Python and Pandas Foundations for Data Analytics

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Python and Pandas Foundations for Data Analytics

Instructor: Matt Bushby

Included with Learn more

3 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

6 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

6 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Explain how Python programming differs from working with spreadsheets and run simple code in Jupyter notebooks.
Load data into Pandas and explain the relationship between DataFrames, Series and indexes.
Generate and interpret summary statistics from tabular data.

Skills you'll gain

Tools you'll learn

Details to know

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There are 3 modules in this course

Learn how to move from spreadsheets to Python for working with tabular data. This course introduces Python code through Jupyter notebook activities in GitHub Codespaces, helping you understand how a programming workflow differs from working in a spreadsheet.

You will then work with Pandas DataFrames and Series, explore the role of the index, and load data from common file formats. Through practical notebook activities, you will learn how these structures relate to each other and generate summary statistics to understand the data they contain. By the end of the course, you will be able to write and run simple Python code, load tabular data into Pandas, and interpret basic summaries of a dataset.

This topic, you will start to learn to recognise Python code and the differences between spreadsheets and programming languages. Before you start, make sure you have signed up to GitHub Codespaces.

What's included

1 video1 assignment7 plugins

1 videoTotal 11 minutes

Guidance Video11 minutes

1 assignmentTotal 30 minutes

Topic Quiz30 minutes

7 pluginsTotal 95 minutes

Overview5 minutes
1.1 Introduction to Python15 minutes
1.2 Python: Try it15 minutes
1.3 Jupyter notebook activity: Writing and running python15 minutes
1.4 Jupyter notebook activity: Programming concepts15 minutes
1.5 Concept summary15 minutes
Conclusion15 minutes

This topic, you will learn about Data Frames, which provide a structured and intuitive way to handle, manipulate, and analyse data. They also integrate with libraries like Pandas that allow for efficiency when working with data. They are extremely versatile and support a range of different tasks.

What's included

1 assignment7 plugins

Learners load a synthetic city bike-share dataset from three common file formats (CSV, Excel, and a SQL database) into DataFrames, work with Series and the index, generate and interpret summary statistics, and explain how Python differs from a spreadsheet for this kind of work. The result is a notebook that profiles the data for a non-technical manager.

What's included

3 readings1 assignment

Instructor

Matt Bushby

Macquarie University

37 Courses34,324 learners

Offered by

Macquarie University

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