Python Data Analytics
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Python Data Analytics
This course is part of multiple programs.
Instructor: Victor Geislinger
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What you'll learn
Sort, query, and structure data in Pandas, the Python library
Describe how to model and interpret data using Python
Create basic data visualizations with Python libraries
Skills you'll gain
Tools you'll learn
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There are 5 modules in this course
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.
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.
In this module you will be introduced to Python and how it can be used in data analytics. You will also learn how to use the Jupyter Notebook programming environment.
What's included
8 videos4 readings2 assignments1 programming assignment
8 videos•Total 31 minutes
- Introduction to the Program•6 minutes
- Instructor Introduction Video•2 minutes
- Approaching Data Analysis with the OSEMN Framework•4 minutes
- Why Python for Data Analysis•3 minutes
- Jupyter Notebook: Where We Write Our Code•1 minute
- Basics of Using Jupyter Notebook•7 minutes
- Using Jupyter Notebook on Coursera•3 minutes
- Reviewing Example of a Typical Notebook on Coursera Activity•5 minutes
4 readings•Total 35 minutes
- Course Syllabus•5 minutes
- Join the Meta Marketing Analytics Community or the Meta Data Analyst Community!•10 minutes
- How to be Successful in this Program•10 minutes
- Tips for Using Jupyter Notebook•10 minutes
2 assignments•Total 25 minutes
- Review Your Community Knowledge•10 minutes
- Practice Quiz: Python for Data Analysis•15 minutes
1 programming assignment•Total 30 minutes
- Activity: Example of a Typical Notebook on Coursera•30 minutes
In this module, 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.
What's included
12 videos1 reading3 assignments5 programming assignments
12 videos•Total 82 minutes
- What Does a Variable Mean in Python?•10 minutes
- Variable Types•5 minutes
- Working with Types in Python•7 minutes
- Reviewing Variables in Python Activity•2 minutes
- Lists & Tuples•12 minutes
- Reviewing Lists & Tuples Activity•7 minutes
- Dictionaries•7 minutes
- Reviewing Dictionaries Activity•4 minutes
- Booleans in Python•7 minutes
- Reviewing Using Booleans Activity•6 minutes
- Conditional Statements•11 minutes
- Reviewing Using Conditionals Activity•5 minutes
1 reading•Total 10 minutes
- Other Python Data Structures•10 minutes
3 assignments•Total 80 minutes
- Graded Quiz: Basic Python Concepts•50 minutes
- Knowledge Check on Variable Types•15 minutes
- Knowledge Check on Conditionals•15 minutes
5 programming assignments•Total 150 minutes
- Activity: Variables in Python•30 minutes
- Activity: Using Lists & Tuples•30 minutes
- Activity: Using Dictionaries•30 minutes
- Activity: Using Booleans•30 minutes
- Activity: Using Conditionals•30 minutes
This week is focused on using a Python library called Pandas. You will learn how to use Pandas to load, select, and clean data.
What's included
12 videos1 reading3 assignments4 programming assignments
12 videos•Total 87 minutes
- Introduction to Libraries•8 minutes
- What is Pandas?•8 minutes
- Working with Pandas Series & DataFrames•9 minutes
- Reviewing Pandas Activity•6 minutes
- Subsets with Pandas•9 minutes
- Reviewing Selective Subsets Activity•6 minutes
- What is Scrubbing?•2 minutes
- Removing Data•10 minutes
- Reviewing Removing Data Activity•7 minutes
- Modifying Values•7 minutes
- Replacing Values•5 minutes
- Reviewing Replacing Values Activity•10 minutes
1 reading•Total 10 minutes
- Python Syntax and Dot Notation Reference•10 minutes
3 assignments•Total 80 minutes
- Graded Quiz: Obtaining and Scrubbing Data with Pandas•50 minutes
- Knowledge Check on Libraries•15 minutes
- Knowledge Check on Pandas•15 minutes
4 programming assignments•Total 120 minutes
- Activity: Using Pandas•30 minutes
- Activity: Selective Subsets•30 minutes
- Activity: Removing Data•30 minutes
- Activity: Modifying and Replacing Values•30 minutes
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.
What's included
17 videos4 assignments4 programming assignments
17 videos•Total 101 minutes
- Why Exploration?•2 minutes
- Exploring Relates to Scrubbing•2 minutes
- Exploration: Basic Statistics•11 minutes
- Exploration: Filtering Data•16 minutes
- Reviewing Basic Exploration Activity•6 minutes
- A Picture is Worth a Thousand Words•3 minutes
- Introduction to the Purpose of Visualizations•1 minute
- Types of Exploratory Visualizations: Distributions•3 minutes
- Types of Exploratory Visualizations: Category•2 minutes
- Types of Exploratory Visualizations: Relationship•3 minutes
- Using Pandas and Matplotlib to Create Visualizations•11 minutes
- Reviewing Creating Visualizations Activity•8 minutes
- Understanding Visualizations for Exploration•8 minutes
- Reviewing Exploring with Visualization Activity•6 minutes
- Where Aggregations Help Us Understand Data•2 minutes
- Working with Groups in Pandas•12 minutes
- Reviewing Aggregations Activity•5 minutes
4 assignments•Total 95 minutes
- Graded Quiz: Exploring Data with Python•50 minutes
- Knowledge Check on Exploration•15 minutes
- Knowledge Check on Basic Statistics•15 minutes
- Knowledge Check on Exploratory Visualizations•15 minutes
4 programming assignments•Total 130 minutes
- Activity: Basic Exploration•30 minutes
- Activity: Creating Visualizations•40 minutes
- Activity: Exploring With Visualizations•30 minutes
- Activity: Aggregations•30 minutes
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.
What's included
9 videos3 assignments1 programming assignment1 discussion prompt
9 videos•Total 76 minutes
- Modeling & Interpreting Data•2 minutes
- Overview of Modeling•3 minutes
- Modeling with Python•14 minutes
- Overview of Interpreting•5 minutes
- Interpreting Model Results•8 minutes
- Exploratory vs. Explanatory Visualizations•4 minutes
- Creating Explanatory Visualizations•12 minutes
- OSEMN: Tying It All Together•12 minutes
- Reviewing Full OSEMN Activity•18 minutes
3 assignments•Total 80 minutes
- Graded Quiz: Modeling and Interpreting Data with Python•50 minutes
- Knowledge Check on Modeling & Interpreting•15 minutes
- Knowledge Check on Interpreting•15 minutes
1 programming assignment•Total 90 minutes
- Activity: Full OSEMN•90 minutes
1 discussion prompt•Total 10 minutes
- Share Your Thoughts!•10 minutes
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Reviewed on Nov 21, 2024
very interesting and easy for first time learner , with real world examples
Reviewed on Oct 25, 2023
I liked the content and lessons learned in using Python. However, platform-wise, some assignments were not graded by the grader.
Reviewed on Jul 21, 2025
Not a fan of Python but still a good primer for some basic knowledge
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