Data science is currently one of the most in-demand professional skills, with good reason. In a world that relies heavily on analytics and insights, modeling and interpreting data is critical to informed decision-making.
Thus, skilled data scientists are invaluable to tech organizations; they analyze large structured and unstructured data sets to help streamline project development. Given how important the role is, it’s important to hire the best talent.
To help you out, we’ll cover how to evaluate data science skills in this article.
Here’s a diagram by IBM that comprehensively represents a data scientist’s workflow:
This diagram accurately depicts the complex day-to-day tasks that data scientists have to manage. To effectively fulfill these responsibilities, professionals should possess various key technical and non-technical skills. These include:
These skills are crucial because data science involves applying mathematical concepts and modeling data sets with statistics to gain deep insights. Data scientists should be proficient in:
Data scientists require strong technical skills to effectively fulfill their responsibilities and meet your organization’s project expectations. Some key technical data science skills include:
Machine learning involves using supervised and unsupervised algorithms for classifying data. Data scientists should be familiar with these algorithms and how to utilize them for practical applications.
Additionally, 57% of data science and machine learning professionals use Python for machine learning; so, candidates should be familiar with the programming language as well.
Some of the most relevant machine learning algorithms include:
Data scientists are tasked with building models, deploying them once they’re validated, and monitoring the models continually to guarantee accurate results. Thus, candidates should be familiar with various model selection and modeling techniques. This includes understanding predictive techniques and when to use them, and how to interpret and validate models.
Visualization helps data scientists make sense of the information and communicate it in a manner that’s easy to understand. Validated models may return data in thousands of rows, so compiling and organizing the information is critical to meaningfully interpret it.
Basic visualization skills include leveraging bar charts or graphs to explain data; however, more advanced techniques use coding languages (like Python) to visualize data. Candidates should thus be well-versed in using programming languages and libraries to create interactive data displays.
SQL is used extensively in data science for managing structured data and finding relevant information using queries. Thus, data scientists should be familiar with Relationship Database Models, Indexes, keys (primary and foreign), and SQL commands, among other SQL skills.
To assess data science skills, employers can test candidates for their knowledge of key concepts in machine learning, modeling, data visualization, statistics, and probability, etc. You can evaluate candidates for these concepts using technical interview questions or other objective methods.
However, it’s vital to evaluate candidates for their practical skills as well, to determine whether they are equipped to help your organization complete projects. To assess candidates for their real-world skills, employers can leverage hands-on skills assessment tests (similar to the one shown below), typically conducted through skills assessment platforms (like We Create Problems).
To evaluate candidates for data science skills, you can use assessment platforms to design a custom test. Employers can let applicants pick from relevant programming languages like Python and R, and use libraries like NumPy and Pandas, to solve the problem.
Additionally, recruitment platforms use efficient IDE’s that check candidates’ answers against various test cases to confirm their accuracy. Tech employers can create data science skill tests with problems of varying difficulty to identify the best talent.
For example, a simple question might assess the candidate’s probability and statistics concepts. The question could ask an applicant to calculate the probability of obtaining certain cards if they drew four from a deck of 52. Depending on the conditions for the drawn cards, the answer will vary.
Additionally, tech recruiters can use more complex questions to evaluate candidates for advanced data science skills, like regression. For example, candidates could be provided with a data set from a Tic-Tac-Toe game, and be asked to predict the result based on all possible moves using logistic regression. Test creators can further customize questions by setting constraints.
Data scientists are some of the most important professionals in modern organizations. They help enterprises interpret, analyze, and model data to obtain meaningful insights. Thus, it’s crucial to onboard only the most qualified talent.
To make sure you hire the best data science candidates, it’s important to thoroughly vet candidates for relevant skills. WeCP (We Create Problems) is a tech recruitment solution that over 5000 organizations trust to help recruit the best tech talent.
WeCP’s platform contains a rich repository of data science questions to evaluate candidates for fundamental concepts and hands-on technical skills. Our cheat-proof platform helps employers conduct bulk assessments to recruit candidates with the best data science skills.
So, schedule a demo with us to start hiring top talent.