Welcome to the repository for the "Data Science in Python: Data Prep & EDA" course, a comprehensive guide to mastering data preparation and exploratory data analysis using Python. This Udemy course is designed for learners who aim to dive deep into the world of data science and gain practical, hands-on experience.
The course is structured in ten detailed sections, each targeting a specific aspect of data science in Python:
- Getting Started: An introduction to the basics of data science.
- Intro to Data Science: Diving deeper into the field's principles.
- Scoping a Project: Learning to define and scope data science projects.
- Installing Jupyter Notebook: Setting up the essential tools for data analysis.
- Gathering Data: Techniques for collecting data effectively.
- Cleaning Data: Ensuring data quality through rigorous cleaning processes.
- Exploratory Data Analysis (EDA): Techniques to uncover insights from data.
- Mid-Course Project: A practical project to apply what you've learned.
- Preparing for Modeling: Setting the stage for predictive modeling.
- Final Course Project: Demonstrating your skills through a comprehensive project.
Through this really well-put course, I focused on:
- Developing and/or engaging domain expertise to make informed data cleaning decisions.
- Ensuring data integrity and consistency to prepare for robust modeling.
- Learning to ask meaningful questions and performing exploratory data analysis (EDA) to discover what the data reveal.
More information about the course materials and additional resources can be found here.