10 Top Difference between Data Science and Data Analytics

7 min readNov 3, 2023

Difference between Data Science and Data Analytics

Data science and data analytics are two related fields that are both in high demand today. Both the terms “Data Science” and “Data Analytics” are often used interchangeably, leading to confusion about their true differences. A lot of people consider data analysts and data scientists the same. However, these two disciplines have distinct roles and objectives. It means that although both are integral to the data-driven decision-making process, they serve different purposes and involve different methods.

Today, we will explore the 10 key differences between Data Science and Data Analytics, shedding light on their respective purposes, tools, and applications. Understanding these distinctions is crucial for individuals aiming to pursue a career in either field and for organizations seeking to leverage data effectively to gain insights and drive business success.

What is Data Science and Analytics?

Data science is a field that combines statistics, computer science, and domain knowledge to extract insights from data. It encompasses a broader spectrum, involving complex processes such as data mining, machine learning, and predictive modeling. Data scientists delve deep into data to extract meaningful insights and build predictive models that can uncover hidden patterns and trends, thus aiding in long-term strategic decision-making. They use a variety of tools and techniques to collect, clean, and analyze data to solve real-world problems.

In contrast, data analytics is the process of collecting, cleaning, and analyzing data to identify trends and patterns. It primarily deals with the examination of data to draw actionable conclusions for immediate decision-making. Moreover, it focuses on generating descriptive reports, dashboards, and visualizations to provide a clear snapshot of the current state of affairs. Data analysts use data to understand past performance and make predictions about the future.

What is the Difference between Data Science and Data Analytics?

Now, let’s understand in detail the common factors that differentiate data science and data analytics.

1. Scope and Focus

Data Science: Data science is a broader field that encompasses the entire data lifecycle. Data scientists deal with everything from data collection and cleaning to the development of complex machine-learning models.
Data Analytics: Data analytics, on the other hand, is more specialized. Analysts work with structured data and use it to generate descriptive reports, dashboards, and visualizations, providing a snapshot of current conditions and trends.

2. Goals

Data Science: The primary goal of data science is to develop predictive models and make data-driven predictions about the future. This often involves solving complex problems and uncovering hidden patterns or trends in data that might not be apparent at first glance.
Data Analytics: Data analytics focuses on providing insights for current decision-making. Analysts aim to understand what happened in the past and why it happened, enabling businesses to make informed decisions in real time.

3. Methods and Tools

Data Science: Data scientists use a wide array of tools and techniques, including machine learning algorithms, statistical analysis, and data mining. They have a strong programming background and are proficient in languages like Python and R.
Data Analytics: Data analysts typically use tools such as Excel, SQL, and data visualization tools like Tableau or Power BI. Their work revolves around querying databases, creating charts and graphs, and presenting data in an understandable format.

4. Skill Set

Data Science: Data scientists need strong statistical and data science math skills, along with expertise in machine learning and programming. They must also possess domain knowledge to understand the context of their analysis.
Data Analytics: Data analyst skills include both hard and soft skills. They require a solid foundation in statistics and data manipulation. Effective communication skills are essential as they need to convey insights to non-technical stakeholders.

5. Education Requirements

Data Science: To pursue a career in data science after 12th or later, a strong educational foundation in mathematics, statistics, and computer science is essential. A bachelor’s degree in a related field is typically the starting point. However, many data scientists hold advanced degrees (master’s or Ph.D.) for in-depth knowledge and research capabilities.
Data Analytics: Data analysts often hold bachelor’s degrees in fields such as mathematics, statistics, economics, or computer science. While advanced degrees can be beneficial, they are not always a strict requirement for entry-level positions.

6. Types of Jobs

Data Science: A data science career offers professionals to work in a variety of roles, including machine learning engineer, data engineer, data scientist, and artificial intelligence specialist. They tackle complex problems involving predictive modeling, algorithm development, and big data analysis.
Data Analytics: Data analysts typically hold positions like business analyst, market research analyst, financial analyst, or operations analyst. They focus on generating actionable insights from data to support immediate decision-making.

