AI fluent nation</h4>
<p>In June 2017, as part of its AI Innovation pillar, AI Singapore launched a program called “<a target="_blank" href="https://aisingapore.org/industryinnovation/100e/">The 100 Experiments</a>”, which aims to create 100 different AI projects. The main challenge was hiring the right skills and people for the project. Competing with big tech companies, such as Facebook, Google, and Microsoft created a talent shortage in Singapore. As a solution, AI Singapore focused on recruiting engineers who are passionate about learning AI skills but have not had the opportunity to obtain real-world experience. Engineers would go through a 9-month apprenticeship program that would allow them to gain the relevant experience to take responsibility for future projects.</p>
<h4 id="upskilling-with-datacamp">Upskilling with DataCamp</h4>
<p>One of AI Singapore’s goals is to demystify AI and make it accessible to everyone. During the early days, they would run a 3-hour long workshop called AI for everyone. Due to the success of the workshop, they saw the need of providing further access to a high quality AI curriculum and training. This need led them to collaborate with DataCamp, which has allowed them to scale their learning program and offer it to anyone who is interested. Laurence explained that DataCamp has become a key part of their initiative thanks to the “Learn today, apply tomorrow” approach. He further mentioned, “I like the way DataCamp structures the courses. You watch a short video and then apply the skills straight away”. Considering that AI Singapore’s customer target is professionals who work 8 to 10 hours per day, they do not have the energy to sit through long video tutorials and learn a new skill. However, with the DataCamp approach AI Singapore experienced a 30-40% completion rate of their program.</p>
<blockquote>
<p>“Datacamp is about to learn today, do tomorrow” -  Laurence Liew, Director of AI Innovation at AI Singapore</p>
</blockquote>
<h2 id="best-practices-rolling-out-nationwide-upskilling-programs">Best practices rolling out nationwide upskilling programs</h2>
<p>There are different challenges associated with every citizen when rolling out nationwide training programs. Inclusivity and accessibility has been a key ingredient in AI Singapore&#39;s success.</p>
<h4 id="maintaining-an-inclusive-program">Maintaining an inclusive program</h4>
<p>Koo explained that AI Singapore is not an AI talent development program, instead,  it is a generational development AI capability program. They are working hard to involve anyone regardless of their age, gender, or social status, as everyone must have equal opportunity and become AI literate. Furthermore, due to Singapore’s linguistic diversity, the AI for Everyone program has been translated into English, Mandarin, and Malay.</p>
<blockquote>
<p>&quot;We want to ensure every strata in society regardless of age, gender, economic status, to have access to best quality AI education.&quot; — Koo Sengmeng, Senior Deputy Director of AI Innovation at AI Singapore</p>
</blockquote>
<p>Koo Sengmeng also discussed how AI Singapore strives to go beyond languages, creating learning programs designed for all ages, from kids to working professionals and senior citizens. Recent partnerships with schools have allowed them to develop AI for students. By following the DataCamp approach, they converted the AI for Everyone program into small bite-size videos to make it accessible online. When Covid-19 hit Singapore, they had an entire learning package toolkit online, ready to be accessed by any school. By expanding the program into schools, AI Singapore has been able to conclude some commonalities and differences that are important to consider when designing a nationwide program:</p>
<ul>
<li>A common foundation is very important across all ages. This can be achieved by standardizing common terms.</li>
<li>Develop a mental narrative that positions AI as a tool and it should not be feared.</li>
<li>Maintain emphasis on creating ethical AI solutions.</li>
<li>Contextualize the skills to fit the needs of different learners. This context will make data literacy and fluency a powerful foundation.</li>
<li>Make the learning experience easy and frictionless.</li>
</ul>
<h4 id="why-ethics-must-be-part-of-the-curriculum">Why ethics must be part of the curriculum</h4>
<p>Laurence emphasized that data biases are really important, but it is not something new. Humans tend to draw conclusions that favor a particular outcome. For this reason, students are required to take at least one AI ethics module during the program. Moreover, AI Singapore is in constant collaboration with industry experts, which has allowed them to develop awareness about potential borderline ethical issues.</p>
<h4 id="building-an-ai-narrative-that-involves-every-citizen">Building an AI narrative that involves every citizen</h4>
<p>Koo mentioned that one of the most important elements to build an upskilling program for a nation is to get the citizens ready for it. In order for the program to be successful, many working professionals and kids need to be AI-aware. Moreover, collaboration with other governmental bodies is key, as no single agency can achieve an AI nation by itself. Laurence further emphasized that you cannot only focus on academic researchers if the industry is not aware and citizens do not embrace technology and AI. National programs need to make sure they empower their citizens to become data-savvy and AI-aware.</p>
<h2 id="how-does-ai-singapore-see-the-future-of-learning">How does AI Singapore see the future of learning</h2>
<p>Laurence explained that AI Singapore envisions a drastic shift in the current learning landscape. Schools need to be more dynamic as the rapid growth in technology is forcing them to readapt the skills they teach. Of course, basic courses such as English or Math, might not change as much, but generally, schools feel the pressure to evolve faster and teach industry-relevant skills. Koo mentioned, “the industry is saying you need to train your students so they can deploy their skills faster”. A few institutions in Singapore are leading the progress as they are looking to allow students to design their own modules, so they learn what they are passionate about. Laurence summarized it as “pursuing your love, rather than institutions forcing structured curriculum paths that might not work for everyone”. This transition is then further accelerated by digitalization and the adoption of Artificial Intelligence.</p>
<p><center><a target="_blank" href="https://www.datacamp.com/business/demo"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1624651228/schedule_a_demo_bfhlx1.png" /></a></p></p>


https://www.datacamp.com/community/blog/event-recap-developing-an-ai-literate-nation

We recently hosted a fireside chat with AI Singapore to discuss how they are developing an AI fluent nation with national upskilling programs. 

