Discover the complete data science course syllabus for the 2024-2025 academic session in India, covering B.Tech, M.Tech, BCA, and more.
This curriculum outlines essential topics from programming and data analysis to machine learning and advanced applications, preparing students for success in the field.
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Data Science course syllabus and curriculum
Here’s a Data Science course syllabus at a glance:
| SL No. | Module Name | Topics Covered | Data Science Projects |
| 1 | Data Science Foundations | – Importance of data in decision-making – The Data Science Lifecycle | 1. Traffic Pattern Analysis: Optimize traffic flow and reduce congestion using traffic data. 2. Predicting House Prices: Predict house prices based on features like the number of bedrooms, bathrooms, and location. |
| 2 | Python for Data Science | – Python Libraries and Frameworks – Advanced Python Concepts | 1. COVID-19 Data Visualization: Load a dataset of COVID-19 cases using Matplotlib and Seaborn to create informative visualizations. 2. Spam Classification: Train a Scikit-learn model to classify emails as spam or not spam. |
| 3 | Statistical Inference and Modeling | – Probability – Hypothesis Testing – Regression Analysis | 1. Coin Flip Simulation: Simulate 10,000 coin flips and calculate the probability of getting a certain number of heads. 2. Credit Risk Assessment: Use logistic regression to predict the probability of a customer defaulting on a loan based on credit information. |
| 4 | Machine Learning Fundamentals | – Supervised Learning – Unsupervised Learning – Model Selection and Evaluation | 1. Credit Card Approval: Predict credit card approval based on credit score, income, and debt-to-income ratio using logistic regression. 2. Titanic Survival Prediction: Predict Titanic passenger survival based on demographic and travel information. |
| 5 | Deep Learning | – Neural Network Architectures – Libraries and Frameworks – Advanced Deep Learning Topics | 1. Image Classification with CNNs: Build a CNN model to classify images into different categories using the CIFAR-10 dataset. 2. Chatbot with Seq2Seq RNNs: Build a chatbot that responds to user queries using a sequence-to-sequence RNN model. |
| 6 | Natural Language Processing (NLP) | – Text Preprocessing and Representation – NLP Applications – Libraries and Frameworks | 1. Language Translation: Use the Transformers library to build a machine translation model. 2. News Navigator: Implement a Named Entity Recognition (NER) model to extract named entities from news articles. |
| 7 | Big Data and Distributed Computing | – Big Data Ecosystem – Spark Programming – Scalable Machine Learning | 1. Twitter Sentiment Analysis: Analyze Twitter tweets in real time using Spark Streaming. 2. Customer Purchase Prediction: Build a machine learning model using Spark MLlib to predict customer purchases. |
| 8 | Data Engineering and Pipelines | – Data Ingestion and Extraction – Data Transformation and Orchestration – Data Quality and Governance | 1. Weather Data Ingestion: Ingest weather data from APIs and web scraping using Apache Airflow. 2. Data Quality Guard: Create a data quality pipeline using Apache Airflow to detect anomalies. |
Module 1: Data Science Foundations
- Importance of data in decision-making
- The Data Science Lifecycle (problem definition, data collection, preprocessing, EDA, feature engineering, model building and evaluation, deployment, and monitoring)
⭐ Hands-on projects to practice:
- Traffic Pattern Analysis: Optimize traffic flow and reduce congestion using traffic data.
- Predicting House Prices: Predict house prices based on features like the number of bedrooms, bathrooms, and location.
- Weather Forecasting: Analyze weather data to predict weather patterns and temperatures.
Module 2: Python for Data Science
Python Libraries and Frameworks
- NumPy for numerical computing
- Pandas for data manipulation and analysis
- Matplotlib and Seaborn for data visualization
- Scikit-learn for machine learning
Advanced Python Concepts
- List comprehensions and generator expressions
- Functional programming (lambda, map, filter, reduce)
- Object-oriented programming principles
- Decorators and context managers
⭐ Hands-on projects to practice:
- COVID-19 Data Visualization: Load a dataset of COVID-19 cases using Matplotlib and Seaborn to create informative and attractive visualizations(line plots, bar charts, and heatmaps).
