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Regression Models

Data Science,

Linear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist’s toolkit. This course covers regression analysis, least squares and inference using regression models. Special cases of the regression model, ANOVA and ANCOVA will be covered as well. Analysis of residuals and variability will be investigated. The course will cover modern thinking on model selection and novel uses of regression models including scatterplot smoothing.
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Rating 4.0 based on 528 ratings
Length 5 weeks
Starts Jun 19 (50 weeks ago)
Cost $49
From Johns Hopkins University via Coursera
Instructors Roger D. Peng, PhD, Brian Caffo, PhD, Jeff Leek, PhD
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science Mathematics
Tags Data Science Data Analysis Probability And Statistics

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What people are saying

data science

Regression Models is the seventh course in the Data Science specialization.

The knowledge gained in this course has tremendous value in the data science workplace.

Good Course The best course in my mind, but I am chocked about how Data Science people approach regression type of problems, it is almost 100% data mining and no theory!!

The Regression Models is an excellent course for a beginner.I would recommend the enthusiastic students for a great start in Data science.

Extremely valuable content to my pursuit of a career in data science.

Very good for anyone wanting to get into the field of Data Science using R Love it I like Brian Caffo's lectures.

One of the most required skill set in the field of data science.

Perfect course toward the data science specialization.

I would recommend it to those wanting to learn more about data science.

Hard but rewarding work that I think is perfect for Data Science.

I am looking for a new Data Scientist career ( did this course to get new knowledge about Data Science and better understand the technology and your practical applications.

It deals with the scientific foundation of how to do data science: regression models, residuals, measures of the quality of the prediction, etc.

Honestly this data science course is getting worse as the months progress, you really should think of updating the content of the course if you want to continue to charge money for it.

easy to understand and full of new idea about using R.especially 'manipulate' package is very useful This course is the first one in the Data Science series to lapse in terms of the clarity of the lectures, and the sense of cohesiveness of the material.

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statistical inference

As with Statistical Inference, it is taught by Brian Caffo and suffers from the same issues as the preceding course.

Just like the previous course in the specialization path (Statistical Inference) the course delves into some relevant topics however it doesn't feel as properly structured.

Very detailed and exhaustive course If you thought that the previous course (Statistical Inference) with Brian Caffo was a horrible experience -- think twice and get ready for Regression Models.

Once again, this and Statistical Inference courses are very challenging to truly completed with insightful understanding.

I was optimistic about this class because it started out fixing some of the pedagogical mistakes the professor made in Statistical Inference, but by the time we got to week 3, it was pretty clear that the course was trying to accomplish too much in 4 weeks, and instead of focusing on the most important parts of regression and making sure they were taught well and understood clearly, I feel the course tried to do far too much.

With the first few videos, I was concerned I would be re-living the nightmare that was the Statistical Inference course.

To summarize Statistical Inference: I hated it.

At least it makes you want to investigate more about the subject.I find frustrating however not to have a proper instructor example of the final assignment, it is hard to review other participants work and realize what they / you have done wrong without actually knowing how best the assignment should have been fulfilled.And as all courses in this specialization, there is not much interaction between participants, and not much effort by mentors to animate it Slightly better than the Statistical Inference course, but many of the same technical and delivery defects persist.

This course was an improvement in teaching modality from the statistical inference course, with more polished content, but the link between the lectures and the actual exercises was still a bit strained.

This was better than the statistical inference course, but Brian still puts too much emphasis on the precision of his language (as if he's teaching to other mathematicians) which makes it difficult to understand.

This course is much improved, when compared with Statistical Inference.

Regression models was almost just as difficult as statistical inference.

Very good course Love the whole course approach on the importance of linear models and how one should interpret them to get a better grasp of the data one possesses - one should definitely take the statistical inference course before attempting this course beforehand.

This course is better than Statistical Inference, and I think it is as useful.

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brian caffo

Another problem was the instructor, Brian Caffo, who seems like a good guy and good researcher, but not an effective teacher.

Brian Caffo is an excellent professor.

Thank you Brian Caffo!

Thanks to Brian Caffo for the wealth of information about regression models taught through this course!

Brian Caffo's lectures in Statistical Inference were good; in this course they seem to veer left and right rather than get straight to the essence of whatever subject he is lecturing about.

I loved studying Regression Models taught by Prof. Brian Caffo.

Once again the concepts are simple and the math not so hard, yet I had to do a lot of research outside the course to be able to understand these simple concepts and derive the not so hard mathematics.Brian Caffo is clearly brilliant and, I would say, seem to be a good lad too, but something is missing.

The quality of the lectures was very high and the information interesting, so compliments to Dr. Brian Caffo on that.

I thank Professor Brian Caffo for sharing his knowledge with us.

Excellent course This course was a great as an intro to regression models, material was good but needs some update on the links, for the structure of topics it would be better if it was more coherent as many topics were covered randomly in different weeks like residuals.Thanks for the instructor Brian Caffo for the good material and and clarification of concepts for a better understanding for students.

Lot of learning , a must take course Excellent course, though I recommend you supplement applied practice by using the principal instructor, Dr. Brian Caffo's book, to answer practice questions if you want to retain these content-packed lessons.

Brian Caffo has a way to explain regression without sinking deep into hard math.

Thank you Brian Caffo and other masters for this course.

Worst teaching by Brian Caffo!

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introduction to regression models

Great course; practical introduction to regression models at the university level.

sufficient depth but explnation is not sufficient in many places Great Wonderful course Really good introduction to regression models.

great introduction to regression models This course failed greatly to balance the workload by week.

Very comprehensive introduction to regression models.

