In the last decade, data science has gone from just a few oblivious seeds to an evergreen forest. It is believed that roughly 1.7 megabytes of new information is created every second, and by 2025 the Artificial Intelligence (AI) market will surpass $100 billion.
For the third year in a row, Data Science has claimed the most sought-after job roles in India by Glassdoor and LinkedIn’s Emerging Jobs Report. The report also records Data Science as the fastest-growing job category, with more than 11 million job openings by 2026.
Read on to find out how to hire productive people for Data Science roles.
What are the skills to look for?
The ideal candidate will have skills under these umbrellas: mathematics/stats engineering/programming.
Libraries/Concepts:
The candidate will have a strong foundation of basic statistics – such as correlation and regression analysis – even if they don’t yet have the machine learning element of the role.
Some of the in-demand skills to look for in productive people include:
- SciPy and statsmodels are some popular Python libraries used in data science for scientific computation like eigenvalue calculations, optimization, statistics, etc. The popular concepts to be tested are statistical distributions, statistical tests, correlation functions, etc. Some of the popular assessment methods are code-based tasks with a specified time limit.
- NumPy and Pandas are popular Python libraries used in data science to perform mathematical and logical operations on arrays. Their role is also to manipulate, clean, and organize data frames. The popular concepts to be tested are broadcasting With arrays, creating and managing arrays, handling missing values, checking outliers, grouping attributes. Some popular assessment methods are the use of code-based tasks with specified time limits for testing skills in the use of these libraries.
- Matplotlib and ggplot are the famous libraries in Python and R respectively used to visualize plots. The popular concepts to be tested are basically the creation and customization of types of plots like graphs, scatterplots, linear plots, box-plots for combining data manipulation and visualization. Some popular assessment methods are using code-based tasks while giving a data frame with a constraint of time limit depending on the implementation.
- Sci-kit Learn is Python’s most valuable and robust machine learning library. It includes classification, regression, clustering, and dimensionality reduction. Other valuable machine learning and statistical modelling tools and metrics like ROC, accuracy, etc. It is assessed using code-based tasks, which include model fitting, validation, and prediction on the constraint of time limit for testing skills in this library.
- Statistics is a foundation in most roles in data science. The concepts that are commonly used are Descriptive and Inferential Statistics. Descriptive statistics contains topics like pdf, pmf, distributions, measures of central tendency. Inferential statistics with topics like hypothesis tests like t-tests etc. It is commonly assessed by using coding tasks for calculating basic metrics and tests.
- Machine Learning has the most contribution in data science roles. Some important concepts for Machine learning are Preprocessing, Regression, Classification, Clustering, Tree-based methods, and Support Vector Machines. It is assessed using tasks on model building, prediction, etc. The evaluation of these concepts is done using various datasets provided in code-based tasks or in takeaway assignments.
Engineering/Programming Languages:
The second skill set to examine is engineering and programming skills. Why? Simply, because, the role relies heavily on knowledge of databases and how these are queried using SQL in order to pull data.
- SQL is a foundational programming language, and topics like JOINS, CLAUSES, etc are very crucial in data science tasks. These skills can be assessed using basic query-based database programming questions.
- Python is one of the most important skills for a data science career. Python is used at every stage of the data science process. Concepts like basic operators, data types, loops, functions, basic data structures, etc are tested. This skill is assessed using a basic programming test related to the application of concepts.
- R is a programming language that is used in data science to handle, store, and analyze data. It can be used for statistical modelling and data analysis. Concepts like data input, visualization, functions, statistical modelling, data handling, etc are tested. This skill is assessed using a basic programming test related to the application of concepts.
Key takeaways in a nutshell:
That brings you to the end of our guide on how to hire an ideal candidate. There are a myriad of skills and each role requires a combination of different skills. If you know how to test the right skills using proper assessment methods, you will always spot productive talent very easily.
We are leaving you with a sample test template to hire a productive candidate for your data science requirements. You can try your hands on our data science coding test template below.
Coding Test for Data Science
Topic
Question Type
Subtopic
Time
Logic Coding
Programming(Algorithms)
Python 2, Python 3, R
15-20
mins
SQL
Database
Joins, Clauses, Basic Queries, Nested Queries, Aggregate Functions
5-10
mins
Data Preprocessing, Data Visualization, Statistical Analysis, Model Selection
Data Science
NumPy, SciPy, Pandas, Matplotlib, Statsmodel, Seaborn, Sci-kit Learn
20-30
mins
Machine Learning Predictive Modelling
Machine Learning
Evaluation of model based on MSE, F1 Score, Precision, Recall, Accuracy, R square, MAE, MSE, RMSE
20-30
mins
Total Time
60-90 mins
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