Data Science Course Syllabus And Subjects

Data Science Syllabus: Data Science is a vast discipline that encompasses a wide range of subjects and topics. Various data science certification courses are available in the market. You can take up a full-stack data science certification course and become a data analyst or a data scientist. In this article, we will briefly cover data science syllabus and subjects.

While going through the data science syllabus, you will come across different processes, methods, algorithms, and instruments. So, it is necessary to go through the syllabus while choosing the data science course that compliments your career aspirations and goals!

Table of Contents

Data Science Course Syllabus Overview

A data science course is a specialised program designed to make you aware of data science concepts and processes. It includes in-depth information about data statistical methods and tools. You will also learn how to manage statistical data or raw data obtained from different sources. An overview of the data science course syllabus is given below:

Data Science Course Syllabus, An Overview
Particulars	Details
Data Science Course Duration	3 months to 3 years or more
Course Mode	Offline or Online
Eligibility Criteria	10+2 Science with Physics, Maths, and Computer Science.
Prerequisites	Basic knowledge of computer science, statistics, and mathematics
Key Subjects	Probability & Statistics Programming in Python R Programming MySQL Mathematics Data Analytics
Jobs Available	Data Analyst, Data Engineer, Data Scientist, Business Analyst, Business Strategist, Project Manager, etc.
Courses	B. Tech in Data Science, BSc. in Data Science, BCA in Data Science, MSc. in Data Science, B. Tech in Data Science and Engineering, and more.

Data Science Course Subjects

Various kinds of data certification courses are available in the market. You can take up these courses either via offline or online mode. The option of taking up BSc Data Science or BCA Data Science course is also available. By enrolling in the degree course, you can get an in-depth knowledge about data science applications and concepts. Some of the common subjects included in most data science certifications and degree programs are listed below:

Data Science Course Subjects
Subject Name	Information
Statistics and Probability	The fundamentals of probability and statistical science are covered in this subject. It also includes the basics of linear algebra.
Data Manipulation	Data manipulation helps you organize the data and present it in a meaningful way.
Machine Learning	The process of making human language understandable to machines is called machine learning. It includes topics such as regression analysis, Naive Bayes Algorithm, and more.
Business Intelligence	The process of converting raw data into insights that helps companies make business decisions is known as business intelligence. The skills of handling various BI (Business Intelligence) methods and tools are covered in this subject.
Programming Languages	Data science includes programming in languages like R and Python. It also includes exposure to database languages like SQL.

Data Science Syllabus for Beginners

Those who have completed their 10+2 can enroll in data science courses designed for beginners. Many platforms offer a data science course for beginners. The key aspects of the data science syllabus for beginners have been covered in the below table:

Data Science Syllabus for Beginners
Subject Name	Syllabus	Details
Introduction to Data Science	Introduction to data analysis tools Data structures Linear Regression Statistical Inference Statistical Models	The fundamentals of data science are covered in this subject.
Cloud Computing	Database Management Visualization Cloud Architecture Model Services Cloud Security	The basics related to cloud computing are covered in this subject.
Data Mining	Mining from different databases Preprocessing data Data Modelling Clustering Data Detecting Anomaly Multi-dimensional Modeling of Data Data Prediction and Classification Techniques	Various data mining techniques and tools are explored in this subject.
Data Visualization	Introduction to Data Visualization Acquiring and Visualizing Data Applications of Data Visualization Data Visualization tools and techniques	This subject provides hands-on knowledge and experience of using data visualization tools
Data Analysis	Probability and Statistics Business Fundamentals Customer Analytics Retail Analytics Social Network Analysis Pricing Analysis Statistics Introduction to BIS (Business Information Systems)	The fundamentals of data analysis, processing, and interpretation are covered in this subject.
Machine Learning	Linear Algebra Statistical Learning Scalable Learning Programming For Data Analysis Big Network Data Deep Learning Machine Learning Models Training Algorithms	The various prerequisites of machine learning and applications of machine learning are discussed in this subject.
Business Intelligence	Introduction to BI Learning objectives of BI Components of BIS (Business Information Systems) Business Intelligence and Analytics BI and Decision-Making	The fundamentals of BI and their applications in various aspects of business are to be studied in this subject. The students learn how to use BI techniques and insights to improve business decisions by going through the topics of this subject
Data Warehousing	Data Warehousing and Business Analysis Components of Data Warehousing How to build a data warehouse? Architectures for Data Warehousing Data Warehousing Design Business Intelligence using SAP Business Objects	The basics of data warehousing, data warehouse building techniques, and their applications are explained in this subject.
Data Dashboards and Storytelling	Creating Static Charts Interactive Dashboards and data stores Data Visualization Using Tableau Storytelling Techniques	The basic concepts and uses of charts, diagrams, and other graphic elements in data visualization are to be studied in this subject. It gives the students a basic idea of how to present data in a meaningful way.