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difficult to follow

He is very unengaging, difficult to follow, and rushes through lectures.

I did find it difficult to follow and understand some of the materials.

I am no used to this educational system so I find difficult to follow without any proof or demonstration of the mathematical tools.

I again found many of the lectures to be difficult to follow along, there seems to be lots of different styles of videos in the way that the person was superimposed on the slides.

Videos were very difficult to follow along with.

The instructor gives too much information and is difficult to follow, some information is even trivial.

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highly recommend

I highly recommend avoid this course, and instead go through the R guide on linear regression; in the end, I used those to get through this course.

I highly recommend new people for this course Expects a level of statistical knowledge already.

I highly recommend it!

Highly recommended course and specialization,There are so many unanswered questions, so many new relationships to uncover.

I highly recommend both of those courses.

Highly recommended.

I highly recommend this course.

Further analysis of the mathematical and statistical theory is highly recommended.

A highly recommended specialization.

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machine learning

Also it would benefit if there was a clear message coming through, like Machine Learning course where things follow a order.If it was not by the book of Mr.Field with Statistics in r, I would never be able to understand what was really being said in this course.

I feel like I only began to understand the material once I finished the course project, and even then I have no idea how regression models work.I'm now going to be taking a month or 2 off from the courses to read more about statistical inference and regression models on my own, since I feel completely unprepared for the upcoming Machine Learning course.

I have the opportunity to explore all the plotting concept and apply them in regression models arena.Good to take this course to step in the concept of machine learning.

Good foundation in the Data Science Certification for Practical Machine Learning.

There are 3 areas that I would like to dig deeper so far: Statistical Inference, Regression Models and Practical Machine Learning (perhaps + Deep Learning).

A well defined learning path to understand the fundation of machine learning techniques.

These lectures on statistics, regression and machine learning are where the rubber hits the road after a lot of prep work to learn R and principles/tools of data science taught in earlier classes.

These regression models help explore the relationships between the variables and build the first analytic models before any machine learning techniques are applied.

Excellent It was a wonderful course for regression models, the full import of which I realized when I took up the next course on machine learning.

The concepts learned here enhanced by confidence to venture into more advanced machine learning.

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swirl exercises

Overall, it felt like there was a bit of a disconnect between the swirl exercises and the lectures, and this led to a lot of self-teaching.

Another proof of week 3 issue: the related swirl exercises start in week2 (2 of them) and finish in week4 (2 more exercises) !!!!

The Swirl exercises for this course help reinforce the topics in a way that is much more engaging than the lectures.

I give the Swirl exercises for this course a score of 3/5 stars.

For me it was more like 20 hours, and more if I did all the Swirl exercises.

The lectures were thorough and the Swirl exercises were very useful.

The swirl exercises are especially useful to revise the course content and apply the theory.

swirl exercises needs to be fixed, could not complete it because of the bug The course is interesting but probably overambitious.

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final project

proof of different theories, the course only provides overview, but didn't go deep enough 2) Project: I find the optional quiz project more interesting, the final project is too simple, and didn't include things we learnt such as GLM etc.

A more comprehensive final project with more aspects of courses knowledge will be much better to re-solidate learning really informative with helpful examples.

Definitely doing the exercises and final project is a must to get all the learnings!

Would have loved a tougher final project (eg.

How about adding two variants for all final projects - 1. lots of things to do vs. 2. more technically complex ?

Also the final project is so unsatisfactory in that we were to analyze the data with 32 obs but 11 variables!

A more structured final project would have been helpful.

The final project is given as 2 hours but it was closer to 15 for me.

The R based practice assignments are wonderful and the final project incorporates things together nicely.

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prof. caffo

Prof. Caffo and his team did a very good job in my opinion.

Prof. Caffo is a great teacher!

Prof. Caffo does an excellent job presenting the material in a way that does not require previous background or expertise.

Prof. Caffo tone of speech and style of lecture delivery is kind of fast and stuffed with information thus you need to stop several times more than you do in other courses for this specialization [ i.e.

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most important

The instructor is very knowledgeable and covers the most important aspects of regression models.

I feel like out of all the professors in this specialization course, there were so many others who could have taught the material better, especially since this is probably the most important course of the entire specialization.

The third week which I think was the most important one have too many information to learn and assimilate whereas the first two weeks could be rearranged to start multivariate regression earlier.

!I think one of the most important expertise and knowledge that a data scientist must know and master was unfairly squeezed in one week leaving no time for the learner/student to do more search/exercises on the subject.

very good practical approach, with good theoretical coverage of most important principles of regression good course, nice teaching!

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real world

The emphasis is on derivation of formulas and techniques, not applications to the real world.

I prefer more practical lectures, where you're shown how to apply what you're learning to the real world.

finally I have learned how to implement regression in real world analysis This course should be targeted for Data Scientists, in my opinion it is more for statisticians.

If there is more introduction about the common problems people may encounter during working in the real world, the course will be better!

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An overview of related careers and their average salaries in the US. Bars indicate income percentile.

Counseling Theories & Models Part-Time Faculty $17k

Trainer of Evidence Based Models $54k

Federal - Regression Tester $55k


Regression Analyst $69k

Senior Functional and Regression QA Analyst $92k

Assistant Adjunct Professor Statistical Models $122k

Risk Analytics Tools and Models Program Manager $136k

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Rating 4.0 based on 528 ratings
Length 5 weeks
Starts Jun 19 (50 weeks ago)
Cost $49
From Johns Hopkins University via Coursera
Instructors Roger D. Peng, PhD, Brian Caffo, PhD, Jeff Leek, PhD
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science Mathematics
Tags Data Science Data Analysis Probability And Statistics

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