Understanding and Visualizing Data with Python
- 4.7
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Course Summary
Learn how to effectively visualize data with this course that covers the principles and techniques of data visualization. Discover the best practices for designing effective charts and graphs and how to communicate complex data through visual representations.Key Learning Points
- Understand the principles and techniques of data visualization
- Learn how to design effective charts and graphs
- Communicate complex data through visual representations
Related Topics for further study
Learning Outcomes
- Understand the principles and techniques of data visualization
- Design effective charts and graphs
- Communicate complex data through visual representations
Prerequisites or good to have knowledge before taking this course
- Basic understanding of data analysis
- Comfortable with basic statistics and probability concepts
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
Similar Courses
- Data Visualization for Storytelling
- Data Visualization and Communication with Tableau
- Applied Data Visualization with Python
Related Education Paths
Related Books
Description
In this course, learners will be introduced to the field of statistics, including where data come from, study design, data management, and exploring and visualizing data. Learners will identify different types of data, and learn how to visualize, analyze, and interpret summaries for both univariate and multivariate data. Learners will also be introduced to the differences between probability and non-probability sampling from larger populations, the idea of how sample estimates vary, and how inferences can be made about larger populations based on probability sampling.
Knowledge
- Properly identify various data types and understand the different uses for each
- Create data visualizations and numerical summaries with Python
- Communicate statistical ideas clearly and concisely to a broad audience
- Identify appropriate analytic techniques for probability and non-probability samples
Outline
- WEEK 1 - INTRODUCTION TO DATA
- Welcome to the Course!
- Understanding and Visualizing Data Guidelines
- What is Statistics?
- Interview: Perspectives on Statistics in Real Life
- (Cool Stuff in) Data
- Where Do Data Come From?
- Variable Types
- Study Design
- Introduction to Jupyter Notebooks
- Data Types in Python
- Introduction to Libraries and Data Management
- Course Syllabus
- Meet the Course Team!
- About Our Datasets
- Help Us Learn More About You!
- Resource: This is Statistics
- Let's Play with Data!
- Data management and manipulation
- Practice Quiz - Variable Types
- Assessment: Different Data Types
- WEEK 2 - UNIVARIATE DATA
- Categorical Data: Tables, Bar Charts & Pie Charts
- Quantitative Data: Histograms
- Quantitative Data: Numerical Summaries
- Standard Score (Empirical Rule)
- Quantitative Data: Boxplots
- Demo: Interactive Histogram & Boxplot
- Important Python Libraries
- Tables, Histograms, Boxplots in Python
- What's Going on in This Graph?
- Modern Infographics
- Practice Quiz: Summarizing Graphs in Words
- Assessment: Numerical Summaries
- Python Assessment: Univariate Analysis
- WEEK 3 - MULTIVARIATE DATA
- Looking at Associations with Multivariate Categorical Data
- Looking at Associations with Multivariate Quantitative Data
- Demo: Interactive Scatterplot
- Introduction to Pizza Assignment
- Multivariate Data Selection
- Multivariate Distributions
- Unit Testing
- Pitfall: Simpson's Paradox
- Modern Ways to Visualize Data
- Pizza Study Design Assignment Instructions
- Practice Quiz: Multivariate Data
- Python Assessment: Multivariate Analysis
- WEEK 4 - POPULATIONS AND SAMPLES
- Sampling from Well-Defined Populations
- Probability Sampling: Part I
- Probability Sampling: Part II
- Non-Probability Sampling: Part I
- Non-Probability Sampling: Part II
- Sampling Variance & Sampling Distributions: Part I
- Sampling Variance & Sampling Distributions: Part II
- Demo: Interactive Sampling Distribution
- Beyond Means: Sampling Distributions of Other Common Statistics
- Making Population Inference Based on Only One Sample
- Inference for Non-Probability Samples
- Complex Samples
- Sampling from a Biased Population
- Randomness and Reproducibility
- The Empirical Rule of Distribution
- Building on Visualization Concepts
- Potential Pitfalls of Non-Probability Sampling: A Case Study
- Resource: Seeing Theory
- Article: Jerzy Neyman on Population Inference
- Preventing Bad/Biased Samples
- Optional: Deeper Dive Reference
- Course Feedback
- Keep Learning with Michigan Online
- Assessment: Distinguishing Between Probability & Non-Probability Samples
- Generating Random Data and Samples
Summary of User Reviews
The course Understanding and Visualizing Data on Coursera is a highly rated course that teaches students how to effectively visualize data. Many users praise the course for its practicality and comprehensive approach.Key Aspect Users Liked About This Course
practicalityPros from User Reviews
- Comprehensive approach to visualizing data
- Practical examples and exercises
- Engaging lectures by knowledgeable instructors
Cons from User Reviews
- Some users found the course challenging
- Not suitable for beginners with no prior knowledge of data visualization
- Limited interaction with instructors and classmates
Brenda Gunderson
4.7 Raiting
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