Syllabus of Data Science Course-Wise – Core Subjects, Guide 2024

Did you know that there has been a wide gap in the demand and supply of data science professionals not just in India but across the globe as well? India produces the maximum number of IT professionals in the world, yet they lack in a few domains like Data Science and Cyber Security. 

By the end of 2020, there were more than 90,000 open jobs in India for data science and data analytics. This wide gap has led to a sudden growth in the need of experienced data science professionals. All this is because BIG DATA upon which the entire data science relies, is growing exponentially every single day! 

This is why various colleges have introduced several Data Science courses to develop experts in the field. These courses are discipline-specific courses that offer in-depth knowledge of Data Science. Therefore, the Data Science syllabus is different from other computer courses. 

This blog is a complete guide to the data science syllabus for various courses from beginner to advanced levels, including a detailed description of the core subjects. 

Data Science Syllabus 2024

The syllabus of Data Science might differ for different courses, colleges, or duration. However, there are some topics and subjects that are common to all as they form the base and are mandatory no matter which type of course or which college you study from. 

Computational Mathematics	Probability and Probability Distribution	Distributed Algorithms
Statistical Inference	Programming (R, Python, Java, C++)	Predictive Analytics
Database Management	Optimization Techniques (Hadoop, Spark, etc.)	Scientific Computing
Project Deployment Tools	Segmentation using Clustering	Stochastic Processes
Data Visualisation	Design and Analysis of Experiments	Business Acumen
Exploratory Data Analysis	Data Structures & Algorithms	Machine Learning
Linear Regression Models	Health Technology Assessment	Deep Learning
Categorical Data Analysis	Image Processing and Analytics	Artificial Intelligence
Time Series Analysis	Longitudinal Data Analysis	Text Mining
Applied Data Analytics	SAS Programming for Analytics	Statistical Modelling
Research Methodology	Nonparametric & Nonlinear Regression Models

What is Data Science?

Before knowing the detailed syllabus of Data Science, you should first clearly know what data science actually is and what are its applications, areas, and focus of study.

This will help you understand the data science syllabus even better.  Data Science is not just a technique or technology, it is an entire process. It is the process of extracting meaningful and valuable insights from raw data to use it further for various tasks and business solutions. Also Read | What Is Data Science with Example?

Every single data science model has to go through a life cycle of roughly 6 following steps:  

1. Data Acquisition- Gathering data based on the business problem 
2. Data Preparation- Involves cleaning, transformation, processing, staging, and architecture of data.  
3. Data Modelling- Defining, refining, and classification of data. Use of ML techniques to identify the best model for the business.  
4. Visualization & Communication- Involves Business Intelligence (BI) and decision-making. 
5. Deploying the Model- Testing the model before actually deploying it for public use. 
6. Exploratory Data Analysis (EDA)- Real-time exploratory analysis after an actual deployment of the model. Includes maintaining the performance of the model through regular qualitative and predictive analysis, text mining, and regression in order to monitor the smooth functioning of the business model. 

Data Science Core Subjects

Data Science is an extremely dynamic subject. The foundation of Data Science lies not just in one but various domains which are- Statistics, Mathematics, Computer Science, and Business. These are the four core subjects that form the base of Data Science. 

The various topics in the data science syllabus fall under these four subjects. Below mentioned are the subject-wise important topics of the Data Science syllabus. 

Subject	Topics
Statistics	Statistical Inference Statistical Modelling Database Management Categorical Data Analysis Segmentation using Clustering Longitudinal Data Analysis Applied Data Analytics Statistical Quality Control  Analytical Tools for Statistics
Mathematics	Probability and Probability Distribution Numerical Analysis  Calculus  Computational Mathematics  Linear Regression Models Stochastic Processes Time Series Analysis  Nonparametric & Nonlinear Regression Models Vector and Matrices
Computer Science	Data Structures & Algorithms Exploratory Data Analysis Programming (R, Python, Java, C++) Distributed Algorithms Predictive Analytics Design and Analysis of Experiments Deep Learning Machine Learning Artificial Intelligence Text Mining Project Deployment Tools Health Technology Assessment  Image Processing and Analytics SAS Programming for Analytics
Business	Data Visualisation Research Methodology Business Acumen Optimisation Techniques (Hadoop, Spark, etc) Business Intelligence (BI) and BI Tools  Marketing Analysis  Data Mining  Big Data Analytics  Communication  Strategic Management  Operations and HR Management

Data Science Syllabus for Beginners

Beginners who are just starting out in the field of data science and do not have any prior knowledge of the subject or freshers who have just graduated the 12th class can look at the topics below. These are the basic but really important topics. Without the knowledge of these topics, you will not be able to completely understand how data science functions. 

