# 7 Must-Have Skills to Get a Job as a Data Scientist

<em><strong>Updated</strong>: May 23, 2022. </em> <h2><span data-preserver-spaces="true">Must-Have Skills for Data Science</span></h2> <blockquote>What are the most in-demand skills for data scientists in 2022? <a href="https://appsilon.com/top-7-data-science-skills-2022/">Read the updated version of our article to find out</a>.</blockquote> <span data-preserver-spaces="true">Everybody and their mother wants to learn data science. And there's no reason not to - the job you do is interesting 95% of the time, the salaries are excellent, and most likely you can get the work done from the comfort of your bed. But what skills separate a superb data scientist from a regular one? There are dozens of areas data scientists must excel at, and we've handpicked seven for today's article. Choosing these particular skills was tough, and some may surprise you because they aren't directly connected to data science and machine learning. </span> <blockquote><strong>Think you have what it takes to join a world-class team? <a class="editor-rtfLink" href="https://wordpress.appsilon.com/careers/" target="_blank" rel="noopener noreferrer">Apply for one or more open positions at Appsilon</a>.</strong></blockquote> <span data-preserver-spaces="true">Today we'll go over seven essential skills every data scientist should have. Here's the complete list:</span> <ul><li><a href="#skill-1">Creativity and critical thinking</a></li><li><a href="#skill-2">Math and stats</a></li><li><a href="#skill-3">Programming</a></li><li><a href="#skill-4">Data analysis and visualization</a></li><li><a href="#skill-5">Machine learning and deep learning</a></li><li><a href="#skill-6">Databases</a></li><li><a href="#skill-7">Education</a></li></ul> <hr /> <h2 id="skill-1"><span data-preserver-spaces="true">Creativity and Critical Thinking</span></h2> <span data-preserver-spaces="true">Tasks in a day-to-day data science job are often vaguely defined - at least at the beginning of the project. More often than not, to provide any benefit from a data science solution, the data scientist must have a lot of domain knowledge. </span> <span data-preserver-spaces="true">For example, how could you possibly develop credit risk models if you don't know anything about the subject? Sure, you could do your best and follow well-established data science principles, but that can only get you so far. As a result, your models won't work optimally, and you won't know what to do about it.</span> <span data-preserver-spaces="true">That's where creativity and critical thinking come into play. Data scientists have to distill a lot of information in a short time frame. Having a team of highly creative people might expose solutions that no one thought of before.</span> <span data-preserver-spaces="true">Critical thinking will help you dig deeper and always ask the right questions, and spot potential biases in the responses. </span> <h2 id="skill-2"><span data-preserver-spaces="true">Math and Stats</span></h2> <span data-preserver-spaces="true">How much math you'll use daily depends on your role. These four areas come up most often when looking at data science prerequisites:</span> <img class="size-full wp-image-6490" src="https://webflow-prod-assets.s3.amazonaws.com/6525256482c9e9a06c7a9d3c%2F65b7d5862210fb3996714f42_70d2bfc3_1-3.webp" alt="Image 1 - Photo by Dhirendra Mirsa on Medium - https://medium.com/@dhirendra.misra/is-mathematics-core-of-machine-learning-1c6a75cb684c" width="1742" height="1092" /> Image 1 - Photo by Dhirendra Mirsa on Medium - https://medium.com/@dhirendra.misra/is-mathematics-core-of-machine-learning-1c6a75cb684c <span data-preserver-spaces="true">It's definitely not something you can pick up in a week, as every listed subject falls into a category of college-level math. </span> <span data-preserver-spaces="true">It doesn't mean you should spend the next year or so learning these subjects in-depth, but you should know the basics. If you're after a junior-level position, basic intuition and understanding of the applicability in data science should do. If you're after a lead researcher position, it's expected these topics are second nature to you.</span> <span data-preserver-spaces="true">There's at least a several-year gap between junior and senior data science positions, so you'll always have the time to learn and explore further. The best part is - you can learn everything entirely for free! Here's a complete reference for beginners:</span> <ul><li><a class="editor-rtfLink" href="https://www.khanacademy.org/math/statistics-probability" target="_blank" rel="noopener noreferrer"><span data-preserver-spaces="true">Statistics and Probability</span></a></li><li><a class="editor-rtfLink" href="https://www.khanacademy.org/math/linear-algebra" target="_blank" rel="noopener noreferrer"><span data-preserver-spaces="true">Linear Algebra</span></a></li><li><a class="editor-rtfLink" href="https://www.khanacademy.org/math/ap-calculus-ab" target="_blank" rel="noopener noreferrer"><span data-preserver-spaces="true">Calculus</span></a></li></ul> <h2 id="skill-3"><span data-preserver-spaces="true">Programming</span></h2> <span data-preserver-spaces="true">All of the math, stats, and critical thinking skills in the world won't help if you don't know how to express your knowledge through code. Let's take a look at the most widely used languages in data science:</span> <img class="wp-image-6491 size-large" src="https://wordpress.appsilon.com/wp-content/uploads/2021/01/2-4-1024x940.png" alt="Image 2 - Programming languages used by data professionals - 2019 Kaggle ML and Data Science Survey" width="1024" height="940" /> Image 2 - Programming languages used by data professionals - 2019 Kaggle ML and Data Science Survey <span data-preserver-spaces="true">In a nutshell - Python and R are industry leaders. SQL is supposedly used more than R, but that's likely for another reason, covered later in this article.