Data Visualisation and Regression with Python

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Data Visualisation and Regression with Python

Instructor: Matt Bushby

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3 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

5 hours to complete

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

5 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Select appropriate plots and improve their presentation to communicate data clearly.
Combine data preparation and plotting in a workflow from raw data to a finished visualisation.
Calculate and interpret linear regression and apply introductory logistic regression.

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There are 3 modules in this course

Use Python to turn prepared data into visualisations and explore relationships through regression. This course introduces different plot types, including line plots and histograms, and helps you choose and refine visualisations that suit your data.

You will then bring together the skills developed across the Specialisation in notebook activities that move from raw data to a completed visualisation. You will identify situations where linear regression may be appropriate, calculate and interpret a linear regression, and explore the application and interpretation of logistic regression. By the end of the course, you will be able to combine data preparation, visualisation and introductory regression techniques in a practical analysis workflow.

This topic you will learn about using plots to visualise your data. You will learn about the different kinds of plots, their uses, and how to improve how plots look. There are many ways to achieve the same outcome, but in this microcredential we look at one method.While there are many ways to generate plots, in this microcredential we will learn the simplest and most general tool which you can transition to very complex analysis as your skills grow.

What's included

1 video1 assignment7 plugins

1 videoTotal 11 minutes

Guidance Video11 minutes

1 assignmentTotal 30 minutes

Topic Quiz30 minutes

7 pluginsTotal 86 minutes

Overview1 minute
1.1 The place of plots15 minutes
1.2 Jupyter notebook activity: Line plots15 minutes
1.3 Jupyter notebook activity: Histograms15 minutes
1.4 Jupyter notebook activity: Other plots15 minutes
1.5 Concept summary15 minutes
Conclusion10 minutes

This topic is about consolidating everything you know and putting it together. You will apply your learning and complete a linear regression and an end-to-end visualisation.

What's included

1 assignment5 plugins

Learners take a synthetic cafe-chain dataset from raw files to finished charts and a prediction. They prepare the data, choose a suitable chart type for four business questions, build the charts with the pandas plot method, calculate and interpret a linear regression, draw the regression line over the measured data, and judge which predictions can be trusted. The result is a notebook that answers an owner's questions with evidence.

What's included

3 readings1 assignment

Instructor

Matt Bushby

Macquarie University

35 Courses34,183 learners

Offered by

Macquarie University

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