Data Science Master Program

Module 1: Introduction to Data Science

• Data science fundamentals
• Data science lifecycle
• Data-driven organizations
• Industry applications

Module 2: Data Science Environment

• Python ecosystem
• Jupyter Notebook
• Development environments
• Package management

Module 3: Python Programming Fundamentals

• Variables
• Data types
• Operators
• Expressions

Module 4: Python Control Flow

• Conditional statements
• Loops
• Iterations
• Comprehensions

Module 5: Python Data Structures

• Lists
• Tuples
• Dictionaries
• Sets

Module 6: Functions & Modules

• Functions
• Parameters
• Return values
• Modules and packages

Module 7: Object-Oriented Python

• Classes
• Objects
• Inheritance
• Encapsulation

Module 8: Python for Data Processing

• File handling
• CSV and JSON
• Exception handling
• Data processing workflows

Module 9: NumPy Fundamentals

• Arrays
• Indexing
• Array operations
• Aggregations

Module 10: Advanced NumPy

• Broadcasting
• Vectorization
• Reshaping
• Numerical operations

Module 11: Pandas Fundamentals

• Series
• DataFrames
• Data selection
• Filtering

Module 12: Data Manipulation with Pandas

• Sorting
• Grouping
• Merging
• Pivoting

Module 13: Data Cleaning

• Missing values
• Duplicates
• Incorrect data
• Data validation

Module 14: Data Transformation

• Encoding
• Scaling
• Binning
• Feature transformations

Module 15: Exploratory Data Analysis

• Data distributions
• Patterns
• Relationships
• Analytical insights

Module 16: Data Visualization Fundamentals

• Visualization principles
• Matplotlib
• Charts
• Plot customization

Module 17: Advanced Data Visualization

• Multivariate visualization
• Distribution analysis
• Interactive concepts
• Data storytelling

Module 18: Statistics Fundamentals

• Descriptive statistics
• Central tendency
• Dispersion
• Statistical measures

Module 19: Probability Fundamentals

• Probability concepts
• Conditional probability
• Random variables
• Probability rules

Module 20: Probability Distributions

• Normal distribution
• Binomial distribution
• Poisson distribution
• Distribution analysis

Module 21: Inferential Statistics

• Sampling
• Estimation
• Confidence intervals
• Statistical inference

Module 22: Hypothesis Testing

• Null hypothesis
• Alternative hypothesis
• P-values
• Statistical significance

Module 23: Correlation & Regression Analysis

• Correlation
• Covariance
• Regression relationships
• Statistical interpretation

Module 24: SQL Fundamentals

• Databases
• SELECT
• Filtering
• Sorting

Module 25: SQL Aggregations

• GROUP BY
• Aggregate functions
• HAVING
• Analytical queries

Module 26: SQL Joins

• INNER JOIN
• LEFT JOIN
• RIGHT JOIN
• Multi-table analysis

Module 27: Advanced SQL

• Subqueries
• CTEs
• Window functions
• Complex queries

Module 28: Data Modelling Fundamentals

• Relational models
• Tables
• Keys
• Relationships

Module 29: Machine Learning Fundamentals

• ML lifecycle
• Supervised learning
• Unsupervised learning
• Model development

Module 30: Feature Engineering

• Feature creation
• Feature transformation
• Encoding
• Feature selection

Module 31: Data Preprocessing for ML

• Scaling
• Normalization
• Missing data
• Pipeline preparation

Module 32: Linear Regression

• Regression fundamentals
• Model fitting
• Coefficients
• Prediction

Module 33: Multiple Regression

• Multiple predictors
• Feature relationships
• Model interpretation
• Regression assumptions

Module 34: Logistic Regression

• Classification fundamentals
• Logistic function
• Probability prediction
• Decision boundaries

Module 35: Decision Trees

• Tree structure
• Splitting
• Classification trees
• Regression trees

Module 36: Random Forest

• Ensemble learning
• Bagging
• Feature importance
• Model tuning

Module 37: Gradient Boosting

• Boosting concepts
• Sequential learning
• Gradient boosting models
• Performance optimization

Module 38: XGBoost Fundamentals

• XGBoost architecture
• Model training
• Parameters
• Practical applications

Module 39: Support Vector Machines

• Hyperplanes
• Margins
• Kernels
• Classification

Module 40: K-Nearest Neighbors

• Distance metrics
• Neighbor selection
• Classification
• Regression

Module 41: Naive Bayes

• Bayes theorem
• Probabilistic classification
• Model assumptions
• Applications

