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Data Analysis & Exploratory Data Analysis | Build EDA App
Basic & Advanced Data Analysis Techniques | Use Streamlit to build EDA App | EDA Libraries | Data Visualization
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
Recent updates
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Jan 2023: EDA libraries (Klib, Sweetviz) that complete all the EDA activities with a few lines of code have been added
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Jan 2022: Conditional Scatter plots have been added
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Nov 2021: An exhaustive exercise covering all the possibilities of EDA has been added.
Testimonials about the course
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“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.
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“He is very clear. It’s a perfect course for people doing ML based on data analysis.” Dasika Sri Bhuvana V.
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“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:
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How to understand the relationship between variables
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How to identify the critical factor in data
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Descriptive Statistics, Shape of distribution, Law of large numbers
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Time Series Forecasting
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Regression and Classification
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Full suite of Exploratory Data Analysis techniques including how to handle outliers, transform data, manage imbalanced dataset
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EDA libraries like Klib, Sweetviz
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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!
Data Analysis Basics
Advanced Data Analysis
Hypothesis Testing
Understanding EDA
Create EDA App Using Streamlit
Classification and Unsupervised Machine Learning
Python Refresher: Data Analysis Using Pandas
Bonus Lecture
You can view and review the lecture materials indefinitely, like an on-demand channel.
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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