Top 5 Data Science Skills Required In 2022

In this blog, you’ll learn what a Data Scientist do, the Data Science skills required to become a Data Scientist, and much more.

An Introduction to the Top 5 Data Science Skills

The average annual salary of INR 16.8 lakhs alone makes Data Science an attractive field for job seekers. Are you wondering what a Data Scientist do to earn such sky-high remunerations? Analysis of big data, deriving meaningful insights, and driving key decisions; a Data Scientist can play a multitude of roles to ensure that businesses stay ahead of the competition and flourish.

The global growth rate of the Data Science sector is a whopping 30%. The projected market value of this industry by 2024 globally is USD140.9 billion. The proliferation of tech-enabled activities in industries like Banking, Insurance, the Internet and e-commerce sectors means that data generation takes place in mind-boggling quantities every minute. 

Professionals in the field of Data Science harness big data and make it work to both the business and end users’ advantage. When applied to health and sciences, Data Scientists potentially also provide insights that could transform the world we live in!  The best part is that with time and dedicated effort, many of the skills can be acquired. Read on to know what you need to be a part of this buzzing field.  

Data Science Skills Required to Excel

Jobs in Data Science typically require an ability to collect, analyse, interpret and present data effectively. In doing so, you may be addressing a business question on hand and carving possibilities for the way ahead. The right mix of technical and non-technical skills can help you stand out from the crowd and thrive in the competitive world of Data Science. Let us delve into the essential Data Science skills and tools:

Technical Skills for Data Science

The basic technical skills required that can aid you in the field of Data Science are: 

• Programming and logical reasoning

You should have a thorough knowledge of at least one programming language. Popular open source languages like Python and R for Data Science are easily accessible and involve a shorter learning curve. These powerful languages can be used for analysis, data manipulation and more. 

Tip: Coding and running small programs using Python or R libraries is a great way to strengthen your programming skills. 

• Querying and database management

The heart of Data Science is big data and converting data into information is an important aspect of addressing business questions. To be able to do this, you need to learn how to work with databases and eventually gain mastery. 

With large quantities of data available, it is vital to know how to organise and manage these using a database. Many activities require you to interact with database management systems regularly. You may need to design a database, create datasets, map, order, and/or interlink key values. Depending on the data modelling need, you may need to work with relational databases (like MYSQL, db2 or PostgreSQL) or NoSQL databases (like MongoDB). While you may have access to an existing database, at times, you may need to build one from scratch.

Tip: To interact with relational databases, you need to be familiar with querying and data manipulations (insert/ delete/ modify entries). Using a language like SQL, you can do all these, clean up data or map data logically.

• Knowledge of statistics and probability

Research and development in fields like Machine learning and AI require a strong conceptual understanding of statistical learning techniques. This is especially useful in predictive modelling, essential for building intelligence into computer systems. You may also need to learn how to work with tools helpful in pattern recognition and machine learning. Depending on the need, you may need to know how to incorporate algorithms that enable supervised, unsupervised or reinforcement-based machine learning. Also, learning to apply Bayesian Statistics can be a big differentiator as this approach can aid in new perspectives- concerning data or events.

The ability to formulate both interesting questions and derive answers that can have a meaningful impact can prove to be critical to your success at work. Working on case studies can prod you to think through several scenarios. This way, you gain knowledge and boost your confidence in dealing with probability distributions.

Tip: Build a knack for investigative studies on a variety of areas using Think stats by Allen Downey. You can use the real-world datasets from this book to apply the basics and develop a deeper understanding of the subject matter.

If you have prior knowledge of linear regression, you can refer to ‘An introduction to statistical learning with applications in r’ to get ahead. This source is a great way to understand pertinent applications through illustrations that address both modelling and prediction techniques.

•  Data interpretation and visualisation

The field also needs people who are well-versed in representing data graphically. For this, you must be able to utilise charts, plots, animations or infographics. These can aid you in putting forth even the most complex data relationships and insights in an easily understandable way to the stakeholders. This expedites not just understanding but also speeds analyses and decision-making that can prove to be critical for business.    

Tip: You can explore an extensive range of data visualisation using ‘R’. This can help you in presenting key information gained via the application of statistics for Data Science.

• Business acumen and soft skills as Data Scientist

Mere knowledge of top technical skills for Data Science cannot guarantee success in the field. Regardless of your role, developing an ability to communicate your ideas, thoughts effectively, and perspectives is important. Also, developing a keen sense of curiosity and critical thinking can help unravel new possibilities while resolving a business crisis. Collaborating with people and being a team player can keep you open to others’ inputs while solving a problem on hand. These are just a few non-technical skills for Data Science that can prove to be handy. 

Conclusion

The qualification of a Data Scientist need not stop at an undergraduate or master’s degree in Computer Science, Mathematics, or some other related field. Self-study and investing time in up-skilling can take you closer to your dream job role. Multiple avenues open up not just eligibility to step into the field but also help to hone their skills. With the best books and courses for data analytics available online, learning and fine-tuning skills are just a click away.  

The entry-level Data Scientist skills acquired via coursework, internships, and projects of personal interest can add to an impressive Data Science skills resume. While experience can add to your ability to add value to the role, it is also vital to stay sharp and maintain your competitive edge via multiple channels. Regardless of the path you choose to follow, it is undeniable that the technical know-how related to Data Science for business can be developed best by the application of concepts. Add keen business acumen to these top Data Science skills, and a lucrative career in Data Science can be yours. UNext is one such platform that can help you become a successful Data Science professional.