Back to Basic Statistics

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3,717 ratings

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942 reviews

Understanding statistics is essential to understand research in the social and behavioral sciences. In this course you will learn the basics of statistics; not just how to calculate them, but also how to evaluate them. This course will also prepare you for the next course in the specialization - the course Inferential Statistics.
In the first part of the course we will discuss methods of descriptive statistics. You will learn what cases and variables are and how you can compute measures of central tendency (mean, median and mode) and dispersion (standard deviation and variance). Next, we discuss how to assess relationships between variables, and we introduce the concepts correlation and regression.
The second part of the course is concerned with the basics of probability: calculating probabilities, probability distributions and sampling distributions. You need to know about these things in order to understand how inferential statistics work.
The third part of the course consists of an introduction to methods of inferential statistics - methods that help us decide whether the patterns we see in our data are strong enough to draw conclusions about the underlying population we are interested in. We will discuss confidence intervals and significance tests.
You will not only learn about all these statistical concepts, you will also be trained to calculate and generate these statistics yourself using freely available statistical software....

PG

Apr 20, 2016

This is a nice course...thanks for providing such a great content from University of Amserdam.\n\nPlease allow us to complete the course as I have to wait till the session starts for week 2 lessions.

DA

Jan 28, 2021

great course with good videos and examples. Very good course for learning the basic statistics. Unfortunately the week 3 is the most misunderstanding module, nevertheless very good and understanding

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By Sanha L

•Dec 7, 2020

good

By Amruta b k

•Aug 30, 2020

good

By S S S V

•Aug 6, 2020

Good

By PADMASHREE B S

•Aug 1, 2020

good

By POOJA K S

•Jul 29, 2020

good

By DOMINIC P

•Jul 14, 2020

poli

By Utsav M

•Jul 14, 2020

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By Cui L

•Jun 22, 2020

good

By Priyanka A

•Jun 4, 2020

GOOD

By avaneesh k

•Apr 25, 2019

good

By Wendong Y

•Feb 19, 2018

good

By Trung-Duy ( N

•Sep 23, 2017

Good

By praveen k

•Apr 3, 2016

Good

By Niharika N L

•Aug 3, 2020

NO

By 김봉현

•Jun 22, 2020

ㅇㅇ

By Padmini M

•Jul 30, 2020

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By MAMATHA G

•Jun 23, 2020

.

By Deepak R

•Apr 1, 2019

V

By Tomislav S

•May 11, 2020

Even though I graded 5 stars, I would actually prefer to grade 4,5 stars this course. I liked many things, but some were really frustrating. Pros: the overall knowledge that is offered in this course (BA level), course structure, quizzes. Many students critized the course integration with R, but I do like this fact. I am aware they could have organized the practical part with some other tools, but for me R is a necessity and a must. However, there are some drawbacks. 1. The presentation quality between two instructors was noticeable. Somehow, one of them was repeating more, had a slower pace, more vivid examples, which I prefer. 2. There are several important mistakes with numbers in the presentation slides. Many students have already noticed that, so it's a pitty no one corrected them. 3. This drawback is more for DataCamp, and not for this course, but the spelling was really bad. Too many obvious mistakes. 4. Final test. It was quite demanding for me. I still don't know how one could answer 30 math questions, many of which include calculating, for only 1 hour of time!? Also, some questions were really tough and I haven't found an answer anywhere in my lectures. There are at least 3 answers that I don't know how to explain why were (in)correct.

I plan to take another statistics course in Coursera, as well as at Khan Academy, so I will be able to compare and evaluate more adequately the knowledge presented. For the time being, I would recommend this course!

By Linda J

•Aug 24, 2017

Very clear course. It really started with the basics, so I could understand everything. The set-up (transcripts with videos, helpful animations in the videos, R labs, quizzes) was really designed to get the most out of it. I learned a lot from it.

Two points of approvement:

1) Feedback on the quizzes! I thought it was really a loss that I couldn't see what I did wrong in the quiz (you can see which questions were wrong, but not which answer you chose and which was the right one) and that there was no explanation of why the answer was wrong. This way, I learned nothing from my mistakes in the quizzes, unfortunetely.

2) Quizzes took me way more time than what was designated for them, because some of the questions had to be answered in R or manually, and the course didn't teach me a quick way to calculate the answer (or sometimes it did, but the small data sets given in the quizzes aren't implemented in R so doing this manually also took quite some time). This way, a simple multiple-choice question could consume way to much work because you sometimes had to do a lot of tedious manual work.

By Vicky G

•Feb 6, 2020

I love the vivid examples, and the all the visual explanations. They really helped with understanding the concepts. The assignments and the R exercises are well designed and align perfectly with the course content which helps as well.

Meanwhile I wish there was more explanation about the math behind the formulas (as to how those formulas were derived) instead of just teaching us how to put numbers into those formulas. That way we'd be able to better understand the "why" behind the fascinating data behaviors. For instance why do we say when n >= 30 then the sampling distribution of sample mean is normally distributed? Why when number of success >= 15 and number of failures >=15 then the sampling distribution of sample proportion is normally distributed? What are the actual reasons behind using t-table instead of z-table when we do not know the population σ? Just a couple examples off the top of my head.

By Eva D

•Aug 11, 2018

This is a good course, and I would recommend it to anyone who needs an introduction to statistics. My main criticism is that now that the course is up and running, nobody seems to care much about maintenance. Some of the lectures and R-labs have mistakes in them. I understand that you cannot redo the videos, but it would be extremely helpful if you would create a printable document containing all identified errors, that students can check while watching the videos or doing the labs. Expecting students to go through years of postings on the forum to figure out if they misunderstood something or whether there was an error in the materials is unrealistic. It would also decrease the workload of tutors that receive the same questions over and over again. On that note, while some questions in the forum get answered very quickly, others remain unanswered for months.

By Lieke S

•Feb 28, 2016

In my opinion this course is a good way to learn basic statistics. I do think the R part and the statistics part could be more integrated in order to learn R better and to use it more efficiently. For me the studytime was double the time that was stated even though I was already familiar with some of the concepts and calculations. I feel like some weeks the subjects should have gotten a little more explanation with video's or other supportive material, maybe some assigments to make in order to check wether you know and understand the material. Maybe you could compare content and way of teaching to the Duke University statistics course, this is not a better course per se but does do some things differently whilst both teaching stats and R.

By Chris L

•Feb 5, 2018

Much better than the Univ. of Ohio introduction to Python e-course. The lecturers are more interesting and better produced. Likewise, the r course is better structured with more questions that build upon one another.

Down side: a lot of spelling mistakes, particularly in the r package portion. Also, some of the r questions were not intuitive, even after seeing the clues and/or answers. Finally, I still don't feel very competent at applying various formulas or the reasoning behind some of the statistical measures. I think the course would be better with some practical coding examples and examples after each lecturer video instead of all grouped up at the end of each week.

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