Data Science Projects with Python

Delve into data science with Python by exploring datasets, building models, and learning logistic regression, decision trees, gradient boosting, and SHAP values. Gain insights into deploying and monitoring models.

Beginner

98 Lessons

24h

Certificate of Completion

AI-POWERED

Explanations

AI-POWERED

Explanations

This course includes

7 Projects

52 Playgrounds

7 Quizzes

This course includes

7 Projects

52 Playgrounds

7 Quizzes

Course Overview

As businesses gather vast amounts of data, machine learning is becoming an increasingly valuable tool for utilizing data to deliver cutting-edge predictive models that support informed decision-making. In this course, you will work on a data science project with a realistic dataset to create actionable insights for a business. You’ll begin by exploring the dataset and cleaning it using pandas. Next, you will learn to build and evaluate logistic regression classification models using scikit-learn. You will...Show More

What You'll Learn

Hands-on experience in data exploration, data processing, data modeling and data visualization using pandas, scikit-learn, and Matplotlib

The ability to evaluate model performance and interpret model predictions

Working knowledge of how predictive models can support business decision-making

An understanding of the mathematical foundations of machine learning models

What You'll Learn

Hands-on experience in data exploration, data processing, data modeling and data visualization using pandas, scikit-learn, and Matplotlib

Course Content

Introduction

Get familiar with machine learning's role in data science and essential Python libraries.

Overview

Course Structure

Data Exploration and Cleaning

Discover the logic behind data exploration and cleaning for effective data science projects.

Introduction: Python for Data Science Exercise: Getting Familiar with Python Different Types of Data Science Problems Introduction to Jupyter and pandas Exercise: Loading the Case Study Data in a Jupyter Notebook Getting Familiar with Data and Performing Data Cleaning Exercise: Verifying Basic Data Integrity Boolean Masks Exercise: Continuing Verification of Data Integrity Exercise: Exploring and Cleaning the Data Exercise: Exploring the Credit Limit and Demographic Feature Deep Dive: Categorical Features Exercise: Implementing OHE for a Categorical Feature Exploring the Financial History Features in the Dataset Summary: Data Exploration and Cleaning Quiz: Data Exploration and Cleaning

(Challenge) Exploring Remaining Financial Features in Dataset

Project

Introduction to scikit-learn and Model Evaluation

Examine scikit-learn tools for model training, evaluation metrics, and generating synthetic data.

Introduction: scikit-learn and Model Evaluation The Response Variable and Concluding the Initial Exploration scikit-learn Generating Synthetic Data Exercise: Linear Regression in scikit-learn Model Performance Metrics for Binary Classification Splitting the Data: Training and Test Sets Classification Accuracy Exercise: Calculating True and False Rates and Confusion Matrix Exercise: Obtaining Probabilities from Logistic Regression Model The Receiver Operating Characteristic (ROC) Curve Precision Summary: Introduction to scikit-learn and Model Evaluation Quiz: Introduction to scikit-learn and Model Evaluation

(Challenge) Logistic Regression and Precision-Recall Curve

Project

Details of Logistic Regression and Feature Extraction

Break down complex ideas in logistic regression, feature extraction, and their practical applications.

Introduction: Logistic Regression and Feature Extraction Examining Relationships Between Features and Response Variable Pearson Correlation Mathematics of Linear Correlation Exercise: F-test and Univariate Feature Selection Finer Points of the F-test Exercise: Visualizing the Feature and Response Variable Relationship Univariate Feature Selection Exercise: Plotting the Sigmoid Function Scope of Functions Why Is Logistic Regression Considered a Linear Model?Exercise: Finding Appropriate Features for Logistic Regression Logistic Regression Predictions Using Sigmoid Exercise: Linear Decision Boundary of Logistic Regression Summary: Details of Logistic Regression and Feature Extractor Quiz: Details of Logistic Regression and Feature Extraction

(Challenge) Logistic Regression Model and Coefficients

Project

The Bias-Variance Trade-Off

Map out the steps for regularization, cross-validation, and gradient descent in logistic regression.

Introduction: The Bias-Variance Trade-Off Estimating the Coefficients and Intercepts of Logistic Regression Gradient Descent to Find Optimal Parameter Values Exercise: Using Gradient Descent to Minimize a Cost Function Assumptions of Logistic Regression The Motivation for Regularization Exercise: Generating and Modeling Synthetic Classification Data Lasso (L1) and Ridge (L2) Regularization Cross-Validation Exercise: The Synthetic Data Classification Problem Options for Logistic Regression in scikit-learn Scaling Data, Pipelines, and Interaction Features in Scikit-Learn Summary: The Bias-Variance Trade-Off Quiz: The Bias-Variance Trade-Off

(Challenge) Cross-Validation and Feature Engineering

Project

Decision Trees and Random Forests

13 Lessons

Tackle decision trees and random forests to enhance predictive modeling and handle non-linear data.

(Challenge) Cross-Validation Grid Search with Random Forest

Project

Gradient Boosting, XGBoost, and SHAP Values

12 Lessons

Master advanced techniques in gradient boosting, XGBoost, and SHAP values for model performance and interpretation.

(Challenge) XGBoost and SHAP Explanation for Case Study Data

Project

Test Set Analysis, Financial Insights, and Delivery to the Client

10 Lessons

Learn how to use test set analysis for model evaluation, financial insights, and client delivery.

(Challenge) Deriving Financial Insights

Project

Appendix

1 Lesson

Create a Jupyter Notebook locally with recommended hardware, software, and Anaconda.

Course Author

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Anthony Walker

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Emma Bostian 🐞

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