Data Science vs Data Analytics: Which Should You Choose?

Data science and data analytics are two closely related fields, but there are key differences that set them apart. Data scientists primarily use data science in their careers, while data analysts use data analytics. To illustrate the differences and similarities between data science and data analytics, we will explore how these roles differ regarding skill sets, responsibilities, and career outlook.

A quick guide to data science and data analytics roles

You will find similarities and discrepancies when exploring job opportunities related to data science and data analytics. While there is some overlap between the two positions, ultimately, they serve different purposes for their organisations: 

• Data scientist: Data scientists rely on machine learning algorithms to automate processes, such as data modelling, to predict future outcomes and deliver insights for their organisations.

• Data analyst: Data analysts sort and analyse data to spot trends and solve specific problems, allowing companies to optimise their processes based on valuable insights. 

What does a data scientist do?

As a data scientist, it would be your job to organise large raw data sets and design machine learning algorithms to process data and build predictive models. Using data visualisation tools, you would create graphs, charts, maps, and other visuals to illustrate trends and patterns in more easily understood forms for non-technical audiences. In an increasingly technical world, data scientists are precious to organisations by allowing them to make data-driven decisions. 

Data science skills 

Data scientists have a very diverse and advanced skill set. With a foundation in computer science, statistics, and business practices, data scientists are highly skilled in many technical areas. Here are some of the primary skills needed to succeed as a data scientist:

• Machine learning

• Big data

• Data visualisation and reporting

• Computer programming 

• Artificial intelligence

• Predictive modelling

What does a data analyst do?

As a data analyst, you would gather, clean, sort, and interpret data to solve complex problems. Data analysts use many of the same programming languages as data scientists, such as SQL, Python, and R. You would use these tools to spot trends and use insights to make better business decisions. Data analysts are highly skilled in working with databases and creating visualisations to demonstrate their findings.

Data analyst skills

Like their data science counterparts, data analysts are highly skilled and bring a depth of knowledge to their company. As such, you need to communicate your findings in this role effectively. While an analyst's knowledge isn’t quite as advanced as a data scientist's in computer programming and mathematics, you would still have an impressively diverse and valuable skill set, including:

• Statistics

• SQL (Structured Query Language)

• Data visualisation 

• Microsoft Excel

• R

• Software as a service (SAS)

• Problem-solving and communication skills

Educational requirements

There are varying educational requirements for careers in data science and data analytics. Although post-secondary education is expected from an employer, hiring managers in both these fields are most concerned about whether or not you have the necessary skills, regardless of what your studies were concentrated on. Because of this, obtaining a degree explicitly focused on data science or analytics is an option as courses that teach you other related technical concepts. 

Data science education

Because data science is a relatively new field, many data scientists don’t have a degree in data science. Instead, computer science, mathematics, and statistics are common degree courses. To progress in your career as a data scientist, getting an advanced degree is valuable as it’s common for data scientists to obtain a master’s degree or even a PhD. However, more institutions are adding data science degrees and courses to their curriculum with the rise in popularity. 

Data analytics education

Like their data science counterparts, data analysts have various options for what they may choose to study. The exact computer science, mathematics, and statistics majors are all popular choices. Some institutions offer specified degree courses, such as business analytics, but any related degree will generally suffice if you have the necessary skills mentioned earlier. 

According to information from Salary Expert, 47 percent of data analysts in India hold a bachelor's degree, while 40 percent have earned their master’s. Six percent have completed a PhD, and 5 percent have only completed secondary school [2]. Regardless of your educational background, you typically have opportunities if you are skilled in data analytics.

Salary and job outlook

A career in data science or analytics will create opportunities to earn an above-average income and see significant job growth in the coming years.

The average data scientist salary in India is ₹23,04,872 [1]. Senior-level positions, which can typically be reached within eight years, come with an increase in salary with average earnings of ₹29,04,781. Based on Salary Expert's data, it anticipates a 30 percent increase in salary over the next five years, which would bring the average estimated salary up to ₹29,89,603 by 2029.

While the earning potential isn’t as high for data analysts as data scientists, analysts still have a powerful outlook and can expect similar growth. According to Salary Expert, the average salary for a data analyst is ₹18,69,606, with senior-level analysts earning ₹23,54,293 [2]. The average salary is estimated to grow to reach ₹24,25,028 by 2029.

Things to consider

Other factors are worth considering beyond education and salary when pursuing a career in data science or analytics. 

Domain knowledge can be a great asset to have. If there’s a specific field you know well, combining your domain-specific knowledge with your ability to work with data can set you apart from other candidates. 

To have long-term success in data science or data analytics, it’s important for you to constantly stay up to date on the latest tools and programs available. Since this space constantly evolves, committing to lifelong learning and keeping up with the latest advancements will help you progress in your career.

Next steps

Many Coursera courses can help start your data science or data analytics career. Coursera offers various courses covering all the skills needed in data science and analytics.

If you’re new to data science, consider enrolling in IBM’s Introduction to Data Science Specialisation. In this course, you’ll have the opportunity to gain familiarity with many popular data science tools and get an introduction to machine learning concepts. The University of Michigan’s Applied Data Science with Python Specialisation is an intermediate-level course that can help you further develop your skills in the popular programming language Python. 

For aspiring data analysts, bolster your resume with a Google Data Analytics Professional Certificate. Courses in this program will help you learn SQL, data visualisation, data analysis, and many other valuable skills to prepare you for a career in data analytics.