Data Science Course Syllabus and Subjects 2024 (PDF included)

If you’re looking to start your career in data science, you should know the key subjects and course syllabus to plan your learning path.

In this guide, I have listed all data science course curriculum syllabus for key streams such as M.tech, B.Sc, BCA, and M.Sc (with free downloadable PDF).

👉 Click here to download the full data science syllabus

Data science course syllabus for beginners

Below, I have listed the complete course curriculum of our data science training program which is available in online and offline Hyderabad and Vijayawada training centres:

Module 1	Python Programming
Module 2	Data science course duration: 6 months | English
Module 3	Statistics
Module 4	Machine Learning and machine learning projects for final year students
Module 5	NLP
Module 6	Deep Learning
Module 7	Computer vision
Module 8	Machine Learning and machine learning projects for final-year students
Data science course dration: 6 months | English
By Codegnan institute

Learn more about the data science course fees and duration before enrolling in any online or offline course.

Module 1. Python Programming

Learning the basics of Python programming is essential for data scientists to manipulate and visualizing data. This section will cover the basic syntax, operators, strings, functions, and other essential details to help you analyse large amounts of data and manipulate them.

• Python Introduction and setting up the environment
• Python Basic Syntax and Data Types
• Operators in Python (e.g., Arithmetic, Logical, Bitwise)
• Strings in Python
• Lists
• Tuples
• Sets
• Dictionaries
• Python conditional statements (e.g., if, if-else, if-elif-else)
• Loops in Python (e.g., while, for, break, continue)
• Getting Started with HackerRank use cases and working on them
• List and Dictionaries comprehension
• Functions
• Anonymous Functions (Lambda)
• Generators
• Modules
• Exceptions and Error Handling
• Classes and Objects (OOPS) (including different types of methods, inheritance, polymorphism, operator overloading, overriding)
• Date and Time
• Regex (e.g., re.search(), re.compile(), re.find(), re.split())
• Files (including opening, closing, reading and writing files)
• APIs the Unsung Hero of the Connected World
• Python for Web Development – Flask
• Hands-On Projects (Web Scraping, Sending Automated Emails, Building a Virtual Assistant)

Module 2. Data Analysis

Data analysis helps you in making informed decisions with data exploration and visualization using advanced tools. This section will cover the basics along with teaching you how to scrape data from websites using libraries like BeautifulSoup and handling and storing them in appropriate formats. 

• Packages (Working on Numpy, Pandas, and Matplotlib)
• Web Scraping (learning about tools, libraries and ethical considerations)
• Exploratory data analysis (EDA) using Pandas and NumPy
• Data Visualization using Matplotlib, Seaborn, and Plotly
• Database Access
• Tableau
• Power BI

Module 3. Statistics

The specific topics covered in the statistics section give you an overview of descriptive statistics and inferential statistics that provide the foundation for understanding and analyzing complex data. Explore the statistical foundations for data signs from this section and apply them to data analysis projects.

• Descriptive Statistics (including central tendency, variance, standard deviation, covariance, correlation, probability)
• Inferential Statistics (including Central limit theorem, hypothesis testing, one-tailed and two-tailed test, and Chi-Square test)

Module 4. Machine Learning

This part of the syllabus comprises mathematical models and algorithms that are needed in coding machines to adapt them to real-world challenges. The course comprises basic knowledge of machine learning and its three main types: supervised, unsupervised, and reinforcement learning among other essential topics.

• Introduction to Machine Learning
• Introduction to data science and its applications
• Data Engineering and Preprocessing
• Model Evaluation and Hyperparameter Tuning
• Supervised Learning – Regression
• Supervised Learning – Classification
• SVM, KNN & Naive Bayes
• Ensemble Methods and Boosting
• Unsupervised Learning – Clustering
• Unsupervised Learning – Dimensionality Reduction
• Recommendation Systems
• Reinforcement Learning
• Developing API using Flask / Webapp with Streamlit
• Deployment of ML Models
• Project Work and Consolidation

Module 5. NLP

Natural Language Processing NLP helps machines understand and create human language. This section will teach you Named Entity Recognition, text pre-processing, and text representation, along with applications ranging from sequential modelling, and building sentiment analysis. 