Event Recap: Developing an AI Literate Nation

<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1625092637/employee-spotlight-jeremaia_xvzeyw.png" /></center>

<h4 id="what-do-you-do-at-datacamp-and-what-does-a-typical-day-look-like-">What do you do at DataCamp and what does a typical day look like?</h4>
<p>I am a Software Engineering intern for the Enterprise team—that’s DataCamp’s service designed for managing our B2B customers’ employees in all things data science. So, my team’s responsibility is to both maintain and expand the Enterprise product.</p>
<h4 id="what-led-you-to-your-role-at-datacamp">What led you to your role at DataCamp?</h4>
<p>For the 12 years prior to joining DataCamp, I worked in the events industry, in ticket sales and marketing-focused roles, for a variety of different organizations: large scale venues, festivals, and prominent ticketing agencies. In these roles, I would closely monitor marketing activity against ticket sales, including using analytics to see how well ads had performed and to gain insights on our customers. I reapplied that knowledge when I founded a ticketing startup shortly after graduating in 2012.</p>
<h4 id="was-this-experience-where-your-interest-in-engineering-began-">Was this experience where your interest in engineering began?</h4>
<p>That’s right, I hired some back-end engineers to develop the platform, taking care of the front-end myself. Through online tutorials and forums, I learned the skills needed to style and design the site’s pages and learned a lot about software development and project management along the way. Those learning experiences really stuck with me.</p>
<p>Last year I was running the ticketing department for The Roundhouse in London, when the pandemic struck and I was put on furlough. At this point, I had the breathing space to rethink my career, and I decided to give coding more of a try. In January of this year I enrolled in a full-stack web development bootcamp called Le Wagon where I was able to hone my skills through three months of intensive learning. After this, I went out and looked for employment within web development, and that was when I came across DataCamp.</p>
<h4 id="was-there-anything-you-would-do-differently">Was there anything you would do differently?</h4>
<p>Ultimately I did make mistakes, and if I was to do it again I would do it completely differently—everything from the project management to the types of languages we were using. All the same, it made for a great learning experience.</p>
<p>Some of the advice I’d give to CEOs and entrepreneurs who encounter knowledge gaps, especially with regards to software engineering, is to hire people who can fill these gaps, form a good relationship with them and trust in what they’re doing. If you would like to learn it yourself, reach out and talk to people. People love to talk about themselves and what they do to people with an interest. So, don’t be afraid to reach out on LinkedIn, Twitter or go to meetups.</p>
<p>Also: use Ruby to code your website. It’s easy enough to learn by yourself, but there are also a sizable number of developers who specialize in it should you require outside help.</p>
<h4 id="you-ve-been-interning-for-a-month-what-do-you-think-so-far-">You’ve been interning for a month, what do you think so far?</h4>
<p>It has been great, this company has a great culture and my team has been amazing to work with. And my mentor, Francisco, is very supportive, if I have any questions or doubts he’ll be in touch to help almost immediately. I spent a couple of weeks in the onboarding process, and although DataCamp has a startup culture it is a fair bit larger than some of the other organizations I’ve worked in previously, so part of it was just to get familiar with who does what and how different departments work. The other major part was getting to know the different projects I work on and the range of technologies and tools we use.</p>
<h4 id="what-do-you-like-doing-in-your-spare-time-">What do you like doing in your spare time?</h4>
<p>Most of my time this past year has been spent studying software development since I’m still learning, and trying to improve my skills. Probably not the most exciting answer admittedly, but it’s true. It takes a lot of time and energy to upskill and switch careers especially in technical fields, so a lot of my free time is focused on that. This internship is definitely helping in that regard, and my top goal this summer will be to get as much out of it as possible.</p>
<p>I do spend quite a bit of free time reading science fiction books, right now I’m reading book six of The Expanse, and now that lockdown is easing I’m going to try to get out and see as many people as possible. Of course, I would love to go on holiday at some point but I guess with everything that’s going on I’ll need to see about that one.</p>
<P><i>Would you like to join Jeremaia at DataCamp? We’re currently hiring in New York; London; Leuven, Belgium; and remote locations. Check out our <a target="_blank" href="https://www.datacamp.com/jobs/">open roles</a>.</i></P>

https://www.datacamp.com/community/blog/employee-spotlight-jeremaia

Jeremaia came to us after a career change, going from marketing and entrepreneurship to webdesign, and now he works with us as a software engineering intern. Read on to find out how one man's knack for learning got him here!

Employee Spotlight: Jeremaia

<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1625585317/DataCamp_s_Professional_Data_Scientist_Certification_Program_pske6o.png" /></center><br>
<p>We started DataCamp to help millions of students build data skills. While we’ve upskilled a lot of students, a massive skills gap continues to exist on the labor market for data talent.</p>
<p>Our <a target="_blank" href="http://datacamp.com/certification">Data Scientist Professional Certification</a> lets qualified individuals signal their capabilities to employers. It helps them stand out and land that job or promotion faster. It also helps employers recognize qualified talent both internally as well as during the hiring process. Employers who interview candidates that have been certified by DataCamp can rest assured that these candidates have the skills necessary to succeed as data scientists.</p>
<h3 id="how-we-created-our-certification-process">How we created our certification process</h3>
<p>Through working closely with our industry partners, we quickly came to realize the need for an accreditation that individuals and businesses can trust. So when creating our certification process, we kept this at the forefront of our mission. We conducted an in-depth practice analysis to identify tasks that are both commonly performed or critical to the role of a data scientist.</p>
<p>We looked at a variety of settings and positions that included:</p>
<ul>
<li>Practical interviews with industry experts</li>
<li>Analyzing the role in various company sizes and industries</li>
<li>Assessing the impact of differences in team structures - distributed vs. a centralized team</li>
<li>The seniority of the position</li>
<li>Differences in geographic locations</li>
</ul>
<h3 id="what-the-product-entails-">What the product entails:</h3>
<p>Our Certification process consists of two parts:</p>
<ol>
<li>The certification process</li>
<li>Career services (for our individual learners)</li>
</ol>
<h3 id="1-the-certification-process-">1. The certification process:</h3>
<p>Our Data Scientist Professional Certification not only tests for the technical skills required but also critically, the ability to communicate findings and insights.</p>
<p>To get certified our learners have to pass through our automated tests coupled with case study submissions and practical one-to-one interviews. These include:</p>
<div class="output_wrapper"><table>
<thead>
<tr>
<th>Step</th>
<th>Challenge</th>
<th>Skills tested</th>
</tr>
</thead>
<tbody>
<tr>
<td>1.</td>
<td>Timed assessments</td>
<td>General data science skills in R, Python, and SQL</td>
</tr>
<tr>
<td>2.</td>
<td>Coding challenge</td>
<td>The ability to write and run complex code</td>
</tr>
<tr>
<td>3.</td>
<td>Case study submission</td>
<td>Taking real-world data sets and analyzing them</td>
</tr>
<tr>
<td>4.</td>
<td>Live case study presentation</td>
<td>Ability to describe and structure analysis to both technical and non-technical audiences</td>
</tr>
</tbody>
</table></div>
<p>During the case study section, learners are evaluated by both technical and non-technical audiences. We go past just automated diagnostic tests and introduce a human element through our case study. Through our thesis-like structure, we thoroughly test a candidate on their ability to communicate their findings.</p>
<p>Once learners have been certified, they get access to our personalized career services team.</p>
<h3 id="2-our-career-services-team-">2. Our career services team:</h3>
<p>Our career services team is specialized in helping qualified candidates land their dream jobs in data science. Through our sessions, we help guide candidates through their job search whether through resume reviews, interview preparation, or general tips. We wanted to give our certificate holders the opportunity to not only prove their skills through a certification but also go that extra mile and help them put their best foot forward during their job searches. Currently, our career services offering is only available to our individual customers.</p>
<h3 id="what-our-first-certificate-holders-have-to-say-">What our first certificate holders have to say:</h3>
<p>We’ve already had over 30,000 learners get started with the certification process and we’re blown away by the enthusiasm we’ve received.</p>
<blockquote>
<p>“It was a worthwhile process, which inspired me to elevate my data science skills to a whole new level.” - Radhika Garg</p>
<p>“This experience with DataCamp really went beyond my expectations" - Qiwei Men</p>
<p>“I enjoyed the certification process, and I&#39;m happy to have been successfully certified.”- Jens Svensmark</p>
</blockquote>
<h3 id="what-comes-next-">What comes next?</h3>
<p>We’re just getting started. This is the first in a series of professional certifications. We’d like to give our learners the opportunity to specialize in the fields that they’re most interested in and help employers hire the best candidates.</p>
<p>Learn more about our Data Science Professional Certification <a target="_blank" href="http://datacamp.com/certification">here</a>.</p>


