Understanding and Visualizing Data with Python
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Understanding and Visualizing Data with Python at Coursera Overview
Duration | 20 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Beginner |
Official Website | Explore Free Course  |
Credential | Certificate |
Understanding and Visualizing Data with Python at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 1 of 3 in the Statistics with Python Specialization
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Beginner Level High school algebra
- Approx. 20 hours to complete
- English Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, Korean, German, Russian, English, Spanish
Understanding and Visualizing Data with Python at Coursera Course details
- 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.
- At the end of each week, learners will apply the statistical concepts they?ve learned using Python within the course environment. During these lab-based sessions, learners will discover the different uses of Python as a tool, including the Numpy, Pandas, Statsmodels, Matplotlib, and Seaborn libraries. Tutorial videos are provided to walk learners through the creation of visualizations and data management, all within Python. This course utilizes the Jupyter Notebook environment within Coursera.
Understanding and Visualizing Data with Python at Coursera Curriculum
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
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