Data Science vs Data Analytics: Which is better for your Career?

Data Science vs Data Analytics

Table of Contents

Are you someone who is interested in data and numbers? Are you someone who wants to know the difference between Data Science vs Data Analytics?

Today, data is the most crucial for businesses to collect decisive insights and enhance their business performance to grow in the market. Worldwide enterprises are looking forward to utilizing the data for their prosperity.

Data analyst or a data scientist are the two most popular job roles in this area.

They are a mishmash of terms that mix and overlay with one another but are still wholly different.

However, both being interconnected, they implement different results and pursue different approaches.

Key Takeaways

  • Data Analytics is someone who examines data sets and creates visual presentations to help businesses make more strategic decisions. 
  • Knowledge of Intermediate Statistics, Stats tools, BI tools, and skills in Excel and SQL databases are the prerequisites to become Data Analytics.
  • Data Scientists are someone who collects, organizes, explains and discovers the right solutions to existing problems. 
  • Knowledge of Math, Advanced Statistics, Predictive Modelling, skills in programming languages, experience with data visualization tools, and proficiency in using big data tools are the prerequisites to become Data Analytics.


Today, anything you do is data. Watching a movie, browsing social media, searching for any product, everything is data for a company.

The company collects all this data, analyzes it, takes out the insights, and utilizes it to enhance their performance. Data plays an important role at different levels, such as in the creation of products, marketing campaigns, and risk management plans. 

So, not every CEO or manager know how to obtain and rightly utilize the data for maximum output, that’s where Data Scientist and Data Analytics are required.

Data science is the home for all the methods and tools, and Data analytics is the small room of that house. 

However, Data analytics is more specific and concentrated than data science. 

What is Data Analysis?

The field of data and analysis deals with finding the solutions to existing problems. 

The process of collecting and organizing data to conclude an insightful output from it is known as Data Analysis. The process uses analytical and logical reasoning to conclude insight from the data.

What does a Data Analysts do?

Data Analysts examine data sets to recognize trends, develop charts, and create visual presentations to help businesses make more strategic decisions. They concentrate on processing and executing statistical analysis of existing datasets.

Their objective is to formulate methods to capture, process, and organize data to develop actionable insights for prevailing problems and manage the best way to present the insights drawn from the data. 

Data Analysts use SQL to make queries to a relational database, programming languages like R and SAS, visualization tools like Power BI and Tableau, and communication skills to develop and deliver the results.

Types of Data Analysis 

To get a better understanding of how and why Data Analysis is important for business, it is divided into four types:

  1. Descriptive Analysis

It analyzes past data and explains what happened. Descriptive Analysis is used for tracking Key Performance Indicator (KPI), revenue, sales leads, and many more. 

It basically tells what positive or negative changes have occurred.

  1. Diagnostic Analysis

It is done with the aim to find the cause behind the happening. 

After finding the change occurred, diagnostic deals with why it happened. 

For example, A business finds out that leads increased in the month of March, then by diagnostic analysis they can find the marketing efforts which increased the leads.

  1. Predictive Analysis

Predictive Analysis deals with finding what can happen in the future, based on the analysis of previous years’ data.

For example, To predict next year’s revenue, you will analyze the previous years’ revenue and find the result.

  1. Prescriptive Analysis

After collecting the data from all the above three analyses, it interprets the data and finds a solution for the problem. 

Skills requisite to become a Data Analyst:

  • Knowledge of Intermediate Statistics and excellent problem-solving skills
  • Skills in Excel and SQL database to slice and dice data
  • Experience working with BI tools like Power BI for reporting
  • Knowledge of Stats tools like Python, R, or SAS

What is Data Science?

The process of implementing mathematical, statistical, and programming skills to find actionable insights from a large set of raw and structured data, is known as Data Science.

It tries to build connections and shapes the questions to answer them for the future.

What does a Data Scientist do?