https://www.datacamp.com/community/blog/datacamps-professional-data-scientist-certification-program

By certifying job readiness for data roles, we’re helping learners progress from learning to earning and we’re helping employers find and recognize the data talent they need.

DataCamp’s Professional Data Scientist Certification Program

<p>Given the fact that it&#39;s one of the fundamental packages for scientific computing, NumPy is one of the packages that you must be able to use and know if you want to&nbsp;do data science with Python. It offers a great alternative to Python lists, as NumPy arrays are more compact, allow faster access in reading and writing items, and are more convenient and more efficient overall.&nbsp;</p>

<p>In addition,&nbsp;it&#39;s (partly) the fundament of other important&nbsp;packages that are used for data manipulation and machine learning which you might already know, namely, Pandas, Scikit-Learn and SciPy:</p>

<ul>
	<li>The Pandas data manipulation library builds on NumPy, but instead of the arrays, it makes use of two other fundamental data structures: Series and DataFrames,</li>
	<li>SciPy builds on Numpy to&nbsp;provide&nbsp;a large number of functions that operate on NumPy arrays, and</li>
	<li>The machine learning library Scikit-Learn builds not only on NumPy, but also on&nbsp;SciPy and Matplotlib.&nbsp;</li>
</ul>
<br>
<p>You see, this Python library is a must-know: if you know how to work with it, you&#39;ll also gain a better understanding of the other Python data science tools that you&#39;ll undoubtedly be using.&nbsp;</p>

<p>It&#39;s a win-win situation, right?</p>

<p>Nevertheless, just like any other library, NumPy can come off as quite overwhelming at start; What are the very basics that you need to know in order to get started with this data analysis library?</p>

<p>This cheat sheet means to give you a good overview of the possibilities that this library has to offer.&nbsp;</p>

<p>Go and check it out for yourself!</p>

<p><a href="http://datacamp-community-prod.s3.amazonaws.com/da466534-51fe-4c6d-b0cb-154f4782eb54" target="_blank"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1625226769/NumPy_Cheat_Sheet-_Data_Analysis_in_Python_oejlva.png" /></a></p>

<p>You&#39;ll see that this cheat sheet covers the basics of NumPy that you need to get started: it provides a brief explanation of what the Python library has to offer and what the array data structure looks like, and goes on to summarize topics such as array creation, I/O, array examination, array mathematics, copying and sorting arrays, selection of array elements&nbsp;and shape manipulation.</p>

<p>NumPy arrays are often preferred over Python lists, and you&#39;ll see that selecting elements from arrays is very similar to selecting elements from lists.</p>

<p>Do you want to know more? Check out DataCamp&#39;s&nbsp;<a href="https://www.datacamp.com/community/tutorials/18-most-common-python-list-questions-learn-python/">Python list&nbsp;tutorial.&nbsp;</a></p>

<p>PS.&nbsp;Don&#39;t miss our other Python cheat cheets for data science that cover&nbsp;<a href="https://www.datacamp.com/community/blog/scikit-learn-cheat-sheet/">Scikit-Learn</a>,&nbsp;<a href="https://www.datacamp.com/community/blog/bokeh-cheat-sheet-python/">Bokeh</a>, <a href="https://www.datacamp.com/community/blog/python-pandas-cheat-sheet/">Pandas</a> and the <a href="https://www.datacamp.com/community/tutorials/python-data-science-cheat-sheet-basics/">Python basics.&nbsp;</a></p>


https://www.datacamp.com/community/blog/python-numpy-cheat-sheet

This Python cheat sheet is a quick reference for NumPy beginners.

This Python cheat sheet is a quick reference for NumPy beginners looking to get started with data analysis.

