Understanding and Visualizing Data with Python
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.
Buy Now AED 170.99 + VAT
Monthly Subscription Starting at AED 99 + VAT
- Level Foundation
- المدة 21 ساعات hours
- الطبع بواسطة University of Michigan
-
Offered by
عن
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.الوحدات
Introduction
-
2
Videos
-
4
Readings
2
Videos
- Welcome to the Course!
- Understanding and Visualizing Data Guidelines
4
Readings
- Syllabus
- Meet the Course Team!
- About Our Datasets
- Help Us Learn More About You!
What is Statistics?
-
2
Videos
-
1
Readings
2
Videos
- What is Statistics?
- Interview: Perspectives on Statistics in Real Life
1
Readings
- Resource: This is Statistics
What is Data?
-
4
Videos
-
2
Readings
-
1
Discussion
-
2
Assignment
2
Assignment
- Practice Quiz - Variable Types
- Assessment: Different Data Types
1
Discussions
- Discussion: Three Guiding Questions
4
Videos
- (Cool Stuff in) Data
- Where Do Data Come From?
- Variable Types
- Study Design
2
Readings
- Let's Play with Data!
- Data management and manipulation
Using Python to read data files and explore their contents
-
3
Videos
-
5
Lab
5
Labs
- Introduction to Jupyter Notebooks
- Data Types in Python
- Introduction to Libraries and Data Management
- Continued Data Basics
- Deeper Dive into Data Management & Python Resources
3
Videos
- Optional: Introduction to Jupyter Notebooks
- Optional: Data Types in Python
- Optional: Introduction to Libraries and Data Management
What Can You Do With Univariate Data?
-
6
Videos
-
2
Readings
-
1
Discussion
-
2
Assignment
2
Assignment
- Practice Quiz: Summarizing Graphs in Words
- Assessment: Numerical Summaries
1
Discussions
- What is There? What isn't There?
6
Videos
- 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
2
Readings
- What's Going on in This Graph?
- Modern Infographics
Using Python for analysis of univariate data
-
1
Readings
-
5
Lab
5
Labs
- Python Libraries and an Introduction to Graphing
- Tables, Histograms, and Boxplots in Python
- Case Study of Univariate Data Analyses using NHANES Data
- More Practice: Univariate Analysis Using NHANES
- More Practice: Univariate Analysis Using NHANES (Solutions)
1
Readings
- Optional: Link to a Graphics Gallery
Week 2 Python Assignment
-
1
Lab
-
1
Assignment
1
Assignment
- Python Assessment: Univariate Analysis
1
Labs
- Univariate Analysis: Assessment Notebook
What Can You Do with Multivariate Data?
-
2
Videos
-
1
Assignment
1
Assignment
- Practice Quiz: Multivariate Data
2
Videos
- Looking at Associations with Multivariate Categorical Data
- Looking at Associations with Multivariate Quantitative Data
Try it Out - What Can YOU Do with Data?
-
2
Videos
-
2
Readings
-
1
Discussion
-
1
PeerReview
1
Peer Review
- Pizza Study Design Assignment
1
Discussions
- Discussion: Find Your Own Example
2
Videos
- Demo: Interactive Scatterplot
- Introduction to Pizza Assignment
2
Readings
- Pitfall: Simpson's Paradox
- Modern Ways to Visualize Data
Using Python for analysis of multivariate data
-
5
Lab
5
Labs
- Multivariate Data Selection
- Multivariate Distributions
- Unit Testing
- Case Study of Multivariate Analyses in NHANES
- More Practice: Multivariate Analyses with NHANES
Week 3 Python Assignment
-
1
Lab
-
1
Assignment
1
Assignment
- Python Assessment: Multivariate Analysis
1
Labs
- Multivariate Analysis: Assessment Notebook
Populations vs. Samples
-
5
Videos
-
3
Readings
-
1
Assignment
1
Assignment
- Assessment: Distinguishing Between Probability & Non-Probability Samples
5
Videos
- Sampling from Well-Defined Populations
- Probability Sampling: Part I
- Probability Sampling: Part II
- Non-Probability Sampling: Part I
- Non-Probability Sampling: Part II
3
Readings
- Building on Visualization Concepts
- More on SRS Probabilities of Inclusion
- Potential Pitfalls of Non-Probability Sampling: A Case Study
Probability Samples --> Sampling Distributions
-
4
Videos
-
1
Readings
4
Videos
- Sampling Variance & Sampling Distributions: Part I
- Sampling Variance & Sampling Distributions: Part II
- Demo: Interactive Sampling Distribution
- Beyond Means: Sampling Distributions of Other Common Statistics
1
Readings
- Cluster Sampling and Design Effects
Inference in Practice
-
3
Videos
-
4
Readings
3
Videos
- Making Population Inference Based on Only One Sample
- Inference for Non-Probability Samples
- Complex Samples
4
Readings
- Resource: Seeing Theory
- Article: Jerzy Neyman on Population Inference
- Preventing Bad/Biased Samples
- Optional: Deeper Dive Reference
Using Python
-
4
Lab
4
Labs
- Sampling from a Biased Population
- Randomness and Reproducibility
- The Empirical Rule of Distribution
- Illustrating sampling distributions using NHANES
Python Assessment
-
1
Assignment
1
Assignment
- Generating Random Data and Samples
Course Feedback
-
2
Readings
2
Readings
- Course Feedback
- Keep Learning with Michigan Online
Auto Summary
"Understanding and Visualizing Data with Python" is a comprehensive course perfect for those diving into the realm of Data Science and AI. Guided by an expert instructor from Coursera, this foundation-level course delves into the essentials of statistics and data management, highlighting key concepts such as data origins, study design, and data visualization. Throughout the course, learners will uncover different types of data and develop skills to visualize, analyze, and interpret both univariate and multivariate data. The curriculum emphasizes the distinction between probability and non-probability sampling, and how to make inferences about larger populations through probability sampling. A unique feature of this course is the hands-on application of statistical concepts using Python. Each week, learners engage in lab-based sessions where they leverage powerful Python libraries like Numpy, Pandas, Statsmodels, Matplotlib, and Seaborn within the Jupyter Notebook environment on Coursera. Tutorial videos provide step-by-step guidance in creating visualizations and managing data effectively. Spanning approximately 1260 minutes, this course offers flexible subscription options including Starter, Professional, and Paid plans, catering to a wide range of learners from beginners to those seeking a professional edge. Ideal for aspiring data scientists and AI enthusiasts, this course equips learners with the foundational skills needed to excel in the data-driven world.
Brenda Gunderson
Brady T. West
Kerby Shedden