UPenn - Data Analysis Using Python
4.3 /5- Offered byCoursera
Data Analysis Using Python at Coursera Overview
Duration | 17 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Beginner |
Official Website | Explore Free Course  |
Credential | Certificate |
Data Analysis Using Python at Coursera Highlights
- Earn a shareable certificate upon completion.
- Flexible deadlines according to your schedule.
Data Analysis Using Python at Coursera Course details
- This course provides an introduction to basic data science techniques using Python. Students are introduced to core concepts like Data Frames and joining data, and learn how to use data analysis libraries like pandas, numpy, and matplotlib. This course provides an overview of loading, inspecting, and querying real-world data, and how to answer basic questions about that data. Students will gain skills in data aggregation and summarization, as well as basic data visualization.
Data Analysis Using Python at Coursera Curriculum
Module 1 : Loading, Querying, & Filtering Data Using the csv Module
Course Introduction
About the Instructor : Brandon Krakowsky
Downloading & Installing Jupyter Notebook
Using Jupyter Notebook
Importing and reading a file using the csv module
Coding demonstration : Analyzing the 500 Greatest Albums of All Time
Coding demonstration : Catching data errors and sorting
Coding demonstration : Calculating max and min
Course Layout & Syllabus
Tips to succeed in this course
Module 1 Resources
Review of dictionaries
Review of lists
Review of loops
Review of functions
Lambda with max and min
Homework 1 - Instructions
Quiz 1 - Loading, Querying, & Filtering Data
Quiz 2 - Catching Errors & Sorting
Module 2 : Loading, Querying, Joining & Filtering Data Using pandas
The pandas module
Loading data
Inspecting data
Querying data
Joining data
Code Along Exercise : Join data
Slicing rows
Querying data using boolean indexing
Code Along Exercise : Dive bar recommendation in Las Vegas
Computations - sum()
Computations - mean()
Other methods
Updating & creating data
Code Along Exercise : Add rating column
Module 2 Resources
Homework 2 - Instructions
Casting Data
Cleaning data & dealing with missing values
Homework 3 - Instructions
Quiz 3 - Loading, Inspecting, & Querying Data
Quiz 4 - Joining & Filtering Data
Module 3 : Summarizing & Visualizing Data
Summarizing groups
The numpy library
Pivot tables
Using an index
Code Along Exercise : Average review count and rating
Aggregate functions
Jupyter notebook magic functions
The matplotlib library
Histograms
Histograms Coding Demonstration : To show distribution of data
Histograms Coding Demonstration : Preparing data
Histograms Coding Demonstration : Setting options for PyPlot
Histograms Coding Demonstration : Displaying the visualization
Scatterplots
Scatterplots Coding Demonstration : To compare data points on different dimensions
Scatterplots Coding Demonstration : Preparing data
Scatterplots Coding Demonstration : Setting options for PyPlot
Scatterplots Coding Demonstration : Displaying the visualization
Module 3 Resources
Homework 4 - Instructions
Bar charts and plotting pivot tables
For reference: Seaborn
Homework 5 - Instructions
Quiz 5 - Summarizing Data
Quiz 6 - Visualizing Data
Data Analysis Using Python at Coursera Admission Process
Important Dates
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