NumPy Cheat Sheet: Data Analysis in Python

<p><center><a target="_blank" href="https://www.datacamp.com/business/demo"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1624651228/schedule_a_demo_bfhlx1.png" /></a></p></p>
<h2 id="organizations-require-winning-data-teams">Organizations Require Winning Data Teams</h2>
<p>Today’s organizations are generating more data than ever before. <a target="_blank" href="https://www.forbes.com/sites/bernardmarr/2018/05/21/how-much-data-do-we-create-every-day-the-mind-blowing-stats-everyone-should-read/?sh=3336938560ba">Forbes claimed</a> over 2.5 quintillion bytes of data were generated every day in 2018 with over 90 percent of the data in the world being generated in the two years before the article was published. Individual countries are generating even more data. In 2018, <a target="_blank" href="https://www.cnbc.com/2019/02/14/china-will-create-more-data-than-the-us-by-2025-idc-report.html">CNBC reported</a> that China generated 7.6 zettabytes of data. This is expected to scale to 48.6 ZB in 2025. The U.S. generates similar amounts of data as well.</p>
<p>There are a lot of actionable insights available in this data that are not taken advantage of. According to <a target="_blank" href="https://go.forrester.com/blogs/hadoop-is-datas-darling-for-a-reason/">Forrester</a>, 60 to 73 percent of an organizations’ data is not leveraged in analytics.</p>
<p>Successful, data-driven organizations are leveraging data at scale to generate value. For example, <a target="_blank" href="https://eng.uber.com/uber-big-data-platform/">Uber</a> invested heavily in the creation of a platform to efficiently deliver the over 100 petabytes of available data to their data teams through a simple interface which <a target="_blank" href="https://eng.uber.com/uber-data-platform-2019/">they scaled</a> to deliver over 1 billion Uber Eats orders with over 24 million miles covered. Through A/B testing, <a target="_blank" href="https://netflixtechblog.com/its-all-a-bout-testing-the-netflix-experimentation-platform-4e1ca458c15">Netflix</a> generates 20 to 30 percent more views by changing the picture associated with a movie or TV show.</p>
<p>There are clearly high-value insights available to organizations that can successfully navigate their complex and large data landscapes. This type of value generation can only be achieved through a high-performing and comprehensive data team.</p>
<h2 id="data-roles-for-a-winning-data-team">Data Roles for a Winning Data Team</h2>
<p>Many key roles go into building a successful data team. In <a target="_blank" href="https://www.datacamp.com/resources/whitepapers/data-leaders-guide-to-upskilling">this white paper</a>, DataCamp describes eight roles, or personas, that can be found in any data-driven organization. While job titles may differ from one organization to another, we’ll outline five of the roles that make up a strong data team: business analyst, data analyst, data scientists, machine learning scientists, and data engineers.</p>
<h3 id="business-analyst">Business Analyst</h3>
<p><strong>Business Analysts</strong> increase profitability and efficiency from data insights. They supplement their deep knowledge of the business domain with data analysis and visualization skills and report on insights to data consumers.</p>
<p><strong>Key Skills:</strong> Data Manipulation, Data Visualization, Reporting, Basic Statistics
Tools: Spreadsheets (Excel, Google Sheets), Business Intelligence tools (Tableau, PowerBI), SQL</p>
<p><strong>Course Recommendations:</strong> <a target="_blank" href="https://www.datacamp.com/courses/introduction-to-sql">Introduction to SQL</a>, <a target="_blank" href="https://www.datacamp.com/courses/data-analysis-in-spreadsheets">Data Analysis in Spreadsheets</a> / <a target="_blank" href="https://www.datacamp.com/courses/data-analysis-in-excel">Excel</a>, <a target="_blank" href="https://www.datacamp.com/courses/marketing-analytics-in-spreadsheets">Marketing Analytics in Spreadsheets</a>, <a target="_blank" href="https://www.datacamp.com/courses/financial-analytics-in-spreadsheets">Financial Analytics in Spreadsheets</a></p>
<h3 id="data-analyst">Data Analyst</h3>
<p><strong>Data Analysts</strong> play a similar role to business analysts in analyzing and drawing insights from data to drive business outcomes. Therefore, their skills also overlap; however, data analysts answer less defined problems that require a higher understanding of the data analysis workflow, and leverage a combination of coding and non-coding tools</p>
<p><strong>Key Skills:</strong> Data Manipulation, Data Visualization, Reporting, Importing and Cleaning Data, Probability, and Statistics</p>
<p><strong>Tools:</strong> R or Python, Spreadsheets (Excel, Google Sheets), Business Intelligence tools (Tableau, PowerBI), SQL</p>
<p><strong>Course Recommendations:</strong> Data Analyst Career in Track (<a target="_blank" href="https://www.datacamp.com/tracks/data-analyst-with-r">R - 16 courses</a>/<a target="_blank" href="https://www.datacamp.com/tracks/data-analyst-with-python">Python - 16 courses</a>), <a target="_blank" href="https://www.datacamp.com/courses/cleaning-data-in-r">Cleaning Data in R</a>/<a target="_blank" href="https://www.datacamp.com/courses/cleaning-data-in-python">Python</a>, <a target="_blank" href="https://www.datacamp.com/courses/time-series-analysis-in-sql-server">Time Series Analysis in SQL</a></p>
<h3 id="data-scientist">Data Scientist</h3>
<p><strong>Data Scientists</strong> play a significantly more technical role in organizations, working mostly with coding tools to investigate, extract, and produce insights and value with data. Data scientists require a strong understanding of data analysis and machine learning workflows and the ability to work with non-standard data types and big data tools.</p>
<p><strong>Key Skills:</strong> Data Manipulation, Data Visualization, Reporting, Importing and Cleaning Data, Probability and Statistics, Machine Learning,</p>
<p><strong>Tools:</strong> R, Python, Scala, Big data tools (Airflow, Spark), SQL, Command-line tools (Git, Shell)</p>
<p><strong>Course Recommendations:</strong> Data Scientist Career Track (<a target="_blank" href="https://www.datacamp.com/tracks/data-scientist-with-r">R - 22 courses</a>/<a target="_blank" href="https://www.datacamp.com/tracks/data-scientist-with-python">Python - 23 courses</a>), <a target="_blank" href="https://www.datacamp.com/courses/sentiment-analysis-in-python">Sentiment Analysis in Python</a>, <a target="_blank" target="_blank" href="https://www.datacamp.com/courses/introduction-to-git">Introduction to Git</a></p>
<h3 id="machine-learning-scientist">Machine Learning Scientist</h3>
<p>Machine Learning Scientists are responsible for developing machine learning systems at scale. They derive predictions from data using machine learning models of all types to solve problems like predicting churn and customer lifetime value, and are responsible for deploying these models for the organization to use.</p>
<p><strong>Key Skills:</strong> Data Manipulation, Data Visualization, Importing and Cleaning Data, Probability and Statistics, Machine Learning, Data Engineering
<strong>Tools:</strong> R, Python, Scala, Big data tools (Airflow, Spark), SQL, Command-line tools (Git, Shell)
<strong>Course Recommendations:</strong> Machine Learning Scientists Career Track (<a target="_blank" href="https://www.datacamp.com/tracks/machine-learning-scientist-with-r">R - 14 courses</a>/<a target="_blank" href="https://www.datacamp.com/tracks/machine-learning-scientist-with-python">Python - 23 courses</a>), <a target="_blank" href="https://www.datacamp.com/courses/image-processing-in-python">Image Processing in Python</a>, <a target="_blank" href="https://www.datacamp.com/courses/machine-learning-with-pyspark">Machine Learning with PySpark</a></p>
<h3 id="data-engineers">Data Engineers</h3>
<p>Data engineers are responsible for creating data pipelines that help organizations get the correct data to the right people. They combine large amounts of data from different sources into one centralized location, enabling the various data roles to work with clean, relevant, compliant, and actionable data.</p>
<p><strong>Key Skills:</strong> Data Manipulation, Importing and Cleaning Data, Data Engineering, Advanced Programming
<strong>Tools:</strong> Python, Scala, Big data tools (Airflow, Spark), SQL, Command-line tools (Git, Shell), Cloud Platforms (e.g., AWS)
<strong>Course Recommendations:</strong> <a target="_blank" href="https://www.datacamp.com/tracks/data-engineer-with-python">Data Engineer with Python Career Track (25 courses)</a>, <a target="_blank" href="https://www.datacamp.com/courses/introduction-to-airflow-in-python">Introduction to Airflow in Python</a>, <a target="_blank" href="https://www.datacamp.com/courses/streaming-data-with-aws-kinesis-and-lambda">Streaming Data with AWS Kinesis and Lambda</a></p>
<h2 id="how-to-create-a-winning-data-team">How to Create a Winning Data Team</h2>
<h3 id="case-study">Case Study</h3>
<p>With a high-level understanding of the responsibilities of each role, let’s now examine how each of these roles may interact in a business setting to drive value by using a real-world example, where a data team extracts value from a customer churn model.</p>
<p>In this context, data engineers ensure that data scientists and machine learning scientists have access to the high quality data they need to develop and operationalize the model.  They would ensure the quality of the data and the correct permissions for each dataset are enforced. They would also deliver the data in an easy to access way with necessary metadata and variables for an effective analysis.</p>
<p>Next, data scientists and machine learning scientists would work together to create an accurate model to predict customer churn. They would need to ensure the model is accurate, interpretable, and can be deployed within a business process, and work to ensure the model remains accurate on unseen data once it is deployed.</p>
<p>Finally, data analysts and business analysts would work together to leverage the outputs of the model to make decisions that drive business value. They can allocate marketing spend based on which customers are more likely to churn, and provide fact-based reasoning to why a certain segment of customers is more likely to churn to decision makers.</p>
<p>Like the majority of data projects, each role plays a part in extracting value from data.</p>
<h3 id="the-path-towards-an-effective-data-team-starts-with-your-people">The path towards an effective data team starts with your people</h3>
<p>Data positions are in high demand despite the recent hiring decreases caused by the pandemic, ranking twice in the top three of <a target="_blank" href="https://business.linkedin.com/content/dam/me/business/en-us/talent-solutions/emerging-jobs-report/Emerging_Jobs_Report_U.S._FINAL.pdf">LinkedIn’s annual emerging jobs report</a>. At the end of 2020, <a target="_blank" href="https://www2.deloitte.com/us/en/insights/industry/technology/ai-talent-challenges-shortage.html">Deloitte claimed</a> that 23% of organizations had a major or extreme gap between AI needs and current abilities. In 2021, <a target="_blank" href="https://www2.deloitte.com/us/en/insights/industry/technology/data-analytics-skills-shortage.html">Deloitte published a report</a> arguing that to face this shortage in data position hiring, organizations must practice selective hiring and targeted upskilling.</p>
<p>Given the data talent shortage, organizations must combine selective hiring with upskilling to create highly skilled data teams that can create value. Organizations can do this by providing personalized learning paths for their people specifically tailored for these roles. At DataCamp, we provide learners personalized learning journeys based on their skill set and desired learning outcomes, enabling them with the tools to assess, learn, practice, and apply new data skills. With DataCamp for Business, learners can benefit from custom learning tracks that enable organizations to tailor learning programs to their specific goals and challenges. Learn more on how you can transform your talent with DataCamp.</p>
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<p><i>DataCamp for Business provides an interactive learning platform for companies that need to upskill and reskill their people on data skills. With topics ranging from data literacy, and data science to data engineering and machine learning, over 1,600 companies trust DataCamp for Business to upskill their talent.<i></p>


