logo
Call Us : 9240206353
Apply Now
logo-mobile
Latest News

Admissions Closed

Program Overview

Executive M.Tech in Data Science & Data Analytics at IIT Bhilai equips working professionals with the skills to analyze complex datasets, derive meaningful insights, and build data-driven solutions. This program emphasizes foundational knowledge, advanced analytics, and real-world applications of data science, including machine learning, big data technologies, and statistical modeling.

Designed for flexibility, the program offers online classes, electives, and project-based learning. It prepares participants for roles such as data scientists, business analysts, and AI/ML specialists in various industries.

Eligibility:

Minimum 2 years of relevant experience within the preceding 3 Years*.

Should have B.Tech. / B.E. / B.Sc. in relevant field (minimum 4-year program) / M.S. or M.Sc. in relevant field (minimum 2-year program) / M.C.A. (minimum 2-year program). For the candidates applying based on the M.Sc./M.S./M.C.A. degree, the undergraduate degree must be in a science or engineering field.

In the qualifying degree, at least 55% marks or a CGPA/CPI equivalent to 55% marks. In case the candidate belongs to the SC, ST, or Persons with Disability (PwD) category, this is relaxed to 50% marks or a CGPA/CPI equivalent to 50% marks. To be eligible to apply for equivalency after the third semester, the candidate must have scored the minimum marks in the qualifying degree as specified in the eligibility criteria of the regular M.Tech. program of IIT Bhilai.

*To be computed as per the last day for application submission of this round.

Eligibility for PhD:

Executive M.Tech. degree will be considered eligible for PhD admissions. Other MEQs will remain as per the institute norms.

Program-specific criteria for Ex. M.Tech. admissions
  1. Relevant Experience: Industrial or Teaching experience only.
  2. Relevant field for the qualifying degree:
    • A. M.Sc./M.S. (2 years): ME / EE / ECE / CS / MT / PI / AE / IN
    • B. B.Sc. (4 years): ME / EE / ECE / CS / MT / PI / AE / IN

Duration:

The nominal duration of the Executive M.Tech. program will be 2 calendar years / 4 semesters (online).

The nominal duration of the Executive M.Tech. program with an equivalency to the regular M.Tech. will be a 5 semesters (3 online, 2 offline). Please contact IIT Bhilai for further details.

Duration

2 years

Program Fee

4,11,000

(Excluding Optional Fee)
Download Brochure

Program Objectives

Build a strong foundation in data science principles, statistical methods, and computational tools.

Gain expertise in big data technologies and advanced machine learning techniques.

Develop the ability to analyze, visualize, and interpret complex datasets to solve real-world problems.

Understand the ethical, legal, and societal implications of data science and AI applications.

Foster innovation in creating data-driven solutions for diverse industries such as healthcare, finance, and IoT.

Cultivate leadership and collaboration skills for managing multidisciplinary data analytics projects.

Learning Outcomes

Demonstrate expertise in core data science concepts, including data preprocessing, analytics, and modeling.

Apply machine learning and advanced analytics techniques to solve complex business and scientific problems.

Design, develop, and implement big data systems for scalable and efficient data processing.

Ensure ethical data usage and address privacy, governance, and compliance in data-driven decision-making.

Communicate data-driven insights and recommendations effectively to technical and non-technical audiences.

Commit to continuous learning, staying updated with emerging trends and technologies in data science and analytics.

Program Highlights

  • An esteemed certification, campus immersion & alumni status from IIT Bhilai
  • Learn through Virtual Instructor-Led Training (VILT)
  • Explore top-notch learning with industry experts

WHO IS THIS PROGRAM FOR?

  • Professionals who are already working in technology-related fields, including IT professionals, software engineers, and data science experts this program provides an opportunity to deepen their expertise.
  • Entrepreneurs, innovators, tech enthusiasts & engineers eager to master the field of Data Science and Data Analytics.
  • Individuals passionate about this field and wants to stay ahead in the digital age.
  • Engineers and software developers seeking a deep understanding of Data Science and Analytics will find this program invaluable for honing their skills.
Course Curriculum:

Semester I (Total Credits - 10)
Course Code Course Name L T P C Category
DSDA01 Programming for Data Science and Data Analytics 2 1 0 3 Core Course
DSDA02 Mathematics for Data Science and Data Analytics 3 1 0 4 Core Course
DSDA03 Introduction to Machine Learning 2 0 2 3 Core Course
Semester II - Total Credits - 9
Course Code Course Name L T P C Category
DSDA04 Big Data Technologies 2 0 2 3 Core Course
DSDAEXX Elective in Machine Learning 3 0 0 3 Elective -1
DSDAEXX Elective in Advanced Analytics 3 0 0 3 Elective -2
Semester Ill - Total Credits - 9
Course Code Course Name L T P C Category
DSDAEXX Expertise-Oriented Electives 3 0 0 3 Elective 3
DSDAEXX Elective-4 (Any) 3 0 0 3 Elective 4
DSDAEXX Elective-4 (Any) 3 0 0 3 Elective 4
Semester IV (Total Credits - 12)
Course Code Course Name L T P C Category
DSDAP01 Capstone Project 0 0 24 12 MTech Project
OR
DSDAT01* Thesis x x x 12 MTech Thesis
Semester V (Total Credits - 14)
Course Code Course Name L T P C Category
DSDATO1* Thesis x x x 14 MTech Thesis
Elective Courses
Category Sem Course Code Course Name L T P C
Elective in Advanced Analytics 2/3 DSDAE01 Text Mining and Natural Language Processing 3 0 0 3
2/3 DSDAE02 Time Series Analysis and Forecasting 3 0 0 3
2/3 DSDAE03 Network Data Analytics 3 0 0 3
2/3 DSDAE04 Data Analytics in the Cloud 2 0 2 3
Elective in Machine Learning 2/3 DSDAEOS Deep Learning 3 0 0 3
2/3 DSDAE06 Computer Vision 3 0 0 3
2/3 DSDAE07 Reinforcement Learning 3 0 0 3
Expertise-Oriented Electives 3/4 DSDAE08 Business Intelligence and Analytics 3 0 0 3
3/4 DSDAE09 Information Security 3 0 0 3
3/4 DSDAE10 Data Governance and Compliance 3 0 0 3
3/4 DSDAE11 Advanced Data Visualization Techniques 3 0 0 3
3/4 DSDAE12 Data-Driven Decision Making 3 0 0 3

