Introduction to Data Science and Supervised Machine Learning

Course

This course is for data analysts who want to apply machine learning. You will begin with an introduction to the fundamental concepts and processes that differentiate data science from other fields. Then you will dive deeper into machine learning algorithms, including underlying math concepts like gradient descent, ensemble models like random forests, and an introduction to neural networks and deep learning. Once you can harness these algorithms, you will apply model evaluation techniques for both accuracy and fairness. The course culminates with advice for effectively communicating findings to stakeholders. Your final project will involve building a machine learning model and writing a blog post about your analysis, to build your data science portfolio.

Advanced
2 weeks
Last Updated January 20, 2025

Skills you'll learn:

Blog postsData storytelling

Prerequisites:

NumPyGithubLinear regressionBasic calculusPandas

Advanced

2 weeks

Last Updated January 20, 2025

Skills you'll learn:

Blog posts • Data storytelling • CRISP-DM • AI algorithms in Python

Prerequisites:

NumPy • Github • Linear regression

Course Lessons

Lesson 1

The Data Science Process

Learn about the basics of data science and machine learning. Walk through the CRISP-DM process and how you can apply it to many data science problems.

Lesson 2

Supervised Machine Learning Algorithms

Explore supervised machine learning algorithms: regression, classification, linear models, decision trees, random forests, and neural networks, with interactive exercises in scikit-learn.

Lesson 3

Machine Learning Model Evaluation

Learn why default accuracy metrics can be misleading with real-world datasets, and the alternative metrics you can utilize to communicate the benefits and limitations of your models.

Lesson 4

Model Interpretability and Fairness

To apply AI ethically and transparently, you need to understand how your models make decisions and whether their impacts are fair. Apply feature importances, SHAP values, and the Aequitas framework

Lesson 5

Communicating to Stakeholders

Create a GitHub repository and Medium blog post to communicate your findings

Lesson 6 • Project

Project: Data Science Blog Post

Complete the CRISP-DM process with a dataset of your choice, and deploy your findings in the format of a blog post.

Taught By The Best

David Elliott

Data Scientist, Data Engineer

David Elliott is both a data scientist and a data engineer at a small data management company. He has extensive experience in education, both as an instructor and as a curriculum developer.

Antje Muntzinger

Professor of Computer Vision

Antje Muntzinger is a professor of computer vision at Stuttgart University of Applied Sciences, where she teaches AI and applied mathematics. Previously, she was an algorithm developer and tech lead for sensor fusion in the field of automated driving at Mercedes. She holds a PhD in engineering and a diploma in mathematics.

Nathan Klarer

CEO

Nathan is an expert in the scientific fields of bioengineering-bioinformatics and machine learning. He is responsible for building multiple successful enterprises that have driven tens of millions of dollars of investment globally.

Joshua Bernhard

Staff Data Scientist, Marketplace

Josh has been sharing his passion for data for over a decade. He's used data science for work ranging from cancer research to process automation. He recently has found a passion for solving data science problems within marketplace companies.

The Udacity Difference

Combine technology training for employees with industry experts, mentors, and projects, for critical thinking that pushes innovation. Our proven upskilling system goes after success—relentlessly.

Demonstrate proficiency with practical projects

Projects are based on real-world scenarios and challenges, allowing you to apply the skills you learn to practical situations, while giving you real hands-on experience.

Gain proven experience
Retain knowledge longer
Apply new skills immediately

Top-tier services to ensure learner success

Reviewers provide timely and constructive feedback on your project submissions, highlighting areas of improvement and offering practical tips to enhance your work.