• Natural Language Processing (NLP) (including NER, text representation, sequential model, sentiment analysis)

Module 6. Deep Learning

This section allows you to master advanced topics like CNN (Convolutional Neural Networks), RNN (Recurrent Neural Networks), Neural Network architecture, and more.

• RISE OF THE DEEP LEARNING
• Artificial Neural Networks
• Convolution Neural Networks
• CNN – Transfer Learning
• RNN – Recurrent Neural Networks
• Generative Models and GANs

Module 7. Computer Vision

The computer vision syllabus allows you to understand how to create algorithms for computers to read and write data sent via images or videos.

• Computer Vision (including reading and writing images, drawing shapes using OpenCV, reason eye detection using OpenCV, VGG, CNN with Keras)

Bonus Module: Projects & Case Study

• Real-Time Rain Prediction using ML
• Real-Time Drowsiness Detection Alert System
• House Price Prediction using LSTM
• Customizable Chabot using OpenAI API
• Fire and Smoke Detection using CNN

M.Tech data science syllabus

M.Tech data science is a 2 years course with four semesters comprising several data science modules and multiple elective papers. All the main topics covered during the course are mentioned in the table. To check the complete syllabus, follow the M.Tech data science syllabus PDF.

Semester	Modules
Semester-I	Computational Linear Algebra Optimization Techniques for Data Science Machine Learning Data Structures and Algorithms for Data Science SC-1 Research Methodology Career Competency – I
Semester-II	Deep Learning SC-2 SC-3 SC-4 Elective 1 Elective 2 Career Competency – II
Semester-III	Elective 3 Elective 4 Dissertation I
Semester-IV	Dissertation II

List of Courses
Core Subjects Computational Linear Algebra Optimization Techniques for Data Science Data Structures and Algorithms for Data Science Machine Learning Deep Learning

Subject Core (SC)
Embedded Computing & Realtime OS for Data Science Research Methodology Introduction to Probabilistic Graphical Models Scientific Computing Text Mining and Analytics Big Data Framework for Data Science Statistical Modelling Advanced Data Visualization and Analytics

Electives

AI Applications for Power Systems Deep Learning in Genomics and Biomedicine Deep Learning for Biomedical Data Analysis Deep Learning for Speech Signal Processing Social Media Analytics Deep Learning for Visual Recognition Deep Learning for Cyber Security Complex Systems in Engineering, Finance & Biology: Modelling & Analysis High Performance Computing Multiscale Fluid Modelling Computer Vision Reinforcement Learning Blockchain Technology Predictive Analytics for Internet of Things Cloud Computing and Security in the Cloud

👉 Download the M.Tech data science course syllabus PDF: M.Tech data science curriculum

B.Sc data science syllabus

B.Sc data science is a 3-year graduate program having 6 semesters that revolves around the core data science subjects. Some of these are compulsory papers and a few of them are elective papers that you can choose from a huge range of options. The main topics covered in the course are mentioned in the table and for more details download the B.Sc data science course syllabus PDF. 

Semester	Modules
Semester-I	Communicative English Linear Algebra & Calculus Computer Architecture Problem Solving and Programming using C Digital System Design Ethics and Human Values
Semester-II	Principles of Data Science Fundamentals of Statistics Operating Systems Database Management System Computer Networks
Semester-III	Probability Theory Cloud Computing Advanced Database Technologies Web Programming Data Mining Operation Research
Semester-IV	Data Handling and Visualization Machine Learning Optimization Techniques Big Data Analytics PEC1
Semester-V	Deep Learning Natural Language Processing PEC 2 PEC 3
Semester-VI	Stream Processing Analytics PEC 4 PEC 5
PEC	Program Elective Course (PEC) -1 Cloud Services for IoT Business Analytics Business Intelligence Intelligent Database System Digital Marketing Analytics Internet of Things
PEC – 2 & 3	Augmented Reality & Virtual reality Linux Programming Image Processing and Analysis Healthcare Analytics Data mining using R Text Analytics
PEC – 4 & 5	High-Dimensional Data Analysis Cyber Forensic analytics Social Network Analytics IoT cloud and data analytics Predictive Modeling Analysis

M.Sc data science syllabus

M.Sc data science course is a 2-year postgraduate course comprising 4 semesters. The below table consists of the major topics, find the M.Sc data science syllabus PDF for more details. 