Electives will be floated based on the availability of faculty members. This list may be updated from time to time and change batch-wise.

Additional Notes

*Only for those who have opted for equivalent degree to the regular Mtech program

  • Students registered only for the Executive MTech (Online) Program: Total Credits : 10 (Semester I)+ 9 (Semester II)+ 9 (Semester - III) + 12 (Semester - IV) = 40
  • Students opted for the equivalent degree to the regular MTech Program: Total Credits : 10 (Semester I) + 9 (Semester II) + 9 (Semester III) + 12 (Semester - IV) + 14 (Semester V) = 54

Thesis Requirements:

To achieve equivalency with the regular offline MTech program, the candidate must complete a total of 26 thesis credits on campus from the fourth semester onwards.

Program Equivalency:

This is an online Executive M.Tech. degree program, which is not equivalent to the regular M.Tech programs of IIT Bhilai. To get a degree equivalent to a regular M.Tech, the student has to opt for 26 credits offline thesis (at IIT Bhilai) after the 3 rd semester. The offline thesis will be for two semesters (semester 4 and semester 5). The selection for the equivalency program will be based on qualifying the mandatory offline written exam and an interaction conducted by IIT Bhilai.

Program Structure

Total Credits: 40
Core Courses: 12-13 Credits
Elective Courses: 13-14 Credits (if the Campus immersion is opted), otherwise 15-16 credits.
Campus Immersion Program: 2 Credits (optional)
Project: 12 Credits (in 4th Semester)

Examination & Evaluation:

End-Semester Offline Exams:

  • First Semester: At IIT Bhilai Campus.
  • Second & Third Semesters: At Designated Centers.

The weightage of online evaluation and end-semester exams will be in the proportion of 40:60. To pass a course, a student must attain a minimum of 40% marks in the total marks.

Students will be evaluated in each semester. In addition to continuous online evaluation (Mid semester exam / Quiz / assignment / assessment) in the core/elective courses running in the first three semesters, the end semester exams will be offline* proctored exams. The end-semester offline exams in the first three semesters will preferably be conducted at the IIT Bhilai campus to give the candidates an opportunity to visit and explore the IIT Bhilai campus. This may also be clubbed with other activities such as document verification, campus immersion, project team formation, and interaction with the mentors/supervisors, etc. Exams may be organized at other IIT Bhilai Centers, subject to higher demand.

Examination & Evaluation:

Students will be evaluated in each semester. In addition to continuous online evaluation (quiz/assignment/assessment) in the core/elective courses running in the first three semesters, the end-semester exam will be offline proctored exams.

  • The first end-semester exam will mandatorily be held at the IIT Bhilai Campus and will be clubbed with document verification. Any student who misses the end - term exam as well as the document verification due to emergency or faces some issues during the document verification must ensure to get the document verification completed during the Compartment/ Improvement Exam of the first semester. If any student fails to complete the document verification even after that exams of the first semester, the student will be terminated from the program.
  • The end-semester offline exams in the second and third semesters will preferably be conducted at the IIT Bhilai campus to give the candidates an opportunity to visit and explore the IIT Bhilai campus again. This may also be clubbed with other activities such as campus immersion, project team formation, and interaction with the mentors/supervisors, etc. Exams may be organised at other IIT Bhilai Centres (mutually agreed between IIT Bhilai and Edtech partner), subject to higher demand.

Grading Weightage:

The weightage of online evaluation and end-semester exams will be in the proportion of 30:70. To pass a course, a student must attain a minimum of 35% marks in the total marks.

