This Data Science vs Data Analytics blog covers the below mentioned topics:
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.
Check out this YouTube tutorial about Data Analytics:
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
Data Analytics Process
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.
Who is a Data Analyst?
Analysts are those who look through data to identify patterns and trends. They
build and offer visualizations and reports to explain the hidden patterns and
information. They can perform statistics and help visualize and communicate the
data to draw the necessary conclusions. As a Data Analyst, you need to have
good knowledge of statistics, an excellent understanding of databases, and the
ability to build new and unique visualizations and extract valuable
now read about the skills required to be a Data Analyst.
Skills to Be a Data Analyst
the must-have skills to become a successful Data Analyst include:
- Understanding of mathematics
- Excellent programming skills
in languages such as Python and R
- Knowledge of data wrangling
- Experience in applications
such as Hive or Pig
Now, in this ‘Data Science vs Data Analytics’ blog, you will read about Data Science and get a better understanding of this IT domain.
What is Data Science?
compared to Data Analytics, Data Science has a broader scope. 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.
Watch this comprehensive video on Data Science:
Prerequisites to Learn Data Science
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
Data Science and its Components
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.
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.
Who is a Data Scientist?
Scientists are IT professionals who interpret data with the help of the skills
and expertise they have in coding as well as mathematical modeling.
Skills to Be a Data Scientist
are the skills that you definitely need to acquire for a career in Data
- Understanding of R, Python,
SAS, and Scala
- Experience in coding in SQL
- Ability to work on
- Basic knowledge of various
- Understanding of Machine
In this Data Science vs Data Analytics blog, you have learned in detail about the two. Now, you will come across the differences between the two.
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 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.
scope is micro. It is limited to analytical methods and techniques using
scope is macro. It includes Machine Learning, Artificial Intelligence, and
Data Analytics leads to lucrative jobs, the Data Analytics professionals are
less-paid when compared to Data Scientists.
Science offers some of the highest-paying jobs in the field of IT.
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.
should have experience in working with BI tools, along with basic knowledge
They should have a basic understanding of languages such as Python/R,
SQL, and SAS.
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.
Data Analytics, issues are already known to the analysts, so they use
analytics to come up with the most relevant solutions for the issues.
Science digs out new and undiscovered business issues that can further be
converted into innovative use cases and business stories.
Analytics is dominantly used in the industries of travel and tourism,
finance, healthcare, gaming, and more.
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
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:
In this ‘Data Science vs Data Analytics’ blog, you learned about what Data Science and Analytics are and also the difference between Data Science and Data Analytics. You also learned about the must-have skills required for professionals in this field. 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 in such courses, you will gain hands-on experience through solving exercises and executing various real-time projects.
provides you with comprehensive Data Science Courses 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