Data Science IN DATA SCIENCE ( S-DS-110 )

Month 1: Introduction to Data Science & Python Programming

Week 1: Introduction to Data Science

• What is Data Science? Scope and Applications
• Data Science Workflow: Data Collection, Cleaning, Exploration, Modeling, and Evaluation
• Tools and Technologies in Data Science
• Introduction to Data Science Projects
• Overview of Data Science Roles (Data Analyst, Data Engineer, Data Scientist)

Week 2: Python Programming for Data Science

• Introduction to Python: Basics, Variables, and Data Types
• Control Flow: Loops and Conditionals
• Functions and Modules
• Working with Data Structures: Lists, Tuples, Dictionaries, and Sets
• Introduction to Libraries: NumPy, Pandas, Matplotlib

Week 3: Data Manipulation with Pandas

• Introduction to Pandas DataFrames
• Reading and Writing Data: CSV, Excel, SQL Databases
• Data Selection, Filtering, and Aggregation
• Data Cleaning: Handling Missing Data, Duplicates, and Outliers
• Combining and Merging Datasets

Week 4: Data Visualization with Python

• Introduction to Matplotlib and Seaborn
• Plotting Basic Graphs: Line, Bar, Histogram, Boxplot, Scatterplot
• Customizing Graphs: Titles, Labels, and Legends
• Understanding and Visualizing Distributions
• Introduction to Plotly for Interactive Visualizations

Month 2: Statistical Analysis and Probability

Week 5: Introduction to Statistics for Data Science

• Basic Concepts of Statistics: Population vs Sample, Types of Data
• Descriptive Statistics: Mean, Median, Mode, Variance, and Standard Deviation
• Data Distribution and Graphical Representation
• Measures of Central Tendency and Dispersion

Week 6: Probability Theory

• Basic Probability Concepts
• Probability Distributions: Normal Distribution, Binomial Distribution, etc.
• Conditional Probability and Bayes' Theorem
• Random Variables and their Types

Week 7: Hypothesis Testing and Statistical Inference

• Null Hypothesis and Alternative Hypothesis
• Types of Errors (Type I and Type II)
• p-value and Statistical Significance
• T-tests, Z-tests, and Chi-Square Tests
• Confidence Intervals and their Applications

Week 8: Exploratory Data Analysis (EDA)

• Understanding the EDA Process
• Univariate and Bivariate Analysis
• Correlation and Covariance
• Data Visualization Techniques for EDA
• Identifying Patterns, Trends, and Outliers

Month 3: Introduction to Machine Learning

Week 9: Introduction to Machine Learning

• What is Machine Learning? Types of Learning: Supervised, Unsupervised, and Reinforcement
• Overview of the ML Workflow: Data Preprocessing, Model Training, Evaluation, and Tuning
• Introduction to Scikit-Learn
• Understanding Overfitting and Underfitting

Week 10: Supervised Learning - Regression

• Introduction to Linear Regression
• Simple Linear Regression: Formula, Assumptions, and Interpretation
• Multiple Linear Regression
• Model Evaluation: R², Mean Absolute Error (MAE), Mean Squared Error (MSE)
• Regularization: Ridge and Lasso Regression

Week 11: Supervised Learning - Classification

• Introduction to Classification Algorithms
• Logistic Regression: Theory and Application
• Decision Trees and Random Forests
• Model Evaluation: Accuracy, Precision, Recall, F1-Score, Confusion Matrix
• K-Nearest Neighbors (KNN)

Week 12: Model Evaluation and Validation

• Cross-Validation Techniques
• Hyperparameter Tuning using GridSearchCV
• Bias-Variance Tradeoff
• Evaluating Model Performance
• Dealing with Imbalanced Datasets

Month 4: Advanced Machine Learning

Week 13: Unsupervised Learning - Clustering

• Introduction to Unsupervised Learning
• K-Means Clustering: Theory, Algorithm, and Application
• Hierarchical Clustering and DBSCAN
• Evaluating Clustering Performance
• Applications of Clustering in Real-World Scenarios

Week 14: Dimensionality Reduction

• Curse of Dimensionality
• Principal Component Analysis (PCA)
• t-SNE (t-Distributed Stochastic Neighbor Embedding)
• Feature Engineering and Feature Selection

Week 15: Model Deployment and Real-World Applications

• Model Deployment Overview
• Saving and Loading Models using Pickle and Joblib
• Introduction to Flask for Building APIs
• Deploying Models to Cloud Platforms (Heroku, AWS)
• Using APIs for Real-Time Predictions

Week 16: Natural Language Processing (NLP) Basics

• Introduction to NLP and Text Processing
• Tokenization, Stemming, and Lemmatization
• TF-IDF and Word Embeddings
• Building Text Classification Models
• Sentiment Analysis and Applications

Month 5: Deep Learning and Neural Networks

Week 17: Introduction to Deep Learning

• What is Deep Learning? Neural Networks and their Components
• Architecture of Neural Networks (Neurons, Layers, Activation Functions)
• Training Neural Networks: Forward Propagation and Backpropagation
• Introduction to TensorFlow and Keras

Week 18: Convolutional Neural Networks (CNNs)

• CNN Architecture: Convolutional Layers, Pooling Layers, Fully Connected Layers
• Image Classification with CNNs
• Transfer Learning and Pretrained Models
• Applications of CNNs in Image Processing

Week 19: Recurrent Neural Networks (RNNs)

• RNNs and Time-Series Data
• Long Short-Term Memory (LSTM) Networks
• Sequence Prediction Problems
• Applications of RNNs in NLP and Time-Series Forecasting

Week 20: Advanced Topics in Deep Learning

• Generative Adversarial Networks (GANs)
• Autoencoders for Dimensionality Reduction and Anomaly Detection
• Reinforcement Learning Basics
• Hyperparameter Tuning in Deep Learning

Month 6: Capstone Project & Advanced Data Science Topics

Week 21: Big Data and Spark for Data Science

• Introduction to Big Data and its Challenges
• Overview of Apache Spark and Hadoop
• Working with Spark using PySpark
• Data Processing and Analysis with Spark
• Distributed Computing Concepts

Week 22: Model Interpretability and Explainability

• Importance of Model Interpretability
• Techniques for Model Interpretability (LIME, SHAP)
• Understanding Feature Importance
• Model Debugging and Fairness Considerations

Week 23: Data Science Capstone Project - Part 1

• Project Planning: Defining Problem Statement and Scope
• Data Collection and Preprocessing
• Exploratory Data Analysis (EDA) and Visualization
• Model Selection and Initial Model Development

Week 24: Data Science Capstone Project - Part 2

• Model Training, Tuning, and Optimization
• Model Evaluation and Validation
• Deploying the Model and Preparing the Final Report
• Presentation and Review of the Capstone Project

• Eligibility Criteria
•  

• Mathematics Foundation:
  Understand key concepts in linear algebra, calculus, probability, and statistics. These are the building blocks for understanding machine learning algorithms and neural networks.

• Programming Skills:
  Learn Python, which is the most popular language for AI/ML. Familiarize yourself with libraries like NumPy, pandas, and matplotlib for data handling and visualization.

• Machine Learning Basics:
  Study the fundamental concepts of supervised and unsupervised learning, common algorithms (e.g., linear regression, decision trees, k-means), and evaluation metrics.

• Practical Application:
  Practice using frameworks like TensorFlow or PyTorch for building models. Use platforms like Kaggle, Google Colab, or local datasets to implement projects and solve real-world problems.