Introduction: Python for Data Science

Get introduced to our topic for this chapter: the fundamental aspects of Python for data science.

We'll cover the following

Chapter overview
Python for predictive modeling
Python for data science
- Indexing and the slice operator
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Chapter overview

In this chapter, you will take your first steps with Python and Jupyter notebooks, some of the most common tools data scientists use. You’ll then take your first look at the dataset for the case study project that will form the core of this course. You will begin to develop an intuition for quality assurance checks that data needs to be put through before model building. By the end of the chapter, you will be able to use pandas, the top package for wrangling tabular data in Python, to do exploratory data analysis, quality assurance, and data cleaning.

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Introduction

Data Exploration and Cleaning

(Challenge) Exploring Remaining Financial Features in Dataset

Introduction to scikit-learn and Model Evaluation

(Challenge) Logistic Regression and Precision-Recall Curve

Details of Logistic Regression and Feature Extraction

(Challenge) Logistic Regression Model and Coefficients

The Bias-Variance Trade-Off

(Challenge) Cross-Validation and Feature Engineering

Decision Trees and Random Forests

(Challenge) Cross-Validation Grid Search with Random Forest

Gradient Boosting, XGBoost, and SHAP Values

(Challenge) XGBoost and SHAP Explanation for Case Study Data

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

(Challenge) Deriving Financial Insights

Appendix

Introduction: Python for Data Science

Chapter overview