- Spam Classification: Train a Scikit-learn model to classify emails as spam or not spam.
- Web Scraper: Use list comprehensions and generator expressions to build a web scraper that extracts data from a website.
- Machine Learning Model Deployment: Use Scikit-learn and Flask to deploy a machine learning model as a web application.
Module 3: Statistical Inference and Modeling
Probability Distributions
- Discrete distributions (Bernoulli, Binomial, Poisson)
- Continuous distributions (Normal, Exponential, Gamma)
- Joint and conditional probability
Hypothesis Testing
- One-sample and two-sample tests
- ANOVA and Chi-square tests
- Non-parametric tests
Regression Analysis
- Linear regression (simple and multiple)
- Logistic regression for classification
- Regularization techniques (Ridge, Lasso, Elastic Net)
⭐ Hands-on projects to practice:
- Coin Flip Simulation: Simulate 10,000 coin flips and calculate the probability of getting a certain number of heads.
- Website Conversion Rate: Use a one-sample t-test to determine if a website’s conversion rate is significantly different from an industry benchmark.
- Energy Consumption Prediction: Use simple linear regression to predict energy consumption based on a single feature (e.g., number of occupants).
- Credit Risk Assessment: Use logistic regression to predict the probability of a customer defaulting on a loan based on credit information.
Module 4: Machine Learning Fundamentals
Supervised Learning
- Linear and logistic regression
- Decision trees and random forests
- Support Vector Machines (SVMs)
- Ensemble methods (bagging and boosting)
Unsupervised Learning
- K-means clustering
- Hierarchical clustering
- Principal Component Analysis (PCA)
- Anomaly detection techniques
Model Selection and Evaluation
- Train-validation-test split
- Cross-validation techniques
- Performance metrics (accuracy, precision, recall, F1-score)
- ROC curves and AUC
⭐ Hands-on projects to practice:
- Credit Card Approval: Predict credit card approval based on credit score, income, and debt-to-income ratio using logistic regression.
- Wine Quality Prediction: Use ensemble methods (bagging and boosting) to predict wine quality based on features like chemical composition and sensory data.
- Gene Expression Analysis: Use hierarchical clustering to identify patterns in gene expression data.
- Titanic Survival Prediction: Predict Titanic passenger survival based on demographic and travel information.
Module 5: Deep Learning
Neural Network Architectures
- Feedforward neural networks
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- Autoencoders and Generative Adversarial Networks (GANs)
Deep Learning Libraries and Frameworks
- TensorFlow and Keras
- PyTorch
Advanced Deep Learning Topics
- Transfer learning and fine-tuning
- Attention mechanisms
- Reinforcement learning
- Interpretability and explainability
⭐ Hands-on projects to practice:
- Image Classification with CNNs: Build a CNN model to classify images into different categories (e.g., animals, vehicles, buildings) using the CIFAR-10 dataset.
- Sentiment Analysis with RNNs: Develop an RNN model to classify movie reviews as positive or negative using the IMDB dataset.
- Generative Adversarial Networks (GANs) for Face Generation: Build a GAN model to generate new face images using the CelebA dataset.
- Chatbot with Seq2Seq RNNs: Build a chatbot that responds to user queries using a sequence-to-sequence RNN model trained on the Cornell Movie Dialog Corpus.
Module 6: Natural Language Processing (NLP)
Text Preprocessing and Representation
- Tokenization and normalization
- Stemming and lemmatization
- Bag-of-words and TF-IDF
- Word embeddings (Word2Vec, GloVe, FastText)
NLP Applications
- Sentiment analysis (using lexicon-based and machine-learning approaches)
- Text classification (using Logistic Regression, SVM, and Deep Learning)
- Named Entity Recognition (NER) (using Conditional Random Fields (CRFs) and Deep Learning)
- Machine translation
- Text generation
NLP Libraries and Frameworks
- NLTK and spaCy
- Gensim for topic modeling
- Transformers for state-of-the-art NLP models
⭐Hands-on projects to practice:
- Language Translation: Use the Transformers library to build a machine translation model to translate sentences from one language to another using the WMT dataset.
