Overview

About this course

If data is the new oil, then machine learning is the drill. As companies gain access to ever-increasing quantities of raw data, the ability to deliver state-of-the-art predictive models that support business decision-making becomes more and more valuable.

In this course, you’ll work on an end-to-end project based around a realistic data set and split up into bite-sized practical exercises. This creates a case-study approach that simulates the working conditions you’ll experience in real-world data science projects.

You’ll learn how to use key Python packages, including pandas, Matplotlib, and scikit-learn, and master the process of data exploration and data processing, before moving on to fitting, evaluating, and tuning algorithms such as regularized logistic regression and random forest.

This course will take you through the end-to-end process of exploring data and delivering machine learning models, including XGBoost, SHAP values, algorithmic fairness, and the ethical concerns of deploying a model in the real world.

By the end of this data science projects with Python course, you’ll have the skills, understanding, and confidence to build your own machine learning models and gain insights from real data.

Intended audience

This course is for anyone who wants to get started with data science and machine learning. If you’re keen to advance your career by using data analysis and predictive modeling to generate business insights, then this course is the perfect place to begin.

Approach

Data Science Projects with Python takes a practical case study approach to learning and teaching concepts in the context of a real-world dataset. Clear explanations will deepen your knowledge, while engaging exercises and challenging activities will reinforce it with hands-on practice.

Prerequisites for this course

It is recommended that you have basic experience with programming in Python or another similar language (R, Matlab, C, etc.) to take this course. Additionally, knowledge of statistics that would be covered in a basic course, including topics such as probability and linear regression, or a willingness to learn about these on your own while reading this course would be useful.