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Machine Learning

Top In-Demand Data Science Skills in 2021

Data Science is one of the fast-growing careers in the world. With a median salary of about $89,000 and 650% job growth since 2012, it is one of the most promising careers in the 21st century. Even with the hiring freezes and layoffs caused by the COVID-19 in the different industries, data science has not been impacted. In fact, this field has become more lucrative than ever.

If you are planning to enter the field of data science, here are the most in-demand skills in 2021:

1. Math

In order to start in the field of data science, you have to dig math textbooks. All data scientists must have a solid foundation in these math concepts:

  • Statistics

You must have a thorough understanding of the key terms like standard deviation, mean, median, mode, distributions, and maximum likelihood indicators. As a data scientist, you need to have an understanding of sampling techniques and ways for avoiding bias in experiments. Through descriptive statistics, you will be able to use charts and graphs to paint a data picture. Also, you will be able to make predictions through data using inferential statistics.

  • Probability

You need to cover probability topics such as Bayes theorem, Central Limit theorem, standard variables, independence, random variables, standard errors, expected values, and probability distribution functions. Through these, you will be able to perform statistical tests and uncover meaningful trends and patterns in the data.

  • Linear Algebra

Linear algebra is considered to be the backbone of algorithms. During your job, you will be using matrices and vectors frequently, especially if you specialize in machine learning.

  • Multivariate Calculus

You must have a deep knowledge of gradient, mean value theorems, limits, product and chain rules, derivatives, beta and gamma functions, and Taylor series. These concepts are often used in logistic regression algorithms. You might be asked about these calculus problems in your interviews as well.


The most commonly used programming language in data science is Python. However, it faces tough competition from R. Python is an object-oriented, multi-purpose programming language that can be easily deployed in websites and applications. It also has the support of an active data science community that makes it the top choice for several tech companies. Here are the programming languages you need to know about to work in the field of data science:

  • Python

As you start studying Python fundamentals, you will come across the Python libraries. These libraries are reusable code pieces that you can use instead of rewriting simple commands. The most popular Python libraries used in data science are Pandas, Matplotlib, NumPy, TensorFlow, Scikit-learn, Seaborn, and SciPy.

  • R Programming

R Programming is another open-source language that is used in the field of data science for statistical analysis. It comes with tools to present and communicate data-driven results. For academic and research work, R is more suited.

  • SAS

This is a software suite that comes with a built-in graphical user interface (GUI) and statistical functions for guiding less technical users. It has one disadvantage over R and Python as the former is an expensive enterprise software while the latter is free to use. 

When you are deciding what language to use, it is important to consider the company and the industry you plan to work for. If you plan to work in machine learning or data engineering, learning other programming languages like C, C++ and Java can be useful. However, since programming involves problem-solving and structured thinking, learning one primary language will make it easier to pick up others.

3.Analytical tools

Analytical tools are used for extracting meaningful insights from the data and providing useful frameworks for big data processing such as SQL, Hoop, Hive, Spark, and Pig.

  • SQL

Through SQL, you can store, manipulate, and query data present in relational database management systems. It is also possible to connect multiple datasets using joins.

  • Spark

Spark, a processing engine, can be integrated with Hadoop to work with large and unstructured data. It can also be used for storing data operations entirely. But, data analysis needs a third-party distribution system.

  • Hadoop

This is an open-source software library created by Apache Software Foundation capable of distributing big data processing into a cluster of computing devices. It uses the Hadoop Distributed File System (HDFS) for storing large datasets and streaming data to user applications such as MapReduce, which then takes care of analytics.

4.Machine Learning

If a company is handling large volumes of data, it is highly likely that they will use machine learning in their day-to-day operations. It is important to note that not every data science role will require knowledge of deep learning, data engineering, or natural language processing. However, you must still make yourself familiar with terms like random forests, ensemble methods, and k-nearest neighbours, especially if you want to work with big data.

5.Data Visualization

Data analysis won’t mean anything if people can understand it. Through data visualization tools such as ggplot, matplotlib, and d3.js, you can transform your data into visuals like charts and graphs. Some of the most commonly used tools for data visualization are:

  • Power BI

It is available in service, mobile, and desktop forms. It uses SQL, Azure, and Excel for generating different forms of visualization. 

  • Tableau

This is a complex tool that offers expanded capabilities and greater speed. There is a drag-and-drop function through which users use to create beautiful dashboards and produce reports that contain line charts, scatter plots, heat maps, etc. 

6.Data Wrangling

Once you have collected data from different data sources, what you will get is a mess that must be cleaned up before it can be used. Data wrangling is needed for building off of programming languages and addressing data imperfections such as date formatting, string formatting, and missing values. For example, any date that comes like “14/12/2020” and “2020-12-14” must be transformed for smooth analysis.

7.Business acumen

Data scientists also need a deep understanding of the business and communication skills for conveying their discoveries to their colleagues and stakeholders. Once you have an understanding of the data, organizations can minimize costs, maximize efficiency, and seize revenue opportunities.  Please note that this is not an exhaustive list. There are a lot of other skills and concepts that you must know to have a successful career in the field of data science. The best way to go through all such skills is through a data science course in India.

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