Data Science Vs Data Analytics: Which one should you choose?

4 min read

Data Science vs Data Analytics

What do you think is the reason businesses are flourishing more rapidly now than they were in the past centuries? Well, the one-word answer is Data. With the number of internet users of approximately 5 Billion in 2021, Data has become a precious asset for every business. Companies want to glean significant insights from this pool of data and convert it into rewarding business solutions. This urgency to employ data has led to the foundation of two major career trends, namely Data Science and Data Analytics. 

Although the duo seems to be similar, the two sectors have their significance, and choosing one of them as a profession can be confusing. Therefore, let’s first understand the difference between Data Science and Data Analytics to get a clear picture of both professions!

Data Science v/s Data Analytics: What is the difference? 

While both Data Scientists and Data Analysts work with Data, the difference lies in how they work with the Data! 

Data science is a multi-functional, multi-disciplinary field that focuses on finding actionable data from massive structured data sets. On the other hand, Data Analytics is the process of examining the existing datasets for valuable insights and converting them into business solutions. To understand this more simply, Data Scientists determine WHAT the data convey, whereas Data Analysts are more concerned with WHY and HOW data transforms and behaves the way it does.

For instance, suppose you work as a Data Scientist at Amazon, whereas your friend is a Data analyst there. Your job will be to provide data-driven insights for rapid business growth. While, your friend needs to design, develop, analyze, and maintain the metrics to drive key business solutions.

Roles and Responsibilities

Roles and responsibilities of Data Science and Data Analytics.

Data Science – 

Data Scientists use prototypes, algorithms, predictive models, and custom analysis in order to develop data production and modeling methods. Responsibilities of a Data Scientist are to-

  • Extract Data by creating ETL pipelines or using APIs.
  • Clean Data by using programming languages.
  • Process and Validate the Data. 
  • Perform statistical analysis with the help of Machine Learning algorithms like NLP, Random Foreset, kNN, etc. 
  • Create automation techniques and ML libraries for simplifying methods to develop and train Machine Learning models. 
  • Process and interpret data in order to catalog valuable insights.

Data Analytics – 

Data analysts accumulate, organize and scrutinize massive datasets to identify data patterns and illustrate them by using visualization techniques such as charts, graphs, etc. Responsibilities of a Data Analyst are to:

  • Interpret data and classify business-related patterns. 
  • Solve Data queries. 
  • Analyze datasets and predict data behaviors. 
  • Apply analytical tools such as predictive analytics, descriptive analytics, diagnostic analytics, and prescriptive analytics.
  • Visualize the cataloged data.
  • Glean insights and share them with business executives to make informed decisions. 

Skills & Tools Required 

Skills and tools required by Data Science and Data Analytics.

Data Science – 

Data Scientists are:

  • Well-versed in data mining/warehousing, data analysis, software development, Machine Learning, and object-oriented programming. 
  •  Familiar with big data platforms like Hadoop and Apache Spark.
  •  Proficient in programming languages like Python, Java, MATLAB, SQL, Julia, and R.

Data Analytics – 

Data Analysts are: 

  • Skilled in data mining/warehousing, statistical analysis, database management, and data modeling. 
  • Compatible with platforms like Excel and SQL databases. 
  • Proficient in Python and R programming. 
  • Well versed with tools like SAS, Power BI, and Tableau.

Career Background 

Career background of Data Scientists and Data Analysts.

Career paths for Data Science and Data Analytics are pretty alike. For pursuing a career in Data Science, one should have an educational background in Computer Science or Software Engineering, or Data Science along with solid mathematical and statistical knowledge. Similarly, for Data analytics, one may have a background in Information Technology, or Mathematics, or Statistics.

 However, you need not worry about your educational background as you can always take online certification courses to advance your career in either of the two. 

Job Outlook 

Job outlook for Data Scientists and Data Analysts.

Both Data Science and Data Analytics are lucrative careers to follow!

According to Glassdoor, the national average salary for a Data Scientist is ₹9,83,500/year, and for Data Analysts, it is ₹5,50,000/year in India. Moreover, the US Bureau of Labour and Statistics suggested that the average pay for data scientists is  $98,860/year and  $86,200/ year for Data Analytics. 

Research also showed that the Data Science sector is expected to increase at a compound annual growth rate(CAGR) of 26% from 2020 to 2027. Whereas, the job opportunities in the Data Analytics field are projected to grow 25% by 2029.

So, Which one should you go for? 

Now that you have a firm idea about both sectors, you can choose the path that suits you the most. But the truth is, it is not that simple! 

To make a firm decision about which career path you should opt for, the foremost thing to consider is your area of interest. 

As you can see, both Data Science and Data Analytics is the game of numbers blended with computer science. Becoming a data scientist requires an interest in mathematics, statistics, and programming as well as an understanding of the business world. If your skills and experience fit with this description, then Data Science is the right path for you. 

On the other hand, Data Analysts being the Sherlock Holmes of the company, are curious about unveiling data points from complex sources and protect sensitive data. They have a passion for numbers, statistics, programming, and research. If this sounds fun to you, go for Data Analytics. 

Knowing which occupation matches your interests will help you get a better idea of the types of jobs you will enjoy. Besides, you are more likely to excel in the work that you appreciate, giving you more satisfaction and happiness.  

Other things to consider are your existing skills and knowledge about the field and the career potential of that sector. Go through the above comparison between Data Science and Data Analytics again to analyze which one is the most suitable career for you. 

Grow a career in Data Science and Data Analytics

If you want to develop your skillset and advance your career in Data Science and Data Analytics, enrolling in an online learning program is probably the best option. At SkilloVilla we have created career tracks on Data Science and Data Analytics with the help of top industry experts to foster your career in this Data-driven world. Not only this, but we also provide individual courses on Data Science and Data Analytics. Moreover, you learn directly from the industry masters via interactive LIVE classes and concept videos. By giving practical knowledge and expertise of various analytical and database tools, we help you thrive in the industry of Data Science and Data Analytics.

Our placement support team helps you get access to the job opportunities in our top 300+ partner companies by helping you build a strong resume and conducting mock interviews.

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