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

Created by: Brenda Gunderson

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Overall Score : 78 / 100

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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.

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Instructor Details

Brenda Gunderson

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.

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Reviews

3.9

30 total reviews

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By Aayush A on 27-Feb-19

Do you do usability tests of your courses? Like you can test a landing page - you pick a random person to perform a certain action on your landing page, and see where they struggle or what is unclear? If you did this with this course before going live, it would benefit everyone. Right now the quality of this course is too low, concepts are not explained enough, and the assignments (especially week 3) contain wrong instructions and errors.

By mahesh on 26-Jan-19

If you want to learn basic and inferential statistics - I would advise checking out the courses with these name from by University of Amsterdam(you can take them without taking the specialization). they are much clearer. And then if you want examples of Python code - take this course. Just check out the forums first. As of jan2019 the Python Notebook used for the week3 assessment had various problems.

By Theo L on 31-Mar-19

This course attempts to cover very useful topics but falls short on several areas. 1. Multiple errors in the assignments. Practice exercises don't have any answers for students to check. 2. Course slides are not provided. 3. Lack of support to questions asked in forum. I learned a lot from the course but a significant amount of time could have been saved if the issues I mentioned were addressed.

By Sanjay M on 25-Feb-19

Alltogether the course was great. I learned so much and understood some principles I did not understand when having read of them before.However in some notebooks, calculations were wrong or notbooks were missing alltogether (week 4, last jupyter notebook). Furthermore it can be annoying if you cannot trust a result of a statistical analysis in a notebook because there were other mistakes before. That's why I give you "only" 4/5 stars.

By Binil K on 30-Jan-19

Great lecture content. Poor quiz design.

By Aadesh N on 21-Mar-19

Good lectures but too little practice and quizzes that don't cover all the material. Very little Python. No lecture slides or "handouts" to summarize procedures or formulae that tend to jumble together for the various scenarios you learn. Some of the lectures told us to find tables needed to do the quizzes online, no more specifications. That was very disappointing.

By Mudambi S S on 16-Apr-19

The course contents are good to an introduction or refreshing in statistics but the assigments are not really well prepared, and contains many unrepaired errors. This drops down the level an educational potential of this course (and the entire specialization) and converts it in a poor educational resource and a waste of time, in my opinion

By Tripat S on 4-Feb-19

Very clear and interesting lectures, but quizzes and Jupyter notebooks could benefit from some additional proofreading and pre-release testing. Material in last week is out of order. Spent a few hours some week just figuring out the mistakes with the help of the course forum.Also, I would have liked to have a bit more background and explanation, e.g. information on why we using a particular distribution or a particular test, not just how. While a complete derivation of all the material would clearly be out of scope, other courses did a better job of introducing the theory behind their methods.

By Abhinav U on 26-Apr-19

I must say that this is a must take course for ones who are aspiring a career in Data Science. All the concepts were laid out so beautifully and it was explained very clearly with visualisations of each real-life-examples. I enrolled in this specialisation before starting my Machine Learning so that I have all the necessary fundamentals of Statistics. Brady Sir & Brendra Ma'am are simply phenomenal, the way they explain the concepts are incredible. The concepts gets etched in one's memory.

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 Jerome G on 28-May-19

This course is a good statistics course, but a poor Python course. Python is practically an after thought in each week's lesson as the focus in the lecturing learning methods is entirely verbal rather than supported by in lecture use of Python. The Python review at the end of each week before the assessment is not connected enough with the lecture materials and makes for a very disjointed week of learning.

By Sergey M on 7-Mar-19

If you are interested in statistics and statistical analysis, this course gets you grounded in the essential aspects of statistics. Excellent instructors.