Module :
Foundations of Data Science
Module 1 - Introduction to Data Science
  1. Overview of Data Science and Its Importance
  2. Key Concepts in Data Science: Data Mining, Machine Learning, and Big Data
  3. Data Types and Structures: Quantitative vs. Qualitative Data
  4. Introduction to Data Science Tools and Technologies
  5. The Data Science Process: From Data Collection to Data Cleaning
  6. Ethical Considerations and Data Privacy in Data Science
Module 2 - Introduction to Data Science
  1. Descriptive Statistics: Central Tendency, Variability, and Distributions
  2. Inferential Statistics: Hypothesis Testing and Confidence Intervals
  3. Correlation and Regression Analysis
  4. Non-parametric Statistical Methods
  5. Probability Distributions and Bayesian Thinking
  6. Experimental Design and Analysis
Module 3 - Machine Learning Foundations
  1. Introduction to Machine Learning and Its Applications
  2. Supervised vs. Unsupervised Learning
  3. Key Algorithms: Regression, Classification, Clustering, and Dimensionality Reduction
  4. Model Evaluation and Selection
  5. Overfitting, Underfitting, and Model Optimization
  6. Practical Implementation of Machine Learning Models
Module 4 - Data Visualization and Communication
  1. Principles of Effective Data Visualization
  2. Tools for Data Visualization (e.g., Tableau, Matplotlib, Seaborn)
  3. Interactive Visualizations and Dashboards
  4. Storytelling with Data: Narrative Techniques
  5. Communicating Complex Data Insights to Non-Technical Audiences
  6. Case Studies in Data Visualization
Module 5 - Advanced Topics and Trends in Data Science
  1. Big Data Analytics: Tools and Techniques
  2. Introduction to Deep Learning and Neural Networks
  3. Natural Language Processing and Text Analytics
  4. Time Series Analysis and Forecasting
  5. Ethical AI and Responsible Data Science
  6. Emerging Trends and Future of Data Science
Statistical Methods for Data Analysis
Module 1 - Introduction to Statistics for Data Analysis
  1. Overview of Statistics in Data Analysis
  2. Types of Data: Nominal, Ordinal, Interval, and Ratio
  3. Measures of Central Tendency: Mean, Median, Mode
  4. Measures of Dispersion: Range, Variance, Standard Deviation
  5. Probability Distributions: Normal, Binomial, Poisson
  6. Sampling Techniques and Data Collection
Module 2 - Hypothesis Testing and Inferential Statistics
  1. Concepts of Hypothesis Testing
  2. Types of Errors in Hypothesis Testing
  3. Confidence Intervals
  4. T-tests (One-sample, Independent sample, Paired sample)
  5. Analysis of Variance (ANOVA)
  6. Chi-Square Tests
Module 3 - Regression Analysis
  1. Introduction to Regression Analysis
  2. Simple Linear Regression
  3. Multiple Linear Regression
  4. Diagnostics and Assumptions in Regression Analysis
  5. Non-linear and Logistic Regression
  6. Model Selection and Validation
Module 4 - Multivariate Statistical Techniques
  1. Exploratory Data Analysis (EDA)
  2. Principal Component Analysis (PCA)
  3. Factor Analysis
  4. Cluster Analysis
  5. Discriminant Analysis
  6. Canonical Correlation
Module 5 - Advanced Topics and Case Studies
  1. Time Series Analysis and Forecasting
  2. Survival Analysis
  3. Bayesian Statistics
  4. Machine Learning in Statistics
  5. Real-world Case Studies and Applications
  6. Ethical Considerations in Statistical Analysis
Big Data Technologies
Module 1 - Introduction to Big Data
  1. Overview of Big Data and its characteristics (Volume, Variety, Velocity, Veracity)
  2. Big Data sources and types
  3. Challenges and opportunities in Big Data
  4. Big Data architecture and ecosystems
  5. Introduction to Hadoop and MapReduce framework
  6. Overview of data storage and management in Big Data (HDFS)
Module 2 - Big Data Processing Technologies
  1. In-depth study of Hadoop ecosystem components (Hive, Pig, HBase)
  2. Real-time processing with Apache Spark
  3. Introduction to NoSQL databases (Cassandra, MongoDB)
  4. Big Data integration and ETL processes
  5. Stream processing technologies (Apache Kafka, Apache Flink)
  6. Cloud-based Big Data solutions (AWS, Azure)
Module 3 - Big Data Analytics
  1. Basics of Big Data analytics and tools
  2. Data mining techniques for Big Data
  3. Machine Learning with Big Data
  4. Advanced analytics: Predictive, prescriptive, and sentiment analysis
  5. Visualization techniques for Big Data
  6. Case studies of Big Data analytics in various sectors
Module 4 - Big Data Security and Privacy
  1. Security challenges in Big Data environments
  2. Data encryption and security tools
  3. Privacy concerns and solutions in Big Data
  4. Regulatory compliance and data governance
  5. Ethical considerations in Big Data analytics
  6. Case studies on Big Data security and privacy
Module 5 - Emerging Trends and Future of Big Data
  1. Internet of Things (IoT) and Big Data
  2. Artificial Intelligence and Big Data
  3. Edge computing in Big Data
  4. Big Data in Industry 4.0
  5. Challenges and future directions in Big Data technologies
  6. Impact of Big Data on society and economy
Data Warehousing and Data Mining
Module 1 - Introduction to Data Warehousing
  1. Overview of Data Warehousing
  2. Data Warehousing Architectures
  3. Data Warehousing Components
  4. Data Modelling for Data Warehouses
  5. ETL Processes: Extraction, Transformation, and Loading
  6. Data Warehouse Performance Optimization
Module 2 - Solar Energy Systems for EVs
  1. Relational Database Design
  2. SQL for Data Warehousing
  3. Data Warehouse vs. Database Systems
  4. Data Quality and Cleaning
  5. Metadata Management in Data Warehouses
  6. Case Studies: Database Systems in Data Warehousing
Module 3 - Introduction to Data Mining
  1. Fundamentals of Data Mining
  2. Classification of Data Mining Systems
  3. Data Mining Process and Models
  4. Data Preprocessing Techniques
  5. Basics of Data Visualization
  6. Privacy and Security in Data Mining
Module 4 - Data Mining Techniques and Algorithms
  1. Classification and Prediction Methods
