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

• 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

Data Visualization with Matplotlib and seaborn

Introduction to Matplotlib and Seaborn

EDA (Exploratory Data Analysis)

Deep Dive into Plots and its attributes

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

Introduction to Machine Learning and its Types

Basics of Models,Model Evaluation, and Cross Validation

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

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

Time Series Analysis and Forecasting

Introduction to Deep Learning, Neural Network, and NLP

Final Project - Day 1 - Presentation

Final Project - Day 2 - Presentation

Cloud Introduction

Fundamentals of Power BI and Tableau

Fundamentals of DB (SQL and No SQL)

Jenkins or Concourse Tool

GitHub