Foundations of Data Science: K-Means Clustering in Python (Coursera)

Organisations all around the world are using data to predict behaviours and extract valuable real-world insights to inform decisions. Managing and analysing big data has become an essential part of modern finance, retail, marketing, social science, development and research, medicine and government.
You will consider these fundamental concepts on an example data clustering task, and you will use this example to learn basic programming skills that are necessary for mastering Data Science techniques. During the course, you will be asked to do a series of mathematical and programming exercises and a small data clustering project for a given dataset.

What You Will Learn

• Define and explain the key concepts of data clustering
• Demonstrate understanding of the key constructs and features of the Python language.
• Implement in Python the principle steps of the K-means algorithm.
• Design and execute a whole data clustering workflow and interpret the outputs.

Syllabus

WEEK 1
Foundations of Data Science: K-Means Clustering in Python
This week we will introduce you to the course and to the team who will be guiding you through the course over the next 5 weeks. The aim of this week's material is to gently introduce you to Data Science through some real-world examples of where Data Science is used, and also by highlighting some of the main concepts involved.

WEEK 2
Means and Deviations in Mathematics and Python

WEEK 3
Moving from One to Two Dimensional DataWeek 4: Introducing Pandas and Using K-Means to Analyse Data

WEEK 4
Introducing Pandas and Using K-Means to Analyse Data

WEEK 5
A Data Clustering Project