https://www.datacamp.com/community/blog/how-to-build-a-winning-data-team

Your organization is generating countless data points, and this data hides many actionable insights that can improve your company’s performance. Learn how to build a winning data-driven team that leverages the right data science skills.

How to Build a Winning Data Team

<p>There are various ways you might hear the name Alan Turing: you might have watched the famous Benedict Cumberbatch film The Imitation Game. You might be wandering around Manchester and see his statue sitting on a bench just outside an area called “The Gay Village”. One day you might finally see, or even use, a £50 note, and if you look at one side you’ll see the face of the man. But since you’re on DataCamp, chances are you first heard his name when getting to know artificial intelligence and machine learning. For all the talk of AI in this day and age and how it will affect the future, it can be easy to forget that it was invented during the Second World War by a man whose brain won Britain the war in intelligence, and hastened the peace that followed.</p>
<br><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1625142287/1200x628_nb7ula.png" /></center>

<h2 id="chapter-i-a-war-of-wits">Chapter I: A War of Wits</h2>
<p>Encryption works on the basis of replacing the letters of a message with others in accordance with a system known as a cypher. A very basic cypher might be that every letter gets replaced with two letters ahead in the alphabet, so that “hello” gets spelled as “jgnnq”. Code-breakers in World War II could break this very easily, so more complicated cyphers were needed. And enigma devices, used extensively by the German military, provided that complication.</p>
<br><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/c_scale,f_auto,q_auto:best,w_254/v1625084719/alan_turing_2_c2wiw8.png" /></center><br>

<p>At its most basic level, one types out a letter which causes a corresponding letter on a keypad to light up. Which letter could light up was dictated by the arrangement of rotors and a plug board in the machine, and this arrangement was enigma’s cypher. This design meant there were 159 quintillion ways messages could be written out (that number is neither made up nor exaggerated). To further complicate matters, Enigma’s cypher was one that changed daily, written out in a book that each operator carried so that they could adjust the settings of their device. Code books were only set for the month ahead and could be sabotaged by being thrown in water, so even if the allies did manage to obtain one by force a more permanent solution was needed.</p>
<p>Turing and his team at Bletchley Park, the UK’s centre of code-breaking efforts, needed a way to know how the enigma machines had been set up that day, and quickly so that intelligence could be relayed in good time. And so, they developed a machine known as the Bombe, which when fully functional could crack enigma messages in under twenty minutes. Based on Turing’s earlier work with machines and mathematics, Bombe worked by process of elimination and mimicry of enigma’s systems. The Bombe could confirm if pairings of letters corresponded to one another and therefore show the operator the way rotors and plugs had been set up that day. And how did they decide which pairings to go with?</p>
<p>Enigma’s biggest flaw was the fact that a letter would never be written as itself, and so this meant a word in German could be compared to a set of letters in the encrypted message. If none of the letters matched up it was a possible combination. Turing’s team would predict these words themselves: as the Germans were sending out daily weather reports, they would look for the word “weather” in German. When the Royal navy planted mines, staying in contact with Bletchley, they would look for the word “mine”. And another set of words they would look for, for obvious reasons, was “heil Hitler”.</p>
<p>Because only one plug could be put into a socket at a time, the Bombe could identify and eliminate impossible combinations until it was only left with settings that made logical sense. Turing’s team, and later the entirety of Bletchley Park, were then able to enter the encrypted messages into captured enigma devices, calibrated the correct way, and obtain the message in plain German.</p>
<br><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/c_scale,f_auto,q_auto:best,w_349/v1625084719/alan_turing_3_qaxj6h.png" /></center><br>

