Understanding And Visualizing Data With Python Class Centralunderstand

Lecturer IV and Research Fellow Understanding and Visualizing Data with Python What You'll Learn - 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 About 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. 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. What You'll Earn - Certificate of Completion: - Certificates of completion acknowledge knowledge acquired upon completion of a non-credit course or program.

Welcome Message Welcome to Understanding and Visualizing Data with Python, a capstone course part of the Python for Everybody series that brings together the full range of skills developed through learning Python for data work. In this course, you will retrieve, process, analyze, and visualize real-world data using Python 3. You will begin with guided visualizations and progress to designing your own data-driven project, building practical experience in data analysis and visualization workflows. This abbreviated syllabus description was created with the help of AI tools and reviewed by staff.

The full syllabus is available to those who enroll in the course. Course Schedule Module 1: Introduction to Data - Video: Welcome to the Course! - Video: Understanding and Visualizing Data Guidelines - Reading: Syllabus - Reading: Meet the Course Team! - Reading: About Our Datasets - Reading: Help Us Learn More About You! - Video: What is Statistics? - Video: Interview: Perspectives on Statistics in Real Life - Reading: Resource: This is Statistics - Video: (Cool Stuff in) Data - Reading: Let's Play with Data!

Video: Variable Types - Graded Assignment: Practice Quiz - Variable Types - Video: Study Design - Reading: Data management and manipulation - Ungraded Lab: Introduction to Jupyter Notebooks - Video: Optional: Introduction to Jupyter Notebooks - Ungraded Lab: Data Types in Python - Video: Optional: Data Types in Python - Ungraded Lab: Introduction to Libraries and Data Management - Video: Optional: Introduction to Libraries and Data Management - Ungraded Lab: Continued Data Basics - Ungraded Lab: Deeper Dive into Data Management & Python Resources - Graded: Assessment: Different Data Types Module 2: Univariate Data - Video: Categorical Data: Tables, Bar Charts & Pie Charts - Video: Quantitative Data: Histograms - Video: Quantitative Data: Numerical Summaries - Video: Standard Score (Empirical Rule) - Video: Quantitative Data: Boxplots - Video: Demo: Interactive Histogram & Boxplot - Graded Assignment: Practice Quiz: Summarizing Graphs in Words - Discussion Prompt: What is There?

Reading: Modern Infographics - Ungraded Lab: Python Libraries and an Introduction to Graphing - Ungraded Lab: Tables, Histograms, and Boxplots in Python - Reading: Optional: Link to a Graphics Gallery - Ungraded Lab: Case Study of Univariate Data Analyses using NHANES Data - Ungraded Lab: More Practice: Univariate Analysis Using NHANES - Ungraded Lab: More Practice: Univariate Analysis Using NHANES (Solutions) - Ungraded Lab: Univariate Analysis: Assessment Notebook - Graded: Assessment: Numerical Summaries - Graded: Python Assessment: Univariate Analysis Module 3: Multivariate Data - Video: Looking at Associations with Multivariate Categorical Data - Video: Looking at Associations with Multivariate Quantitative Data - Graded Assignment: Practice Quiz: Multivariate Data - Video: Demo: Interactive Scatterplot - Reading: Pitfall: Simpson's Paradox - Discussion Prompt: Discussion: Find Your Own Example - Reading: Modern Ways to Visualize Data - Video: Introduction to Pizza Assignment - Ungraded Lab: Multivariate Data Selection - Ungraded Lab: Multivariate Distributions - Ungraded Lab: Unit Testing - Ungraded Lab: Case Study of Multivariate Analyses in NHANES - Ungraded Lab: More Practice: Multivariate Analyses with NHANES - Ungraded Lab: Multivariate Analysis: Assessment Notebook - Graded: Pizza Study Design Assignment - Graded: Python Assessment: Multivariate Analysis Module 4: Populations and Samples - Reading: Building on Visualization Concepts - Video: Sampling from Well-Defined Populations - Video: Probability Sampling: Part I - Reading: More on SRS Probabilities of Inclusion - Video: Probability Sampling: Part II - Video: Non-Probability Sampling: Part I - Video: Non-Probability Sampling: Part II - Reading: Potential Pitfalls of Non-Probability Sampling: A Case Study - Video: Sampling Variance & Sampling Distributions: Part I - Reading: Cluster Sampling and Design Effects - Video: Sampling Variance & Sampling Distributions: Part II - Video: Demo: Interactive Sampling Distribution - Video: Beyond Means: Sampling Distributions of Other Common Statistics - Video: Making Population Inference Based on Only One Sample - Reading: Resource: Seeing Theory - Reading: Article: Jerzy Neyman on Population Inference - Reading: Preventing Bad/Biased Samples - Video: Inference for Non-Probability Samples - Video: Complex Samples - Reading: Optional: Deeper Dive Reference - Ungraded Lab: Sampling from a Biased Population - Ungraded Lab: Randomness and Reproducibility - Ungraded Lab: The Empirical Rule of Distribution - Ungraded Lab: Illustrating sampling distributions using NHANES - Reading: Course Feedback - Reading: Keep Learning with Michigan Online - Graded: Assessment: Distinguishing Between Probability & Non-Probability Samples - Graded: Generating Random Data and Samples Grading Policy This course includes quizzes, written peer-reviewed work, and programming assessments completed in a Jupyter Notebook environment.

Some assessments may not be mobile-friendly. All assignments are worth between 10% and 20% of your final grade. Professor Research Associate Professor Course content developed by U-M faculty and managed by the university. Faculty titles and affiliations are updated periodically. Beginner Level High school algebra

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