- Topic Tracker: Apply topic modeling using Gensim to extract underlying topics from a dataset of news articles.
- News Navigator: Implement a Named Entity Recognition (NER) model to extract named entities (e.g., people, organizations, locations) from news articles.
- Word Wizard: Use Word2Vec, GloVe, and FastText to create word embeddings and calculate text similarity between sentences.
Module 7: Big Data and Distributed Computing
Big Data Ecosystem
- Hadoop (HDFS, MapReduce, Hive, Spark)
- Apache Spark, Spark Streaming, and Kafka
- NoSQL databases (MongoDB, Cassandra, HBase)
Spark Programming
- RDDs and DataFrames
- Spark SQL and Datasets
- Spark MLlib for machine learning
- Spark Streaming for real-time data processing
Scalable Machine Learning
- Distributed training and inference
- Hyperparameter tuning at scale
- Model serving and deployment
⭐Hands-on projects to practice:
- Twitter Sentiment Analysis: Analyze Twitter tweets in real time using Spark Streaming, Spark MLlib, and MongoDB.
- Customer Purchase Prediction: Build a machine learning model using Spark MLlib to predict customer purchases based on transaction data.
- Scalable Recommendation System: Build a scalable recommendation system using Apache Spark, Spark MLlib, and TensorFlow Serving.
Module 8: Data Engineering and Pipelines
Data Ingestion and Extraction
- Batch and streaming data sources
- APIs and web scraping
- Data lakes and data warehouses
Data Transformation and Orchestration
- ETL pipelines with Apache Airflow
- Data transformation with Apache Beam
- Containerization and orchestration (Docker, Kubernetes)
Data Quality and Governance
- Data profiling and anomaly detection
- Data lineage and provenance
- Privacy and security considerations (laws like GDPR, CCPA, HIPAA)
⭐Hands-on projects to practice:
- Weather Data Ingestion: Ingest weather data from APIs and web scraping using Apache Airflow, and load into a data warehouse using Apache Beam.
- Data Quality Guard: Create a data quality pipeline using Apache Airflow to detect anomalies and perform data profiling, with data lineage and provenance using Apache Atlas and Apache Beam.
- ETL Flow: Build a scalable ETL pipeline by packaging it with Docker and managing it with Kubernetes, using Apache Beam to move and prepare batch data (e.g., CSV files) for a PostgreSQL database.
- Privacy Shield: Implement data privacy and security considerations in a data pipeline using Apache Airflow and Apache Beam, with access control and encryption using Apache Ranger and Apache Knox.
B.Sc Data Science Syllabus
The BSc (Hons) in Data Science is a 3-year undergraduate (UG) program that provides students with a strong foundation in Data Science principles and practices.
The average fees for the BSc (Hons) in Cyber Security course range from INR 30,000 to 4,00,000 per annum, depending on the college and location.
| Semester | Name | Topics Covered |
| I | Fundamentals of Data Science | Introduction to Data Science, Linear Algebra, Basic Statistics, Programming in C, Communication Skills in English, Python Programming, Introduction to Geospatial Technology |
| II | Programming for Data Science | Probability and Inferential Statistics, Discrete Mathematics, Data Structures and Program Design in C, Computer Organization and Architecture, Machine Learning, Advanced Python Programming for Spatial Analytics, Image Analytics |
| III | Data Management and Analytics | Programming in C Lab, Microsoft Excel Lab, Research Proposal, Natural Language Processing, Genomics, Data Warehousing and Multidimensional Modeling |
| IV | Advanced-Data Science Techniques | Data Structure Lab, Exploratory Data Analysis, Programming in R Lab, Research Publication |
| V | Machine Learning and Big Data | Machine Learning II, Introduction to Artificial Intelligence, Big Data Analytics, Data Visualizations, Programming in Python Lab |
| VI | Capstone and Practical Experience | Elective papers, Grand Viva, Major Project |
B.Tech Data Science syllabus
The B.Tech in Data Science is a 4-year undergraduate program that equips students with the knowledge and skills to analyze and interpret complex data to make informed decisions. You must complete 12th grade with a minimum of 45-60% marks, including Mathematics.