Data Analysis	Data Mining
Introduction to Data Science	Business Intelligence and its tools
Data Visualisation	Data Warehousing
Machine Learning Techniques	Programming Language (preferably Python)
Data Modelling, Selection, Evaluation	Data Dashboards and Storytelling

There are various online beginner-level data science courses out there that can build your basics in data science and prepare you well for further studies. Online platforms like Upgrade, Coursera, Udemy, etc. However, you must only attend the course from certified online platforms and beware of online fraudsters. 

Apart from online, there are some reputed institutions in the country that offer online beginner-level certificate courses for data science. You can check them out as we have listed such colleges further in this blog.    

Data Science Course-wise Syllabus 

For those candidates who already have a base in Data Science, for instance, the students who studied Mathematics and Statistics/Computer Science in their 12th standard, these candidates can directly take admission in the professional degree level courses. Data Science courses are available at undergraduate as well as postgraduate levels. 

Undergraduate Courses of Data Science Program

At the undergraduate level, the following degree courses are available in data science- B.Sc, B.Tech, and BCA: 

B.Sc Degree Course Data Science Syllabus 

Bachelor of Science (B.Sc) is a 3-year (6 semesters) undergraduate degree course. The semester-wise syllabus for B.Sc in Data Science is as follows: 

Semester	B.Sc Degree Course (Syllabus)
Semester 1	Introduction to Data Science  Statistics Basics  Linear Algebra  C Programming Language  Microsoft Excel and English Communication
Semester 2	Discrete Mathematics  Probability  Inferential Statistics  Data Structures (theory and practical lab) C- Program Design  R (theory and practical lab) Data Warehousing  Computer Organisation & Architecture  Multidimensional Model
Semester 3	Operating Systems  Object Oriented Programming (theory and practical lab) Database Management (theory and practical lab) Design & Analysis of Algorithms
Semester 4	Time Series Analysis  Cloud Computing  Machine Learning Basic (theory and practical lab) Data Warehousing and Multidimensional Modelling (theory and practical lab) Operations Research  Optimisation Techniques
Semester 5	Machine Learning Advance  Introduction to Artificial Intelligence  Python Programming (practical lab)  Big Data Analytics (theory and practical lab) Data Visualisations Minor Project
Semester 6	Elective 1  Elective 2  Viva voce  Major Project

B.Tech Degree Course Data Science Syllabus 

Bachelor of Technology (B.Tech) is a 4/3 year (8/6 semesters) undergraduate degree course. The semester-wise syllabus for B.Tech in Data Science is as follows: 

Semester	B.Tech In Data Science (Syllabus)
Semester 1	Mathematics 1 Programming  Engineering Physics  Introduction to AI & ML  Principles of Electrical & Electronics Engineering  Computer-aided drafting & designing  Soft skills 1
Semester 2	Mathematics 2 Application-based programming (Python) Engineering Chemistry  Advanced Physics  Multimedia Application (practical lab) Soft skills 2
Semester 3	Introduction to Biology Engineering  Applied Statistical Analysis  Discrete Structures  Data Structures (using C) Computer Organisation & Architecture  Object Oriented Programming (OOPs) using JAVA Project-based learning 1 Industrial Internship 1
Semester 4	Data Acquisition  Computer Networks  Database Management System  Advance JAVA (practical lab) Principles of Operating Systems  Management skills  Project-based learning 2 CTS- Communicate to Conquer
Semester 5	Data Warehousing  Algorithm Design and Analysis  Computation Theory Software Engineering  Testing Methodologies  Linux Programming (practical lab)  Elective 1  Industrial Internship 2  CTS- Impress to Impact
Semester 6	Compiler Design  Data Mining  Advanced Artificial Intelligence  Statistical Analysis (practical lab) Elective 2 Elective 3  Project based learning 3  CTS- Ace the Interview
Semester 7	Business Intelligence  Web Technologies  Professional Ethics  Comprehensive Examination  Elective 4  Major Project 1  CTS- Campus to Corporate
Semester 8	Big Data Analytics  Elective 5  Elective 6 Major Project 2
Elective Subjects
Elective 1	Introduction to Graph Theory  Introduction to Statistical & Mathematical Techniques in Computer Science
Elective 2	Cloud Computing (introduction) Business Process Management
Elective 3	Social Media Analytics
Elective 4	Deep Learning
Elective 5	Web Technology
Elective 6	Cluster Computing  Internet of Things (IoT)

BCA Degree Course Data Science Syllabus 

Bachelor of Computer Applications (BCA) is a 3-year (6 semesters) undergraduate degree course. The semester-wise syllabus for BCA in Data Science is as follows: 