<p>Cracking enigma was a major victory for the allies that they did not even realize they had won outside of military intelligence circles (and nor did the Germans, until 1974). It was used in almost every military campaign, from Norway to North Africa to Normandy. Yet the most crucial difference cracking enigma made was the ability to know the positions of German submarines known as U-boats. Roaming the North Atlantic, these mechanical sea monsters were the terror of every sailor running the gauntlet between Britain and the US, a vital supply line between the only allied nation in Europe and the industrial powerhouse of the world. Churchill said they were the only thing he had feared during his time as Prime Minister. Discovery of their positions enabled allied convoys to be diverted away from the threat and prevent the UK from starving—and for allied navies to close in for the kill.</p>
<p>This part of the story tends to end with historians’ estimates that breaking enigma shortened the war by two years and saved 14 million lives. And that is true, but there is something more that needs to be said: people who theorize Germany could have won the war often do so on the basis that technologies they were developing in the war’s late stages could have given them the upper hand had it not been for the war coming to an end before they could be deployed. Most historians are in agreement that at the least it would have made the war far more destructive, so the bottom line is: 14 million lives is a very conservative estimate.</p>
<br><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/c_scale,f_auto,q_auto:best,w_512/v1625084719/alan_turing_4_pmzfqr.png" /></center>
<center>Photo credit: <a href="https://unsplash.com/@tesa22?utm_source=unsplash&amp;utm_medium=referral&amp;utm_content=creditCopyText">TS</a> on <a href="https://unsplash.com/s/photos/alan-turing?utm_source=unsplash&amp;utm_medium=referral&amp;utm_content=creditCopyText">Unsplash</a></center>

<h2 id="chapter-ii-the-mind-of-a-machine">Chapter II: The Mind of a Machine</h2>
<p>Turing’s story might have ended with WWII and thanks to the Official Secrets Act might not have been known to the public until much later. He continued to work at Bletchley Park for two years until he felt that the culture of secrecy was hindering his work, and subsequently transitioned into civilian life. In 1936 Turing had already laid the foundations of what came to be known as Turing Machines, machines capable of solving any mathematical problem through algorithms, and Turing built upon these during this period when working upon the earliest iterations of computers.</p>

<p>In 1950 he published what might be his most influential piece, a philosophical paper that began with the question “Can Machines Think?” He theorized that the human brain, through training, education, and life experiences, becomes a “universal machine”, and therefore it ought to be possible to construct a machine that can take prior information into account and learn likewise. This was how Turing came to be known as the father of artificial intelligence.</p>

<p>Turing proposed a test that would confirm if a brain had been built called the Imitation Game. Based on an earlier Victorian parlor game, it would involve a human judge having a conversation with a machine, though not knowing this for a fact. If, through speech patterns, word choice, etc, the machine could fool the judge into thinking they were having a conversation with a human, the machine passed the Turing Test. Turing’s theory was that by the year 2000 a machine could easily have passed this test.</p>

<p>The involvement of Turing’s brilliance could well have meant this prophecy would have been fulfilled. But a year later, Turing’s story entered a much darker phase.</p>


<h2 id="chapter-iii-betrayal">Chapter III: Betrayal</h2>
<p>Alan Turing was a homosexual man born into a homophobic England. In the Victorian era laws criminalizing homosexual behavior had been passed, and were active and applied over half a century later when it was discovered that Turing had been having a relationship with a man called Arnold Murray.</p>

<p>Turing had been open about his homosexuality during his time at King’s College, described as “an oasis of acceptance” in an otherwise intolerant society. He kept it a secret during his time at Bletchley Park except in 1941 when he revealed it to his fiancee and colleague Joan Clarke, deciding he could not proceed with the engagement. His former colleague Professor I.J. Good later remarked “it was probably a good thing that the security people didn&#39;t know because he might then have been fired and we might have lost the war&quot;.</p>

<p>Turing was offered the choice between prison and chemical castration, with him choosing the latter so that he could continue his research work. Taking medications designed to render him impotent, these along with the stripping of his security clearance and ability to travel abroad reportedly caused a depression that would eventually lead to his suicide by eating a cyanide-laced apple.</p>

<p>Ideas are impossible to kill, however. As Britain transitioned into a more tolerant society Turing became an icon in the LGBTQ+ world. In 2009 Prime Minister Tony Blair issued an apology on behalf of the British government for how Turing had been treated, and four years later Queen Elizabeth issued a Royal pardon. 2015 saw the introduction of a set of laws that pardoned 49,000 men convicted under the act, wiping their criminal records clean. These were informally called the Alan Turing laws.</p>

<h2 id="chapter-iv-data-science-and-the-lgbtq-community">Chapter IV: Data Science and the LGBTQ+ community</h2>
<p>Imagining what would have happened if Turing were able to see the world as it is now is not that hard to do. Born 109 years ago, had he lived a long and happy life he would still have been with us. He would have been both delighted and disappointed.</p>

<br><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1625084719/alan_turing_5_srzm36.png" /></center><br>

