This series of related courses is designed to help you master current Data Science and Computer Science topics both from a professional and an academic perspective.
Module
Statistics and Clustering in Python
Module information>
This is the sixth of the series of eight courses aimed to give you an understanding of Data Science Clustering in Python.
About this course:
You will learn how to understand key mathematical and statistical concepts pertinent to data clustering. You will access the Python notebook environment and use it to edit and run an existing notebook and compute one-dimensional mean and deviation in Python.
The topics we cover include:
- Introduction to Mathematical Concepts of Data Clustering
- Mean of One-dimensional Lists
- Multidimensional Data Points and Features
- Storing 2D Coordinates in a Single Data Structure
- Using the Pandas Library to Read CSV Files
By the end of this course, you will be able to understand mathematical concepts of k-means algorithm, how to compute the mean and variance, and demonstrate your understanding of the key mathematical and statistical concepts pertinent to data clustering. These are multidimensional mean and deviation, data point, feature, distance metric and outlier.
Other courses in this series are:
- The Data Science Profession - Student View (Goldsmiths)
- What is Data Science? (IBM)
- Tools for Data Science (IBM)
- Problems, Algorithms and Flowcharts (Goldsmiths)
- Python for Data Science, AI Development (IBM)
- Statistics and Clustering in Python (Goldsmiths)
- Data Science Project Capstone: Predicting Bicycle Rental (Goldsmiths)
- Python Project for Data Science (IBM).
