Statistics and Clustering in Python

Statistics and Clustering in Python

This course is part of Data Science Foundations Specialization

Instructor: Robert Zimmer

2,668 already enrolled

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4 modules

Gain insight into a topic and learn the fundamentals.

21 reviews

Beginner level

No prior experience required

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

21 reviews

Beginner level

No prior experience required

2 weeks to complete

at 10 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.

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

34 assignments¹

AI Graded see disclaimer

Taught in English

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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
Earn a shareable career certificate

There are 4 modules in this course

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.

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

10 videos Total 38 minutes

Introduction to this course in the specialisation 2 minutes
Introduction to Mathematical Concepts of Data Clustering 2 minutes
Mean of One Dimensional Lists 2 minutes
Variance and Standard Deviation 4 minutes
Jupyter Notebooks 6 minutes
Variables 4 minutes
Lists 5 minutes
Computing the Mean 3 minutes
Better Lists: NumPy 4 minutes
Computing the Standard Deviation 6 minutes

7 readings Total 75 minutes

Course Syllabus 10 minutes
Getting ready for this course 10 minutes
Population vs Sample, Bias 10 minutes
Variability, Standard Deviation and Bias 10 minutes
How to back-up your virtual lab work 5 minutes
Python Style Guide 10 minutes
Numpy and Array Creation 20 minutes

10 assignments Total 84 minutes

Population vs Sample – Review Information 10 minutes
Mean of One-Dimensional Lists – Review Information 3 minutes
Variance and Standard Deviation – Review Information 3 minutes
Jupyter Notebooks – Review Information 5 minutes
Variables – Review Information 5 minutes
Lists – Review Information 5 minutes
Computing the Mean – Review Information 3 minutes
Better Lists – Review Information 5 minutes
Computing the Standard Deviation – Review Information 5 minutes
Week 1 Summative Assessment 40 minutes

1 peer review Total 30 minutes

Use Jupyter Notebooks 30 minutes

1 ungraded lab Total 15 minutes

Jupyter Notebook Environment 15 minutes

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

14 videos Total 52 minutes

Multidimensional Data Points and Features 2 minutes
Multidimensional Mean 3 minutes
Dispersion: Multidimensional Variables 3 minutes
Distance Metrics 5 minutes
Normalisation 1 minute
Outliers 1 minute
Basic Plotting 3 minutes
Storing 2D Coordinates in a Single Data Structure 6 minutes
Multidimensional Mean 5 minutes
Adding Graphical Overlays 6 minutes
Calculating the Distance to the Mean 4 minutes
List Comprehension 4 minutes
Normalisation in Python 6 minutes
Outliers and Plotting Normalised Data 3 minutes

10 readings Total 120 minutes

Multidimensional Data Points and Features Recap 10 minutes
Multidimensional Mean Recap 10 minutes
Multidimensional Variables Recap 10 minutes
Distance Metrics Recap 10 minutes
Normalisation Recap 10 minutes
Note on Matplotlib 10 minutes
Matplotlib Scatter Plot Documentation 20 minutes
Matplotlib Patches Documentation 10 minutes
List Comprehension Documentation 20 minutes
Errata 10 minutes

14 assignments Total 110 minutes

Multidimensional Data Points and Features – Review Information 3 minutes
Multidimensional Mean – Review Information 3 minutes
Dispersion: Multidimensional Variables – Review Information 5 minutes
Distance Metrics – Review Information 10 minutes
Normalisation – Review Information 5 minutes
Outliers – Review Information 5 minutes
Basic Plotting – Review Information 6 minutes
Storing 2D Coordinates – Review Information 5 minutes
Multidimensional Mean – Review Information 5 minutes
Adding Graphical Overlays – Review Information 10 minutes
Calculating Distance – Review Information 5 minutes
Normalisation in Python – Review Information 5 minutes
Outliers – Review Information 3 minutes
Week 2 Summative Assessment 40 minutes

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

6 videos Total 36 minutes

Using the Pandas Library to Read csv Files 5 minutes
Sorting and Filtering Data Using Pandas 8 minutes
Labelling Points on a Graph 4 minutes
Labelling all the Points on a Graph 3 minutes
Eyeballing the Data 6 minutes
Using K-Means to Interpret the Data 9 minutes

6 readings Total 60 minutes

Code Resources 5 minutes
Pandas Read_CSV Function 15 minutes
More Pandas Library Documentation 10 minutes
The Pyplot Text Function 10 minutes
For Loops in Python 10 minutes
Documentation for sklearn.cluster.KMeans 10 minutes

7 assignments Total 68 minutes

Using the Pandas Library to Read csv Files – Review Information 5 minutes
Sorting and Filtering Data Using Pandas – Review Information 5 minutes
Labelling Points on a Graph – Review Information 5 minutes
Labelling all the Points on a Graph – Review Information 5 minutes
Eyeballing the Data – Review Information 5 minutes
Using K-Means to Interpret the Data – Review Information 3 minutes
Week 3 Summative Assessment 40 minutes

1 peer review Total 60 minutes

Create a Labelled Plot of the Happiness Data 60 minutes

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

8 videos Total 28 minutes

Can a Machine Detect Fake Notes? 2 minutes
Working for a Client 5 minutes
How to Organize Work on Your Project 4 minutes
Dealing With Difficulties 3 minutes
No Data no Data Science: Introduction of the Dataset 5 minutes
Modelling 5 minutes
Presenting the Project Results 3 minutes
End of course 1 minute

3 readings Total 25 minutes

Week 4 Code Resource – the Dataset for our Project 10 minutes
Saving plt.scatter Outputs as Figures 10 minutes
Additional Recommended Reading for Week 4 5 minutes

3 assignments Total 22 minutes

How Would You Help? – Review Information 2 minutes
Python – Review Information 5 minutes
Week 4 Summative Assessment 15 minutes

3 peer reviews Total 180 minutes

Exploratory Data Analysis 60 minutes
Clustering 60 minutes
Your Report 60 minutes

5 discussion prompts Total 70 minutes

What Is Required to Train a Machine to Detect Fake Notes? 10 minutes
Your Project Plan 30 minutes
Self-reflection 10 minutes
Tips for Other Learners 10 minutes
Do You have Data Science Plans? 10 minutes

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Instructor

Robert Zimmer

University of London

5 Courses 19,102 learners

Offered by

University of London

IBM

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To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

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