Master Data Science Workshop in Tamil

Module 01: Introduction to Data Science

• No Code Data Science
• Data Cleaning, Data Preprocessing
• Statistical Analysis, Feature Engineering
• Machine Learning Basics, Supervised Machine Learning, Cross-Validation and Model Evaluation
• Feature Importance and Model Interpretation, Unsupervised Machine Learning
• Model Deployment and Maintenance

Module 02: Introduction to Python For Data Science

• Introduction to Python and Virtual Environment
• Python for Data Science, File Handling using Pandas
• Deep Dive into Python Libraries and its usage for Data Science
• Data Pre-Processing Basics
• Data Wrangling
• Model Deployment and Maintenance

Module 03: Data Visualization with Matplotlib and seaborn

• Data Visualization with Matplotlib and seaborn
• Introduction to Matplotlib and Seaborn
• EDA (Exploratory Data Analysis)
• Deep Dive into Plots and its attributes
• Data Wrangling

Module 04: Feature Engineering Techniques

• Data Pre-Processing Advanced - Feature Engineering, Dimensionality Reduction (PCA, t-SNE)
• Pre-Processing Tools: ColumnTransformer and Pipeline
• Case Study 1: Program Guidance Implementing all the above processes
• Case Study 2: Program Guidance Implementing all the above processes

Module 05: Machine Learning Basics

• Introduction to Machine Learning and its Types
• Basics of Models, Model Evaluation, and Cross Validation

Module 06: Supervised Learning

• Supervised Learning - Evaluation Metrics - MSE, F1 score, R2, RMSE, Precision, Recall, Confusion matrix
• Supervised Learning - Classification
• Supervised Learning - Decision Tree and Ensemble Model
• Supervised Learning - Regularization, Model Performance and Optimization - Overfitting, Underfitting, Pruning

Module 07: Unsupervised Learning

• Unsupervised Learning - Clustering - KMeans, Hierarchical Clustering, Noise Reduction
• Unsupervised Learning - Evaluation Metrics - Cross Validation and Confusion Matrix
• Hyperparameter Tuning - Grid Search, Bayesian, Random Search
• Basics of Model Deployment
• Case Study 3: Program Guidance Implementing all the above processes

Module 08: Advance Data science ( Time Series , Deep learning)

• Time Series Analysis and Forecasting
• Introduction to Deep Learning, Neural Network, and NLP
• Final Project - Day 1 - Presentation
• Final Project - Day 2 - Presentation

Module 09: Cloud , Power BI, Tableau, Database

• Cloud Introduction
• Fundamentals of Power Bi and Tableau
• Fundamentals of DB (SQL and No SQL)
• Jenkins or Concourse Tool
• GitHub