What you'll learn

Introduction to the field of Data Analysis

How to build an Analyst's mindset

An insight into the day-to-day tasks of a Data Analyst

Introduction to Descriptive Statistics

Terminology: Sample, Population, Statistic and Parameter

Classification

Prediction

Association

Pattern recognition

Descriptive statistic

Measures of Central Tendency and Dispersion

Introduction to data visualisation using Python

Univariate graphs, bi-variate graphs, and when to use which

Inferential Analytics/Statistics

Sample vs Population

What is a Hypothesis: Null and alternate hypothesis, test statistic and how to frame a hypothesis for your test

Normality and Central Limit theorem: Application to hypothesis testing and inference

Types of test statistics

How to perform the hypothesis test, p-value, Statistical Power, alpha (confidence), Hypothesis Rejection and Failure to Rejection

Impact of sample size on confidence and statistical power

Predictive Analytics

Introduction to Predictive Analytics and various predictive tasks

Introduction to Machine Learning, with examples of how each applies to real-life predictive analytics tasks

Deep Dive into supervised (regression and classification) and unsupervised tasks

Code examples

Requirements

No previous knowledge required! Suitable for candidates from any domain.