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.
Statistics and Clustering in Python
Statistics and Clustering in Python
This course is part of Data Science Foundations Specialization
Instructor: Robert Zimmer
Sponsored by ARS SCINet/AI-COE
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
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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.
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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
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