Data Scientists are those who collect, organize, explain and discover the right solutions to the existing problems. They design and create new data modeling methods and production using prototypes, algorithms, predictive models, and custom analysis.

Their objective is to propose questions and discover potential avenues of study, with less interest in definite answers and more stress overwhelmed on attaining the right question.

Are you wondering how to become a Data Scientist?

Data Scientists are proficient in the field of mathematics and statistics along with the skills of a hacker to think and deal with problems innovatively.

Lifecycle of Data Scientist

These are the steps, Data Scientist takes while finding the right solution for the problem.

1. Data Capturing, the first step deals with finding the data and data entry.

2. Data Maintaining, this step involves data warehousing and data cleaning.

3. Data Processing, this step involves mining classification and modeling of data.

Communicating Data, after processing the data, its reporting is done in this step, and to ensure the readability of data, it is visualized. 

Data Analysis, qualitative analysis, predictive analysis, and regression are done in this step depending on the business problem.

Skills required to become a Data Scientist:

  • Knowledge of Math, Advanced Statistics, Predictive Modelling
  • Proficiency in using big data tools like Hadoop and Spark
  • Expertise in SQL and NoSQL databases like Cassandra and MongoDB
  • Experience with data visualization tools like QlikView, D3.js, and Tableau
  • Skill in programming languages like Python, R, and Scala

Data Science or Data Analytics: Which is better?

Data Analytics and Data Science are the different sides of the same coin, and their functions are highly interconnected. 

To understand the differences between Data Analytics and Data Science, we have considered some of the fundamental dimensions like the scope, objectives, application area, and many more.

Data Science vs Data Analytics: Infographic

Which is better for pursuing a career?

Now after understanding the differences between data analytics and data science. The question is, what each career entails? 

Data Analysts’ and Data Scientists’ differences are rooted in their professional and educational backgrounds.

While deciding between the two for pursuing a career, there are few things that need to be considered:

  1. Personal Background

Every career has its own set of requirements. If you are choosing between data analysts and data scientists, consider your background, education, work experience, and other important factors to see which field aligns best with your future goals.

  1. Your interest

If you are someone who is interested in numbers, statistics, and programming, then Data Analyst will be a perfect career for you.

  1. Desired Salary and Career Path

Salary and career path are the most essential factor when deciding between Data Scientists and Data Analysts. 
Different levels of experience and education are required to pursue a career as a scientist or analyst. 

And in terms of salary, Data Scientist earns more than Analysts. 

Conclusion: Data Science vs Data Analytics

Data Analysts and Data Scientists are one of the industry’s most in-demand job roles. 

Thinking of these two disciplines, it’s essential to forget about viewing them as data science vs. data analytics. 

We should see them as two parts of a whole vital to understanding the information we have and how to analyze it better and review it.

Organizations embrace these professionals to lead the path towards technological change and maintain the competitive stride. And it is the right time to learn more about this and upskill your career.

According to Glassdoor, both are on the list of the best jobs in America for 2021.

Frequently Asked Questions

What are the differences between Data Analytics and Data Science?

For your better understanding, we have incorporated the differences between Data Analytics and Data Science in an infographic, which you can find under the sub-heading, Differences between Data Analytics and Data Science.

Does Data Analysis and Data Science have a scope in the future?

Yes, Data Analysis and Data Science have a great scope in the future as today, data is most crucial for businesses to collect decisive insights and enhance their business performance to grow in the market.

Are Data Analysis and Data Science interconnected?

Yes, Data Analysis and Data Science are interconnected but still, they implement different results and pursue different approaches. 

Which is better- Data Analysis or Data Science? 

There is nothing that one is better than the other, both implement different results, pursue different approaches, both are different professions. It depends on your interest and skills which is better for you.

How to select between Data Analysis or Data Science, which is better as a career option?

You can decide which is better as a career option on the basis of your personal background, your interest, desired salary, and career path.

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