  2. Clustering Techniques
  3. Association Rule Mining
  4. Decision Trees and Random Forests
  5. Neural Networks and Deep Learning in Data Mining
  6. Time Series Analysis and Mining
Module 5 - Advanced Topics and Applications
  1. Big Data Analytics and Data Mining
  2. Text Mining and Web Mining
  3. Social Network Analysis
  4. Real-Time Data Warehousing and Mining
  5. Trends and Future Directions in Data Mining
  6. Case Studies: Data Mining in Industry
Predictive Analytics
Module 1 - Fundamentals of Predictive Analytics
  1. Introduction to Predictive Analytics: Definition, Scope, and Importance
  2. Data Exploration and Preprocessing: Handling Missing Values, Outliers, and Data Transformation
  3. Statistical Foundations: Probability Theory, Distributions, and Descriptive Statistics
  4. Predictive Modelling Process: Problem Definition, Data Splitting, and Cross-Validation
  5. Overview of Predictive Models: Linear Regression, Logistic Regression, Decision Trees
  6. Model Evaluation Metrics: Accuracy, Precision, Recall, AUC-ROC
  7. Introduction to Software Tools: R, Python, and Specific Libraries (e.g., scikit-learn, pandas)
Module 2: Statistical Methods in Predictive Analytics
  1. Linear and Logistic Regression: Model Building, Assumptions, and Interpretation
  2. Time Series Analysis: Components, ARIMA Models, Seasonality Adjustments
  3. Survival Analysis: Concepts, Kaplan-Meier Estimate, Cox Proportional Hazards Model
  4. Bayesian Statistics: Bayesian Inference, Prior and Posterior Distributions
  5. Dimensionality Reduction Techniques: Principal Component Analysis, Factor Analysis
  6. Model Selection and Validation Techniques: Train-Test Split, K-Fold Cross-Validation
Module 3: Machine Learning for Predictive Analytics
  1. Overview of Machine Learning: Supervised vs. Unsupervised Learning
  2. Classification Algorithms: K-Nearest Neighbors, Support Vector Machines, Naive Bayes
  3. Regression Techniques: Polynomial Regression, Ridge, Lasso
  4. Ensemble Methods: Random Forests, Gradient Boosting Machines
  5. Neural Networks and Deep Learning: Basics, Feedforward Neural Networks
  6. Unsupervised Learning for Prediction: Clustering Techniques, Association Rules
  7. Advanced Topics: Overfitting, Underfitting, Hyperparameter Tuning
Module 4: Applications and Advanced Techniques
  1. Industry Applications: Marketing, Finance, Healthcare, and Supply Chain
  2. Text Analytics and Natural Language Processing: Sentiment Analysis, Topic Modeling
  3. Recommendation Systems: Collaborative Filtering, Content-Based Systems
  4. Deep Learning for Predictive Analytics: Convolutional Neural Networks, Recurrent Neural Networks
  5. Big Data Analytics: Hadoop, Spark, and Their Role in Predictive Modeling
  6. Ethical Considerations and Bias in Predictive Analytics
  7. Future Trends and Innovations in Predictive Analytics
Text Mining and Natural Language Processing
Module 1 - Introduction to Text Mining and Natural Language Processing
  1. Overview of Text Mining and NLP
  2. Challenges in Text Mining and NLP
  3. Basic Text Processing and Cleaning
  4. Regular Expressions and Tokenization
  5. Introduction to Syntax and Parsing
  6. Basic Machine Learning Concepts in NLP
Module 2 - Text Representation and Feature Engineering
  1. Bag of Words and TF-IDF
  2. Word Embeddings: Word2Vec and GloVe
  3. Contextual Embeddings: BERT and GPT
  4. Feature Selection Techniques
  5. Dimensionality Reduction in Text Data
  6. Advanced Text Features
Module 3 - Machine Learning for Text Mining
  1. Supervised Learning Algorithms in Text Mining
  2. Unsupervised Learning: Clustering Techniques
  3. Semi-supervised and Reinforcement Learning in NLP
  4. Evaluation Metrics for Text Mining Models
  5. Text Classification and Categorization
  6. Sequence Modeling and Prediction
Module 4 - Advanced Topics in NLP
  1. Sentiment Analysis and Opinion Mining
  2. Named Entity Recognition and Entity Linking
  3. Topic Modeling and Latent Dirichlet Allocation
  4. Question Answering and Chatbots
  5. Language Generation and Summarization
  6. Advanced Parsing and Semantic Analysis
Module 5 - Applications and Case Studies
  1. Text Mining in Social Media Analysis
  2. NLP in E-commerce and Customer Service
  3. Text Mining in Healthcare and Biomedical Research
  4. Natural Language Understanding in Intelligent Agents
  5. Ethical Considerations and Bias in NLP
  6. Future Trends and Research Directions in Text Mining and NLP
Time Series Analysis and Forecasting
Module 1 - Introduction to Time Series Analysis
  1. Fundamentals of Time Series Data
  2. Components of Time Series: Trend, Seasonality, Cycle, and Irregular Variations
  3. Stationarity and Differencing
  4. Correlation and Autocorrelation Functions
  5. Introduction to Time Series Decomposition
  6. Basic Visualization Techniques for Time Series
Module 2 - Time Series Modeling
  1. ARIMA Models: Concepts and Applications
  2. Seasonal ARIMA (SARIMA) Models
  3. Exponential Smoothing and Holt-Winters Method
  4. Moving Average and Smoothing Techniques
  5. Non-Stationary Time Series and Unit Root Tests
  6. Model Selection and Diagnostic Checking
Module 3 - Advanced Time Series Models
  1. Vector Autoregression (VAR) Models
  2. State-Space Models and Kalman Filtering
  3. ARCH and GARCH Models for Volatility Forecasting
  4. Multivariate Time Series Analysis
  5. Nonlinear Time Series Models
  6. Introduction to Time Series Analysis in Frequency Domain
Module 4 - Machine Learning in Time Series
  1. Regression Analysis in Time Series
  2. Time Series Classification and Clustering
  3. Neural Networks for Time Series Forecasting
  4. Deep Learning Approaches (LSTM, GRU)
  5. Feature Engineering in Time Series
  6. Model Evaluation and Performance Metrics
Module 5 - Applications and Case Studies
  1. Financial Time Series Analysis and Forecasting
  2. Economic Time Series: GDP, Inflation, and Employment Data
  3. Time Series Analysis in Retail and Sales Forecasting
