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Inferential Statistical Analysis with Python

This specialization is designed to teach learners beginning and intermediate concepts of statistical analysis using the Python programming language. Learners will learn where data come from, what types of data can be collected, study data design, data management, and how to effectively carry out data exploration and visualization. They will be able to utilize data for estimation and assessing theories, construct confidence intervals, interpret inferential results, and apply more advanced statistical modeling procedures. Finally, they will learn the importance of and be able to connect research

3.9

30 reviews

Created by: Brenda Gunderson

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Overall Score : 78 / 100

Course Description

In this course, we will explore basic principles behind using data for estimation and for assessing theories. We will analyze both categorical data and quantitative data, starting with one population techniques and expanding to handle comparisons of two populations. We will learn how to construct confidence intervals. We will also use sample data to assess whether or not a theory about the value of a parameter is consistent with the data. A major focus will be on interpreting inferential results appropriately. At the end of each week, learners will apply what they've learned using Python within the course environment. During these lab-based sessions, learners will work through tutorials focusing on specific case studies to help solidify the week's statistical concepts, which will include further deep dives into Python libraries including Statsmodels, Pandas, and Seaborn. This course utilizes the Jupyter Notebook environment within Coursera.

Time: 20h

Instructor Details

Brenda Gunderson

3.9 Rating
30 Reviews

Brenda Gunderson received her PhD in Statistics from The University of Michigan in 1989. She has stayed on at UM and is a Senior Lecturer. She coordinates and teaches the largest undergraduate statistics course, Statistics and Data Analysis, with approximately 1800 students each term. She is also an undergraduate advisor for students electing to major or minor in Statistics. Her research focuses on Statistical Education, in particular using technology to enhance teaching and learning. Brenda received the UM Teaching Innovation Prize for her work on Infusing Technology for Guided Continuous Learning in a Large Gateway Course. She is co-investigator for a UM grant called: Enhancing Undergraduate Education through the Deployment of Quality Learning Objects. Her work on this grant led to receiving the Innovative Use of MERLOT Award (2009) and a Sloan-C Effective Practice Award (2012). She is also part of an NSF project to expand the UM E2Coach system (Expert Electronic Coaching) to students in introductory statistics courses -- computer tailored communication technology allows us to provide individualized coaching and advice to students using their individual background, goals, and current standing in the course.

Fitting Statistical Models to Data with Python
4.3 (19 Reviews)

- Provider: Coursera
- Time: 14h

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3.9

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By Vijai K S on 29-Oct-19

Wish there were more practice problems.

By Hongbing K on 14-Oct-19

Good Python tutorials that gives a good paratactical introduction to the theoretical core of the course.

By gaston F on 7-Oct-19

Very well done. A lot of practice.

By Miao J on 6-Oct-19

Some parts can be explained better

By Mahmoud F on 20-Sep-19

I found Brady T West's videos in Week 4 to be unnecessarily confusing causing me to have to go back to Week 3 lectures to clarify the steps of hypothesis testing.

By Taehee J on 11-Sep-19

It was very good course, everything was very well explained and the activities were challenging enough to practice the knowledges obtain.

By ELINGUI P U on 24-Aug-19

In this course, they cover making confidence intervals and calculating p-values given a specific test scenario (compare sample proportion to population proportion, sample mean to population mean, two sample means to each other, etc). While they go though each statistical procedure clearly, I feel like a lot of underlying context is missing. What is the different between a z- and t-distribution? Why do we use those distributions? How do the different tests relate to each other? Etc. It feels like this course needed an extra 50-60 minutes of lecture time to tie all these concepts together. A textbook to follow along would have been great too.

By Tomasz J on 8-Aug-19

I really appreciate the course and let me accumulate a lot of knowledge about statistics. And I have developed a good impression of the University of Michigan teaching level.

By Sara E E on 17-Jul-19

The content explanation is excellent and one of the best I have seen.

By Lech G on 13-Jul-19

A complete course focused on teaching the details and intuition of experiment design, inferential analysis for decision making through confidence interval ans hypothesis testing and how to state effective questions. I would recommend this course to everyone who are seeeking for more explainability and improvements in its ability to solve complex problems through data analysis.

By Willismar M C on 12-Jul-19