Course Modules
Browse Data Science lessons, notes, notebooks, and datasets.
1 HOUR
Introduction to Data Science
Learn what data science is, where it is used, and the basic workflow of a data science project.
- What is Data Science?
- Data Science lifecycle
- Tools used in Data Science
- Real-world applications
Lecture Slides
PPT
8 HOURS
Introduction to Python Programming
Python Programming
- Data Wrangling
- Data Types
- Comparison
- Functions
- Lambda Functions
Lecture Slides
PPT
10 HOURS
Numpy Pandas Matplotlib for Data Analysis
Learn how to load, clean, filter, group, and analyze data using Pandas.
- Series and DataFrame
- Reading CSV ./files
- Filtering data
- GroupBy
- Handling missing values
Slide
Slide
7 HOURS
Linear Regression
Learn how to m and b for y = mx+b
- Linear Regression
- Derivation of m_best
- Derivation of b_best
- Implementation of Linear Regression from scratch
- Linear Regression from Sklearn Library
Advertising.csv
Dataset
Code
Cheatsheet
Notebook of June 8th
Notebook
6 HOURS
Gradient Descent
Learn about gradient descent
- Gradeient Descent
- Derivation of dJ/dm
- Derivation of dJ/db
- Implementation of Gradient Descent from scratch
- Stochastic Gradient Descent from Sklearn Library
Cheatsheet
Cheatsheet
6 HOURS
Data Visualization & working on titanic datasets
Learn about data science with dataset
- Titanic Dataset
- Sigmoid Function
- Filling NAN
- Logistic Regression
- CLassification Matrix
Titanic.csv
Dataset
EDA Notebook
Notebook
4 HOURS
Logistic Regression
Learn about logisitic Regression
- Logistic Regression
- Sigmoid Function
- Loss Function
- Implementation of Logistic Regression from scratch
- Logistic Regression from Sklearn Library
Logisitc Regression implementation
Notebook
2 HOURS
Evaluation of algorithms
Learn about Confusion Matrix
- Precision
- Recall
Precision Recall TP FP TN FN
Cheatsheet
2 HOURS
Machine Learning Algorithms
One Shot Recap
- Algorithms used in the project
- KNN
- Clustering
- XGBoost
- Random Forest
ML Slide by Jason Mayes
Slide