Lecturer IV and Research Investigator
Statistics with Python Using NumPy, Pandas, and SciPy
What You'll Learn
- Use vector operations in NumPy for applied mathematics.
- Visualize and analyze data distributions using NumPy and SciPy.
- Use statistics to describe patterns in data distributions.
- Conduct statistical inference using hypothesis testing with computational methods.
4 Modules
20 Hours
5 hrs per module (approx.)
Rating
About Statistics with Python Using NumPy, Pandas, and SciPy
“Statistics with Python Using NumPy, Pandas, and SciPy” explores how to apply statistical and mathematical techniques to data science problems.
Throughout the first half of the course, you’ll work on reviewing vector dot products, interpreting text as vectors, and matrix multiplication. You’ll also explore the basics of probability, laying the groundwork for statistical analysis. In the second half, you’ll cover how to interpret data distributions, reason about probability, explore the special properties of normal distributions, understand linear relationships in data, and the connection between probability and uncertainty.
This is the third course in the four-course series “Data-Oriented Python Programming and Debugging,” where you’ll work to strengthen your programming capabilities and enhance your problem-solving skills.
Skills You'll Gain
- Bayesian Statistics
- Data Analysis
- Matplotlib (Python Package)
- Pandas (Python Package)
- Python For Data Analysis
- Python (Programming Language)
- Statistical Programming
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 Statistics with Python Using NumPy, Pandas, and SciPy. This course builds applied statistics skills through hands-on data analysis using Python libraries. Learners practice data manipulation, visualization, and statistical inference in real-world contexts.
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: Vector Operations and Text Representation in Data Science
- Video: Welcome to the Course and Specialization
- Reading: Course Syllabus
- Discussion Prompt: Introduce Yourself
- Reading: Help Us Learn About You
- Video: Welcome to 'Course 3'
- Ungraded Lab: Jupyter Lab Environment
- Video: Vector Dot Products - Code
- Video: Text as Vectors - Code
- Video: Matrix Multiplication
- Video: Debugging Challenge
- Ungraded Lab: Jupyter Lab 1
- Graded: Assessment 1
Module 2: Understanding and Visualizing Data Distributions
- Ungraded Lab: Jupyter Lab Environment
- Video: Basic Probability with a Bernoulli
- Video: Discrete Data Distributions
- Video: Continuous Data Distributions
- Video: Debugging Challenge
- Ungraded Lab: Jupyter Lab 2
- Graded: Assessment 2.1
- Graded: Assessment 2.2
Module 3: Understanding and Analyzing Data Distribution Characteristics
- Ungraded Lab: Jupyter Lab Environment
- Video: Learning From Data Distributions
- Video: Reasoning About Probability from a Distribution
- Video: Special Properties of Normal Distributions
- Video: Linear Relationships in Data
- Video: Probability and Uncertainty
- Video: Debugging Challenge
- Ungraded Lab: Jupyter Lab 3
- Graded: Assessment 3
Module 4: Sampling Methods and Statistical Inference
- Ungraded Lab: Jupyter Lab Environment
- Video: Sampling Distributions
- Video: How Sample Size Affects Variance of the Sampling Distribution
- Video: Bootstrap Sampling
- Video: The Central Limit Theorem
- Video: Statistical Inference Part 1: Null Hypothesis Testing
- Video: Statistical Inference Part 2: Simulating the Null with Shuffling
- Video: Statistical Inference Part 3: Bootstrap Confidence Intervals
- Video: Statistical Inference Part 4: a Bayesian Approach
- Video: Debugging Challenge
- Ungraded Lab: Jupyter Lab 4
- Reading: Post Course Survey
- Reading: Attributions
- Graded: Assessment 4
Grading Policy
Learners must earn an overall grade of 80%. Assessments in each module account for 25% of your final grade.
Michael D. Cohen Collegiate Professor of Information
Lecturer IV
Course content developed by U-M faculty and managed by the university. Faculty titles and affiliations are updated periodically.
Intermediate Level
Learners should complete "Python 3 Programming" on Coursera or have equivalent experience with Python programming basics.
