Coursera

Python Data Analytics (Coursera)

Offered by Meta,

This course introduces the use of the Python programming language to manipulate datasets as an alternative to spreadsheets. You will follow the OSEMN framework of data analysis to pull, clean, manipulate, and interpret data all while learning foundational programming principles and basic Python functions. You will be introduced to the Python library, Pandas, and how you can use it to obtain, scrub, explore, and visualize data.

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By the end of this course you will be able to:
• Use Python to construct loops and basic data structures
• Sort, query, and structure data in Pandas, the Python library
• Create data visualizations with Python libraries
• Model and interpret data using Python
This course is designed for people who want to learn the basics of using Python to sort and structure data for data analysis.
You don't need marketing or data analysis experience, but should have basic internet navigation skills and be eager to participate. Ideally, you have already completed course 1: Marketing Analytics Foundation, course 2: Introduction to Data Analytics, and course 3: Data Analysis with Spreadsheets and SQL.
This course is part of the Meta Marketing Analytics Professional Certificate.

Syllabus

Introduction to Python
This week you will be introduced to Python and how it can be used in data analytics. You will learn basic programming principles such as variables and variable types using Python. You’ll also delve into basic Python statements such as Booleans and conditional statements.

Obtaining and Scrubbing Data with Pandas
This week is focused on using a Python library called Pandas. You will learn how to use Pandas to load, select, and clean data.

Exploring Data with Python
This week you will further explore and analyze datasets with Python. You will learn how to calculate basic statistics and create data visualizations with Pandas and Matplotlib, another Python library.

Modeling and Interpreting Data with Python
This week you will focus on modeling data with Python and interpreting the model results. You complete a data analytics challenge that applies the knowledge of Python and the application of the OSEMN framework you have gained throughout the course.

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