  4. Energy Demand Forecasting
  5. Time Series in Environmental Science
  6. Case Studies: Real-World Applications
Advanced Statistical Modeling
Module 1 - Foundations of Statistical Modeling
  1. Overview of Statistical Modeling: Concepts and Applications
  2. Probability Distributions and Their Properties
  3. Principles of Estimation: Maximum Likelihood and Bayesian Methods
  4. Hypothesis Testing and Statistical Inference
  5. Linear Regression Models and Assumptions
  6. Model Validation and Diagnostic Techniques
Module 2 - Multivariate Statistical Techniques
  1. Introduction to Multivariate Analysis
  2. Principal Component Analysis (PCA) and Factor Analysis
  3. Discriminant Analysis and Cluster Analysis
  4. Canonical Correlation Analysis
  5. Multivariate Regression Models
  6. Handling Missing Data in Multivariate Analysis
Module 3 - Time Series Analysis and Forecasting
  1. Basics of Time Series Data and Their Characteristics
  2. Autoregressive (AR) and Moving Average (MA) Models
  3. ARIMA and Seasonal ARIMA Models
  4. Time Series Decomposition and Forecasting Techniques
  5. Non-Stationary Models and Cointegration
  6. Case Studies in Time Series Analysis
Module 4 - Bayesian Statistics and Advanced Inference
  1. Introduction to Bayesian Statistics
  2. Bayesian Estimation and Prediction
  3. Hierarchical and Mixed Models
  4. Markov Chain Monte Carlo (MCMC) Methods
  5. Bayesian Model Selection and Averaging
  6. Applications of Bayesian Methods in Real-world Problems
Module 5 - Advanced Topics in Statistical Modeling
  1. Nonparametric and Semiparametric Models
  2. Structural Equation Modeling (SEM)
  3. Generalized Linear Models (GLMs) and Extensions
  4. Machine Learning Techniques in Statistical Modeling
  5. Survival Analysis and Censored Data
  6. Recent Trends and Developments in Statistical Modeling
Energy Storage and Conversion
Module 1 - Introduction to Cloud Computing and Data Analytics
  1. Overview of Cloud Computing: Concepts and Models
  2. Data Analytics in the Cloud: An Introduction
  3. Cloud Service Models: IaaS, PaaS, SaaS
  4. Cloud Deployment Models: Public, Private, Hybrid, and Community
  5. Basic Cloud Architecture and Components
  6. Introduction to Virtualization and its role in Cloud Computing
Module 2 - Cloud Platforms for Data Analytics
  1. Overview of Major Cloud Platforms (AWS, Azure, GCP)
  2. Cloud Storage Options and Database Services
  3. Data Processing Services in the Cloud
  4. Big Data Analytics in the Cloud
  5. Real-time Analytics and Stream Processing
  6. Cloud Security and Compliance in Data Analytics
Module 3 - Building Data Analytics Solutions in the Cloud
  1. Designing Data Pipelines in the Cloud
  2. Data Ingestion, Storage, and Processing Techniques
  3. Implementing ETL (Extract, Transform, Load) Operations
  4. Data Modeling and Warehousing in the Cloud
  5. Introduction to Data Visualization Tools in the Cloud
  6. Best Practices for Scalable Cloud Analytics Solutions
Module 4 - Advanced Analytics and Machine Learning in the Cloud
  1. Machine Learning Services in the Cloud
  2. Implementing Predictive Analytics in the Cloud
  3. Deep Learning and AI Services in Cloud Platforms
  4. Integrating Machine Learning Models with Data Pipelines
  5. Automation and Orchestration of Analytical Workflows
  6. Case Studies: Real-world Applications of Cloud-Based Analytics
Module 5 - Performance Optimization and Future Trends
  1. Performance Tuning for Cloud Analytics Solutions
  2. Cost Management and Optimization in Cloud Analytics
  3. Emerging Trends in Cloud Computing and Data Analytics
  4. Scalability and Reliability in Cloud-Based Analytics
  5. The Future of AI and Analytics in the Cloud
  6. Final Project: Building an End-to-End Cloud Analytics Solution
Deep Learning and Neural Networks
Module 1 - Introduction to Neural Networks
  1. Overview of Artificial Intelligence and Machine Learning
  2. Fundamentals of Neural Networks: Perceptrons and Activation Functions
  3. Feedforward Neural Networks: Architecture and Learning
  4. Backpropagation Algorithm and Gradient Descent
  5. Loss Functions and Optimization Techniques
  6. Practical Implementations and Frameworks Overview
Module 2 - Convolutional Neural Networks (CNNs)
  1. Principles of Convolutional Operations
  2. Architecture of CNNs: Layers and Feature Extraction
  3. Applications of CNNs in Image and Video Processing
  4. Advanced Techniques in CNNs: Pooling, Dropout, and Batch Normalization
  5. Transfer Learning and Pre-trained Models
  6. Case Studies: Image Classification and Object Detection
Module 3 - Recurrent Neural Networks (RNNs) and LSTMs
  1. Introduction to Sequential Data Processing
  2. Architecture of Recurrent Neural Networks
  3. Long Short-Term Memory (LSTM) Networks
  4. Applications in Natural Language Processing and Time Series Analysis
  5. Challenges in Training RNNs: Vanishing and Exploding Gradients
  6. Advanced RNN Variants and Attention Mechanisms
Module 4 - Generative Models and Unsupervised Learning
  1. Overview of Unsupervised Learning in Deep Learning
  2. Autoencoders: Architecture and Applications
  3. Introduction to Generative Adversarial Networks (GANs)
  4. Variational Autoencoders (VAEs)
  5. Applications of Generative Models in Data Augmentation and Synthesis
  6. Challenges and Future Directions in Generative Modeling
Module 5 - Deep Reinforcement Learning and Ethical Considerations
  1. Basics of Reinforcement Learning
  2. Deep Q-Networks (DQNs) and Policy Gradient Methods
  3. Applications of Deep Reinforcement Learning in Games and Robotics
  4. Ethical Implications and Responsible AI in Deep Learning
  5. Future Trends and Research Directions in Deep Learning
  6. Industry Applications and Case Studies
Reinforcement Learning
Module 1 - Introduction to Reinforcement Learning
  1. Overview of Reinforcement Learning
  2. The RL Problem: An Introduction
  3. Elements of Reinforcement Learning
  4. The RL Framework: Markov Decision Processes
  5. Dynamic Programming in RL
  6. Policy and Value Iteration
Module 2 - Model-Free Reinforcement Learning
  1. Basics of Model-Free Prediction
  2. Monte Carlo Methods
  3. Temporal-Difference Learning
  4. TD(λ) and Eligibility Traces
  5. Q-Learning and SARSA
  6. Comparison of Model-Free Algorithms
Module 3 - Value-Based Methods
  1. Basics of Value Function Approximation
  2. Methods of Value Function Approximation
  3. Deep Q-Networks (DQN)
  4. Extensions of DQN (Double DQN, Dueling DQN)
  5. Value-Based Learning in Continuous Spaces
  6. Challenges in Value-Based Learning
Module 4 - Policy-Based Methods
  1. Introduction to Policy Gradients
  2. REINFORCE Algorithm
  3. Actor-Critic Methods
  4. Proximal Policy Optimization (PPO)
  5. Trust Region Policy Optimization (TRPO)
  6. Application of Policy-Based Methods
Module 5 - Advanced Topics and Applications
  1. Partially Observable Markov Decision Processes
  2. Multi-Agent Reinforcement Learning
  3. Inverse Reinforcement Learning
  4. Reinforcement Learning in Real-World Scenarios
  5. Ethical Considerations in RL
  6. Future Trends in Reinforcement Learning
Supervised and Unsupervised Learning Techniques
Module 1 - Introduction to Machine Learning
  1. Overview of Machine Learning and its real-world applications.
  2. Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning.
  3. Data Preprocessing: Cleaning, Normalization, and Transformation.
  4. Introduction to Python and relevant libraries for machine learning (NumPy, Pandas, Scikit-learn).
  5. Basic Statistics and Probability Theory in Machine Learning.
  6. Evaluation Metrics for Machine Learning Models.
Module 2 - Supervised Learning Techniques
  1. Linear Regression and Logistic Regression.
  2. Decision Trees and Random Forests.
  3. Support Vector Machines (SVM).
  4. Neural Networks and Deep Learning Basics.
  5. Ensemble Methods and Boosting Algorithms.
  6. Case Studies and Practical Applications of Supervised Learning.
Module 3 - Unsupervised Learning Techniques
  1. Clustering Algorithms: K-Means, Hierarchical, and DBSCAN.
  2. Dimensionality Reduction Techniques: PCA and t-SNE.
  3. Association Rule Learning and Market Basket Analysis.
  4. Anomaly Detection Techniques.
  5. Self-Organizing Maps and Autoencoders.
  6. Case Studies and Practical Applications of Unsupervised Learning.
Module 4 - Advanced Topics and Trends
  1. Introduction to Semi-supervised and Reinforcement Learning.
  2. Deep Learning Advanced Topics: CNNs, RNNs, and GANs.
  3. Big Data and Machine Learning.
  4. Ethical and Societal Implications of Machine Learning.
  5. Model Selection, Optimization, and Hyperparameter Tuning.
  6. Current Research and Future Trends in Machine Learning.
Module 5 - Practical Implementations and Project Work
  1. Data Collection and Preprocessing for Machine Learning Projects.
  2. Implementing Supervised Learning Projects.
  3. Implementing Unsupervised Learning Projects.
  4. Model Evaluation and Optimization Techniques.
  5. Presentation of Projects and Peer Review.
  6. Future Directions and Career Paths in Machine Learning.
Machine Learning at Scale
Module 1 - Introduction to Machine Learning at Scale
  • Overview of Machine Learning and Big Data
  • Scalability Challenges in Machine Learning
  • Distributed Computing Fundamentals
  • MapReduce and Hadoop Ecosystem
  • Data Partitioning and Parallel Processing
  • Introduction to Apache Spark
    • Module 2 - Scalable Machine Learning Algorithms
    • Linear Regression at Scale
    • Large-Scale Classification Techniques
    • Clustering Algorithms for Big Data
    • Dimensionality Reduction in High-Volume Data
    • Ensemble Methods and Random Forests at Scale
    • Scalable Deep Learning Frameworks
      • Module 3 - Data Handling and Preprocessing at Scale
      • Big Data Acquisition and Storage
      • Data Cleaning and Preprocessing Techniques
      • Feature Engineering at Scale
      • Handling Missing and Noisy Data
      • Stream Processing and Real-time Analytics
      • Data Security and Privacy Considerations
        • Module 4 - Advanced Topics in Machine Learning at Scale
        • Hyperparameter Tuning in Distributed Environments
        • Scalable Text Mining and Natural Language Processing
        • Graph Processing and Network Analysis at Scale
        • Time Series Analysis in Big Data
        • Recommendation Systems at Scale
        • Case Studies: Real-world Applications
          • Module 5 - Tools and Technologies for Machine Learning at Scale
          • Overview of Big Data Platforms (Hadoop, Spark, Flink)
          • Machine Learning Libraries (MLlib, TensorFlow, PyTorch)
          • Cloud-Based Machine Learning Services
          • Performance Optimization Techniques
          • Monitoring and Evaluation of Scalable ML Systems
          • Emerging Trends and Future Directions
Business Intelligence and Analytics
Module 1 - Introduction to Business Intelligence and Analytics
  • Overview of Business Intelligence and Analytics
  • Role of Data in Business Decision Making
  • Introduction to Data Warehousing and Data Mining
  • Business Analytics and its Types (Descriptive, Predictive, Prescriptive)
  • Key Performance Indicators (KPIs) and Dashboard Basics
  • Case Studies in Business Intelligence
    • Module 2 - Data Warehousing and ETL Processes
    • Architecture of Data Warehouses
    • ETL (Extract, Transform, Load) Processes
    • Data Modeling and Design
    • Data Warehouse Implementation and Management
    • Data Governance and Quality
    • Tools and Techniques in Data Warehousing
      • Module 3 - Data Mining and Predictive Analytics
      • Introduction to Data Mining Techniques
      • Classification, Clustering, and Association Rule Mining
      • Predictive Modeling and Regression Analysis
      • Decision Trees and Random Forests
      • Neural Networks and Machine Learning Basics
      • Applications of Predictive Analytics in Business