Module 42: Model Evaluation

• Accuracy
• Precision
• Recall
• F1 score

Module 43: Advanced Model Evaluation

• ROC-AUC
• Confusion matrix
• Regression metrics
• Cross-validation

Module 44: Hyperparameter Tuning

• Grid search
• Random search
• Parameter optimization
• Validation strategies

Module 45: Machine Learning Pipelines

• Pipeline design
• Preprocessing pipelines
• Model integration
• Reproducibility

Module 46: Clustering Fundamentals

• Unsupervised learning
• K-Means
• Cluster evaluation
• Customer segmentation

Module 47: Advanced Clustering

• Hierarchical clustering
• DBSCAN
• Density-based methods
• Cluster interpretation

Module 48: Dimensionality Reduction

• PCA
• Feature compression
• Variance preservation
• Visualization

Module 49: Anomaly Detection

• Outliers
• Isolation techniques
• Fraud detection concepts
• Monitoring applications

Module 50: Time Series Fundamentals

• Time-dependent data
• Trends
• Seasonality
• Time series components

Module 51: Time Series Forecasting

• Moving averages
• Exponential smoothing
• Forecast evaluation
• Business forecasting

Module 52: Introduction to Deep Learning

• Neural networks
• Neurons
• Layers
• Activation functions

Module 53: Neural Network Training

• Forward propagation
• Backpropagation
• Loss functions
• Optimizers

Module 54: TensorFlow & Keras

• TensorFlow fundamentals
• Keras API
• Model creation
• Model training

Module 55: Convolutional Neural Networks

• CNN architecture
• Convolution
• Pooling
• Image classification

Module 56: Sequence Models

• Sequential data
• RNN concepts
• LSTM
• Sequence prediction

Module 57: Natural Language Processing

• NLP fundamentals
• Text preprocessing
• Tokenization
• Text representation

Module 58: Text Analytics

• Sentiment analysis
• Text classification
• Keyword extraction
• NLP pipelines

Module 59: Transformers

• Transformer architecture
• Attention mechanisms
• Embeddings
• Transformer models

Module 60: Generative AI Fundamentals

• Generative AI concepts
• Foundation models
• Large language models
• Generative applications

Module 61: Prompt Engineering

• Prompt structure
• Instruction design
• Few-shot prompting
• Prompt optimization

Module 62: Embeddings & Vector Search

• Embeddings
• Vector representations
• Similarity search
• Vector databases

Module 63: Retrieval-Augmented Generation

• RAG architecture
• Document retrieval
• Context augmentation
• Response generation

Module 64: LLM Application Development

• LLM workflows
• API integration concepts
• Structured outputs
• Application architecture

Module 65: Responsible AI

• AI fairness
• Bias
• Explainability
• Responsible deployment

Module 66: Big Data Fundamentals

• Big data concepts
• Distributed computing
• Data architectures
• Large-scale processing

Module 67: Apache Spark Fundamentals

• Spark architecture
• DataFrames
• Transformations
• Actions

Module 68: PySpark for Data Science

• PySpark DataFrames
• Data processing
• Aggregations
• Distributed analytics

Module 69: Data Engineering Fundamentals

• ETL
• ELT
• Data pipelines
• Data integration

Module 70: Data Warehousing

• Data warehouses
• Fact tables
• Dimension tables
• Star schemas

Module 71: Cloud Data Science

• Cloud computing
• Cloud storage
• Cloud processing
• Scalable analytics

Module 72: Model Deployment Fundamentals

• Deployment lifecycle
• Model serialization
• Prediction services
• Deployment patterns

Module 73: Building ML APIs

• API concepts
• Model endpoints
• Request processing
• Prediction responses

Module 74: Docker for Data Science

• Containers
• Docker images
• Containerized models
• Deployment workflows

Module 75: Introduction to MLOps

• MLOps lifecycle
• Model versioning
• Experiment tracking
• Reproducibility

Module 76: Model Monitoring

• Model performance
• Data drift
• Model drift
• Monitoring metrics

Module 77: Data & ML Governance

• Data quality
• Model governance
• Documentation
• Risk management

Module 78: Data Science Solution Design

• Business problem definition
• Solution architecture
• Technology selection
• Scalability considerations

Module 79: End-to-End Data Science Workflow

• Data acquisition
• Data preparation
• Model development
• Deployment planning

Module 80: Master Data Science Project

• Business problem analysis
• Data exploration
• Model development
• Model evaluation
• Deployment
• Results presentation