Course Structure
Get an overview of the structure and strengths of this data science course.
We'll cover the following
About this course
This course consists of 9 chapters, 98 lessons, and 7 projects (challenges). A brief explanation of each chapter is provided below:

Introduction: Provides an overview of the course, including the intended audience, prerequisites, and course structure.

Data Exploration and Cleaning: Gets you started with Python and Jupyter notebooks. The chapter then explores the case study dataset and delves into exploratory data analysis, quality assurance, and data cleaning using pandas.

Introduction to scikitlearn and Model Evaluation: Introduces you to the evaluation metrics for binary classification models. Youâ€™ll learn how to build and evaluate binary classification models using scikitlearn.

Details of Logistic Regression and Feature Exploration: Dives deep into logistic regression and feature exploration. Youâ€™ll learn how to generate correlation plots of many features and a response variable and interpret logistic regression as a linear model.

The BiasVariance TradeOff: Explores the foundational machine learning concepts of overfitting, underfitting, and the biasvariance tradeoff by examining how the logistic regression model can be extended to address the overfitting problem.

Decision Trees and Random Forests: Introduces you to treebased machine learning models. Youâ€™ll learn how to train decision trees for machine learning purposes, visualize trained decision trees, and train random forests and visualize the results.

Gradient Boosting, XGBoost, and SHAP Values: Introduces you to two key concepts: gradient boosting and shapley additive explanations (SHAP). Youâ€™ll learn to train XGBoost models and understand how SHAP values can be used to provide individualized explanations for model predictions from any dataset.

Test Set Analysis, Financial Insights, and Delivery to the Client: Presents several techniques for analyzing a model test set for deriving insights into likely model performance in the future. The chapter also describes key elements to consider when delivering and deploying a model, such as the format of delivery and ways to monitor the model as it is being used

Appendix: This chapter contains only one lesson, which is about how to set up the local system environment for this course.
Course strengths
The following benefits for the learners in this course enhance the overall strength of the course, making it an appealing choice for those who wish to improve their skills in data science and machine learning.
Topic  Description 
Data Science Concepts  The data science concepts covered in this course are essential because they provide a foundational understanding of key concepts in data science and machine learning. This understanding is crucial for building effective predictive models. 
Python Packages  The Python packages covered in this course are beneficial because they enable you to effectively use key Python packages such as pandas, Matplotlib, and scikitlearn for data exploration, processing, visualization, and machine learning modeling. 
Data Processing  Learning data processing techniques is beneficial because it helps you to handle large and complex datasets efficiently, reduce errors in analysis, and improve the accuracy of your models. 
Data Visualization  You will gain a solid understanding of data visualization techniques using Matplotlib, a powerful Python library for creating visualizations. You will learn how to create effective visualizations to explore and present data, which is crucial for making informed decisions based on data analysis. 
Machine Learning Models  By mastering this, you'll be able to build predictive machine learning models with scikitlearn and XGBoost. 
Regression Techniques  You can reduce model overfitting by acquiring knowledge of lasso and ridge regression. 
Course Projects  This course provides you with the opportunity to work on an endtoend project based on a realistic dataset and engage in practical exercises. 
Updated Content  The benefit of updated content is that you can gain knowledge and understanding of the latest advancements and techniques in the field of data science and machine learning. 