Semester	Modules
Semester-I	Linear Algebra Probability and Statistics Principles of Data Science Database Management Systems Python and R Programming Elective-I
Semester-II	Optimization Techniques Using MATLAB Multivariate Data Analysis Machine Learning Techniques Data Mining IoT Analytics Elective-II
Semester-III	Big Data Analytics Deep Learning Techniques Data Privacy and Security Elective-III Elective-IV
Semester-IV	Project Work Elective papers Elective – I Design of Algorithms Artificial Intelligence Business Intelligence Elective – II Transforms and Applications Predictive Analytics Software Project Management Elective – III Natural Language Processing Social Media Analytics Health Care Analytics Nature Inspired Computing Elective – IV Cloud Security Sentiment Analysis Text Analytics Marketing Analytics

BCA data science syllabus

BCA data science course is a 3-year graduate program that comprises 6 semesters. It covers multiple areas of data science and the main topics are mentioned in the below table. For more details on the course, you can check out the BCA data science syllabus PDF. 

Semester	Modules
Semester-I	General English-I Algebra, calculus and trigonometry Computer fundamentals & organization Programming in C Operating system
Semester-II	General English-II Probability and statistics Introduction to data science OOPS with C++ Data structure using C Environmental studies
Semester-III	Reasoning and thinking Database Management systems Computer networks Linear algebra Scientific programming using R
Semester-IV	Inferential statistics NoSQL Databases Big Data Analytics Cloud computing Exploratory data analysis Employability skills
Semester-V	Time series analysis Data analytics using Ms Excel and SQL Machine learning algorithms Elective-I Elective-II
Semester-VI	Elective-III Project
Elective Papers	Elective-I Internet of Things Artificial Intelligence Mobile Computing Elective-II Python Programming Java Programming Data Visualization Elective-III Data Science Project Management Software Engineering Agile Methodology

Data science course subjects and topics to learn

1. Python Programming

Learning Python for data scientists allows them to analyse data and build machine learning models. This data science module teaches you the basic syntax and data types, operators, strings, lists, tuples, sets, dictionaries, conditional statements, loops, functions, generators, exceptions and error handling, classes and objects, files, and more.

Proper understanding and implementation of Python programming can help you solve real-world problems with data.

2. Data exploration and visualization

Data exploration helps you understand data by summarising them, identifying patterns and making inferences. On the other hand, data visualisation refers to communicating the data through visual representations. These are essential skills for data scientists to better understand and communicate data and make informed and critical decisions. 

3. Statistics

Statistics and probability in data science help you understand data distributions, make inferences and build data models. Mastering statistical methods is essential for insightful data analysis and complex business forecasting.

4. Machine Learning

Machine learning is also the core part of data science that teaches you on how to develop algorithms that help computers learn data patterns and make complex decisions. Subfields of machine learning mainly comprise three topics: supervised learning, unsupervised learning, and reinforcement learning. 

It also comprises metrics used in evaluating ML models. Learning these concepts of machine learning will help you solve real-world problems. 

5. Natural Language Processing NLP

NLP covers everything about text analysis and tokenization, as well as text preprocessing and representation. This allows students to analyse text to extract better meaning and draw conclusions. 

6. Deep Learning

Understanding of deep learning can help you explore advanced topics such as Neural Network architecture and Transfer Learning techniques. These are often used for natural language processing, image recognition, and more.

7. Computer Vision

It allows you to understand how to develop algorithms that allow computers to interpret and understand visual data often used for video and image analysis.

8. Database Access

Knowledge of databases and SQL is critical for data retrieval and storage, as it allows data scientists to interact with structured datasets and perform complex analyses.