Here is the B.Tech Data Science syllabus semester-wise.
| Semester | Course Name | Topics Covered |
| 1 | Problem-Solving Using C | Basics of C programming, algorithms, and problem-solving techniques |
| Data Structures | Linear and non-linear data structures, algorithms for data manipulation | |
| Python for Data Science | Python programming, data structures, and libraries for data analysis | |
| 2 | Analytical Mathematics | Advanced calculus, differential equations, and applications |
| Data Structures | Linear and non-linear data structures, algorithms for data manipulation | |
| 3 | Applied Linear Algebra | Vector spaces, linear transformations, and matrix theory |
| Design and Analysis of Algorithms | Algorithm design techniques, complexity analysis, and optimization | |
| Database Management Systems | Database design, SQL, and data modeling | |
| Java Programming | Object-oriented programming concepts and Java applications | |
| R for Data Science | Statistical computing and graphics using R | |
| 4 | Discrete Mathematics | Set theory, combinatorics, graph theory, and logic |
| Data Wrangling | Techniques for data cleaning, transformation, and preparation | |
| Data Handling and Visualization | Techniques for data visualization and presentation | |
| 5 | Probability and Statistics | Probability theory, statistical inference, and data analysis |
| Business Intelligence and Analytics | BI tools, data analysis, and decision-making processes | |
| Predictive Modeling and Analytics | Techniques for predictive modeling and analysis | |
| Artificial Intelligence | Introduction to AI concepts and applications | |
| 6 | Machine Learning | Supervised and unsupervised learning techniques |
| Data Warehousing and Data Mining | Concepts of data warehousing and mining techniques | |
| Modern Software Engineering | Software development methodologies and practices | |
| 7 | Text Analytics and Natural Language Processing | Techniques for analyzing and processing text data |
| Big Data and Analytics | Big data technologies and analysis techniques | |
| Time Series Analysis and Forecasting | Methods for analyzing time series data | |
| Deep Learning | Neural networks and deep learning architectures | |
| 8 | Project & Viva-Voce | Comprehensive project presentation and evaluation |
| Capstone Project | Final project demonstrating cumulative knowledge and skills |
BCA Data Science syllabus
The BCA in Data Science is a 3-year undergraduate program that equips students with the knowledge and skills to analyze and interpret complex data to make informed decisions. You must complete your 12th grade with a minimum of 45-60% marks, including Mathematics.
- This course is designed to provide you with knowledge in both computer applications and data science, bridging the gap between the two fields.
Here are some key details about the BCA Data Science program:
| Semester | Course Name | Topics Covered |
| 1 | Problem-Solving Using C | Basics of C programming, algorithms, and problem-solving techniques |
| Data Structures | Linear and non-linear data structures, algorithms for data manipulation | |
| Computer Essentials for Data Science | Basics of computer systems and applications | |
| 2 | Statistics and Probability | Statistical methods and probability theory |
| Database Management Systems | Database concepts, SQL, and data modeling | |
| Data Structure and Algorithm | Data structures and algorithm design | |
| 3 | Introduction to Data Mining | Data mining techniques and applications |
| Python Programming | Python programming for data science | |
| Object Oriented Programming using C++ | OOP principles and C++ programming | |
| 4 | Data Modelling and Visualization | Techniques for data modeling and visualization |
| R Programming for Data Sciences | R programming for statistical analysis | |
| Machine Learning | Introduction to Machine Learning Algorithms | |
| 5 | Big Data Analytics | Techniques and tools for big data analysis |
| Natural Language Processing | Techniques for processing and analyzing natural language data | |
| Information and Data Security | Data security principles and practices | |
| 6 | Project | Capstone project demonstrating cumulative knowledge and skills |
| Minor Project | Smaller scale project for practical experience |
M.sc Data Science Syllabus
The M.Sc in Data Science is a 2-year program focused on advanced data analysis, machine learning, and big data technologies.
