Data science and data analytics are both incredibly important fields in
the modern world. Both deal with the use of data to help make
decisions and solve problems.
Data analytics and data science are two areas that are often confused
with each other. The biggest difference between these two fields is
their goals.
Data analytics focuses more on analyzing an existing dataset, whereas
data science focuses on creating new models to generate the best
outcomes possible.

What is Data Science?
Data Science is the study of data-driven decision-making. Data Scientists use data to
make predictions and to find patterns in data. They create algorithms and models
that help them make predictions.
Data science is a broad term that includes many disciplines, including statistics,
machine learning, and computer science. These fields are used to create new
algorithms and models for processing large amounts of data.
What is Data Analytics?
Data analytics is the process of collecting, analyzing, and interpreting data to make
better business decisions.
It’s a unique way to get an objective picture of your business, and understand
what’s working, what isn’t and what needs to change. Data analytics help find out
if your marketing strategy is working or not.

Here’s a Table that Compares Data Science vs. Data Analytics:
Data Science Vs. Data Analytics Comparison
DATA SCIENCE DATA
ANALYST
PROGRAMING SKILL= DEEP KNOWLEGE OF
PROGRAMING LANGAUAGE
CODING LANGUAGE = PHYTHON IS MOST
COMMONLY USED FOR DATA SCIENCE
DATA SCIENCE MOSTLY DEAL WITH UNSTRUCTURE
DATA
STATISTIC SKILL ARE IMPORTANT FOR DATA SCIENCE
DATA SCIENCE NEED MACHINE LEARNING ALOGRITHM
PROGRAMING SKILL= BASIC KNOWLEDGE OF
PROGRAMING LANGUAGE
CODING LANGUAGE =PHTHON LANGUAGE
DATA ANALYST MOSTLY DEALS WITH STRUCTURE DATA
STATISTIC SKILL NOT REQUIRED
MACHINE LEARNING NOT REQUIRED

Roles & Responsibilities of Data Scientists & Data Analysts
Data science is a broad field that covers a wide range of topics. It’s the
study of data and how it can be used to reach certain goals, like
improving business processes or creating better products or services.
Data analysts are more focused on the analysis of data, but they’re not
necessarily involved in creating the information from which they
analyze.
Data scientists are more likely to be involved in the creation of data as
well as its analysis, but not all data scientists do both tasks equally well.
There are many different types of professionals who fall under this
umbrella term—the difference between them depends on how much
emphasis they place on each part of their job. (creation vs analysis)
It also depends on what tools they use for each task and how far along in
their career path they’ve progressed (junior vs senior).

THIS IS A NEW CHANNEL FOR DATA SCIENCE STUDENT -
SO START WITH 1ST SKILL SQL