Essential Statistics for Data Science

Data Science is an inter disciplinary fields combining Statistics, Programming, Machine Learning and Business Knowledge.

Statistics is the key field in analyzing the data to extract insights for business decisions.  Though, Statistics as a field is vast, a limited concepts involving quantitative methods are useful in data science.

The science of collecting, describing, and interpreting data is popularly known as Statistical leveraging in Data Science

Two areas of Statistics in Data Science:

Descriptive statistics – Methods of organizing, summarizing, and presenting data in an informative way

Inferential statistics – The methods used to determine something about a population on the basis of a sample

A strong statistics foundation is mandatory for  data science professionals, as statistics is basis for any data analysis.

Statistics is also predominantly used in Machine Learning for feature engineering.

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This is an introductory course on Statistics for Data Science for Beginners.

There are no hard prerequisites for this course. Anyone interested can pursue.

The goal of this course is to provide a statistics with simple examples and learning the learners to get comfortable with Statistics as they move on to more advanced statistical methods.

Curriculum

INTRODUCTION

1. Statistics  Overview – Introduction

2. Statistics Basic Terminology

3. Types of Data

HARNESSING DATA

1. Introduction –  Sampling Methods

2. Sampling Methods

3. Cluster Sampling

4. Systematic Sampling

5. Biased Sampling

6. Sampling Error

EXPLORATORY DATA ANALYSIS

1. EDA – Central Tendencies

2. EDA – Variability

3. EDA – Histogram, Z-Value, Normal Distribution

Happy Learning

Team DataMites