Data Science vs. Data Analytics

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The data scientist and the data analyst represented two of the “most in-demand, high-paying jobs in 2021.” The previous year, the World Economic Forum Future of Jobs Report 2020 listed these jobs at the top of a list representing most in-demand jobs across industries.

In data analytics, which is often referred to as business analytics, datasets are analyzed with the sole mission of discovering trends or actionable insights. These trends and insights aid enterprise decision-making or enable trustworthy business decisions.

Three Dominant Data Science Trends that Changed the Data World

Forbes author Bernard Schroeder discusses three dominant trends that are currently noticeable in the rising Data Science profession, creating profitable opportunities for smaller business operations and startups:

  • According to the U.S. Bureau of Labor Statistics, the Data Science field will grow almost 28% till 2026. Moreover, the emergence of advanced AI technologies has fueled the use of highly sophisticated analytics techniques across mainstream businesses, which includes small businesses and startups. This trend indicates a strong growth in demand for data scientists, business analysts, and other related data-technology positions, which in turn, signals the growth of Data Science skills.
  • Artificial intelligence and machine learning together will revolutionize all industry sectors and human life in general, in the next two decades. These advanced technologies are here to stay and prosper—transforming the way “consumers interact with devices” in their daily lives.
  • Growing regulatory restrictions related to “first-party data” will create a bubbling community of data exchanges and data marketplaces for buying and selling data. While the data marketplaces indicate financial transactions, data exchanges will not involve any such transactions. Data exchanges will work on a mutual-benefit model.

These three trends are making the jobs of data scientists and data analysts increasingly important in the global business world.

Data Science vs. Data Analytics: How Do They Differ?

In a nutshell, Data Science raises specific questions about data, and data analytics answers them. The data scientists develop questions, while the data analysts respond to these questions from the business side. In the business world, the data scientist is perceived as the technical expert and the data analyst is perceived as the business expert.  

While both data science and data analytics deal with big data, Data Science can be considered more of an overall data-processing field, and data analytics as a sub-field. Data Science combines knowledge from various disciplines like computer science, statistics, mathematics, information science and more. It helps extract trends and patterns from data by combining techniques of data mining, predictive modeling, and machine learning. On the other hand, data analytics is more focused on mathematical or statistical analysis of data.

While Data Science establishes correlations between data sets through trends and patterns, data analytics helps uncover meaning of actionable insights. And Data Science states unique business problems through queries, while data analytics provides solutions to those business problems in the form of answers.

Data Science vs. Data Analytics: Contrasting Job Roles

In terms of mindsets, data scientists are undoubtedly more mathematics-oriented, while data analysts tend to view data through a statistical lens.

In terms of hierarchy, the data scientist is usually an expert in the field, with a minimum of 10 years industry experience and superior domain knowledge. In sharp contrast, the data analyst role is a junior role. In the industry, Data Science aspirants with strong computer science and statistics backgrounds make good candidates for data analyst jobs.

Generally, the job responsibilities of a data scientist involve:

  • Processing and validating data
  • Conducting data mining via ETL
  • Conducting deep analysis with ML algorithms
  • Conducting exploratory analysis of large datasets
  • Writing code for ML libraries
  • Identifying trends for business decisions
  • Extracting actionable insights with ML tools.

 The typical job responsibilities of a data analyst involve:

  • Identifying patterns in data sets
  • Collecting and interpreting insights
  • Querying data via SQL
  • Experimenting with different types of analytics such as descriptive, predictive, prescriptive, and diagnostic
  • Using data visualization tools like Tableau for presenting the analyzed information

Here, you will find listings of academic programs in both fields with some career guidance.

Data Science vs. Data Analytics: The Difference in Skill Sets

Data scientists generally bring many skills to the table—computer science, mathematics, statistics, predictive modeling, and machine learning. On the other hand, data analysts are expected to be skilled in data mining, data warehousing, statistical analysis, DBM, visualization, and also some data modeling.

So what are the core common skills between the two? Critical thinking and problem-solving skills. Without these two fundamental skills, an aspirant can neither be a data analyst nor a data scientist.

An article by Kristin Burnham of Northeastern University indicates that data analysts often assume a wide variety of job titles like “database analyst, business analyst, market research analyst, sales analyst, financial analyst, marketing analyst, advertising analyst, customer success analyst, operations analyst, pricing analyst, and international strategy analyst.” Interestingly, although their job titles represent their business functions, they all have two basic skills: data analysis skill and strong communication skills.

Data Science vs. Data Analytics: Which Is Right for You?

In a flourishing data-driven business world, the data analyst or the data scientist gathers and analyzes mission-critical data to extract trends, patterns, and actionable insights to improve business performance. Thus, these two job roles are sought after across businesses of all shapes and sizes today.

So, how do you decide which role is more suitable for you? The answer is to review the sections above to help you make the right decision. Also, read the article from Medium, which provides helpful information about these job roles.

Here are some useful lessons from McKinsey that teach you how to perform better as a data scientist:

  • Be the first to propose solutions, not just raise problems
  • Brainstorm and propose hypotheses, and then use them to guide later analysis by other team members
  • Be the bridge between the data and the business teams. Be your own translator
  • All important communications must begin with a key message, followed by supporting arguments—each reinforced with data
  • Technical gobbledygook must be interpreted in layman’s language for easy comprehension by senior business leaders

Data Science vs. Data Analytics: Industry Applications

The Data Science team members, comprising the data scientist, the data analyst, and others, often work together to deliver the following types of services to the enterprise:

  • Business Budgets and Financial Forecasting: By analyzing and assessing an organization’s past revenue, sales records, costs, and future business goals, the Data Science team can identify and forecast the budgetary and financial requirements for the future.
  • Risk Assessment: By analyzing the current business risks and their associated expenses, the data team can make future recommendations to mitigate the risks. Read about the importance of Data Science and analytics in the finance and banking industry read The Importance of Data Science and Analytics in the Finance Industry.
  • R & D Efforts: Based on past customer data, such as reactions to product features, sales volumes, expressed likes and preferences, the data team can guide the R & D function about the future design and development of products and services for maximum results.
  • Sales and Marketing Forecasts: By analyzing sales and marketing metrics, the data team can measure and calculate the volume of leads the data-driven decisions must generate to fulfill sales and marketing goals.

Here is a unique case study, which describes the joint effort of Data Science and predictive analytics teams to help companies like AccuWeather keep their customers accurately and reliably informed about weather forecasts.

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