Data Analysis & Exploratory Data Analysis | Build EDA App

Basic & Advanced Data Analysis Techniques | Use Streamlit to build EDA App | EDA Libraries | Data Visualization

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

Recent updates

• March 2024: Expanded coverage of non parametric hypothesis tests

• Jan 2023: EDAlibraries (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 EDAhas 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

• EDAlibraries 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 AIand tech veteran and comes to you straight from the oven!