<p>We’ve written a lot about the <a href="https://www.datacamp.com/community/blog/data-literacy-for-responsible-ai-infographic">ethics of AI and data science</a> as a whole here at DataCamp, and the common thread is that it can only be as ethical as the people and the data behind it. As data science affects more of our daily lives, this holds true for the LGBTQ+ community, and there are cases of it being used wrongly that would truly shock Mr Turing. There was the <a href="https://www.vice.com/en/article/j5jmj8/google-artificial-intelligence-bias">case</a> of Google’s Sentiment Analyzer assigning negative sentiment to the phrase “I’m a homosexual”. There was the <a href="https://www.pinknews.co.uk/2017/10/09/russia-targeted-lgbt-americans-with-facebook-propaganda-during-2016-us-election/">instance</a> of a propaganda campaign targeting individuals identified as LGBTQ+ (through their Facebook data) with adverts to persuade them to not vote in the 2016 US Presidential elections. And there was the exceptionally amoral <a href="https://www.theguardian.com/technology/2018/jul/07/artificial-intelligence-can-tell-your-sexuality-politics-surveillance-paul-lewis">case</a> of an AI that could purportedly identify homosexual individuals through facial analysis.</p>
<p>However when one adds the good to the bad, there are more reasons to believe Mr Turing would have been pleased by what he saw. There are examples of apps such as <a href="https://www.prnewswire.com/news-releases/geosure-introduces-first-ever-neighborhood-lgbtq-travel-safety-ratings-300719057.html">Geosure</a> using data to protect LGBTQ+ travelers, and dating apps using human-assisted AI to verify profiles and protect users, of whom LGBTQ+ individuals make up a significant portion. More broadly speaking, data has been used to combat anti-LGBTQ+ misconceptions, and data science is by and large a progressive field that welcomes people of every background, including sexual orientation.</p>
<h2 id="final-words">Final words</h2>
<p>Alan Turing was a brilliant man who was instrumental to protecting what would become the free world and paved the way for technologies that dominate the headlines today. He was a man ahead of his time who sadly got punished for it by a prejudicial system. The world was robbed of a gift—yet not entirely. The fact that I write and you read this from what was once a Turing Machine is testament to that. And so, to honor the memory of a great man, let us ensure that our technological accomplishments are matched, nay, exceeded, by our moral ones.</p>
<h2 id="additional-details">Additional details</h2>
<p>This article could have been much longer than it was had we not glossed over certain details, that nonetheless bear some mentioning for the curious reader.</p>
<ul>
<li><p><a href="https://www.nature.com/articles/d41586-018-06149-y">The key role Polish mathematicians and resistance fighters played in cracking enigma</a></p>
</li>
<li><p><a href="https://www.turing.org.uk/scrapbook/early.html">Turing’s pre-war life</a></p>
</li>
<li><p><a href="https://www.cl.cam.ac.uk/projects/raspberrypi/tutorials/turing-machine/one.html">How a Turing Machine works</a></p>
</li>
<li><p><a href="https://www.pbs.org/newshour/science/8-things-didnt-know-alan-turing">Turing’s additional scientific accomplishments and personal qualities</a></p>
</li>
<li><p><a href="https://www.youtube.com/watch?v=mcX7iO_XCFA">How enigma machines work</a></p>
</li>
<li><p><a href="https://academic.oup.com/mind/article/LIX/236/433/986238">Turing’s famous paper on artificial intelligence</a></p>
</li>
</ul>


https://www.datacamp.com/community/blog/alan-turing-a-life-and-a-legacy

In this post, we explore the life of Alan Turing, the long-lasting impact his work had on data science as we know it today, and in light of his memory the interactions between data science and the LGBTQ+ community.

