Today’s digitally-led business landscape has led to big data becoming an essential component of an organization’s efficient functioning. The actionable insights and exceptional results brought about by the use of big data are many, thus pushing organizations toward greater returns and high levels of success. At the back end of it all are two fields of comprehending big data- data analytics and data science.

Although often used synonymously, data science and data analytics differ from each other. Both are integral elements of big data analytics and business intelligence tools, which is why it can sometimes be confusing to differentiate between the two. Both are undoubtedly interconnected, but how they are still different from each other is something that must be known to people who plan to employ either of the two.

In this article, we will talk about data analytics vs data science. Let’s begin.

What Is Data Science?

As a multidisciplinary field, data science focuses on large data sets of raw, structured, and unstructured data. Data science employs several disciplines, like mathematics, statistics, computer science, machine learning, programming, etc., to unearth meaningful information and insights. It can do so from massive amounts of data available in different formats, which is why data scientists are currently some of the most in-demand skilled professionals in the job market.

What Is Data Analytics?

Data analytics helps get conclusions by processing raw data and converting figures into findings that can help in effective decision-making. It focuses on performing statistical analysis after processing existing datasets.

With various tools, data analysts try to create methods for capturing, processing, and organizing data in order to unearth actionable insights from existing problems, additionally establishing the finest way to present said data.

Data analytics aims to produce results that can cause immediate enhancements. To facilitate this, it might also encompass different branches of broader analysis and statistics to combine diverse sources of data.

Data Analytics vs Data Science: What Is the Difference?

Often used synonymously, data science and data analytics are unique fields that cannot be interchanged. Here are a few ways data science and data analytics differ from each other:

Scope

  • Data science encompasses a group of fields used to mine large datasets, hence is considered as an umbrella term sometimes.
  • Data analytics is more focused and a part of the bigger picture, as it aims at extracting actionable insights that can be implemented quickly.

Exploration

  • Data science focuses on parsing through large datasets to expose insights, either in a structured way or an unstructured one. In simpler terms, it explores insights that concentrate on uncovering questions that need to be asked and establishing potential trends that are based on existing data.
  • Data analytics is supposed to be focused on efficient functioning, which is why its exploratory activities are limited to discovering answers to the questions already being asked.
  • Data science also explores better ways to analyze and model data.

Tools & Techniques

  • In the field of data science, a combination of statistical, mathematical, and machine learning techniques is used to clean, process, and interpret data. Insights are then extracted from it, as advanced data modeling processes are designed with the use of prototypes, custom analysis, machine learning algorithms, and predictive models.
  • In the field of data analytics, data sets are examined to identify trends and draw conclusions by identifying relevant patterns in massive amounts of data once it is collected, organized, and analyzed. Findings are then presented through data visualization techniques, transforming complex insights into business-friendly language for non-technical members to understand it just as well.

Data Science and Data Analytics are both fields that perform data collection, data cleaning, and data analysis, however, to varying degrees. The ultimate aim is to drive efficient decision-making based on actionable insights, thus causing their responsibilities to overlap and enabling people to use the terms synonymously.

Data Analytics can easily be considered as a branch of Data Science, but understanding the differences between the two can enable you to employ better solutions and leverage the power of big data more efficiently.

Now that you understand the differences between the two fields, let’s take a look at how the roles and core skill requirements of professionals in these fields differ as well.

Data Analytics vs Data Science: Roles and Responsibilities

Here are the different roles and responsibilities of data scientists and data analysts:

Data Scientists:

  • Processing and cleaning data
  • Validating data integrity
  • Creating ETL pipelines for data mining
  • Writing the code for automation
  • Extracting business insights through machine learning tools
  • Constructing useful machine learning libraries
  • Identifying new trends in data to facilitate business predictions
  • Performing exploratory data analysis on massive datasets

Data Analysts:

  • Collecting and interpreting data
  • Experimenting with various analytical tools like prescriptive analytics, predictive analytics, diagnostic analytics, etc.
  • Identifying or discovering relevant patterns in datasets
  • Data querying through SQL
  • Using data visualization tools for presenting extracted insights

Data Science vs. Data Analytics: Core Skills

Here are the different required core skills for data scientists and data analysts:

Data Scientists:

  • Proficiency in mathematics and statistics
  • Expertise in programming, machine learning, and predictive modeling
  • Excellence in problem-solving and critical thinking
  • Proficiency in database management, machine learning, data wrangling, and use of Big Data platforms

Data Analysts:

  • Expertise in data visualization tools like Excel, Tableau, PowerBI, etc.
  • Proficiency in data modeling, data mining, data warehousing, visualization, statistical analysis, and database management
  • Excellence in problem-solving and critical thinking
  • Proficiency in programming (R, Python, SQL, etc.)
  • Expertise in data visualization tools like Excel, Tableau, PowerBI, etc.
  • Proficiency in data modeling, data mining, data warehousing, visualization, statistical analysis, and database management
  • Excellence in problem-solving and critical thinking
  • Proficiency in programming (R, Python, SQL, etc.)

Conclusion


An organization’s data can take it to enormous heights, but only if it is understood and leveraged appropriately. Data Science and Data Analytics are widely known and popularly used branches of big data. Many people tend to use the two interchangeably, but it is crucial to understand the differences between the two in order to adopt the best solutions for your use case.