Also read: Data Scientist Course Syllabus – Become A Data Scientist

Data Science Course Syllabus

The data science syllabus for various programs are given in the below table:

Data Science Course Syllabus
SR. No.	Data Science Course Name	Eligibility	Core Topics
1	Full Stack Data Science Pro by PW Skills	Anyone with a basic knowledge of computers and mathematics can apply for this course.	Machine Learning Deep Learning Prompt Engineering Implementations Data Engineering Data Exploration Plotly NumPy Seaborn Spyder Tensor Flow Hadoop Power BI Docker Flask Python MySQL Jupyter Matplotlib
2	IIT Data Science Program (B. Tech in Data Science and Engineering)	10+2 in Science with Physics, Computer Science, and Mathematics. The students must also clear the entrance test to qualify for this course.	Data Handling and Visualization Machine Learning Mathematical Foundations of Data Science Matrix Computations Stochastic Models Statistical Learning Information Security and Privacy Optimization Techniques Python Programming Lab Scientific Computing
3	BSc Data Science Course	10+2 in Science with Physics, Computer Science, and Mathematics. The college or university in which the students apply may or may not hold an entrance test.	Introduction to Data Science Data Visualisations Cloud Computing C Programming Language Machine Learning Basic Linear Algebra Optimization Techniques Big Data Analytics Probability Statistics Basics
4	B. Tech Data Science Program	10+2 in Science with Physics, Computer Science, and Mathematics. The students must also clear the entrance test to qualify for this course.	Algorithm Design and Analysis Engineering Physics Data Warehousing Database Management System Data Acquisition Introduction to Artificial Intelligence and Machine Learning Mathematics Object Oriented Programming (OOPs) Programming Python Statistics
5	MSc Data Science Course	The students who have completed their B. Tech or BSc. in Data Science can apply for this course. Entrance tests are usually not conducted but some colleges or universities may conduct their CET (Common Entrance Test) to select the eligible candidates.	Artificial Intelligence Applied Statistics Computational Mathematics Database Management Deep Learning Machine Learning Optimization Technologies Python and R Spatial Sciences Mathematics
6	Full Stack Data Analytics Course by PW Skills	Anyone with a basic understanding of Math, Statistics, and Computer Science can enroll in this online course.	Data processes like data manipulation, visualization, organization, processing data, etc. Power BI Python MySQL Pandas Tableau Matplotlib MS Excel NumPy

These were some key components and topics of the data science syllabus. We hope you have understood how the syllabus varies as per the course. Do you want to pursue a bright career in data science? At PW Skills, we offer data science courses for everyone. Our courses are developed by industry experts and they help you to become a master of full-stack data analytics. Consult us today and know how we can redefine your career path!

Data Science Course Syllabus And Subjects (FAQs)

What does a data scientist do?

A data scientist interprets the data and converts it into meaningful insights by using machine learning and statistical tools. He has the ability to process the data, organize it, and present it in a meaningful way. So, he possesses data analytical, data visualization, and data manipulation skills.

How much time is required to become a data scientist?

You can take up a degree course in data science like BSc. Data Science or B. Tech in Data Science. After that, you can enroll in a professional data science course and increase your competence in various data analytical and processing skills. Now, you can seek an entry-level position as a data scientist.

Can data science be studied online?

Yes, various data science courses can be studied online. For example, the full stack data science pro course by Physics Wallah develops your skills as a versatile data analyst. You get a full spectrum of data analytical and processing skills that help you take up job roles like data scientist.

What do you mean by data manipulation?

Data manipulation is the method to modify the data and organize it. The end goal is to make the data readable and understandable to a common man. DML (Data Manipulation Languages) are used to make changes to the existing data.

Which are the main subjects in the data science course?

The main subjects in the data science course include Algorithms, Business Intelligence, Data Manipulation, Coding, Statistics, Data Structures, Mathematics, and Machine Learning. The subjects may vary as per the specific course in which you have enrolled.

Who is eligible for data science courses?

Anyone who has completed 10+2 from the science stream and knows the fundamentals of STEM (Science, Technology, Engineering, and Mathematics) can enroll in a data science course.

Can I do data science if I am weak in math?

Though being strong in subjects such as mathematics and statistics helps, it is not mandatory to enroll in a data science course. The basic requirement is the ability to identify a problem and being able to solve it using data analytical tools and techniques. You must also possess strong communication skills to be a data scientist.

Which stream is best for a data science course?

Students from any stream can do a data science course. However, the ones who have completed their 12th class in Science with subjects like Physics, Mathematics, and Computer Science have the best chance to enroll in the top data science certification courses.

Which skills are required to become a data scientist?

You must be able to collect data and organize it. You must also know to store, manipulate, interpret, process, and analyse the data to become a successful data scientist.

How do I learn data science?

In order to learn data science, you must go through the data science syllabus and subjects carefully. You must have a solid foundation in computer science, math, and statistics. Also, you must be familiar with languages like Python or R and must have a basic understanding of data sets.

Why is data science difficult?

Data science can be difficult for beginners, especially if they are weak in mathematics and statistics. It involves use of programming codes and data analytical tools. The students must also be able to perform complex computations using advanced applications.

Which should I learn first, data science or machine learning?

Machine Learning or ML is a part of data science. So it is expected to learn the basics of data science before moving onto complex techniques like machine learning or AI (Artificial Intelligence)