Python Bootcamp for Data Analysis #7: EDA

Name: Python Bootcamp for Data Analysis #7: EDA
Rating: 4.7 (17 reviews)

From Zero to Hero: The Seventh and Final Module of Miuul's Python Bootcamp

Created byMiuul Data Science & Deep Learning

Last updated 7/2024

English

What you'll learn

Conduct advanced functional exploratory data analysis
Analyze categorical variables using various techniques
Perform in-depth analysis of numerical variables
Understand and apply correlation analysis to identify relationships between variables

Course content

1 section • 8 lectures • 1h 46m total length

Course Materials0:03
Advanced Functional Exploratory Data Analysis11:16
Explore advanced functional exploratory data analysis with a reusable check_df function to summarize data, including the Titanic dataset and its Survived variable, and set up numpy, pandas, seaborn, and matplotlib.
Analyzing Categorical Variables I21:51
Develop a programmatic approach to identifying categorical variables using type information and numeric encodings, and build a reusable cat summary function to show class counts and percentages.
Analyzing Categorical Variables II15:24
Learn to analyze categorical variables with scalable practices, add simple visuals like bar plots using seaborn, and handle boolean data robustly by converting to integers while keeping a one-thing approach.
Analyzing Numerical Variables7:48
Learn to analyze numerical variables using descriptive statistics and programmatic type checks to select numeric columns. Build reusable functions for numeric summaries and optional histogram plots.
Capturing Variables16:21
Learn to automatically capture variables and generalize data processes with a grab_col_names function that separates categorical, numerical, and cardinal variables, plus docstring guidance.
Analyzing Target Variable11:22
Analyze the target variable using categorical and numerical features to reveal factors affecting survival. Apply target summary with cat and target summary with num for scalable, automated insights.
Correlation Analysis22:20
Explores correlation analysis with heatmaps to identify and remove highly correlated variables, using a 0.90 threshold and a drop list function on numerical data from breast cancer and fraud datasets.

Requirements

No advanced programming experience needed.

Description

Welcome to the seventh and final module of Miuul's Python Bootcamp for Data Analysis!

This module is a crucial step in your journey as it introduces you to advanced data analysis techniques. We are excited to guide you through the foundational and advanced skills needed to perform comprehensive exploratory data analysis.

In this module, you'll start with advanced functional exploratory data analysis, learning how to deeply explore your datasets. You'll move on to analyzing categorical variables, with detailed lectures covering various techniques for understanding and interpreting these variables. We will also cover analyzing numerical variables, providing you with methods to extract meaningful insights from numerical data.

Additionally, you'll learn how to capture variables effectively, analyze target variables to understand your data's outcomes better, and perform correlation analysis to identify relationships between different variables.

This comprehensive exploration of advanced data analysis techniques will prepare you for real-world data challenges and enhance your ability to draw meaningful conclusions from complex datasets.

Join us at Miuul's Python Bootcamp for Data Analysis, where learning to code becomes an adventure, empowering you to write, analyze, and innovate. Each analysis you perform brings you one step closer to mastering the art of data analysis with Python.

Who this course is for:

Beginner Python developers curious about advanced data analysis techniques
Data analysts and scientists looking to enhance their analytical skills
Students and researchers who need to perform comprehensive data analysis