Data is everywhere, from the media to the health sciences, and from financial forecasting to engineering design. It drives our decisions, and shapes our views and beliefs. But how can we make sense of it?
This course introduces some of the key ideas and concepts of statistics, the discipline that allows us to analyse and interpret the data that underpins modern society.
In this course, you will explore the key principles of statistics for yourself, using interactive applets, and you will learn to interpret and evaluate the data you encounter in everyday life.
No previous knowledge of statistics is required, although familiarity with secondary school mathematics is advisable.
Logo image: (C) The University of Edinburgh 2016 CC BY, derived from Waverley Bridge, by Manuel Farnlack on Flickr, 2010 CC BY
Week 1: Introducing Data
What is statistics? We begin the course with this question, and see how data lies at the heart of statistics. We look at common techniques for presenting and summarising data.
Week 2: Patterns in Data
We look further into the science of data analysis, focusing on finding and interpreting relationships between different data sets, and on using trends in data to make predictions.
Week 3: Collecting Data
We look at key methods of data collection, seeing how we generally use samples of a population to make predictions about the whole population. We learn about how to choose a representative sample, and how to set up a statistical experiment.
Week 4: Uncertainty in Data
Using samples to make predictions about a population brings uncertainty into our data. As the study of risk and uncertainty, probability is therefore key to understanding statistics. We introduce the ideas here for describing and quantifying uncertainty via probabilities.
Week 5: Distribution
We look again at probability and describe a range of common situations that lead to standard forms for describing the associated probability of different possible outcomes. The ability to describe such probabilities provides the basis for building up the knowledge and understanding needed to study deeper statistical methods.
Week 6: Estimation
We will build on the idea of estimating properties of a population using sample data. Further, as the answer that we provide is only an estimate of the (unknown) true value, we will also describe how we may construct an associated uncertainty interval for the parameter being estimated, using properties of the sampling distribution.
We introduce the testing method that is fundamental to all of science: the hypothesis test. We learn how to set up and perform a hypothesis test, and look at how such tests are used in scientific research.
Week 7: Statistical Testing
We introduce the concepts of the testing method that is fundamental to all of science: the hypothesis test. We learn how to set up and perform simple hypothesis tests.
Week 8: Further Statistical Testing
We build on the ideas of the hypothesis test and look at further tests that are commonly used in scientific research.
Adelyne Chan completed this course, spending 3 hours a week on it and found the course difficulty to be medium.
Note: Although this course is slated as being for beginners, I do have experience of learning statistics in university. Admittedly slightly out of touch with the field, but looking to take more advanced courses in handling big data so I thought this would...
Note: Although this course is slated as being for beginners, I do have experience of learning statistics in university. Admittedly slightly out of touch with the field, but looking to take more advanced courses in handling big data so I thought this would be a nice refresher session. I didn't really see the need for a certificate, so I took this course in Audit Mode on EdX but I completed all the course materials and exercises.
TL;DR version: Good course for understanding basics of statistics, accessible to beginners but questions designed at reinforcing concepts in a more real-world setting can be somewhat misleading.
The course itself is nicely structured, with the video lectures mostly formatted around conversations between the lead instructor and one of several other supporting instructors who are all introduced in the first video. Concepts are very accessible to beginners and lectures are filled with real-life examples to help relate - this is something I've always felt difficult to get to grips with when it comes to statistics. There is also much less emphasis on the actual computing, but more towards understanding how to use values like p-Value and t-Value, so students only really need to know which test to use and how to interpret the results rather than the nuts and bolts of how they are actually computed.
Many of the exercises include interactive applets (built in to EdX, worked fine for me but a friend who was taking the course at the same time had some trouble getting it to work) which really help to demonstrate how the statistical values that output differ based on different inputs.
My main problem with the course is quite sadly with the exercises, which can be quite subjective and therefore a) frustrating and b) confusing. When an incorrect answer is submitted, there is often a hint pointing in the right direction but having completed the course I have to say there are some which I don't necessarily agree with that I feel have nothing to do with statistics.
All in all though I think it was a good experience, and would recommend to other learners especially those who are new to statistics.