</span> <span data-preserver-spaces="true">If you're entirely new to programming, there are some great news - both Python and R are easy to learn. On the other hand, if you're coming from languages such as C or Java, these two shouldn't be a problem to pick up. </span> <span data-preserver-spaces="true">After all, Python was designed for teaching programming concepts to kids, so how complicated can it be for well-educated professionals?</span> <span data-preserver-spaces="true">As for R, here's what you can do with it (assuming a basic knowledge of programming concepts):</span> <ul><li><a class="editor-rtfLink" href="https://wordpress.appsilon.com/r-for-programmers/" target="_blank" rel="noopener noreferrer"><span data-preserver-spaces="true">What Can I Do with R? 6 Essential R Packages for Programmers</span></a></li></ul> <h2 id="skill-4"><span data-preserver-spaces="true">Data Analysis and Visualization</span></h2> <span data-preserver-spaces="true">To become an efficient data scientist, your data analysis and visualization skills have to be top-notch. Your results are here to tell a story, and nobody wants to read an incomplete and poorly presented one.</span> <span data-preserver-spaces="true">There's a whole suite of data analysis and visualization packages available for both R and Python. R's most popular analysis package is <code>dplyr</code>, and for Python, that's <code>pandas</code>.</span> <blockquote><span data-preserver-spaces="true">Want a complete beginner guide on data analysis with R? </span><a class="editor-rtfLink" href="https://wordpress.appsilon.com/r-dplyr-tutorial/" target="_blank" rel="noopener noreferrer"><span data-preserver-spaces="true">Check out our detailed guide to R's dplyr</span></a><span data-preserver-spaces="true">.</span></blockquote> <span data-preserver-spaces="true">When it comes to data visualization, a lot will argue that R takes a point here. The visualizations look better, especially with the default stylings. It's most popular visualization library is <code>ggplot2</code>, and we have an entire series to get you started:</span> <ul><li><a class="editor-rtfLink" href="https://wordpress.appsilon.com/ggplot2-bar-charts/" target="_blank" rel="noopener noreferrer"><span data-preserver-spaces="true">How to Make Stunning Bar Charts in R: A Complete Guide with ggplot2</span></a></li><li><a class="editor-rtfLink" href="https://wordpress.appsilon.com/ggplot2-line-charts/" target="_blank" rel="noopener noreferrer"><span data-preserver-spaces="true">How to Make Stunning Line Charts in R: A Complete Guide with ggplot2</span></a></li><li><a class="editor-rtfLink" href="https://wordpress.appsilon.com/ggplot-scatter-plots/" target="_blank" rel="noopener noreferrer"><span data-preserver-spaces="true">How to Make Stunning Scatter Plots in R: A Complete Guide with ggplot2</span></a></li></ul> <span data-preserver-spaces="true">To conclude - proper analysis and visualization skills are a must. It's not enough to know how to write code, but also to ask the right question. That's why creativity and critical thinking are so important.</span> <h2 id="skill-5"><span data-preserver-spaces="true">Machine Learning and Deep Learning</span></h2> <span data-preserver-spaces="true">This is where all the hype is. Machine learning has been a trending topic over the last couple of years. It's not that new of a concept - as it's been introduced back in the 1950s - but the improvements in computing power made it accessible to almost anyone.</span> <span data-preserver-spaces="true">As a result, most companies included machine learning in their core service. This goes from something as basic as flower species classification to autonomous vehicles. </span> <span data-preserver-spaces="true">The applications of machine learning are endless, so the learning path shouldn't be the same for a business user and an aspiring computer vision engineer. Still, starting from the basics can't hurt.</span> <span data-preserver-spaces="true">Here's a couple of basic machine learning articles to get you started:</span> <ul><li><a class="editor-rtfLink" href="https://wordpress.appsilon.com/r-linear-regression/" target="_blank" rel="noopener noreferrer"><span data-preserver-spaces="true">Machine Learning with R: A Complete Guide to Linear Regression</span></a></li><li><a class="editor-rtfLink" href="https://wordpress.appsilon.com/r-logistic-regression/" target="_blank" rel="noopener noreferrer"><span data-preserver-spaces="true">Machine Learning with R: A Complete Guide to Logistic Regression</span></a></li></ul> <span data-preserver-spaces="true">These two articles by no means capture the essence of "basic machine learning". It's a broad and fastly evolving field, so a single book or course won't be enough to cover everything.