Updated on 04th Jan, 22 545 Views

This Data Science vs Data Analytics blog covers the below-mentioned topics:

Check out this detailed video on Data Science vs Data Analytics:

What is Data Analytics?

Data Analytics mainly deals with performing and processing statistical analysis on available data. It mainly concentrates on processing and organizing data to gain insights for solving business issues. In simple terms, it helps in solving issues that we know we cannot find answers to on our own. Mostly, it assists in producing outcomes that can help in business improvement immediately.

Data Analytics consists of mechanical or algorithmic processes to gain business insights. Numerous organizations use it to make improved and well-informed business decisions while disproving or verifying the existing models and theories.

Data Analytics Process

The process of Data Analytics involves the use of a number of tools as well as techniques that help analyze huge volumes of data, which is not humanly possible. The process involves the following steps:

  • Determining the requirements and grouping of data
  • Gathering information from numerous online and offline data sources
  • Organizing data in spreadsheets for analysis
  • Removal of inconsistent, incomplete, and repetitive data
  • Cleaning the data by correcting errors before the process of data analysis

Before learning about Data Science vs Data Analytics, let’s read about who Data Analysts are and what the required skills are for them to excel in this domain.

What is Data Science?

When compared to Data Analytics, the scope of Data Science is broader. In other words, you may also think of Data Analytics as a process that is contained in Data Science. It can be considered as one of the integral phases of the complete and complex life cycle of Data Science. All the phases that occur before and after the Data Analytics process constitute Data Science.

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Prerequisites to Learn Data Science

Besides having knowledge of SQL, Python, and other such programming languages, professionals in the field of Data Science must have the ability to combine their statistical and domain knowledge to derive insights from the business data for improving the business drastically. Moreover, these professionals apply various Machine Learning algorithms in structured, semi-structured, and unstructured data.

Data Science and its Components

Data Science helps in tackling Big Data by involving the processes of data preparation, cleansing, and analysis. These processes apply several Machine Learning concepts, sentiment analysis, and predictive analytics for extracting significant information from the gathered data.

The three main components of Data Science are as follows:

  • Statistics: It mainly focuses on collecting, organizing, analyzing, and presenting data using mathematical methodologies.
  • Data Visualization: The Data Science outcomes are visually displayed in the form of charts, tables, diagrams, and graphs that allow the other employees in the organization to comprehend the information gained. Besides, data visualization also allows you to make faster decisions by highlighting important information.
  • Machine Learning: It is the most significant component of Data Science that allows you to use self-learning algorithms and also predict the natural human behavior in certain situations in the most accurate manner.

Let’s now learn who Data Scientists are and the skills you need to become a Data Science professional in this extensive ‘Data Science vs Data Analytics’ blog.

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Difference Between Data Science and Data Analytics

Now that you have got a better and clearer understanding of both Data Science and Data Analytics concepts, and gain insight into the main topic of this blog, Data Analytics versus Data Science.

Data Analytics Data Science
Data Science is an essential part of Data Science that revolves around organizing, processing, and analyzing business information to solve business issues. Data Science is a multidisciplinary field that involves expertise in statistical research, mathematics, Machine Learning, Data Analytics, and computer science.
Its scope is micro. It is limited to analytical methods and techniques using statistical tools. Its scope is macro. It includes Machine Learning, Artificial Intelligence, and engine exploration.
Although Data Analytics leads to lucrative jobs, the Data Analytics professionals are less-paid when compared to Data Scientists. Data Science offers some of the highest-paying jobs in the field of IT.
Data Analysts must have a good understanding of SQL and other databases and should have good programming skills in Spark/Hadoop, R/Python, etc. Data Scientists should have a good understanding of concepts such as data modeling, Machine Learning, advanced statistics, etc.
They should have experience in working with BI tools, along with basic knowledge of statistics. They should have a basic understanding of languages such as Python/R, SQL, and SAS.
The data received is generally structured on which Data Analysts apply numerous data visualization techniques and design principles. Data Scientists use raw and unstructured data to further clean and organize so that it can be sent for analytics.
In Data Analytics, issues are already known to the analysts, so they use analytics to come up with the most relevant solutions for the issues. Data Science digs out new and undiscovered business issues that can further be converted into innovative use cases and business stories.
Data Analytics is dominantly used in the industries of travel and tourism, finance, healthcare, gaming, and more. Data Science is used in the areas of Internet research, speech recognition, image recognition, recommender systems, digital marketing, and more.

Learn about the difference between Data Science, Data Analytics and Big Data in our comparison blog on Data Science vs Data Analytics vs Big Data.

In this Data Science vs Data Analytics blog, let us now discuss the salary earned by these professionals.

Data Science vs Data Analytics Salary

According to Glassdoor, the average income of a Data Scientist in the United States is about US$113k per annum while the same of a Data Analyst is US$62k per annum.

Check out this detailed video on Data Science vs Data Analytics:

Conclusion

In this ‘Data Science vs Data Analytics’ blog, you learned about what Data Science and Data Analytics are and also the differences between the two. You also learned about the must-have skills required for professionals in this field.

As you can see, Data Science and Data Analytics tread on similar lines. Whether you are looking at the scope or salary, both Data Science and Data Analytics are great options. To conclude, the choice between the two largely depends on the individual’s interests and career goals.

To become a professional and be proficient in the necessary skills for each of these domains, you can choose online training courses. By signing up for such courses, you will gain hands-on experience through solving exercises and executing various real-time projects.

Intellipaat provides you with comprehensive Data Science certification and Data Analytics Training, in which you not only gain the theoretical and practical aspects of Data Science and Data Analytics as mentioned above but also will receive online technical support and job assistance.

Grab high-paying analytics jobs with the help of these Top Data Science Interview Questions!

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