Data Analytics and Data Science: A practice guide

Devote 3 hours daily.

P.S.: More than 3 hours will be better.

Table of Contents

Statistics:

Mean
Median
Standard Deviation
Correlation Coefficient
Regression coefficient
Statistically Figuring Sample Size
Hypothesis testing
Probability Distributions
Bayesian Statistics
Moving Average

Start one tool along with Excel:

Tableau
PowerBi

Start learning one Programming language:

R
Python

Implement the statistics learning in a programming language

Regression Model: Steps
Linear Regression
Simple Linear
Multiple linear
Logistic Regression

Univariate time series:

Linear model
Stationarity
Autocorrelation
Partial Autocorrelation
Multicollinearity
ARIMA
ARMA process.

##Stationarity and Unit Roots Tests: Introduction, Unit Roots tests, Stationarity tests:

Time Series:

Basics of time series
Components of time series
Time series forecasting

Deploying predictive models:

Box-Jenkin Method,
Principal Component Analysis (PCA)

Start solving/practicing on the Kaggle platform or any other platform.

Make your own git repository.

I think it is enough for 100 days.