7. Salary

Data Science Salary: Data scientists are among the highest-paid professionals in the tech industry. Salaries can vary based on experience, location, and industry, but experienced data scientists often earn six-figure incomes. To give you an idea experienced data scientist’s salary in India, on average, is ₹13.6 lakhs and $82,898 in the U.S.
Data Analytics Salary: Data analyst salaries are competitive and can also vary based on factors such as experience and location. While they may not reach the same salary heights as data scientists, data analysts can earn attractive incomes. For example, a data analyst salary in India is an average of ₹10 lakhs for an experienced professional.

8. Demand

Data Science: The demand for data scientists is consistently high, and it is expected to remain so in the coming years. Organizations are continually seeking experts who can extract value from data and develop predictive models to gain a competitive edge.
Data Analytics: Data analysts are also in demand, particularly as companies increasingly recognize the importance of data-driven decision-making. This demand spans various industries, including finance, healthcare, retail, and marketing.

9. Career Growth

Data Science: The Data science career path often have various career growth opportunities, including becoming data science managers, machine learning researchers, or advancing into specialized fields like natural language processing or computer vision.
Data Analytics: Data analysts can progress to roles such as senior data analyst, business intelligence analyst, or even transition into management positions where they oversee teams of analysts.

10. Impact

Data Science: Data science has a transformative impact on businesses and industries. It enables organizations to make strategic decisions based on data-driven insights, leading to improved efficiency, cost reduction, enhanced customer experiences, and innovation.
Data Analytics: Data analytics plays a critical role in day-to-day operations. It empowers businesses to optimize processes, identify market trends, and address operational challenges swiftly, resulting in increased productivity and competitiveness.

Is Data Science and Data Analytics Same?

No, data science and data analytics are not the same. There are quite a lot of data science and data analytics differences. However, they are indeed interrelated. For aspiring professionals in any of these fields, it is important to understand the relationship between the two, whether it is similar or different. Because of their interchangeable nature, there is an overlap in skill requirements as well. If you want to learn data science or analytics, there are quite a few recommendations that you must explore.

Data Science Courses: Recommendation

There are different options available today, and picking the right one depends on some of the important factors that include — your learning goals, the stage of learning you are in, budget, course credibility, etc.

Here are some of the handpicked courses from top data science certifications:

IBM Data Science Professional Certificate by Coursera: This IBM Data Science Professional Certificate is one of the most liked courses out there. The USP of this course is its inclusion of Virtual Labs and a beginner-friendly curriculum.
Post Graduate Program in Data Analytics and Data Science by Skill-Lync: The 24-week-long Post Graduate Program in Data Analytics and Data Science is an instructor-paced course. It offers live classes and even job assistance to learners who successfully complete this course.
Professional Certificate in Data Science by edX: Offered in collaboration with Harvard University, this Professional Certificate in Data Science is 17 months long

Data Analytics Courses: Recommendation

Here are some of the handpicked courses from top data analytics certifications:

Google Data Analytics Professional Certificate by Coursera: This 6-month Google Data Analytics Professional Certificate course is for beginners and offered on Coursera in collaboration with Google. It offers learners an immersive understanding of the practices and processes used by a junior or associate data analyst in their day-to-day job.
Professional Certificate in Data Analyst by edX: In this 10-month log Professional Certificate in Data Analyst course, you will be taught in 9 modules. It is offered in collaboration with IBM.
Post Graduate Program in Data Analytics and Data Science by Skill-Lync: This instructor-paced Post Graduate Program in Data Analytics and Data Science is 24 weeks long. It is a great course for structured learning with hands-on practice.

In a Nutshell

Both data science and data analytics are crucial components of the data ecosystem. With this blog, we hope now you know the distinguishing factors between both. This will surely help you decide which field to choose as a career. Overall, if you are interested in building and applying models to extract insights from data, then a career in data science may be a good fit for you. If you are interested in collecting, cleaning, and analyzing data to identify trends and patterns, then a career in data analytics may be a good fit for you.

To learn the skills required for any of these career options, you must take the help of available online data analytics and data science courses on Careervira.