Day 1 : Introduction, Data collection

Day 2 : Pandas Library

Day 3 : Numpy and data concepts

Day 4 : Matplotlib, seaborn

Day 5 : Project 1 - Exploratory Data Analysis on Sales Dataset

Day 6 : Project 2 - World Population Data Analysis

Day 7 : Project 3 - Powerbi project - Population dataset

Day 8 : Statistics 1 - Sampling, Randomization, Frequency histogram and distribution, time series, bar and pie graphs, variance, correlation

Day 9 : Statistics 2 - Frequency table and stem and leaf, central tendency, variation measure, percentile and box - whisker plot, scatter diagram

Day 10 : Statistics 3 - Linear correlation, normal distribution , empirical rule, z-score and probabilities, central limit theorem

Day 11 : Machine learning for data science - preprocessing (scaling, encoding)

Day 12 : ML  - outlier detection & handling, null handling

Day 13 : ML - feature selection, feature extraction, train_test_split

Day 14 : ML Algorithms

Day 15 : ML Hyperparameter tuning, Evaluation

Day 16 : project 4-  Customer churn prediction using Data Science

Day 17 : project 5 - Supply Chain Optimization for a FMCG Company using DS

Day 18 : project 6 - Music dataset clustering

Day 19 : Time series introduction

Day 20 : project 7 -  CO2 Emission prediction using Time Series prediction

Day 21 : project 8 - Time series analysis on Microsoft stock data