If you want to know how to get hired in data science, you’re in luck. We’ve prepared a complete guide on the 7 skills you need to land your first job or internship. These also apply if you’re looking to make a career switch or to improve your performance as a data scientist. It’s hard to get hired in data science. Chances are you'll never see an ad like “50 data scientists wanted for a 3-year-long project.” Something you're more likely to see in, let’s say, Java development. Smaller companies usually don’t need entire departments, so they end up hiring a couple of seniors. And big companies will likely filter you out due to a lack of experience. So what can you do to improve your chances? We bring you 7 skills to get hired in data science, irrelevant of the company and position: <ul><li><a href="#skill-1">Critical Thinking and Problem-Solving</a></li><li><a href="#skill-2">Teamwork and Communication</a></li><li><a href="#skill-3">Programming</a></li><li><a href="#skill-4">Data Visualization and Storytelling</a></li><li><a href="#skill-5">Math and Statistics</a></li><li><a href="#skill-6">Machine Learning and AI</a></li><li><a href="#skill-7">Model Deployment</a></li></ul> <hr /> <h2 id="skill-1">Critical Thinking and Problem-Solving</h2> Data science is all about finding a needle in a haystack. You need a keen problem-solving mind to figure out what goes where and why, and how it fits into the big picture. Being able to think critically means you’ll make well-informed, appropriate decisions based on data and facts. That means checking your personal opinions at the door and trusting in the data - within reason. It’s challenging to be unbiased in your analysis, oftentimes more than it seems at first. Critical thinking is not something you’re born with. It’s a skill, and like any other skill, you can learn and master it over time. Always be ready to ask questions and adjust your perspective, even if it means you’ll have to start from scratch. Like with any other skill, you’ll just get better over time - there’s no hack or magic pill we can give you. Make sure you’re working on challenging projects you’re interested in, as that should get your creative juices going. <h2 id="skill-2">Teamwork and Communication</h2> Data scientists rarely work in isolation. Maybe during the lockdown, but that’s not what we had in mind. Even if you’re the only data scientist in the company, you’ll be closely working with other departments. Regardless of your data science role, you’ll need to communicate and collaborate with clients, developers, other data professionals, designers, and even executives. Data science is somewhat different from other IT professions because it’s heavily focused on math. You need strong communication skills to translate your technical knowledge and results to non-technical team members. This is a crucial step in data science and why data professionals are highly sought after. Turning the secret magic sauce of statistics and data into something digestible for the laymen to make better decisions beyond an educated guess. It’s important to be a good team player - you can’t be the best at everything, so be welcoming to thoughts and opinions from people working in other departments. After all, they have much more domain knowledge than you. <h2 id="skill-3">Programming</h2> There’s no data science without programming. How else would you give instructions to the computer? All data scientists must be comfortable with writing code, these days likely in either Python, R, or SQL. The scope of what you’ll do with programming languages differs from regular programming jobs, as you’ll gravitate towards dedicated libraries for data analysis, visualization, and machine learning. Still, being able to think as a programmer means more than knowing how to solve problems. If there’s one thing data science sees plenty of, it's problems that need solving. But there’s nothing worse than knowing how to solve a problem, and failing to translate it into a durable and production-ready code. We have a couple of resources you can follow for the R programming language: <ul><li><a href="https://appsilon.com/best-practices-for-durable-r-code/" target="_blank" rel="noopener noreferrer">Best Practices for Durable R Code</a></li><li><a href="https://appsilon.com/how-to-write-production-ready-r-code/" target="_blank" rel="noopener noreferrer">How to Write Production-Ready R Code</a></li></ul> <h2 id="skill-4">Data Visualization and Storytelling</h2> Visualizing data is fun. It depends on who you ask of course, but a lot of folks consider it the most satisfying part of data science and machine learning. Data visualization expert knows how to visualize data according to the business needs, and how to connect the visualization so they tell a story. It can be as simple as embedding a couple of plots in a PDF report, or as complex as building an interactive dashboard tailored to the client’s needs. <blockquote>Interested in dashboard development? <a href="https://appsilon.com/how-to-start-a-career-as-an-r-shiny-developer/" target="_blank" rel="noopener noreferrer">Here’s how you can start a Career as an R Shiny Developer</a>.</blockquote> Data visualization tools you’ll use depend on the language. Plotly might be the best option if you’re looking for a cross-platform interactive solution, as it works with R, Python, and JavaScript. If you’re visualizing data with a BI tool, consider Tableau and PowerBI. With the recent launch of <a href="https://appsilon.com/r-shiny-shinytableau/" target="_blank" rel="noopener noreferrer">shinytableau</a>, you can now create Tableau extensions with R Shiny. <blockquote><a href="https://appsilon.com/dash-vs-shiny/" target="_blank" rel="noopener noreferrer">Python Dash vs. R Shiny</a> - Which one should you choose?