Designed for graduates with a relevant background, the program typically requires 50-60% in a bachelor’s degree and may include entrance exams or interviews.
| Semester | Course Name | Topics Covered |
| 1 | Introduction to Data Science | Data science lifecycle, data types, data collection, and preprocessing |
| Programming for Data Science | Python/R programming, data manipulation, libraries (NumPy, Pandas) | |
| Probability and Statistics | Probability theory, random variables, descriptive and inferential statistics | |
| Machine Learning I | Supervised learning algorithms, regression, classification, and decision trees | |
| 2 | Data Visualization | Data visualization principles, tools, interactive visualizations |
| Machine Learning II | Unsupervised learning, clustering algorithms, dimensionality reduction | |
| Big Data Technologies | Hadoop, Spark, streaming data processing, NoSQL databases | |
| Data Mining | Data mining process, association rule mining, anomaly detection | |
| 3 | Natural Language Processing | Text preprocessing, sentiment analysis, named entity recognition |
| Deep Learning | Neural networks, deep learning architectures, CNNs, RNNs | |
| Computer Vision | Image processing, object detection, facial recognition | |
| 4 | Capstone Project | Comprehensive data science project, applying learned concepts |
M.Tech Data Science Syllabus
The M.Tech in Data Science is a 2-year postgraduate program focused on advanced data analysis, machine learning, and big data technologies.
It’s designed for graduates with a relevant background and typically requires 50-60% in a bachelor’s degree, along with qualifying in entrance exams like GATE, followed by an interview.
| Semester | Course Name | Topics Covered |
| 1 | Mathematical Foundation for Data Science | Probability theory, statistics, random processes, linear algebra, matrices |
| Data Structures and Algorithms | Algorithm analysis, data structures (lists, trees, graphs), sorting, searching | |
| Machine Learning | Supervised and unsupervised learning algorithms, model evaluation | |
| Big Data Management | Hadoop ecosystem, NoSQL databases, distributed processing frameworks | |
| 2 | Data Visualization | Data visualization principles, tools (Tableau, D3.js, Matplotlib) |
| Elective I: Natural Language Processing | Text processing, sentiment analysis, speech recognition | |
| Elective II: Deep Learning | Neural networks, deep learning architectures, CNNs, RNNs | |
| Elective III: Big Data Analytics | Big data analytics tools, predictive modeling, anomaly detection | |
| 3 | Research Methodology | Research design, data collection methods, quantitative and qualitative analysis |
| Seminar | Literature survey, research presentation, peer review | |
| 4 | Dissertation | Comprehensive research project, thesis writing and defense |
Diploma Data Science Course Syllabus
The Diploma in Data Science is a comprehensive program designed to provide practical skills in data analysis, machine learning, and data management.
Typically lasting 6 months to 1 year, it is suitable for those seeking a focused introduction to data science.
Admission usually requires a basic understanding of mathematics and computer science, with entry based on academic qualifications or entrance tests.
| Semester | Course Name | Topics Covered |
| 1 | Introduction to Data Science | Overview of data science, data types, data collection |
| Programming for Data Science | Python programming basics, data structures, control structures | |
| Probability and Statistics | Probability theory, random variables, descriptive statistics | |
| Machine Learning I | Supervised learning algorithms, regression, classification | |
| 2 | Data Visualization | Data visualization principles, creating visualizations |
| Machine Learning II | Unsupervised learning, clustering algorithms, dimensionality reduction | |
| Big Data Technologies | Introduction to Hadoop and Spark, NoSQL databases | |
| Capstone Project | Applying learned concepts to a data science problem, project presentation |
Data Science course subjects and topics to learn
If you want to start a career in data science, below are the topics you need to learn:
- Programming (Python or R)
- Statistics and mathematics
- Data wrangling, manipulation, and management
- Data visualisation
- Machine learning and deep learning
1. Programming (Python or R)
Python and R are often a minimum requirement in entry-level data science roles. Python ranks first as a programming language as per TIOBE and PYPL Index. R is a top option for many data scientists for data manipulation, processing, and so on.
Also, tech Giants like Google, Microsoft, and Netflix heavily rely on Python and R for data science tasks.
Hence, learning these languages will increase your chances of employability, be it internships or placements. You can also learn SAS, SQL, or Julia.