Semester	BCA In Data Science (Syllabus)
Semester 1	Discrete Mathematics  Computer Essentials for Data Science  Computational Mathematics & Thinking Programming in C (theory and practical lab) Soft Skills 1
Semester 2	Statistics  Probability  Data Structures (theory and practical lab) Operating System  Algorithms  Database Management System (theory and practical lab)  Professional Communication & Ethics  Soft Skills 2
Semester 3	Data Collection Ethics (theory and practical lab) Computer Networks  Descriptive Statistics  Software Engineering  Object Oriented Programming (C++) Scripting Technology (practical lab)
Semester 4	Data Mining  Programming in Python (theory and practical lab) Introduction to JAVA (theory and practical lab)  Web Programming Elective 1
Semester 5	Programming in R Data Modelling  Data Visualisation  Machine Learning (theory and practical lab)  Data Optimisation  Parallel programming (theory and practical lab) Elective 2 Minor Project
Semester 6	Natural Language Processing  Information & Data Security  Big Data Analytics (theory and practical lab) Elective 3  Major Project

Data Science Electives for UG Degree Programs 

Artificial Intelligence	Exploratory Data Analysis (EDA)	Pattern Recognition
Business Intelligence (BI)	Text Mining	Soft Computing
Internet of Things (IoT)	Cloud Computing	Embedded Systems
Computational Linear Algebra	Distributed Computing	Quantum Computing
Bioinformatics	Graphics and Multimedia	Visual Programming with C#
Wireless Technologies	Inferential Statistics	Image Processing
Non-relationaText Mining l Databases	–	–

Postgraduate Courses of Data Science Program

At the postgraduate level, the following degree courses are available in data science- M.Sc, M.Tech, MCA, and MBA.

M.Sc Degree Course Data Science Syllabus 

Master of Science (M.Sc) is a 2-year (4 semesters) undergraduate degree course. The semester-wise syllabus for M.Sc in Data Science is as follows: 

Semester	M.Sc In Data Science (Syllabus)
Semester 1	Applied Statistics  Spatial Sciences Mathematics  Data Science Fundamentals Python and R Spatial Science Programming  Database Management  Statistical Inference  Computational Mathematics  Probability & probability Distribution
Semester 2	Analysis and Design of Experiments  Categorical Data Analysis  Distributed Algorithms  Generalised Linear Models Linear Regression Models  Optimisation Technologies (Hadoop, Spark, etc) Stochastics Processes  Mini Project
Semester 3	Bayesian Statistical Modelling  Deep Learning  Longitudinal Data analysis  Machine learning  Text mining  Predictive Analytics  Elective 1  Elective 2
Semester 4	Applied Data Analytics  SAS Programming for Analytics Web Analytics  Research Methodology  Artificial Intelligence Major Project
Elective Subjects
Deep Learning	Genomics & Health Technology Assessment
Image processing and analytics	Time Series Analysis
Nonparametric & nonlinear regression models	Spatial UI Design and Implementation
Internet of Things (IoT)	Data Modelling and Implementation
Image & Video Analytics	R or Python Programming
Multivariate Analysis	Data Collection and Identification

M.Tech Degree Course Data Science Syllabus

Master of Technology (M.Tech) is a 2 year (4 semesters) undergraduate degree course. The semester wise syllabus for M.Tech in Data Science is as follows: 

Semester	M.Tech In Data Science (Syllabus)
Semester 1	Data Analytics Mathematics  Data Mining  Data Warehousing  Data Science Programming  Econometrics  Elective 1 Lab
Semester 2	Advanced Data Analytics  Big Data  Data Analytics and Graphs  Empirical Research  Elective 2 Lab  Project 1
Semester 3	Elective 3 Elective 4 Project 2 Project 3
Semester 4	Research Methodology Project Evaluation  Viva  Internship/Training
Elective Subjects
Artificial Intelligence & COmputational Intelligence	Big Data Systems
Deep Learning	Natural Language Processing
Optimisation Techniques for Analytics	Data Mining, Algorithms & Graphs
Data Analytics Systems	Stream Processing & Analytics
Data Warehousing	Data Science Ethics
Data Visualisation & Interpretation	Information Retrieval

MCA Degree Course Data Science Syllabus 

Master of Computer Applications (MCA) is a 2 year (4 semesters) undergraduate degree course. The semester wise syllabus for MCA in Data Science is as follows: 

Semester	MCA Degree In Data Science (Syllabus)
Semester 1	Linear Algebra and Statistical Techniques  Data Structures (theory and practical lab) Object Oriented Programming in JAVA (theory and practical lab) Python Programming (practical lab) Elective 1 Elective 2
Semester 2	Data Communications  Analysis & Design of Algorithm  Android Application Development (practical lab) Artificial Intelligence  Programming in R (practical lab) Elective  Vocational Training/Capstone Project
Semester 3	Advance Database Systems (theory and practical lab) Software Engineering and Testing  .NET Programming (theory and practical lab) Big Data Analytics (theory and practical lab)  Cloud Computing (theory and practical lab) Elective
Semester 4	JAVA (Advance Level) (theory and practical lab) Software Project Management  Data Mining  Data Warehousing  Project  Elective
Elective Subjects
Computer Graphics	System Programming
Web Applications Development using PHP	Data Fundamentals in Azure
Cyber Security	Artificial Intelligence in Azure
Mobile Computing	Security Fundamentals in Azure
Network Security and Cryptography	Database Administration
E-commerce Technologies	Advanced AI Tools
Data Visualisation with Power BI	Security Engineering using Azure
Machine Learning Technologies	Designing & implementing Data Science Solutions

