STATISTICS FOR DATA SCIENCE

Introduction to Statistics

Two areas of Statistics in Data Science

Applied statistics in business

Descriptive Statistics

Inferential Statistics

Statistics Terms and definitions

Type of Data

Quantitative vs Qualitative Data

Data Measurement Scales

Harnessing Data

Sampling Data, with and without replacement

Sampling Methods, Random vs Non-Random

Measurement on Samples

Random Sampling methods

Simple random, Stratified, Cluster, Systematic sampling.

Biased vs unbiased sampling

Sampling Error

Data Collection methods

Exploratory Analysis

Measures of Central Tendencies

Mean, Median and Mode

Data Variability : Range, Quartiles, Standard Deviation

Calculating Standard Deviation

Z-Score/Standard Score

Empirical Rule

Calculating Percentiles

Outliers

Distributions

Distribtuions Introduction

Normal Distribution

Central Limit Theorem

Histogram - Normalization

Other Distributions: Poisson, Binomial et.,

Normality Testing

Skewness

Kurtosis

Measure of Distance

Euclidean , Manhattan and Minkowski Distance

Hypothesis & computational Techniques

Hypothesis Testing

Null Hypothesis, P-Value

Need for Hypothesis Testing in Business

Two tailed, Left tailed & Right tailed test

Hypothesis Testing Outcomes : Type I & II erros

Parametric vs Non-Parametric Testing

Parametric Tests ,  T - Tests : One sample, two sample, Paired

One Way ANOVA

Importance of Parametric Tests

Non Parametric Tests : Chi-Square, Mann-Whitney, Kruskal-Wallis etc.,

Which Test to Choose?

Ascerting accuracy of Data

Correlation & Regression

Introduction to Regression

Type of Regression

Hands on of Regression with R and Python.

Correlation

Weak and Strong Correlation

Finding Correlation with R and Python