Data Analysis & Exploratory Data Analysis | Build EDA App

Data Analysis & Exploratory Data Analysis | Build EDA App, available at $59.99, has an average rating of 4.43, with 62 lectures, 2 quizzes, based on 272 reviews, and has 17685 subscribers.

You will learn about 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 This course is ideal for individuals who are Data Scientists or Beginners in Machine Learning or Data Analysts or Python Programmers or ML Practitioners or IT Managers managing data science projects or Business Analysts It is particularly useful for Data Scientists or Beginners in Machine Learning or Data Analysts or Python Programmers or ML Practitioners or IT Managers managing data science projects or Business Analysts.

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Summary

Title: Data Analysis & Exploratory Data Analysis | Build EDA App

Price: $59.99

Average Rating: 4.43

Number of Lectures: 62

Number of Quizzes: 2

Number of Published Lectures: 46

Number of Published Quizzes: 2

Number of Curriculum Items: 67

Number of Published Curriculum Objects: 51

Original Price: $29.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

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!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course Introduction

Lecture 2: Introduction to Data Analysis

Lecture 3: Correlation Vs Causation

Lecture 4: What would be the height?

Chapter 2: Data Analysis Basics

Lecture 1: Dependent Vs Independent Variables

Lecture 2: Descriptive Statistics, Shape of distribution, Types of Data

Lecture 3: Box Plots & Outlier Analysis

Lecture 4: The Three Types of Means

Chapter 3: Advanced Data Analysis

Lecture 1: Scatter Plot and Regression

Lecture 2: Pearson correlation coefficient, Spearman correlation & Kendall's Tau

Lecture 3: Finding the critical factor

Lecture 4: Conditional Scatter Plots and Heatmaps

Lecture 5: Time Series Forecasting

Chapter 4: Hypothesis Testing and Parametric Tests

Lecture 1: Introduction to Hypothesis Testing

Lecture 2: Summary of Different Parametric Tests

Lecture 3: t-Test

Lecture 4: Anova

Lecture 5: Relevance of the test in the era of machine learning

Chapter 5: Non Parametric Tests

Lecture 1: Differences between Parametric Vs Non Parametric Tests

Lecture 2: Summary of Different Non Parametric Tests

Lecture 3: Mann Whitney Test

Lecture 4: Wilcoxon Signed-Rank Test

Lecture 5: Kruskal Wallis Test

Lecture 6: Moods Median Test

Lecture 7: Friedman Test

Lecture 8: Chi Square Test

Chapter 6: Understanding EDA

Lecture 1: Dependent and Independent Variables & Data Type

Lecture 2: Null Values and Encoding

Lecture 3: Outliers and Data Transformation

Lecture 4: Multi Collinearity

Lecture 5: Imbalanced Dataset

Lecture 6: Data Scaling

Lecture 7: Code Walkthrough

Lecture 8: EDA Apps/Libraries – Klib, Sweetviz

Chapter 7: Create EDA App Using Streamlit

Lecture 1: Context Setting

Lecture 2: Infrastructure for Streamlit

Lecture 3: Creating a very simple web app and Getting started with streamlit

Lecture 4: Header and Sub Header

Lecture 5: Reading and displaying contents of a file

Lecture 6: Uploading a file

Lecture 7: EDA app

Chapter 8: Classification and Unsupervised Machine Learning

Lecture 1: Logistic Regression

Lecture 2: Unsupervised | Clustering

Chapter 9: Quiz

Chapter 10: Python Refresher: Data Analysis Using Pandas

Lecture 1: Getting Started with Pandas

Lecture 2: Data Analysis Using Pandas

Chapter 11: Bonus Lecture

Lecture 1: Bonus Lecture

Instructors

SeaportAi .
Artificial Intelligence and Business Transformation Experts

Rating Distribution

1 stars: 8 votes
2 stars: 16 votes
3 stars: 44 votes
4 stars: 95 votes
5 stars: 109 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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