        • Module 4 - Visualization and Reporting
        • Principles of Data Visualization
        • Tools for Data Visualization (e.g., Tableau, Power BI)
        • Dashboard Design and Best Practices
        • Interactive Reports and Data Storytelling
        • Advanced Visualization Techniques
        • Case Studies in Data Visualization
          • Module 5 - Advanced Topics and Emerging Trends
          • Big Data Analytics
          • Real-time Analytics and IoT Data
          • Artificial Intelligence in Business Intelligence
          • Cloud Computing and its Impact on BI
          • Ethical Considerations and Data Privacy
          • Future Trends in Business Intelligence and Analytics
Data Governance and Compliance
Module 1 - Introduction to Data Governance
  • Definition and Scope of Data Governance
  • Importance of Data Governance in Business
  • Key Components of Data Governance
  • Data Governance Frameworks
  • Roles and Responsibilities in Data Governance
  • Challenges in Implementing Data Governance
    • Module 2 - Data Compliance and Regulations
    • Overview of Data Compliance
    • Global Data Protection Regulations (GDPR, HIPAA, CCPA, etc.)
    • Compliance Requirements and Standards
    • Data Privacy and Security Laws
    • Role of Compliance in Data Governance
    • Case Studies on Data Compliance
      • Module 3 - Implementing Data Governance
      • Steps in Developing a Data Governance Strategy
      • Data Quality Management
      • Data Lifecycle Management
      • Policies and Procedures in Data Governance
      • Tools and Technologies for Data Governance
      • Metrics and KPIs for Data Governance
        • Module 4 - Risk Management in Data Governance
        • Identifying and Assessing Data Risks
        • Risk Management Frameworks
        • Data Breach and Incident Response
        • Ethical Considerations in Data Governance
        • Business Continuity and Data Governance
        • Case Studies on Risk Management
          • Module 5 - Advanced Topics in Data Governance
          • Emerging Trends in Data Governance
          • Role of AI and Machine Learning in Data Governance
          • Data Governance in Cloud Computing
          • Data Governance in Big Data Analytics
          • Legal and Ethical Implications of Advanced Data Technologies
          • Future of Data Governance and Compliance
Advanced Topics in Data Science
Module 1 - Big Data Analytics
  • Introduction to Big Data: Concepts and Challenges
  • Big Data Technologies: Hadoop, Spark, and NoSQL Databases
  • Data Mining Techniques for Large Datasets
  • Advanced Data Preprocessing and Transformation
  • Real-time Analytics and Stream Processing
  • Case Studies in Big Data Solutions
    • Module 2 - Machine Learning at Scale
    • Overview of Scalable Machine Learning
    • Distributed Computing for Machine Learning
    • Advanced Algorithms: Deep Learning, Neural Networks
    • Implementing Machine Learning Pipelines
    • Optimization Techniques in Large Scale Learning
    • Practical Applications of Machine Learning at Scale
      • Module 3 - Predictive Analytics and Modeling
      • Principles of Predictive Modeling
      • Advanced Regression Techniques
      • Time Series Analysis and Forecasting
      • Ensemble Methods and Model Stacking
      • Model Evaluation and Validation
      • Predictive Analytics in Business and Finance
        • Module 4 - Advanced Data Visualization and Interpretation
        • Innovative Data Visualization Techniques
        • Interactive and Dynamic Visualizations
        • Advanced Reporting Tools and Dashboards
        • Visualizing Big Data and High-Dimensional Data
        • Storytelling with Data
        • Ethical Considerations in Data Visualization
          • Module 5 - Emerging Trends in Data Science
          • Artificial Intelligence and its Role in Data Science
          • Internet of Things (IoT) Data Analytics
          • Blockchain Technology in Data Science
          • Advanced Topics in Data Privacy and Security
          • Data Science in Healthcare and Genomics
          • Future Directions in Data Science
Data-Driven Decision Making
Module 1 - Introduction to Data-Driven Decision Making
  • Overview of Data-Driven Decision Making
  • Importance of Data in Modern Businesses
  • Basic Concepts of Data Analytics
  • Tools and Techniques for Data Analysis
  • Data Quality and Data Governance
  • Case Studies in Data-Driven Decision Making
    • Module 2 - Statistical Methods for Decision Making
    • Descriptive Statistics in Decision Making
    • Inferential Statistics and Hypothesis Testing
    • Regression Analysis for Predictive Insights
    • Time Series Analysis and Forecasting
    • Non-parametric Methods in Data Analysis
    • Application of Statistical Methods in Real-World Scenarios
      • Module 3 - Data Visualization and Interpretation
      • Principles of Effective Data Visualization
      • Tools for Creating Data Visualizations (e.g., Tableau, Power BI)
      • Interpreting Graphs and Charts for Decision Making
      • Interactive and Dynamic Data Visualization Techniques
      • Dashboard Design and Implementation
      • Case Studies on Effective Data Visualization
        • Module 4 - Ethical Considerations in Data-Driven Decisions
        • Understanding Data Privacy and Security
        • Ethical Implications of Data Collection and Analysis
        • Regulatory Compliance (e.g., GDPR, HIPAA)
        • Bias and Fairness in Data Analysis
        • Ethical Decision Making Frameworks
        • Case Studies on Ethical Dilemmas in Data Usage
          • Module 5 - Communicating Data Insights
          • Techniques for Effective Data Storytelling
          • Presentation Skills for Data Professionals
          • Crafting Data-Driven Reports and Proposals
          • Data-Driven Decision Making in Leadership
          • Stakeholder Management and Communication Strategies
          • Real-world Examples of Effective Data Communication
Program Fee :

