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Inferential Statistical Analysis with Python

Inferential Statistical Analysis with Python

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8.8/10 (Our Score)
Product is rated as #122 in category Python

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. The mission of the University of Michigan is to serve the people of Michigan and the world through preeminence in creating, communicating, preserving and applying knowledge, art, and academic values, and in developing leaders and citizens who will challenge the present and enrich the future.

Instructor Details

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.

Specification: Inferential Statistical Analysis with Python

Duration 10 hours
Year 2019
Level Intermediate
Certificate Yes
Quizzes Yes

43 reviews for Inferential Statistical Analysis with Python

3.9 out of 5
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  1. Avatar

    Yaron K

    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.

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  2. Avatar

    David Z

    Great lecture content. Poor quiz design.

    Helpful(3) Unhelpful(0)You have already voted this
  3. Avatar

    Iver B

    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.

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  4. Avatar

    Frank S Y R

    I really enjoyed the course.

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  5. Avatar

    Tobias R

    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.

    Helpful(4) Unhelpful(0)You have already voted this
  6. Avatar

    Emil K

    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.

    Helpful(14) Unhelpful(0)You have already voted this
  7. Avatar

    Rajesh R

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

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  8. Avatar

    Daniel R

    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.

    Helpful(3) Unhelpful(0)You have already voted this
  9. Avatar

    Jin S

    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.

    Helpful(7) Unhelpful(0)You have already voted this
  10. Avatar

    Varga I K

    Absolutely great course of inferential statistics!

    Helpful(0) Unhelpful(0)You have already voted this
  11. Avatar

    Jose A G P

    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

    Helpful(3) Unhelpful(0)You have already voted this
  12. Avatar

    cameron g

    Excellent

    Helpful(0) Unhelpful(0)You have already voted this
  13. Avatar

    Aayush G

    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.

    Helpful(1) Unhelpful(0)You have already voted this
  14. Avatar

    Michael D

    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.

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  15. Avatar

    Yaroslav B

    This course is significantly better than the previous one. Nevertheless, if you want to get knowledge about Python, it’s not about this course.

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  16. Avatar

    EDILSON S S O J

    Amazing course! Very useful for all kind of Analysis!

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  17. Avatar

    JIANG X

    A very in–depth learning material for inferential statistics. Very good explanation of p–value which clarifies some of the prevailing misunderstandings.

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  18. Avatar

    Jafed E

    I enjoy the lectures. The professor has a good speaking and teaching style which keeps me interested. Lots of concrete math examples which make it easier to understand. Very good slides which are well formulated and easy to understand

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  19. Avatar

    Yury P

    Good theoretical foundation, but lacks explanation on python libraries extensively used in the course.

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  20. Avatar

    Vinicius G d O

    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.

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  21. Avatar

    Parvatharajan K

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

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  22. Avatar

    Bonnie

    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.

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  23. Avatar

    ILYA N

    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.

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  24. Avatar

    Maria G

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

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  25. Avatar

    Christine B

    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.

    Helpful(0) Unhelpful(0)You have already voted this
  26. Avatar

    Divyam A

    Some parts can be explained better

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  27. Avatar

    Marina P

    Very well done. A lot of practice.

    Helpful(0) Unhelpful(0)You have already voted this
  28. Avatar

    Jonas N

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

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  29. Avatar

    Kevin K

    Wish there were more practice problems.

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  30. Avatar

    Beatriz J F

    Very satisfied.

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  31. Avatar

    Darien M

    Python does not deserve to be in the title of this course.

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  32. Avatar

    Gabriel G B

    It is absolutely great. Instructors are veeeery pasionated with what they do, and the course material is very good. I really like this course.

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  33. Avatar

    Wenlei Y

    The teaching team is great. But the assignments are not very helpful. And yes, this is more a statistics course than a python course. The application with python, which I am more interested in, seems just the supplementary portions to the lectures of concepts of statistics. There is not much introduction to how we use python to perform statistics, how we debug, and how we interpret the outcomes of programs.

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  34. Avatar

    Marnix W

    I’d like a little more interaction with Python during the explaination itself.

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  35. Avatar

    Sagar T

    From the introduction, the course is supposed to build the knowledge ground up for a beginner in Statistics. However, it falls short in clearing many concepts and the principals end up being vague in a lot of sense, hence, there is a lack of cohesiveness in the concepts spread across weeks. Fortunately, I took an open course offered by Stanford University of the Inference Concepts explained in this course; before taking up this course. Overall, this is a good course for someone who is familiar with the Inference concepts. For a beginner, a significant amount effort would be required to catch up to these concepts.

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  36. Avatar

    Benjamin F

    Well presented and rich content.

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  37. Avatar

    Andres R d S

    Need to improve slides

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  38. Avatar

    Felipe N N B

    the fundamentals and intuition are greatly explained. The python part feels a little rushed though.

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  39. Avatar

    Sam F

    Overall solid course. Could do without peer review assignment, more of a hassle than anything.

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  40. Avatar

    Khaled S A

    Perfect Course, It was very useful to understand the basics of inferential statistics

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  41. Avatar

    Flo

    It’s an amazing course! very clear, very informative!!

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  42. Avatar

    Shuran X

    Detailed explanation and clear syllablus.

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  43. Avatar

    Sumit M

    It’s just an exceptional course, 101 out of 100 would recommend, Learned a lot from it. Thank You

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    Inferential Statistical Analysis with Python
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