Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses. Furthermore, there are broad theories (frequentists, Bayesian, likelihood, design based, …) and numerous complexities (missing data, observed and unobserved confounding, biases) for performing inference. A practitioner can often be left in a debilitating maze of techniques, philosophies and nuance. This course presents the fundamentals of inference in a practical approach for getting things done. After taking this course, students will understand the broad directions of statistical inference and use this information for making informed choices in analyzing data....

JA

Oct 26, 2018

Course is compressed with lots of statistical concepts. Which is very good as most must know concepts are imparted. Lots of extra reading is required to gain all insights. Very good motivating start .

MI

Sep 25, 2020

the teachers were awesome in this course. I liked this course a lot.Understood it properly.Thanks to all the beloved teachers and mentors who toiled hard to make these course easy to handle.Gracious!

筛选依据：

创建者 Do H L

•Jun 17, 2016

This course is tough, informative. Good for people who want a summary of all the statistical concepts you can use for data science. You'll get the most out of this course not by expecting it to be beginner, because it is not. This course is best supplemented by having background knowledge in statistics. Meaning, learners would be much better off if he/she did some statistical course before. This course will provide the practical experience of implementing statistical concepts in R.

创建者 Ritwik V

•Jun 08, 2020

Nice course,enjoyed it the most till now out of previous courses of Data Science Specialization. But is tough for people from non-Statistics background. I am a Statistics Major and I have studied all these topic in great detail so I didn't need to watch much videos.

创建者 Long H

•Jan 31, 2016

I found this course really good introduction to statistical inference. I did find it quite challenging but I can go away from this course having a greater understanding of Statistical Inference

创建者 Pankaj K

•Oct 16, 2018

This course covers the very basics of statistical inference which will help to strengthen your base concept. I loved doing the course especially the practice assignments on swirl.

Thanks.

创建者 John D M

•Feb 01, 2019

This is an excellent course, though it is fast-paced. I didn't have time to watch the lectures and also do the practice exercises in Swirl in the time allotted. As usual, the time estimates for completion are wonky. I ended up just watching the lectures and taking the tests, which is far from ideal (I am taking some time to do those valuable exercises now that the course is done). Although I got 100% in the course, I felt the learning experience could have been better as a result.

创建者 Audun T H B

•Oct 01, 2019

Thorough course. A bit difficult to follow the lectures at times.

创建者 Andrew

•May 05, 2019

Not my favorite course in the series, but I did learn a lot. I highly recommend following along with the course book provided in the course. The videos alone are not enough. I also recommend printing out a sheet with statistical formulas to use (not provided from the course, but you can find easily on the web). The stat sheet with formula helped me connect all the dots and better understand when to use a formula.

创建者 Mingda W

•Jun 05, 2018

My most recent experience with statistics was about 2 years ago, and it was college level statistics. Still, I find this class is hard to keep up sometimes. In general, I felt like the professor explaining too much on the mathematical meaning behind equations instead of talking about the real-world meaning of equation components, and why those calculation make sense.

创建者 Stefan K

•May 02, 2020

I found the lectures hard to follow, they didn't help me one bit. If you get his book, read it, and do the exercises, you can save yourself some time.

创建者 Jiapeng S

•Dec 10, 2019

The materials offered from this course is far away enough from understand the content :(

创建者 Robert K

•Apr 16, 2019

A lot of material to cover - can be a strain, but well explained for the most part.

创建者 Tomasz S

•Jan 18, 2020

Very fast course... Additional reading required.

创建者 John M

•Sep 30, 2019

This course was very hard to complete. The lectures were harder to follow than the previous courses.

创建者 Alexander D

•Jan 31, 2020

Wouldn't recommend for those learning stats. Try Duke's course instead. This one was poorly taught.

创建者 Tongke Z

•Oct 07, 2020

The most boring and nonsense course I have on the Coursera so far. I have a PhD degree in Stem, and had taken statistics courses during my undergraduate, and also had some teaching experience. I can't believe they can made a course like this quality. It downgrades the reputation of the department of biostatistics at the JHU. I saw some criticizing comments before I took the course, but I thought it would be OK and I would get through it. But after taking two weeks' courses, I just feel so frustrated and furious and can't help to write down my comments.

The format of this course is like, first, read out the parameter, and then read out the notation, without giving any explanation about how to calculate this out, why we want to introduce this parameter, and how we use this parameter. And then the instructor gives an example, but I don't see any of the examples emphasize the notions.

I just can't help to write down my comments. I don't want to give even one star to this course!!!!! Such a shame.

There should be some teaching centers at the JHU where some teaching professionals can help to improve the structure of these courses and give instructions about how to deliver the contents organically. I beg you to have some improvements.

创建者 HIBRAIM A P M

•May 05, 2020

Los ejercicios están completamente desactualizados y no corren con versiones actuales de los programas. Es necesario que den mantenimiento a este curso, ya que los últimos comentarios que se respondieron por parte de los instructores, lo hicieron hace más de dos años.

