A focused 30-day learning sprint covering the core data skills I use day-to-day: Python, NumPy, pandas, SQL, visualization, exploratory data analysis, and introductory machine learning.
Projects completed
Progress
| Day |
Topic |
| Day 01 |
Introduction to Machine Learning |
| Day 02 |
Python Refresher (for ML) |
| Day 03 |
Data Handling With Pandas |
| Day 04 |
100 Pandas Puzzles part 1 |
| Day 05 |
100 Pandas Puzzles part 2 |
| Day 06 |
Back to SQL (Day1) |
| Day 07 |
Back to SQL (Day2) |
| Day 08 |
Data Aggregation and Summarization with SQL (Day 3) |
| Day 09 |
Back to SQL Subqueries (Day 4) |
| Day 10 |
Numpy Fundamentals Part 1 |
| Day 11 |
Numpy Fundamentals Part 2 |
| Day 12 |
100 Numpy Exercises (01 - 50) |
| Day 13 |
Matplotlib Fundamentals |
| Day 14 |
Matplotlib Advanced |
| Day 15 |
Seaborn Fundamentals |
| Day 16 |
Seaborn Complete |
| Day 17 |
Plotly for Data Visualization |
| Day 18 |
EDA Capstone Project @ Cleaning and Preprocessing FIFA dataset |
| Day 19 |
EDA Capstone Project @ Analysis and Visualizations with FIFA dataset |
| Day 20 |
EDA Capstone Project @ Final Insights with FIFA dataset |
| Day 21 |
EDA Basic Project 1 @ Super Heroes |
| Day 22 |
EDA Basic Project 2 @ Russia Ukraine War |
| Day 23 |
EDA Basic Project 3 @ Trekking Trails in Nepal |
| Day 24 |
EDA Basic Project 4 @ Best 50 Workouts |
| Day 25 |
Scikit Learn Basics |
| Day 26 |
Text Classification with Scikit Part 1 (Data Preparation) |
| Day 27 |
Text Classification with Scikit Part 2 (Model Training) |
| Day 28 |
Text Classification with Scikit Part 3 (Evaluation and Saving) |
| Day 29 |
Deep Learning Basics |
| Day 30 |
Getting Started with NLP |
Github Repository
30 Days of Data GitHub Repository