</blockquote> If you’re an R user, here are a couple of data visualization guides to get you started: <ul><li><a href="https://appsilon.com/ggplot2-bar-charts/" target="_blank" rel="noopener noreferrer">Bar Charts with R</a></li><li><a href="https://appsilon.com/ggplot2-line-charts/" target="_blank" rel="noopener noreferrer">Line Charts with R</a></li><li><a href="https://appsilon.com/ggplot-scatter-plots/" target="_blank" rel="noopener noreferrer">Scatter Plots with R</a></li><li><a href="https://appsilon.com/how-to-make-stunning-boxplots-in-r-a-complete-guide-with-ggplot2/" target="_blank" rel="noopener noreferrer">Boxplots with R</a></li><li><a href="https://appsilon.com/ggplot2-histograms/" target="_blank" rel="noopener noreferrer">Histograms with R</a></li></ul> <h2 id="skill-5">Math and Statistics</h2> The opinions are somewhat divided when it comes to the importance of math in machine learning. A college-level understanding is required, no arguing there. Topics like linear algebra and calculus shouldn’t sound like a foreign language. But if you’re aiming for an intern/junior role, there’s no need to be a math wizard. There are years separating you from a senior/team lead role, so there’s time to learn. If you’re aiming for a research position at a top university then you should have more than a solid background in math. After all, research moves the industry forward, and you won’t be able to do much without a deep understanding of how things work. <strong>Moral of the story:</strong> Just because data science libraries allow you to do complicated math without breaking a sweat, doesn’t mean you shouldn’t know what’s going on below the surface. Get the basics right, and get better at it as you progress with your career. <h2 id="skill-6">Machine Learning and AI</h2> Machine learning is a core skill for every data scientist. It’s used to build predictive models - anything from simple linear regression to a state-of-the-art image generation with generative adversarial networks. There’s a lot to learn when it comes to machine learning. You have your traditional machine learning algorithms (supervised and unsupervised) - regression, decision trees, SVM, Naive Bayes, clustering, and so on. Then you have neural networks - feed-forward, convolutional, recurrent, LSTM, GRU, GAN. There’s also reinforcement learning, but you get the gist - machine learning is a broad field. In fact, it’s such a broad area there are dedicated job openings for machine learning engineers. Long story short, it’s a great time to be a data professional with a keen interest in machine learning. Here’s our list of curated articles on machine learning: <ul><li><a href="https://appsilon.com/r-decision-treees/" target="_blank" rel="noopener noreferrer">Machine Learning with R: Decision Trees</a></li><li><a href="https://appsilon.com/r-keras-mnist/" target="_blank" rel="noopener noreferrer">Deep Learning with R and Keras</a></li><li><a href="https://appsilon.com/cnn-for-image-classification/" target="_blank" rel="noopener noreferrer">CNN For Image Classification</a></li><li><a href="https://appsilon.com/reinforcement-learning-with-tictacjoe-a-simple-brain-coded-explicitly-in-r/" target="_blank" rel="noopener noreferrer">Reinforcement Learning With TicTacJoe</a></li></ul> <h2 id="skill-7">Model Deployment</h2> Business users don’t care if you have a highly accurate model if they can’t use it. What good is a ship if it can't float? It’s unreasonable to expect non-technical users to connect to dedicated virtual machines or Jupyter notebooks just to see how your model performs. That's why being able to deploy a model is oftentimes a requirement for machine learning engineer positions. The most straightforward way is to create an API around your model and deploy it as any other application - hosted on a virtual machine running in the cloud. Things get complicated if you want to deploy models to mobile, as mobile devices are inferior when it comes to hardware. If speed is the key, making an API request and relying on an Internet connection isn’t optimal. Consider deploying the model to the mobile app itself. Machine learning engineers don’t necessarily know how to create mobile apps, but they could consider network architectures that are lighter, and will hence have lower inference time on lower-end hardware. We strongly suggest learning how REST APIs work if you want to get into model deployment. Here’s an article to get you started: <ul><li><a href="https://appsilon.com/r-rest-api/" target="_blank" rel="noopener noreferrer">How to Make REST APIs with R</a></li></ul> <hr /> <h2>Conclusion</h2> And there you have it - the top 7 skills you need to get hired in data science. Keep in mind that the skill levels or skills themselves may vary from company to company. Some data science roles are more database and programming orientated, while others focus more on math. Nevertheless, we found these 7 data science skills to be a must if you're looking to get hired in 2022. <em>What are your thoughts? Which skills do you think are required to get hired in data science? How does your typical workday look like as a data scientist?</em> Please share in the comment section below.