Executive MTech in Data Science & Data Analytics
Application Fee (Non Refundable ) 5,000
Admission Fee Instalment 1 Instalment 2 Instalment 3 Instalment 4
1,01,500 1,01,500 1,01,500 1,01,500
Total Fee (Excluding Optional Fee) 4,11,000
Optional :

Executive MTech in Data Science & Data Analytics Sem 1 Sem 2 Sem 3 Sem 4
Optional Campus Immersion Fee - 10,000 - 10,000
Optional Institute Alumni Fee - - - 6,000
Total Optional Fee 26,000

Refund Policy:

Click Here to check the refund policy

  • Application/Registration fees is non-refundable.
  • Fees will be collected only after IIT Bhilai confirms the batch start date.
  • 90% refund of the paid Course fee is applicable if requested on or before 30 days of the batch commencement date.
  • No refund is applicable post- batch commencement.

Admission Process

Step 1

Fill up an online application form, upload the required documents & submit application

Step 2

Make the application payment

Step 3

Shortlisting based on eligibility criteria fulfilled by the applicant

Step 4

If shortlisted, you will receive an offer letter

Step 5

Pay the entire fees on admission confirmation within 7 days of receiving the offer letter

Selection process will be scheduled post-counseling & application process, depending on the number of eligible applications as per seat availability for the program. This entire process will be online.

Campus Immersion :


Program Certificate

Executive MTech in Data Science & Data Analytics

Executive MTech in Data Science & Data Analytics

Complete the program successfully to obtain this valuable certificate.

Enquire Now
Apply Now