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eMasters in Data Science and Data Analytics

The eMasters in Data Science and Data Analytics is designed to provide students with a comprehensive understanding and skills in the fields of data science and analytics. The curriculum of eMasters in Data Science and Analytics focuses on teaching students to harness the power of data, employ analytics techniques, and apply these in real-world settings. Students will gain expertise in data handling, analysis, interpretation, and decision-making support, crucial for today's data-driven world.

Eligibility:

Should be a working professional with at least two (2) years of experience.

Should have B.Tech/ BE/ M.Tech/ MSc (4 Semester Program)/ MCA (4 Semester Program)/ MS Degree (min. 4 Semester Program).

In the qualifying degree at least 55 percent marks or equivalent 5.5 CGPA/CPI must be there. In case of the candidate belonging to SC, ST, or Persons with Disability (PwD) category, this is relaxed to 50% or equivalent 5.0 CGPA/CPI. For MCA/MSC passed graduates, the percentage score of MCA/MSC would be considered. For BE/BTech Engineering graduates without PG specialization, the percentage score of the undergraduate degree would be considered. For a post graduation in the Engineering field of study, PG score qualification can be considered.

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.

Duration:
2 years

Program Fee:

3,25,000

(Excluding Optional Fee)
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Program Objectives

  • To provide an understanding of fundamental statistical concepts and their application in data analysis.

  • To develop skills in handling and analyzing large datasets using Big Data tools and platforms.
  • To learn about various Big Data processing techniques and their applications in real-world scenarios.
  • To explore various data mining algorithms and their applications in solving real-world problems.
  • Equip students with an in-depth understanding of advanced statistical modeling techniques and their applications.
  • Develop proficiency in interpreting complex data sets using modern statistical methods.
  • Enhance skills in selecting and applying appropriate statistical models for various types of data and research questions.
  • Foster critical thinking and analytical skills for evaluating model performance and validity.

Learning Outcomes

  • Ability to apply data science techniques to extract meaningful information from data.

  • Development of a critical approach to evaluating data sources, data quality, and analytical methods.
  • Ability to apply statistical concepts and techniques for data analysis.
  • Capability to select and apply the correct statistical methods for different types of data.
  • Ability to identify and utilize appropriate Big Data tools for different types of data and analysis tasks.
  • Awareness of ethical, security, and privacy concerns in Big Data and the ability to address them in professional practices.
  • Ability to apply advanced statistical models to analyze real-world data.
  • Proficiency in using statistical software for model building and interpretation.
  • Skills in critically assessing the suitability and effectiveness of different statistical models.
  • Competence in communicating statistical findings effectively to both technical and non-technical audiences.

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 Structure:

Sem 1 Foundations of Data Science Statistical Methods for Data Analysis Big Data Technologies Data Warehousing and Data Mining
Sem 2 Elective in Advanced Analytics Elective in Machine Learning Open Elective -
Sem 3 Elective in Big Data Thesis/Project in Data Science Elective in Data Visualization -
Sem 4 Thesis/Project in Data Analytics - - -
Elective Courses
Predictive Analytics Text Mining and Natural Language Processing
Time Series Analysis and Forecasting Advanced Statistical Modeling
Data Analytics in the Cloud Deep Learning and Neural Networks
Reinforcement Learning Supervised and Unsupervised Learning Techniques
Machine Learning at Scale Business Intelligence and Analytics
Data Governance and Compliance Advanced Topics in Data Science
Data-Driven Decision Making -
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 :

eMasters in Data Science and Data Analytics Sem 1 Sem 2 Sem 3 Sem 4
Application Fee (Non Refundable ) 5,000 - - -
Admission Fee 80,000 80,000 80,000 80,000
Instalment 1 40,000 - - -
Instalment 2 40,000 - - -
Optional Virtual Labs 4,000 4,000 - -
Optional Campus Immersion Fee - 10,000 - 10,000
Optional Institute Alumni Fee - - - 6,000
Total Fee (Excluding Optional Fee) 3,25,000

Admission Process


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.

Program Certificate


eMasters in Data Science & Data Analytics

eMasters in Data Science and Data Analytics

Complete the program successfully to obtain this valuable certificate.