• Introduction to databases
• SQL basics
• MySQL setup and queries
• Analyzing scraped datasets using SQL

9. Excel and Visualization Tools

Excel and visualization tools like Tableau and Power BI are vital for communicating data insights visually, making complex data more accessible and understandable.

• Excel basics and functions
• Visualization with Tableau
• Power BI for data analysis and visualization

9. Reinforcement Learning

Why learn this: Reinforcement learning principles are valuable for creating systems that learn from interactions, allowing data scientists to build models capable of making sequential decisions and learning from feedback.

• Basics of reinforcement learning
• Markov Decision Processes (MDP)
• Q-learning
• Working with OpenAI Gym

Apply for data science classes with codegnan

Data science is a trending profession today, and the demand for data scientists is on the rise. If you want to be a part of it, take up a data science course from Codegnan. 

You can get a clear understanding of data science subjects and experience real-world projects. We also share data science trends or news related to it for your knowledge growth. 

Why join codegnan’s data science training programs:

• 6-month core data science course 
• Classes will be taken 2 hours daily for 6 days a week (Monday-Saturday) 
• In-hand training along with theoretical knowledge
• Gain experience on 25 real use cases during the course
• Get online assistance on doubt clearance, career guidance, monitoring session, interview preparation & mock interviews
• In-hand practice on real-world projects for different modules
• Improve knowledge with our assignments and quizzes for each module
• Get access to training materials like lab exercises, codes, data sets, and case studies on real data
• Check-out recorded live session
• Real-time training with live scenarios and applications

👉 Enroll for codegnan’s classroom training:

• Data science training course in Hyderabad
• Vijayawada data science training

👉 Contact our team for online data science courses.

Data scientist study material

If you’re looking for a clear data science career roadmap and career paths, check out our free webinar where our experts have shared a guide on how to start your data science career.

Here are other free resources where you start learning relevant data science skills:

1. Free Data Science course by Barton Poulson

This course is specially designed for beginners. You can learn the core of Data Science within 6 hours and have the flexibility to complete the course at your own pace. 

It covers the introduction to data science, data sourcing, coding, mathematics, and statistics. 

Follow the link:

2. Free Data Science course for beginners by codebasics

It is a course for beginners; even the ones with no computer science background can follow the entire series to learn core data science. 

The course is subdivided into multiple parts and mainly covers Python, Jupyter Notebook, Numpy, Pandas, Matpotlib, machine learning and deep learning tools, and more.

How much does a data scientist earn in India?

Data science is among the highest-paying tech jobs in India with an average salary ranging from ₹3.9-27.9 lakhs per annum for professionals with relevant skills and some years of experience.

👉 Learn about the future career scope of data scientists in India.

Data science course eligibility in 2024 

To qualify for the data science course, candidates must have a Science or Engineering graduation with 50% aggregate and Mathematics/Statistics/Computer Science/Information Technology as a core subject.

However, to enroll in codegnan’s data science training classes, you need to secure a minimum of 60% in B.Tech/B.Sc/MCA/BCA, and 60% or above in both Intermediate and 10th class examinations.

👉 Talk to our experts to learn how our course will help in your data science career.

Is data science a subject in CSE?

Yes, data science is now a subject in Computer Science and Engineering (CSE) that puts emphasis on core data science subjects with related computational mathematics, statistics, and computer science subjects.

What are the subjects in data science in the first year?

The core subjects in data science in the first year comprise linear algebra, probability, basic statistics, discrete mathematics, probability, business intelligence, programming languages like Python and R, machine learning, and data manipulation.

Is data science a high-paying job in 2024?

Data scientists are highly paid in India and the US as the demand for data keeps increasing across most industries. This is mainly because the demand for data scientists is increasing massively globally, but the supply of qualified scientists is fairly low. According to AmbitionBox, data scientists in India can earn nearly ₹27.9LPA, which is nearly ₹2.3L per month. 

Who can study data science?

Usually, students from science, maths, technology, or engineering backgrounds qualify for data science courses. However, professionals from non-tech domains can also start learning data science through online courses and transition into these roles. Programming experience is a must to effectively leverage data science.