MBA in Data Science Syllabus 

Master of Business Administration (MBA) is a 2 year (4 semesters) undergraduate degree course. The semester wise syllabus for MBA in Data Science is as follows: 

Semester	MBA Degree In Data Science (Syllabus)
Semester 1	Accounting for Managers  Managerial Economics  Managerial Accounting  Data for Decision Making  Marketing Management  Professional Communication  Statistics for Management
Semester 2	Data, Technology & Analytics for Business  Business Research Methods  Conflict Resolution and Management  Financial Management  Financial Reporting & Analysis Human Resource Management  Legal Aspects of Business
Semester 3	Professional Ethics  Strategic Management  Business Leadership and Organisational Behaviour  Corporate Finance  Design Thinking & Innovation Minor Project
Semester 4	Management in Action- Social Economics and Ethical Issues  Digital Marketing  Operations Management Major Project

Data Science Syllabus Tools & Technologies

Data Science is quite a technical job and hence demands proficiency in the practical aspect of the subject. There are a lot of tools and technologies that a data science professional must have expertise in to get better jobs in the field. 

Some of the most popular and in-demand data science tools and technologies are given below: 

1. SAS– offers statistical libraries to help with modelling and organisation
2. Apache Spark– An analytics engine to help with Stream Processing  
3. BigML–  Used for the processing of Machine Learning Algorithms  
4. Javascript– Scripting language used for interactive visualisation 
5. MATLAB– Used for the processing of Mathematical information and for simulating neural networks 
6. Excel– used for Data Analytics 
7. ggplot2– advanced data visualisation tool used with R programming 
8. Tableau– Data Visualisation software
9. Jupyter– an open source tool, also used in making predictive machine learning models 
10. Matplotlib– plotting and visualising tool mostly used for graphs  
11. NLTK– a Natural Language PRocessing Tool  
12. Scikit-learn– a library mostly used for implementation of ML Algorithms 
13. TensorFlow– Machine Learning Tool 
14. Weka– Machine Learning Software based in JAVA  

Is Coding Needed for Data Science? 

While many people might argue that Coding might not be mandatory for pursuing Data Science, however, experts believe that coding can be the backbone of data science. So, YES, knowledge of coding is quite essential when it comes to Data Science. 

This is because, in most of the data science models today, artificial intelligence and specifically machine learning are a core part. And for Machine Learning, one can not go about without the knowledge of coding. Even if a model does not require machine learning, knowledge of coding essentially helps data science professionals to efficiently analyze and organize unstructured data. Also Read | Data Science Vs Artificial Intelligence Vs Machine Learning.

Python is considered as a great option for those who are just starting out in data science as it is easy to understand and is a scripting language which can be a good advantage to data science professionals. Other popular programming languages for data science are Java, C, C++, and SQL. 

Soft Skills Needed for Data Science 

As a data scientist, you do not just develop a product or a model for the stakeholder, you also communicate it. Therefore, you must possess proficiency in soft skills like being able to communicate and describe your model and also understand what the stakeholders demand so that you can efficiently understand what the problem is and then solve it accordingly. 

Some of the important soft skills needed for a career in Data Science are:  

• Business Acumen 
• Critical Thinking 
• Curiosity 
• Effective Communication 
• Problem solving Attitude 

Best Colleges for Data Science Program

Data Science is a discipline that is usually thought to be under computer science, however, there are other important disciplines like Statistics, Mathematics, and Business that govern the data science field. So, if you study only the computer science degree, that would not be enough for an excellent career in data science.

This is why many colleges have come up with degree courses specifically in the data science domain. These courses are offered in the online mode to offer flexibility to students. Some of the best online colleges for online data science courses are: 

• IIT Madras 
• Online Manipal University 
• Manav Rachna Centre for Distance and Online Education 
• Amity University Online 
• NMIMS 
• BITS Pilani (WILP)  

Data Science Syllabus PDF (Free download)

Here we are mentioning a few pdfs from different universities & some credible sites providing you with general information about the subjects and syllabus of each. For a better understanding, you can download them by clicking on it. 

Udacity- Nanodegree Program for Data Scientist	B.tech in Data Science from Hindustan Online
B.Sc in Data Science from Andhra University	Master Program in Data Science from Simplilearn
M.Sc. in Data Science & Computing from SSSIHL	B.Sc in Data Science from Andhra University