University of London
IBM
Statistics and Clustering in Python
University of London
IBM

Statistics and Clustering in Python

Robert Zimmer

Instructor: Robert Zimmer

Sponsored by Google People Development

Gain insight into a topic and learn the fundamentals.
Beginner level
No prior experience required
17 hours to complete
3 weeks at 5 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Beginner level
No prior experience required
17 hours to complete
3 weeks at 5 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

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

Details to know

Shareable certificate

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Assessments

34 assignments

Taught in English
Recently updated!

October 2024

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This course is part of the Data Science Foundations Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
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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.

What's included

10 videos7 readings10 assignments1 peer review1 ungraded lab

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

What's included

14 videos10 readings14 assignments

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.

What's included

6 videos6 readings7 assignments1 peer review

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.

What's included

8 videos3 readings3 assignments3 peer reviews5 discussion prompts

Instructor

Robert Zimmer
University of London
5 Courses5,148 learners

Offered by

IBM

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