Statistics and Clustering in Python

IBM and University of London via Coursera

Coursera based on 11 ratings

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Overview

The course “Statistics and Clustering in Python” introduces learners to fundamental statistical concepts and unsupervised machine learning techniques using the Python programming language. Designed for beginners and intermediate learners alike, the course focuses on understanding how to analyze data, identify patterns, and group similar data points through clustering methods. Starting with essential statistics—including measures of central tendency, variability, probability distributions, hypothesis testing, and correlation—participants will gain a solid foundation to interpret and summarize data effectively. The course then transitions into...

Syllabus

Week 1: Means and Deviations in Mathematics and Python

This week, we will delve into the core concepts of mean, variance, and other basic statistics, laying the groundwork for a solid understanding of data analysis principles. Through hands-on exercises and demonstrations in Python and Jupyter notebooks, we'll explore practical techniques for calculating and interpreting statistical measures.

Week 2: Moving from One to Two Dimensional Data

This week, we will explore mathematics for multidimensional data. You will also learn how to work with multidimensional data in Python.

Week 3: Introducing Pandas and Using K-Means to Analyse Data

This week, we will explore data manipulation and visualisation with Python's Pandas library. We will dive deep into the versatile capabilities of Pandas, empowering you to efficiently manipulate, analyse, and interpret data.

Week 4: A Data Clustering Project

This week, we will embark on a journey through the fascinating world of unsupervised learning, where patterns emerge from data without explicit guidance. You will implement the K-means algorithm to solve a real-world problem.