创建者 Nelly C

•Dec 13, 2019

There is a lot of theory in the course but it is not always treated with the necessary rigorousness; this creates confusion and makes it difficult to understand the basic concepts.

创建者 Alessandro F

•May 20, 2020

I don't find the button to leave the course!!!!

创建者 Christopher C

•Mar 09, 2016

I learned so much from this course. Brian has an occasional irreverence and dry wit that keep things lively. I will say that I disagree with some of his interpretations, but this is OK!

I would like to see some integration of type s errors, capture intervals, and all the other things the cool kids are doing nowadays.

I am now taking Bayesian statistics online via Richard McElreath's course and this one does help a bit in understanding likelihood functions.

创建者 Boris K

•Oct 13, 2019

This is so far the most difficult course in the specialization, but also the most useful. In this course you are taught to think like a scientist, to test hypothesis and provide evidence for your analysis. The lectures are succint and clear, the quizzes are clever and useful and the final project will make you create a very beautiful report while doing scientific work, which is the reason I started studying data science in the first place!

创建者 Angela W

•Oct 19, 2017

I really liked this course, especially the course project at the end - the second part felt like (a really simplified version of) a task one might actually have to do as a data scientist, and I liked that through this course and the previous ones, I knew exactly what I had to do. The course itself is pretty mathematical and I think intellectually the most challenging so far, especially since it's a lot of content for 4 weeks.

创建者 Kaie K

•Jan 16, 2016

Even as a mathematician I found it super useful to participate this class. I have learned similar material in an undergrad course, but I forgot most of it. In fact this course is so much better than the undergrad course I took, because quizzes and the project help me to learn the material by practical exercises. I am really thankful for the Data Science team for this course and all the Data Science Specialization!

创建者 Lloyd N

•Jun 05, 2017

I thought most of the lessons in this lecture were enjoyable, since it went into the theory of decision-making from data. I feel you need to take an introduction to statistics course before taking this course though, since the lecturer goes too fast at times. I recommend Udacity's Intro to Statistics course, as it helped me understanding the lectures in this course. A+ material though in my opinion.

创建者 amit p

•Oct 04, 2018

This course is one of the most difficult to comprehend, particularly if one does not have any prior knowledge of statistics and probability. But Swirl package of Statistical Inference helps a lot and is a good heuristic approach to learn.

P.S. I would recommend to read this lecture along with any textbook. I referred Probability and Statistics (Schaum Series).

创建者 Prashanth R

•Jan 02, 2018

I absolutely loved this course and felt like i learned a lot about statistics. This was very informative and the peer graded assignment was a perfect way to conclude the course, by having to perform all of the phases in Data Science that I learned by taking other courses in this series. Thank you for this course! Looking forward to the next set of courses.

- Finding Purpose & Meaning in Life
- Understanding Medical Research
- Japanese for Beginners
- Introduction to Cloud Computing
- Foundations of Mindfulness
- Fundamentals of Finance
- 机器学习
- 使用 SAS Viya 进行机器学习
- 幸福科学
- Covid-19 Contact Tracing
- 适用于所有人的人工智能课程
- 金融市场
- 心理学导论
- Getting Started with AWS
- International Marketing
- C++
- Predictive Analytics & Data Mining
- UCSD Learning How to Learn
- Michigan Programming for Everybody
- JHU R Programming
- Google CBRS CPI Training

- Natural Language Processing (NLP)
- AI for Medicine
- Good with Words: Writing & Editing
- Infections Disease Modeling
- The Pronounciation of American English
- Software Testing Automation
- 深度学习
- 零基础 Python 入门
- 数据科学
- 商务基础
- Excel 办公技能
- Data Science with Python
- Finance for Everyone
- Communication Skills for Engineers
- Sales Training
- 职业品牌管理职业生涯品牌管理
- Wharton Business Analytics
- Penn Positive Psychology
- Washington Machine Learning
- CalArts Graphic Design

- 专业证书
- MasterTrack 证书
- Google IT 支持
- IBM 数据科学
- Google Cloud Data Engineering
- IBM Applied AI
- Google Cloud Architecture
- IBM Cybersecurity Analyst
- Google IT Automation with Python
- IBM z/OS Mainframe Practitioner
- UCI Applied Project Management
- Instructional Design Certificate
- Construction Engineering and Management Certificate
- Big Data Certificate
- Machine Learning for Analytics Certificate
- Innovation Management & Entrepreneurship Certificate
- Sustainabaility and Development Certificate
- Social Work Certificate
- AI and Machine Learning Certificate
- Spatial Data Analysis and Visualization Certificate

- Computer Science Degrees
- Business Degrees
- 公共卫生学位
- Data Science Degrees
- 学士学位
- 计算机科学学士
- MS Electrical Engineering
- Bachelor Completion Degree
- MS Management
- MS Computer Science
- MPH
- Accounting Master's Degree
- MCIT
- MBA Online
- 数据科学应用硕士
- Global MBA
- Master's of Innovation & Entrepreneurship
- MCS Data Science
- Master's in Computer Science
- 公共健康硕士