</span> <h2 id="skill-6"><span data-preserver-spaces="true">Databases</span></h2> <span data-preserver-spaces="true">It's likely you won't work with CSV and Excel files most of the time. Instead, datasets will be stored in databases. There are many database vendors out there, such as Microsoft, IBM, and Oracle, and all of them have a single thing in common - SQL.</span> <span data-preserver-spaces="true">It's a language for storing, extracting, and manipulating data within the database. SQL syntax varies a bit from database vendor to vendor, but the differences subtle, so it won't take you much time to feel comfortable again if you decide to change a vendor.</span> <span data-preserver-spaces="true">You can go as simple or as complex as you want with SQL. The term "simple" indicates you're just using it to drag the dataset to the memory (e.g., with Python or R), and "complex" indicates you're doing most of the computations and aggregations in the database.</span> <span data-preserver-spaces="true">The second approach is a way to go if speed is critical, but it's also a bad practice to pull the data you don't need. </span> <span data-preserver-spaces="true">Learning the basics of SQL shouldn't take you much time. From a Python/R perspective, there are pre-made packages for establishing connections with any database, both on-premise and cloud-based. These packages are also well documented (usually), so establishing connections shouldn't be an issue.</span> <strong><span data-preserver-spaces="true">To recap</span></strong><span data-preserver-spaces="true"> - learn the basics of SQL so you can do the "heavy lifting" within the database, and pull only the data you need to Python/R.</span> <h2 id="skill-7"><span data-preserver-spaces="true">Education</span></h2> <span data-preserver-spaces="true">Less than 30% of data scientists have a bachelor's degree or less, and around 20% are PhDs, according to the 2018 study made by </span><a class="editor-rtfLink" href="https://engineering.indeedblog.com/blog/2018/12/where-do-data-scientists-come-from/" target="_blank" rel="noopener noreferrer"><span data-preserver-spaces="true">Indeed</span></a><span data-preserver-spaces="true">. In a nutshell - a master's degree is an expected common ground.</span> <span data-preserver-spaces="true">Here's a complete overview per profession and education level:</span> <img class="size-full wp-image-6492" src="https://webflow-prod-assets.s3.amazonaws.com/6525256482c9e9a06c7a9d3c%2F65b7d5872c3f301b93864ce4_518a147f_3-4.webp" alt="Image 3 - Distribution of tech professionals by profession and education level (Indeed)" width="1100" height="600" /> Image 3 - Distribution of tech professionals by profession and education level (Indeed) <span data-preserver-spaces="true">This doesn't mean you can't get hired as a data scientist without a college degree, but only under two conditions:</span> <ul><li><span data-preserver-spaces="true">The HR department doesn't automatically filter you out for not having a college degree (read: apply for positions in small companies, they most likely don't have an HR department)</span></li><li><span data-preserver-spaces="true">You demonstrate more knowledge than, well, everyone who applied - as you're the last one on the food chain</span></li></ul> <span data-preserver-spaces="true">Yes, a degree is a useful thing for data science jobs, but a degree in what? Let's take a look at the following chart:</span> <img class="size-full wp-image-6493" src="https://webflow-prod-assets.s3.amazonaws.com/6525256482c9e9a06c7a9d3c%2F65b7d587a42dfbe3c048f3ef_d74ad891_4-3.webp" alt="Image 4 - Distribution of degree field studies by profession (Indeed)" width="1100" height="600" /> Image 4 - Distribution of degree field studies by profession (Indeed) <span data-preserver-spaces="true">As you can see, most data scientists have a background in either computer science, business, or math/stats. The number of data scientists with an official degree in data science is expected to rise, as there are more and more universities offering this specialization.</span> <h2><span data-preserver-spaces="true">Conclusion</span></h2> <span data-preserver-spaces="true">And there you have it - 7 essential skills you should have for a job in data science. The take-home point is: knowing the basics of each should be enough to get you an entry-level position. Only years of experience will help you climb the corporate ladder, and there's always time to dive further into specific areas. Data scientists spend a lot of time communicating their ideas and findings with decision-makers, so an extroverted personality is also nice to have, especially in team lead level positions.</span> <strong><span data-preserver-spaces="true">If you want to implement machine learning in your organization, you can always reach out to </span></strong><a class="editor-rtfLink" href="https://wordpress.appsilon.com/" target="_blank" rel="noopener noreferrer"><strong><span data-preserver-spaces="true">Appsilon</span></strong></a><strong><span data-preserver-spaces="true"> for help.</span></strong>

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