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What you'll 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.

Skills you'll gain

Data Visualization

Statistics

Pandas (Python Package)

Probability & Statistics

Unsupervised Learning

Data Manipulation

Machine Learning Algorithms

NumPy

Data Science

Data Analysis

Matplotlib

Python Programming

Descriptive Statistics

There are 5 modules in this course

This MOOC, designed by an academic team from Goldsmiths, University of London, will quickly introduce you to the core concepts of Data Science to prepare you for intermediate and advanced Data Science courses. It focuses on the basic mathematics, statistics and programming skills that are necessary for typical data analysis tasks. 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.

Week 2: Means and Deviations in Mathematics and Python

Week 3: Moving from One to Two Dimensional Data

Week 4: Introducing Pandas and Using K-Means to Analyse Data

Week 5: A Data Clustering Project