Data Science Skills to Stay Relevant in Industry

Data Science Skills to Stay Relevant in Industry

The Hindu Newspaper’s, Business Line has published a data science jobs report on February 28, 2019. It states that there are 97,000 job vacancies in analytics and data science for 2019. Are you ready to grab those vacancies? Then, what data science skills should you learn to stay top in society.

Another interesting fact is that by 2024, there will be a shortage of 250,000 data scientists in the USA alone.

Job Security with Data Science

You may well ask, Why should I learn data science and do I have job security after learning skills in data science.

You should learn data science because there is a huge skill scarcity in the data field which has created the vacancies you have seen above. So if you learn data science now, you will be immediately taken in for a job role in data science.

Do you have job security in a data science job role? Well, again a yes to this question but with some proof about the current and future state of the data science industry.

Data science is an evolving technology

Data science is evolving, associating with it all other recent technologies in the IT field. Thus, you can compete only if you upgrade your data science skills.

Since the skill supply is low, your training in data science will guarantee you a stable job future in the field of data science.

You Can Help Organizations Facing Difficulty in Using the Data Generated

Research by Gemalto, reveals that 65% of organizations are unable to analyze or categorize the volumes of data that they store. Therefore as a skilled data science professional you help the organizations to process their data and draw out positive insights.

In-Demand Skill Set

Data scientists who are already in the industry possess in-demand data science skills.

You have to possess current data science skills like, Machine Learning; R and Python programming; Predictive Analysis; AI; and Data Visualization to stay competitive.

The explosion of Humongous Amount of Data Every day

There is a vast amount of data generated from all sectors of society. There are approximate, 5 million consumers who interact with the internet daily.

This number is set to increase to 6 million by 2025. Hence, these many internet users are responsible for generating voluminous data. Who will analyze and derive results from all these data that is generated?

Undoubtedly, it is the data scientists who will be responsible to guard and process these overflowing data.

Data generation is perennial and thus is your data science job.

You Can Get Easy Promotion in your Job

LinkedIn has reported on the most promising jobs of 2019. And the top most promising job is that of a Data Scientist. It has given a score of 9 out of 10 as the Career Advancement Score for the job of a Data Scientist.

Furthermore, the key skills that LinkedIn has pointed out for a Data Scientist are,

  • Data Science
  • Data Mining
  • Machine Learning
  • Data Analysis and
  • Python

Moreover, data science jobs also pay you high.

The above points give strong reasons for your long time job security in data science job roles.

Now its time to briefly explore the prominent data science skills you need for a successful data science career.

Data Science Skills to Stay Relevant in the Data Field

Let us look into some of the core skills that you have to learn to get a data scientist job.

Machine Learning, prominent among data science skills

A part of artificial intelligence, machine learning involves data processing in order to make predictions and decisions without being programmed to do so.

Furthermore, machine learning combines data science, math and software engineering that requires an extensive skill set.

Skills that you will learn in machine learning are,

  • Computer science fundamentals and programming
  • Probability and statistics
  • Data modeling and evaluation
  • Machine learning algorithms and libraries
  • Software engineering and system design

You have to master machine learning skills that are important among the data science skills.

Python Coding

Python is a useful programming language for data scientists. It can be used for all steps involved in data science processes. Python is a powerful data and visualization tool.

It also has a set of libraries. Among others it has NumPy, SciPy, scikit learn and Pandas. Furthermore, Python can take various formats of data and you can easily import SQL tables into your code.

With Python, you can create datasets and also find any type of dataset you need on Google.


R is very popular among statisticians. Moreover, R is used for problem solving in data science. R is useful for statistical manipulation and graphical representation.

With R, data scientists can involve in data analytics such as statistical and predictive analysis with real-time data.

SQL, powered among data science skills

SQL is a programming language that helps you to carry out tasks such as add, delete, and extract data from a database. You can also carry out analytical functions and transform database structures.

SQL is important among data science skills. SQL helps to edit database table and index structures to help keep the information accurate.


As data science involves big data analyzes, exploring large datasets, mining them and providing data-driven innovation, you must learn Hadoop.

Hadoop is useful for managing and manipulating large datasets from multiple repositories. Moreover, you must be familiar with Hadoop components like Distributed File System, MapReduce, Pig, Hive, Sqoop, and Flume.

Above all, a data scientist benefits a lot from cloud computing tools like Amazon S3 too.


Now you are clear with the core data science skills you need to acquire to become successful data scientists. Deep learning is also one of the data science skills.

In the time series that will follow across the decades, data science will spell magic in rendering fruitful results for various industries. Some of the industries that will employ you with data science skills are,

  • Decision-making sector
  • Marketers
  • Product managing Sector
  • Human resources
  • Banking
  • Gaming
  • Finance
  • Pharmaceuticals
  • Telecom and
  • Government

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