Data Analysis & Exploratory Data Analysis Using Python

Parametric & Non Parametric Hypothesis Tests | Build EDA App with Streamlit | 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

Requirements
Basic Knowledge of Python

Description
Recent updatesMarch 2024: Expanded coverage of non parametric hypothesis testsJan 2023: EDA libraries (Klib, Sweetviz) that complete all the EDA activities with a few lines of code have been addedJan 2022: Conditional Scatter plots have been addedNov 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 VWelcome 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 variablesHow to identify the critical factor in dataDescriptive Statistics, Shape of distribution, Law of large numbersTime Series ForecastingRegression and ClassificationFull suite of Exploratory Data Analysis techniques including how to handle outliers, transform data, manage imbalanced datasetEDA libraries like Klib, SweetvizBuild a web application for exploratory data analysis using StreamlitProgramming Language UsedAll 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 DeliveryThis 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