Data Analysis & Exploratory Data Analysis Using Python

Name: Data Analysis & Exploratory Data Analysis Using Python
Rating: 4.3 (536 reviews)

Parametric & Non Parametric Hypothesis Tests | Build EDA App with Streamlit | EDA Libraries | Data Visualization

Created bySeaportAi .

Last updated 7/2025

English

What you'll learn

What are the four types of data analysis?
What is the difference between data analysis and exploratory data analysis
How to identify the critical factor in your data
How to identify outliers
What is descriptive statistics
How to identify relationship between variables
What is multi collinearity
What is EDA
Why EDA is needed
How to transform data
Central Tendency Vs Dispersion
How to handle missing values in your dataset
How to apply EDA (through an assignment)
How to derive maximum value for your data
What are non parametric hypothesis tests
ANOVA
Mann Whitney Test
Kruskal Wallis Test
Moods Median Test
t-Test
Why do we need geometric and harmonic means

Course content

11 sections • 46 lectures • 4h 53m total length

Course Introduction2:00
Introduction to Data Analysis5:02
Explore data analysis to inspect, clean, and transform data to uncover insights and trends, using descriptive, diagnostic, predictive, and prescriptive methods plus exploratory data analysis to ensure reliable results.
Correlation Vs Causation2:06
What would be the height?2:29
Explore how correlation does not imply causation and how regression toward the mean explains why extreme results tend to normalize, guiding handling of outliers in data analysis using Python.
Understand the data fallacies and challenges in data analysis

Scatter Plot and Regression13:07
Test your understanding
Pearson correlation coefficient, Spearman correlation & Kendall's Tau8:58
Explore how Pearson, Spearman, and Kendall's tau measure correlations in data analysis using Python, highlighting when to use each nonparametric method for monotonic or ordinal relationships.
Finding the critical factor5:09
Conditional Scatter Plots and Heatmaps10:35
Time Series Forecasting10:16

Differences between Parametric Vs Non Parametric Tests4:23
Summary of Different Non Parametric Tests8:42
Mann Whitney Test4:35
Wilcoxon Signed-Rank Test3:24
Kruskal Wallis Test3:19
Moods Median Test4:35
Use Mood's median test to compare medians across three strategies via a contingency table, chi-square, and p-value, illustrating median-based central-tendency analysis and Python implementation.
Friedman Test2:48
Apply the Friedman test, a non-parametric alternative to repeated-measures analysis of variance, to compare three or more related groups using rank-based data, compute the chi-square statistic, and interpret the p-value.
Chi Square Test1:36

Requirements

Basic Knowledge of Python

Description

Recent updates

March 2024: Expanded coverage of non parametric hypothesis tests
Jan 2023: EDA libraries (Klib, Sweetviz) that complete all the EDA activities with a few lines of code have been added
Jan 2022: Conditional Scatter plots have been added
Nov 2021: An exhaustive exercise covering all the possibilities of EDA has been added.

Testimonials about the course

"I found this course interesting and useful. Mr. Govind has tried to cover all important concepts in an effective manner. This course can be considered as an entry-level course for all machine learning enthusiasts. Thank you for sharing your knowledge with us." Dr. Raj Gaurav M.
"He is very clear. It's a perfect course for people doing ML based on data analysis." Dasika Sri Bhuvana V.
"This course gives you a good advice about how to understand your data, before start using it. Avoids that you create a bad model, just because the data wasn't cleaned." Ricardo V

Welcome to the program on data analysis and exploratory data analysis!

This program covers both basic as well as advanced data analysis concepts, analysis approaches, the associated programming, assignments and case studies:

How to understand the relationship between variables
How to identify the critical factor in data
Descriptive Statistics, Shape of distribution, Law of large numbers
Time Series Forecasting
Regression and Classification
Full suite of Exploratory Data Analysis techniques including how to handle outliers, transform data, manage imbalanced dataset
EDA libraries like Klib, Sweetviz
Build a web application for exploratory data analysis using Streamlit

Programming Language Used

All the analysis techniques are covered using python programming language. Python's popularity and ease of use makes it the perfect choice for data analysis and machine learning purposes. For the benefit of those who are new to python, we have added material related to python towards the end of the course.

Course Delivery

This course is designed by an AI and tech veteran and comes to you straight from the oven!

Who this course is for:

Data Scientists
Beginners in Machine Learning
Data Analysts
Python Programmers
ML Practitioners
IT Managers managing data science projects
Business Analysts

Data Analysis & Exploratory Data Analysis Using Python

What you'll learn

Explore related topics

Course content

Introduction4 lectures • 12min

Data Analysis Basics4 lectures • 37min

Advanced Data Analysis5 lectures • 48min

Hypothesis Testing and Parametric Tests5 lectures • 33min

Non Parametric Tests8 lectures • 33min

Understanding EDA8 lectures • 50min

Create EDA App Using Streamlit7 lectures • 34min

Classification and Unsupervised Machine Learning2 lectures • 20min

Quiz0

Python Refresher: Data Analysis Using Pandas2 lectures • 25min

Requirements

Description

Who this course is for: