Data Analysis & Exploratory Data Analysis | Build EDA App
Basic & Advanced Data Analysis Techniques | Use Streamlit to build EDA App | EDA Libraries | Data Visualization
Ratings 4.20 / 5.00
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
Description
Recent updates
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 Should Attend!
- Data Scientists
- Beginners in Machine Learning
- Data Analysts
- Python Programmers
- ML Practitioners
- IT Managers managing data science projects
- Business Analysts
