Statistics and Clustering in Python

Instructor: Robert Zimmer

Beginner Level • 2 weeks to complete at 10 hours a week • Flexible Schedule

What You'll Learn

  • In this course you will engage in a variety of mathematical and programming exercises while completing a data clustering project.

Skills You'll Gain

Matplotlib
Pandas (Python Package)
Statistical Analysis
Probability & Statistics
Unsupervised Learning
Statistics
Data Science
Jupyter
Data Manipulation
NumPy
Descriptive Statistics
Python Programming
Data Visualization Software
Machine Learning Algorithms
Data analysis

Shareable Certificate

Earn a shareable certificate to add to your LinkedIn profile

Outcomes

  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 4 modules in this course

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.

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

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.

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.