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

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

Feb 1, 2024 16.6K Reads

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. 

What Is Data Science? ➤ Get Data Science Certification with Job from IIIT

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. 

life cycle of data science

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. 

Data Science Core subjects

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 
  • 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 
  • 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
  • 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. 

Data Science Degree Courses

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 

FAQs (Frequently Asked Questions)

Data Science is not just a subject but an entire process that requires knowledge of varied domains including Computer Science, Statistics, Mathematics, and Business. Data Analysis, Big Data, Machine Learning, Business Intelligence, and Data Modelling are some of the most important subjects of data science.

Yes, you can pursue BCA in data science specialization in the online mode. One of the best colleges for this course is Manav Rachna Centre for Distance and Online Education.

Yes, actually degree courses that specialize only in the data science discipline are in the online mode. In the offline mode, you can study data science as a sub-domain of computer science. For in-depth knowledge, there are discipline specific data science courses in the online mode.

Some of the best colleges for data science courses in the online mode are- IIT Madras, Manav Rachna Centre for Distance and Online Education, Online Manipal University, Amity Online University, and NMIMS.

Yes, coding is quite necessary for data science. Coding is quite helpful for data science in organizing and managing unstructured data. Also, as the data is becoming more and more complex, machine learning (ML) techniques are being used more often in data science models, and ML is all about coding. So yes, coding is now a necessity for data science professionals.

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