Statistics and Clustering in Python

Overview

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This course is the sixth of eight courses. This project provides an in-depth exploration of key Data Science concepts focusing on algorithm design. It enhances essential mathematics, statistics, and programming skills required for common data analysis tasks. You will engage in a variety of mathematical and programming exercises while completing a data clustering project using the K-means algorithm on a provided dataset.

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.