Data Science Projects with Python

Course Curriculum

Chapter 1: Data Exploration and Cleaning

Lecture 1: Course Overview

Lecture 2: Installation and Setup

Lecture 3: Lesson Overview

Lecture 4: Python and the Anaconda Package Management System

Lecture 5: Different Types of Data Science Problems

Lecture 6: Loading the Case Study Data with Jupyter and pandas

Lecture 7: Getting Familiar with Data and Performing Data Cleaning

Lecture 8: Boolean Masks

Lecture 9: Data Quality Assurance and Exploration

Lecture 10: Deep Dive: Categorical Features

Lecture 11: Exploring the Financial History Features in the Dataset

Lecture 12: Lesson Summary

Lecture 13: Actitvity 1 : Exploring Remaining Financial Features in the Dataset

Lecture 14: Solution 1 : Exploring Remaining Financial Features in the Dataset

Chapter 2: Introduction to Scikit-Learn and Model Evaluation

Lecture 1: Lesson Overview

Lecture 2: Exploring the Response Variable and Concluding the Initial Exploration

Lecture 3: Introduction to Scikit-Learn

Lecture 4: Model Performance Metrics for Binary Classification

Lecture 5: True Positive Rate, False Positive Rate, and Confusion Matrix

Lecture 6: Obtaining Predicted Probabilities from a Trained Logistic Regression Model

Lecture 7: Lesson Summary

Lecture 8: Activity 2: Performing Logistic Regression and Creating a Precision-Recall Curve

Lecture 9: Solution 2: Performing Logistic Regression and Creating a Precision-Recall Curve

Chapter 3: Details of Logistic Regression and Feature Exploration

Lecture 1: Lesson Overview

Lecture 2: Examining the Relationships between Features and the Response

Lecture 3: Finer Points of the F-test: Equivalence to t-test for Two Classes and Cautions

Lecture 4: Univariate Feature Selection: What It Does and Doesn't Do

Lecture 5: Generalized Linear Models (GLMs)

Lecture 6: Lesson Summary

Lecture 7: Activity 3: Fitting a Logistic Regression Model & Directly Using the Coefficient

Lecture 8: Solution 3: Fitting a Logistic Regression Model & Directly Using the Coefficient

Chapter 4: The Bias-Variance Trade-off

Lecture 1: Lesson Overview

Lecture 2: Estimating the Coefficients and Intercepts of Logistic Regression

Lecture 3: Assumptions of Logistic Regression

Lecture 4: How Many Features Should You Include?

Lecture 5: Lasso (L1) and Ridge (L2) Regularization

Lecture 6: Cross Validation: Choosing the Regularization Parameter and Other Hyperparameter

Lecture 7: Reducing Overfitting on the Synthetic Data Classification Problem

Lecture 8: Options for Logistic Regression in Scikit-Learn

Lecture 9: Lesson Summary

Lecture 10: Activity 4: Cross-Validation and Feature Engineering with the Case Study Data

Lecture 11: Solution 4: Cross-Validation and Feature Engineering with the Case Study Data

Chapter 5: Decision Trees and Random Forests

Lecture 1: Lesson Overview

Lecture 2: Decision Trees

Lecture 3: Training Decision Trees: Node Impurity

Lecture 4: Using Decision Trees: Advantages and Predicted Probabilities

Lecture 5: Random Forests: Ensembles of Decision Trees

Lecture 6: Fitting a Random Forest

Lecture 7: Lesson Summary

Lecture 8: Activity 5: Cross-Validation Grid Search with Random Forest

Lecture 9: Solution 5: Cross-Validation Grid Search with Random Forest

Chapter 6: Imputation of Missing Data, Financial Analysis, and Delivery to Client

Lecture 1: Lesson Overview

Lecture 2: Review of Modeling Results

Lecture 3: Dealing with Missing Data: Imputation Strategies

Lecture 4: Cleaning the Dataset

Lecture 5: Mode and Random Imputation of PAY_1

Lecture 6: A Predictive Model for PAY_1

Lecture 7: Using the Imputation Model and Comparing it to Other Methods

Lecture 8: Financial Analysis

Lecture 9: Final Thoughts on Delivering the Predictive Model to the Client

Lecture 10: Lesson Summary

Lecture 11: Activity 6: Deriving Financial Insights

Lecture 12: Solution 6: Deriving Financial Insights