A Data Science Central Community

Some interesting stuff you can do with Lego's to introduce analytic thinking, computational complexity, and experimental design to kids 6-12 years old, and get them interested in analytics.

Let's say you purchase 2 sets of Lego's (one to build a car, another one to build another car). Let's assume that the overlap between the two sets is substantial. There is three different ways that you can build the two cars. The first step consists in sorting the pieces (Lego's) by color, and maybe also size. The three ways to proceed are:

**Sequentially**: Build one car at a time. This is the traditional approach.**Semi-parallel**system: sort all the pieces from both sets simultaneously (so pieces will be blended - some in the red pile will belong to car A, some to car B). Then build the two cars sequentially, following the instructions in the accompanying leaflets.**In parallel**: Sort all the pieces from both sets simultaneously, and build the two cars simultaneously, progressing simultaneously with the two sets of instructions.

Which is the most efficient way to proceed? The least efficient is sequential. Why? If you are a good at multitasking, the full parallel approach is best. Why? Note that with the semi-parallel approach, the first car that you build will take less time than the second car (due to easier way to find the pieces that you need because of higher redundancy), and less time than needed if you used the sequential approach (for the same reason).

You can have your kid build two cars A, B in parallel, then two other cars C and D sequentially, to test my assumptions, and to help get familiar with the concept of distributed architecture.

Other concepts that can be introduced: building a 80-piece car takes more than twice as much time as building a 40-piece car. Why? (the same also applies to puzzles). Note that if the overlap between A and B (the proportion of Lego pieces that are identical in both A and B) is small, then the sequential approach will work best.

**DSC Resources**

- Career: Training | Books | Cheat Sheet | Apprenticeship | Certification | Salary Surveys | Jobs
- Knowledge: Research | Competitions | Webinars | Our Book | Members Only | Search DSC
- Buzz: Business News | Announcements | Events | RSS Feeds
- Misc: Top Links | Code Snippets | External Resources | Best Blogs | Subscribe | For Bloggers

**Additional Reading**

- 50 Articles about Hadoop and Related Topics
- 10 Modern Statistical Concepts Discovered by Data Scientists
- Top data science keywords on DSC
- 4 easy steps to becoming a data scientist
- 13 New Trends in Big Data and Data Science
- 22 tips for better data science
- Data Science Compared to 16 Analytic Disciplines
- How to detect spurious correlations, and how to find the real ones
- 17 short tutorials all data scientists should read (and practice)
- 10 types of data scientists
- 66 job interview questions for data scientists
- High versus low-level data science

Follow us on Twitter: @DataScienceCtrl | @AnalyticBridge

Tags:

© 2019 AnalyticBridge.com is a subsidiary and dedicated channel of Data Science Central LLC Powered by

Badges | Report an Issue | Privacy Policy | Terms of Service