Alan Turing: A Life and a Legacy

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<h2 id="what-is-data-governance-">What is Data Governance?</h2>
<p>In a recent <a target="_blank" href="https://www.datacamp.com/resources/webinars/how-data-governance-enables-scalable-data-science">webinar</a>, Aaren Stubberfield, Data Scientist at Microsoft, described how data governance enables scalable data science, where he started by addressing the basic question of what data governance is.</p>
<p>Data governance, as defined by the <a href="https://www.dama.org/">Data Management Association (DAMA)</a>, is the exercise of authority and control (<strong>planning</strong>, <strong>monitoring</strong>, and <strong>enforcement</strong>) over the management of data assets.</p>
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<p><strong>(1) Planning:</strong>  Creating rules that datasets need to conform to (e.g. format standardization of date fields e.g. YYYY-MM-DD)
<br><strong>(2) Monitoring:</strong> Measuring compliance to rules defined in the Planning phase (e.g. tracking the percentage of ‘Date’ fields with missing values)
<br><strong>(3) Enforcement:</strong> Remediation actions in the event of data rule breaches (e.g. immediate corrective action if critical data fields such as social security number are found to contain major discrepancies)</p>
<p>While there are different data governance frameworks available, they all revolve around the same key elements of people (i.e. data stewards and stakeholders overseeing data governance programs), processes (i.e. data policies and procedures that make up the rules datasets need to conform to), the datasets in question, and <a target="_blank" href="https://www.datacamp.com/community/blog/a-survey-into-data-governance-tools">tools</a> (i.e. technology and infrastructure that allow the monitoring and enforcement of policies).</p>
<br><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1624920044/scaling_data_science_2_ekqjue.png" /></center><br>
<h2 id="why-is-data-governance-important-">Why is Data Governance Important?</h2>
<p>For data to be useful as a strategic asset, it must be kept compliant, clean, and accessible through formal procedures and accountability. The purpose of data governance is to establish the methods, processes, and responsibilities that enable organizations to ensure the quality, compliance, and usability of their data.  </p>
<p>Data quality is crucial for the democratization of data science because <a target="_blank" href="https://www.datacamp.com/community/podcast/creating-trust-in-data-with-data-observability">organization-wide trust in data</a> is of utmost importance. Just as consumers would not purchase or use products from retailers they do not trust, business stakeholders and data practitioners will be hesitant in using data (or insights gleaned from data) for decision-making if they lack trust in it.</p>
<p>Ultimately, the most successful businesses are the ones who can effectively manage data as a trusted asset and are able to marry the best data science capabilities with the best data quality.</p>
<h2 id="what-does-good-data-quality-look-like-">What does Good Data Quality look like?</h2>
<p>Data governance programs ensure the scalability and maintenance of data quality but what exactly does good data quality look like? <a target="_blank" href="https://atlan.com/what-is-data-quality/">Atlan</a> nicely summarized it with the following seven characteristics:</p>
<ol>
<li>Accurate</li>
<li>Available and accessible</li>
<li>Complete</li>
<li>Relevant and reliable</li>
<li>Timely</li>
<li>Gives the right degree of granularity</li>
<li>Helps you in decision-making</li>
</ol>
<h2 id="what-are-the-benefits-of-data-governance-">What are the benefits of data governance?</h2>
<p>When properly implemented, a solid data governance framework can help companies reap numerous significant benefits:</p>
<ul>
<li><p><strong>Common Data Language -</strong> A common verbiage around metrics and datasets creates a common data language and consistent terminology that is easily used and understood across the organization.</p>
</li>
<li><p><strong>Increased confidence in data quality -</strong> Improved data quality from good data governance promotes trust and confidence in the data and its corresponding documentation, thereby leading to greater utilization of the data while making decisions.</p>
</li>
<li><p><strong>Improved reusability of data -</strong> Data governance creates standardization and mapping of enterprise-wide data. This greatly enhances efficiency through the reuse of data across different business units and varying use cases within the enterprise.</p>
</li>
<li><p><strong>Better data management mechanisms -</strong> Central control mechanisms establish clear policies and rules that guide best practices and codes of conduct around data usage.  This provides a bird’s eye view for the consistent management of key areas like compliance and security, while also minimizing costs in overall data management.</p>
</li>
<li><p><strong>Scalable compliance -</strong> A strong data governance program ensures that organizations remain compliant with regulatory pressure (e.g. <a target="_blank" href="https://oag.ca.gov/privacy/ccpa">California Consumer Privacy Act (CCPA)</a> and <a target="_blank" href="https://gdpr.eu/">General Data Protection Regulation (GDPR)</a>) around how to properly manage the privacy and confidentiality of personal data.</p>
</li>
<li><p><strong>Single Source of Truth -</strong> Data governance facilitates the integration of different data sources such that data is no longer siloed. This establishes a consistent and complete single source of truth of the customer profiles (or profiles of other key entities) that business stakeholders can agree upon to make better cooperative decisions.</p>
</li>
<li><p><strong>Increased Agility from Data Democratization -</strong> The high quality of data created by good governance makes it readily available even for non-technical units to utilize for their business goals. This democratization of data will then enable business units to be more agile through self-service analytics.</p>
</li>
</ul>
<p>All these benefits will in turn enable companies to reduce costs through improved efficiency, minimize risks through prudent use of data, stay compliant to data regulations, and maximize business performance through consistent use of data-driven decision making.</p>
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<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1624920044/scaling_data_science_4_iu9ox8.png" /></center><br>
<h2 id="how-to-operationalize-data-governance-">How to Operationalize Data Governance?</h2>
<p>A recent <a target="_blank" href="https://assets.kpmg/content/dam/kpmg/xx/pdf/2018/02/guardians-of-trust.pdf">KPMG study</a> mentioned that only 35% of data leaders trust their data, while a <a target="_blank" href="https://www.gartner.com/smarterwithgartner/how-data-and-analytics-leaders-can-master-governance/">Gartner survey</a> revealed that a staggering 42% of data leaders do not assess or monitor their companies’ data governance structures.</p>
<p>This shows that many organizations are still behind in their data quality journeys. In the webinar, Aaren mentioned numerous best practices for organizations to get started on their path towards operationalizing data governance.</p>
<ul>
<li><p><strong>Start with the right people -</strong> Successful data governance is built on the agenda set by the right people. It is important to start by enrolling the best talents and key stakeholders to form the core team that drives data governance implementation.</p>
</li>
<li><p><strong>Track your metrics -</strong> Data quality metrics need to be identified and monitored right from the start to track the progress and value of data governance systems. Some examples include percentage completion of data glossary, and percentage of contact details that are complete, valid, and accurate.</p>
</li>
<li><p><strong>Communication is key -</strong> Continuous support and sponsorship of data governance programs rely on frequent and constructive communication of the successes, setbacks, and progress to the key business stakeholders.</p>
</li>
<li><p><strong>Be in it for the long haul -</strong> Implementing a data governance program is a long-term investment that requires consistent work. This means that the operationalization of data governance should be framed as an iterative process towards continuous improvement, rather than an ad-hoc initiative.</p>
</li>
<li><p><strong>Invest in data governance tools -</strong> The ever-expanding volumes of data render it impossible for data governance activities to be done manually. This is where data governance tools come in to serve as vital enablers in the automation of governance operations and data stewardship efforts.</p>
</li>
<li><p><strong>Avoid starting from scratch -</strong> There are many resources documenting successful use cases, frameworks, and examples of effective data governance, so there is no need to start from scratch. For example, DAMA, EDM Council, and PwC have frameworks that can be leveraged and repurposed by organizations.</p>
</li>
</ul>
<br><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1624920044/scaling_data_science_5_alat2k.png" /></center><br>
<i>Example of a comprehensive data governance framework | <strong>Source: <a target="_blank" href="https://www.pwc.in/assets/pdfs/consulting/technology/data-and-analytics/govern-your-data/insights/global-and-industry-frameworks-for-data-governance.pdf">PwC</a></strong></i>

<h2>Building the Business Case</h2>

<p>In order to drive long-term change and gain executive buy-in on data governance initiatives, it is important to present a strong business case to stakeholders to maintain strong executive support. This can be done by first identifying pain points where low data quality is the problem. For example, incomplete customer contact details could be resulting in a significant loss of sales opportunities.</p>

<p>From there, business benefits (e.g. increased revenue, improved customer experience etc.) from improved data quality resulting from data governance endeavors can be clearly highlighted and communicated.</p>

<br><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1624920044/scaling_data_science_6_kybxm8.png" /></center>

<h2 id="data-skills-enable-successful-data-governance">Data skills enable successful data governance</h2>
<p>A <a target="_blank" href="https://www.bcg.com/publications/2019/good-data-starts-with-great-governance">BCG study</a> showed that more than 60% of the companies assessed their data governance capabilities as ‘underdeveloped’, leading to an inability to leverage advanced analytics in their business. This shows that there are many challenges to the successful implementation of data governance, including inadequate data steward representation, misaligned ownership of data governance elements, and low organizational priority.</p>
<p>A common theme in these challenges is that people in the organization are at the center of making it happen. To achieve the data governance outcomes, the people in the organization must be empowered with the right skills to handle the governance tasks involved. To enable successful data governance, organizations should look to upskill their people on a myriad of data skills, from data literacy, to data cleaning and exploration, and more.</p>
<p>Ultimately, data governance is a team effort built on a common data language across the enterprise.</p>
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https://www.datacamp.com/community/blog/scaling-data-science-with-data-governance

The immense potential of data science and analytics is well recognized by businesses across all industries. But for these data science initiatives to succeed and scale, the data must first be relevant, accessible, and of high quality. This is where data governance comes in to set the right foundations for scalable data science.

The immense potential of data science and analytics is well recognized by businesses across all industries. But for these data science initiatives to succeed and scale, the data must first be relevant, accessible, and of high quality. 

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