Data Science With Python
Preface
Welcome to my DataCamp Data Science Portfolio repository!
This repository serves as a showcase of my journey through various DataCamp courses in the field of data science. Here, you’ll find a collection of projects, practice notebooks, and code snippets that I’ve completed and worked on during my learning process.
Contents
Practice Notebooks:
Here, you’ll find notebooks where I’ve practiced various data science concepts, techniques, and algorithms taught in DataCamp courses. Feel free to explore and learn alongside me!
- Introduction to Python
- Intermediate Python
- Data Manipulation with pandas
- Joining Data with pandas
- Introduction to Statistics in Python
- Introduction to Data Visualization with Matplotlib
- Introduction to Data Visualization with Seaborn
- Python Data Science Toolbox (Part 1)
- Python Data Science Toolbox (Part 2)
- Intermediate Data Visualization with Seaborn
- [Exploratory Data Analysis in Python]
- Working with Categorical Data in Python
- [Data Communication Concepts]
- Introduction to Importing Data in Python
- Cleaning Data in Python
- [Working with Dates and Times in Python]
- Writing Functions in Python
- Introduction to Regression with statsmodels in Python
- [Sampling in Python]
- [Hypothesis Testing in Python]
- [Supervised Learning with scikit-learn]
- [Unsupervised Learning in Python]
- [Machine Learning with Tree-Based Models in Python]
- [Intermediate Importing Data in Python]
- [Preprocessing for Machine Learning in Python]
- [Developing Python Packages]
- [Machine Learning for Business]
- [Introduction to SQL]
- [Intermediate SQL]
- [Joining Data in SQL]
- [Introduction to Git]
Course Projects:
This section contains projects completed as part of DataCamp courses. Each project includes a detailed analysis, code implementation, and insights gained from the data.