- All DegreesExplore Bachelorâ€™s & Masterâ€™s degrees
- BusinessExplore MBA & Business degrees
- Computer Science & EngineeringExplore Computer Science & Engineering degrees
- Data ScienceExplore Data Science degrees
- MasterTrackâ„¢Earn credit towards a Masterâ€™s degree
- University CertificatesAdvance your career with graduate-level learning

Back to Statistical Inference

stars

4,392 ratings

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, â€¦) 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 25, 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 24, 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!

Filter by:

By Charles M

â€¢May 27, 2019

Elegant presentation materials and contains evaluation materials that target essential concepts and learner's ability to apply course information. Very well done and looking to take the biostatistics bootcampe alluded to in the lectures, by the same professor (Caffo).

By Balsher S

â€¢Feb 3, 2017

This is a good course to set up for further learning. One gets exposure to topics in intro and intermediate statistic and starts to grasp how intricate the web of statistics it all the while the focus is on Hypothesis testing which is one cornerstones of statistics.

By Craig L

â€¢Dec 5, 2016

This is the toughest content yet of the Data Science specialisation but probably the most valuable piece so far. Video content is good but moves along very quickly so finding another book on statistics to back up the course content will be a great benefit.

By Greg A

â€¢Feb 22, 2018

Very good course, but definitely a challenge. There is no shame in watching some of these lectures multiple times. I would recommend taking all of these quizzes until you can get 100%. It will help you out a lot in the regression and machine learning

By Marcos S

â€¢May 2, 2022

Excelent for people from other areas (engineering for ex.) to get the initial grip on these valuable tools. The companion book and the interactive swirl exercises are great to complement the pragmatic explanation of Dr. Brian. I sincerely recomend.

By Donald M

â€¢Nov 27, 2020

excellent course, although it had a certain degree of difficulty for me for not being a mathematician. I liked him and learned many things that I had only seen little of during my college career. This experience will be of great help in my career.

By Nino P

â€¢May 24, 2019

It's basically introduction to statistics. I have taken them as part of my education so it was a bit easier for me, but I think somebody new to this can lear a lot. It's a bit harder than first 5 courses, but still important and well teached.

By EdÃ©n S

â€¢Jul 27, 2020

At the beginning I've found a bit hard to store so much information on Statistics, but it is worth making the efforts, since the shown tools equipped a data scientist with strong arguments so as to refuse or accept some conclusions on data.

By Roberto D

â€¢Dec 13, 2016

I learned a great deal from this course. Methods, testing and most of all logical processes with proven with evidence. I understand this course only touches the surface, but it will serve me as a catalyst to continue exploring the field.

By Rosa C V

â€¢Feb 3, 2020

Muy contenta con el curso. Tenia un poco de temor de que se me haga muy complicado, pero las clases estuvieron bien diseÃ±adas y pude concluir este y otros 4 cursos de la especializaciÃ³n en Data Science con exito :-). Muchas gracias!!

By Damian

â€¢Jul 9, 2017

This one is one of the more mathematical course in this specialization, few times to the library and help with friends who are in the field of statistics or biomathematics would be very beneficial.

Dont skip any swirl practices ..

By Regis O

â€¢Aug 29, 2016

This course covers a wide range of powerful statistical concepts. The best way to work through this is to run R code as you go through the examples. If you are not comfortable with R, make sure to take the intro to R course first.

By Julio C R N

â€¢May 17, 2020

Loved it. Some courses have an automated voice. But I was lucky enough to get an iteration with a human. I always wondered the reasoning behind some statistical analysis and this shows you the basics to understand those concepts.

By Tarek L

â€¢Dec 13, 2018

Dr Brian DeCaffo is a talented and forward thinking educator. The amount of supplementary material he brings to the course is a bountiful bonus that really helped me grasp concepts. One of the best courses I've taken on Coursera.

By Pablo D C

â€¢May 9, 2020

Such a good course! it is a very dense one, but Brian is a really great teacher and knows who to explain wonderfully. Furthermore, there are many examples and online activities which help you to understand better the concepts!

By jess f

â€¢Nov 24, 2020

It is really informative especially for those who are not into statistics. Maybe you can also explain MSE, estimators other than t and z, and also insert some information why we use the corrected sample variance (n/(n-1))s^2.

By Rongbin Y

â€¢Sep 17, 2019

The course provided a great overview of the base statistical knowledge required for advanced data analytics. I had gained great experience and exposure to sophisticated hypothesis making and data wrangling skills. Thank you.

By Tine M

â€¢Mar 2, 2018

The course was at times difficult, I found that extra research was needed to fully understand what was going on. The extra questions related to the homework questions are a great way to test your understanding of the class.

By Matthew C

â€¢Nov 2, 2017

One of the better courses so far in the Data Science Specialization. If you have no background in statistics, expect to spend a lot of extra time in this course, especially weeks 3 and 4. Tough, but lots of good material.

By Matt S

â€¢Feb 10, 2019

Excellent course if you have some background in math or stats already. This course might be difficult if you don't have that background. The peer graded assignment does a good job tying everything together in my opinion.

By Bill S

â€¢Oct 2, 2017

This was challenging and informative. I think the time estimates are way off though. Some things estimated at 2 hours really took 10, and things that are estimated to take hours are one paragraph to read and then over.

By Vinodkumar V

â€¢Oct 10, 2017

Rigorous but worth... though it skims the SI topic, at least introduces to the vast dimensions of the subject. It takes time, but one learns. Follow the exercises in the book in addition to swirling. It helps a lot.

By Marc T

â€¢Mar 31, 2019

Not only did this course help me to understand concepts that I have encountered in my job over the length of my career, but it also introduced me to using R Markdown, which will come in handy for future projects.

By Angela K

â€¢Dec 3, 2018

really great course! takes a few minutes to get used to Brian after having all courses taught by Roger. I finally understand hypothesis testing and confidence intervals after taking several classes on this topic.

By Sk M

â€¢Jul 17, 2017

This is one of the most important course in data science specialization series, everyone should take this course very attentive way, because it give very deep insight about the role of statistics in data science.

- AWS Cloud A Practitioner's Guide
- Basics of Computer Programming with Python
- Beginners Python Programming in IT
- Developing Professional High Fidelity Designs and Prototypes
- Get Google CBRS-CPI Certified
- Introduction to MATLAB Programming
- Learn HTML and CSS for Building Modern Web Pages
- Learn the Basics of Agile with Atlassian JIRA
- Managing IT Infrastructure Services
- Mastering the Fundamentals of IT Support

- Basics of Computer Programming with Python
- Beginners Python Programming in IT
- Building a Modern Computer System from the Ground Up
- Getting Started with Google Cloud Fundamentals
- Introduction to Cryptography
- Introduction to Programming and Web Development
- Introduction to UX Design
- Learn HTML and CSS for Building Modern Web Pages
- Mastering the Fundamentals of IT Support
- Utilizing SLOs & SLIs to Measure Site Reliability

- Building an Agile and Value-Driven Product Backlog
- Foundations of Financial Markets & Behavioral Finance
- Getting Started with Construction Project Management
- Getting Started With Google Sheets
- Introduction to AI for Non-Technical People
- Learn the Basics of SEO and Improve Your Website's Rankings
- Mastering Business Writing
- Mastering the Art of Effective Public Speaking
- Social Media Content Creation & Management
- Understanding Financial Statements & Disclosures