Meta - Python Data Analytics
- Offered byCoursera
Python Data Analytics at Coursera Overview
Duration | 28 hours |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Beginner |
Official Website | Explore Free Course  |
Credential | Certificate |
Python Data Analytics at Coursera Highlights
- Earn a certificate of completion
- Add to your LinkedIn profile
- 17 assignments
Python Data Analytics at Coursera Course details
- 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. 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.
Python Data Analytics at Coursera Curriculum
Introduction to Python
Introduction to the Program
Course Introduction Video
Instructor Introduction Video
Introduction: Introduction to Python
Approaching Data Analysis with the OSEMN Framework
Why Python for Data Analysis
Jupyter Notebook: Where We Write Our Code
Basics of Using Jupyter Notebook
Using Jupyter Notebook on Coursera
What Does a Variable Mean in Python?
Variable Types
Working with Types in Python
Reviewing Variables in Python Activity
Lists & Tuples
Reviewing Lists & Tuples Activity
Dictionaries
Reviewing Dictionaries Activity
Booleans in Python
Reviewing Using Booleans Activity
Conditional Statements
Reviewing Using Conditionals Activity
For Loops
More Control Over Control Flow
Reviewing Control Flow Activity
Functions are Little Machines
Built-in Python Functions
Writing Our Own Functions
Reviewing Writing Functions Activity
Weekly Review: Introduction to Python
Course Syllabus
How to be Successful in this Program
New Reading
New Reading
Other Python Data Structures
Common Built-in Python Functions
Practice Quiz: Python for Data Analysis
Knowledge Check on Variables
Knowledge Check on Variable Types
Knowledge Check on Conditionals
Knowledge Check on Control Flow
Knowledge Check on Built-in Functions
Graded Quiz: Introduction to Python
Activity: Example of a Typical Notebook on Coursera
Activity: Variables in Python
Activity: Using Lists & Tuples
Activity: Using Dictionaries
Activity: Using Booleans
Activity: Using Conditionals
Activity: Using Iterators
Activity: Control Flow with Data Structures
Activity: Writing Functions
Meet and Greet
Obtaining and Scrubbing Data with Pandas
Introduction: Obtaining and Scrubbing Data with Pandas
Introduction to Libraries
What is Pandas?
Working with Pandas Series & DataFrames
Reviewing Pandas Activity
Subsets with Pandas
Reviewing Selective Subsets Activity
What is Scrubbing?
Removing Data
Reviewing Removing Data Activity
Modifying Values
Replacing Values
Reviewing Replacing Values Activity
Weekly Review: Obtaining and Scrubbing Data with Python
Knowledge Check on Libraries
Knowledge Check on Pandas
Graded Quiz: Obtaining and Scrubbing Data with Pandas
Activity: Using Pandas
Activity: Selective Subsets
Activity: Removing Data
Activity: Modifying and Replacing Values
Exploring Data with Python
Introduction: Exploring Data with Python
Why Exploration?
Exploring Relates to Scrubbing
Exploration: Basic Statistics
Exploration: Filtering Data
Reviewing Basic Exploration Activity
A Picture is Worth a Thousand Words
Introduction to the Purpose of Visualizations
Types of Exploratory Visualizations: Distributions
Types of Exploratory Visualizations: Category
Types of Exploratory Visualizations: Relationship
Using Pandas and Matplotlib to Create Visualizations
Reviewing Creating Visualizations Activity
Understanding Visualizations for Exploration
Reviewing Exploring with Visualization Activity
Where Aggregations Help Us Understand Data
Working with Groups in Pandas
Reviewing Aggregations Activity
Multivariate Visualizations
Introducing Seaborn Visualization Library
Reviewing Seaborn Activity
Seaborn Multivariate Visualizations
Reviewing Multivariate Visualizations Activity
Weekly Review: Exploring Data with Python
New Reading
Knowledge Check on Exploration
Knowledge Check on Basic Statistics
Knowledge Check on Exploratory Visualizations
Graded Quiz: Exploring Data with Python
Activity: Basic Exploration
Activity: Creating Visualizations
Activity: Exploring With Visualizations
Activity: Aggregations
Activity: Using Seaborn
Activity: Using Seaborn for Multivariate Visualizations
Modeling and Interpreting Data with Python
Introduction: Modeling and Interpreting Data with Python
Modeling & Interpreting Data
Overview of Modeling
Modeling with Python
Overview of Interpreting
Interpreting Model Results
Exploratory vs. Explanatory Visualizations
Creating Explanatory Visualizations
OSEMN: Tying It All Together
Weekly Review: Modeling and Interpreting Data with Python
Course Conclusion & Congratulations
New Reading
Knowledge Check on Modeling & Interpreting
Knowledge Check on Interpreting
Graded Quiz: Modeling and Interpreting Data with Python
Graded Activity: Full OSEMN
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