Linked Index to Videos
Chris Wild, Department of Statistics, The University of Auckland
In addition to being a MOOC introducing its students to statistical data analysis, "Data to Insight"
(https://www.futurelearn.com/courses/data-to-insight) prototypes a much-further-much-faster, more-data-more-quickly Introductory Statisics Course. The acceleration should be evident from the course outline below, particularly since it is "covered" in about 10% of the "class time" of a standard introductory course.
This page has been created for university and college teachers of statistics and research methods to enable them to dip in and see "what is being done and how" by jumping directly to particular movies.
You are welcome to come, dig around and "steal" ideas, but I do ask three things:
- Please do not critique the course on the course site itself
- Leave the course blogging/commenting facilities for the real MOOC students.
- If you watch or download any of the movies, please mark the "step" Complete on the course site
- (a pink button towards the bottom right of the video page)
- If there is a community you are in contact with that could benefit from this course, please send them to https://www.futurelearn.com/courses/data-to-insight.
- So long as they sign up before the course ends (30 November) they can do it and at their own pace.
- For the links below to work, you need to be signed in to the course site on FutureLearn.
- Register here for a free login.
TABLE OF CONTENTS
(Only our movies are linked. To see other content, click on the title of any Week.)- Publicity trailer
- Week 1: Getting Started
- Week 2: Boot Camp
- Week 3: Relationships
- Week 4: More Relationships
- Week 5: Why what we see is never quite the way it really is
- Week 6: Estimation With Confidence
- Week 7: Randomised Experiments and Statistical Tests
- Week 8: Time Series
- Strategies used for acceleration
TRAILER (marketing)
- Course Trailer () [2:40]
Week 1: GETTING STARTED
- Introduction to the Course () [3:25]
- How do we use data analysis? (Interviews with 4 statisicians working in a range of fields) [4:36]
- Data Organisastion [5:15]
Week 2: BOOT CAMP
- Statistics Boot Camp (Introduction to the week) [1:17]
- Categorical Variables (Frequency tables and barcharts) [4:59]
- Ordering Categories (Alphanumeric, natural and frequency ) [2:20]
- Numeric variables (Dotplots & histograms; Centre (means & medians), where and why different) [5:44]
- Feature Spotting (Spread, shapes & oddities; Colouring by a 2nd variable) [5:26]
- Comparing groups ((for numeric variable) Comparing features; Shifts & background varianbility; Colouring by a 3rd variable) [7:53]
- Time Travel (Case study subsetting over a 3rd variable using tiled plots and movement) [3:22]
Week 3: RELATIONSHIPS
- Introduction to Relationships (Why we care; Outcome & predictor variables) [2:52]
- Relationships between Categorical Variables (Exploration using separate bar charts and side-by-side bar charts) [6:22]
- Changes across subgroups (Exploring effects of a 3rd and 4th variable on a relationship via subsetting, tiling & movement) [4:52]
- Relationships between numeric variables (Scatter plots; Trend, scatter & outliers; Clustering) [5:17]
- Trend, Scatter & Outliers (Examples; Prediction & prediction intervals; Training the eye) [6:42]
Week 4: MORE RELATIONSHIPS
- More Relationships (Introduction to week's coverage) [1:32]
- Lines, curves and smoothers (Lines, curves & smoothers; Least squares) [4:01]
- Overcoming Perceptual Problems (Problems with large datasets; Overprinting; Jitter; Varying transparency & point size; Running quantiles; Tile-density plots) [7:05]
- Diving deeper with more variables (Additional variables using colour, subsetting & movement; different trends per group ) [5:20]
- Our Changing Health and Wealth (Case study using up to 6 variables at once by employing colour, size, subsetting, matrices of plots and movement ) [5:41]
Week 5: WHY WHAT WE SEE IS NEVER QUITE THE WAY IT REALLY IS
- Why what I see is never quite the way it really is (Intro to week; Facts & artefacts) [03:04]
- Bad Data ("Measurement" issues/ biases; "Selection" biases; missingness) [07:02]
- Causes and Lurking Variables, Part I (Confounding & adjustment) [06:38]
- Causes and Lurking Variables, Part II (Confounding & adjustment) [03:57]
- Random Error, Part I (Random variation/error; effect of sample size) [07:06]
- Random Error, Part II (Random variation/error; effect of sample size; biases) [06:05]
Week 6: ESTIMATION WITH CONFIDENCE
- Estimation With Confidence (Motivation, background, intuition) [04:52]
- Confidence Intervals from Bootstrap Resampling (Bootstrap idea; Comparing BS re-sampling variation & sampling variation; Construction of bootstrap CI) [08:27]
- Do CIs work? (Simulation coverage) [05:41]
- Numeric Outcomes (Use of CIs and comparison intervals in anova-type situation) [05:41]
- Categorical Outcomes (Use of CIs and comparison intervals; [At end:bootstrapping trends on scatter plot]) [05:39]
Week 7: RANDOMISED EXPERIMENTS AND STATISTICAL TESTS
- Randomised Experiments [05:24]
- Randomisation Variation (Effects of pure randomisation variation) [06:01]
- The Randomisation Test [07:45]
- Percentages and Multiple groups (Applied to proportions/percentages in a 2- and 3-groups situations) [07:48]
- Out of the Fog and into Power (Power, Type I error and "sample size") [08:27]
Week 8: TIME SERIES
- Introducing Time Series [04:38]
- Seasonal Decomposition & Forecasting, Part I [06:01]
- Seasonal Decomposition & Forecasting, Part II [05:12]
- Comparing Series [05:16]
Strategies used for acceleration
The most novel strategies used are:- Being intensely visual and driving all argument off things you can see supplemented by metaphor;
- Building software solutions that prevent "how do I get this out of the software?" limiting the speed at which students can encounter new situations and new ideas;
- Finding some powerful, conceptually-undemanding "extender-capabilities" that immediately open much wider horizons.