2. Statistics and Mathematics
As a data scientist, you should know how to collect, present, and interpret data. Therefore, you should learn different concepts like mean, median, mode, etc., in statistics. You must understand statistical techniques.
You should also cover areas like calculus, linear algebra, matrices, probability, and other important mathematical concepts.
This helps you write high-quality algorithms and machine-learning models.
3. Data wrangling, manipulation, and management
These topics help you work with raw, real-world data and perform complex queries.
These tasks are foundational in data science as you must prepare the data to provide accurate business insights. Data wrangling deals with cleaning and organising data sets for easier analysis.
You are also expected to learn database management to extract data and transform it into suitable formats.
Data wrangling tools:
- Altair
- Alteryx
- Talend
Data manipulation tools:
- Pandas
- NumPy
- scikit-learn
Database management tools:
- MySQL
- MongoDB
- Oracle database
4. Data visualisation
Being able to present data is important to being a data scientist. You will need to master reporting and visualisation to present business insights to key stakeholders. So, learn how to create charts, graphs, dashboards, and tables.
Learning the tools below will prepare you well in this area:
- Tableau
- Power BI
- QlikView/Qlik Sense
- Matplotlib
- Plotly
5. Machine learning and deep learning
As per Stanford University, machine learning is the most in-demand skill followed by NLP. With this skill, you can develop algorithms and models that make predictions and automate decision-making.
Students who learn these techniques can solve real-world problems. These skills are highly sought-after in the job market.
To begin, master the fundamentals of statistics and programming. Then, explore introductory courses on machine and deep learning.
Data Science Course Fees and Duration 2024
What is the course fee for Data Science courses?
The fees of a data science course typically start from INR 30,000 and can reach up to INR 3 lakhs. You can find various institutes that offer both online and offline data science courses. For instance, IIT Madras offers the following fee structure:
Another example: ExcelR in Hyderabad:
The course fees for data science vary on different factors:
- Brand affiliation or partnerships with Microsoft, Google, NASSCOM, etc.
- Opting for certification
- Topics covered (advanced/foundational)
- Learning Format (instructor-led, real-time support)
- Job placement assistance
To get a clearer picture, explore the types of data science jobs, including job responsibilities, and prerequisites, and the latest job statistics, and trends for 2024. Additionally, gain experience on the data science lifecycle, and explore various data science career paths providing a complete guide on how to start a career in data science.
Data science course duration
On average, a data science course spans from 6 months to 3 years, depending on the curriculum, projects, and student availability.
For instance, the data science course from IIT Madras is at least 2 years long and can stretch up to 3 years.
ExcelR, a reputable choice among data science learners, provides a 6-month data science course. It also has various branches in different locations in India.
Who is eligible for Data Science courses?
If you want to enroll in any online training course for Data Science, there are no such criteria or eligibility. However, knowing the basics of computers and data science fundamentals will be helpful.
For academic courses in India: Students are eligible for Data Science courses after completing their 12th grade, with specific criteria depending on the course type:
- Diploma in Data Science: Open to any stream with 10+2 completion.
- BTech in Data Science: Requires 10+2 with Physics, Chemistry, and Mathematics, along with a minimum of 50% marks.
- B.Sc/ BCA in Data Science: Eligible for students who have completed 10+2 with Mathematics, also need at least 50% marks.
- Postgraduate Courses: A bachelor’s degree in IT or related fields is necessary, with a minimum of 50% marks required.
Somrita Shyam is a content writer with 4.5+ years of experience writing blogs, articles, web content, and landing pages in multiple domains. She holds a master’s degree in Computer Application (MCA) and is a Gold Award winner at Vidyasagar University. Her knowledge of the tech industry and experience in crafting creative content helps her write simple and easy-to-understand tech pieces for readers of all ages. Her interest in content writing began after helping PhD scholars in submitting their assignments. Later in 2019, she started working as a freelance content writer at Write Turn Services, and has worked with numerous clients, before joining Experlu (An UK based accounting firm) in 2022 and working as a full-time content writer in GigDe (2022-2023).