Data Science Skills

Top 10 Most In-Demand Data Science Skills in 2022

Data science is easily among the top rapidly growing fields in the world. However, despite the hundreds of job opportunities, not every applicant excels. The primary reason for this is applied data science is not the same as the data science taught in courses. Applicants have to demonstrate data science skills to be considered eligible. Hence, this article lists the top 10 most in-demand data skills for 2022 that recruiters are looking for!

What is data science?

Data science and analytics is a field that uses scientific methods, algorithms, and many other kinds of systems to extract trends and patterns from comprehensive, unstructured databases. These trends and patterns are then used to make project-based or customer-based decisions such as guiding customer journeys, marketing campaigns, etc.

Why is data science important?

In today’s world, applied data science is of extreme importance because a majority of human interaction is achieved online. As a result, an average user continues to create new data points and trends with each click on the internet.

This creates a gigantic database for companies that can be used as a base for making all decisions. Furthermore, if understood, the trends in this database can be used to create revenue forecasts, sales, and many other business decisions.

As a result, AI and data science have become one of the most popular and most rapidly growing industries in the world. It is estimated that there will be approximately 11.5 million data science jobs in 2022, and this makes knowing the most popular data science skills all the more important.

Given below are the ten applied data science skills that are expected to be in demand in 2022.

10 in-demand skills to master in 2022

Structured Query Language (SQL)

Structured query language or SQL is the language used to communicate with the database. SQL allows the data scientist to organise, upgrade queries, and analyse the data.

Most companies in the world use SQL to create the database, and, as a result, it is one of the most important data science skills to learn. One advantage of this is that SQL is easy to learn and versatile enough to be used in all companies.

Statistics and probability

One cannot deny that data science and analytics are, in fact, based on statistics. Therefore, to have a successful career in data science, it is important to have a strong foundation in statistics. Even if you do not have formal education in statistics, you should know the concepts, ideas, and strategies taught in statistics.

A majority of data sorting and analysis depends on statistics and probability, and therefore, this is a non-negotiable skill. These skills can help you avoid bias and logical errors and ensure your results and insights are trustworthy and infallible.

Data visualisation

Analysing the data to find patterns is one major part of the data scientist’s job. Another part is using this data to tell a story or create a narrative. Data scientists need to know how to visualise the data and depict it graphically in charts, maps, etc. It is only when the scientist can visualise the data can they create insights that can be used for further processing.

This visualisation empowers the data scientist to create dashboards, data models, reports, and software that companies can use to create decisions.


Econometrics is a skill that may not be applicable for all data science roles. However, it does have a high value. Econometrics allows data scientists to apply statistics and mathematical data models to economics. The main aim of this is to predict future trends and patterns that can influence the market, customer behaviour, and demand or supply.

Econometrics is a must-learn data science skill if you are looking to apply for data science roles in finance and investment fields. Econometrics is a part of the predictive analysis (which finds a place in the job description for many job roles) and is an expected skill for many applied data science careers.

Programming languages

A majority of data science depends on programming languages. Programming is the backbone of many data science and analysis roles, such as data system developments. Therefore, for an aspiring data scientist, programming languages are a must.

Some of the most popular programming languages used in data science are Python, R, and Java. Therefore, these are some of the data science skills that you cannot overlook if you wish to create a career in this field.

Missing data handling and imputation

Datasets may not always be complete. In many cases, a few data points may be missing. This makes creating patterns or coming to conclusions difficult. As a result, a key skill that all data scientists must know is how to handle the missing data.

Imputation is the most common technique used to handle missing data. It requires replacing missing data with values. However, this demands extensive learning and understanding, and as a result, imputation is a field of its own.

Domain knowledge

Data scientists can work in a variety of fields, including BFSI, media, healthcare, automobile, digital marketing, etc. It is critical to note that all fields analyse data differently and for different results. For BFSI, data may be analysed to understand lending patterns, but for digital marketing, data analysis may be used to understand trending keywords.

Therefore, a data scientist needs to have expert domain knowledge to understand the purpose of analysing the data. This will help them understand trends and create forecasts more accurately.

Social media mining

A relatively less-known term, social media mining is one of the up-and-coming data science skills. Instagram, Facebook, and Twitter patterns of users can easily be analysed to understand user behaviour.

Skilled data scientists can extract data from social media to understand patterns and get insights. Since all businesses and people now have social media accounts, this skill’s importance cannot be ignored in the long run.

Microsoft Excel

Most data science roles have moved away from Microsoft excel. However, this is not to say that for some jobs, Microsoft Excel is still used. As many people know the basics of Microsoft Excel, there are many functions that can be completed more easily now.

Aspiring data scientists need to know all possible functions, tools and methods that can be completed on Microsoft Excel. From auto-sum to creating macros – everything is included and must be learned!


We have already talked about the importance of statistics and probability; however, it is equally important to know about the significance of high-level mathematics. Machine learning and data analysis algorithms are based on both programming and mathematics.

Therefore, to be considered eligible for many data science roles, you need to have more than a working knowledge of mathematics. Mathematics is another non-negotiable skill that most recruiters ask for when considering candidates for a data scientist or analyst role.

Bottom line

These are the top 10 data science and analytics skills that are expected to be in demand in 2022. If you plan to pursue a career in AI and data science this year, these are some of the skills that you must have. Learning these and adding them to your resume will ensure you have a fair shot in the job application process!

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