# Data Science: An Introduction/Thinking Like a Statistician

Data Science: An Introduction
* Welcome to Data Science
** 01: A History of Data Science
** 02: A Mash-up of Disciplines
** 03: Definitions of Data
** 04: The Impact of Data Science
* Thinking about the World
** 05: Thinking Like a Visual Artist
** 06: Thinking Like a Data Engineer (Oct 13, 2012)
** 07: Thinking Like a Hacker
** 08: Thinking Like a Programmer
** 09: Thinking Like a Scientist
** 10: Thinking Like a Mathematician
** 11: Thinking Like a Statistician
** 12: Thinking Like a Domain Expert
* Analyzing and Visualizing, Part One
** 13: Single Variable Analysis
** 14: Single Variable Tables and Plots
* Setting up the Problem
** 15: Theory-Based Inquiry
** 16: Theoretical vs Measured Variables
** 17: Hypothesis Testing
* Collecting, Ingesting, Transforming Data
** 18: Collecting vs Finding Data
** 19: Data Dictionaries and Schemas
** 20: Data Preparation and Metadata
* Analyzing and Visualizing, Part Two
** Scientific data visualisation
** 21: Two Variable Analysis
** 22: Two Variable Tables
** 23: Two Variable Plots
* Emergent Answers to Free Form Problems
** 24: Non-Theory-Based Inquiry
** 25: Exploratory Analysis
* Analyzing and Visualizing, Part Three
** 26: Scientific data visualisation
** 26: Multiple Variable Analysis
** 27: Multiple Variable Tables
** 28: Multiple Variable Plots
* Presenting Results
** 29: Statistical Significance vs Substantive Significance
** 30: Telling the Story
** 31: Writing Good Data-Rich Paragraphs
** 32: Creating Good Data-Rich Slides
* Appendices
** 01. Glossary
** 02. Working in Groups
** 03. 250 R Commands
** 04. Research Design
** 05. Data Dictionary
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## Chapter Summary

When a data scientist thinks like a statistician, they think in terms of variables. The tasks are to understand the central tendencies, the distributions, the correlations, and the clusters of the variables associated with the problem and its solution.

## Assignment/Exercise

This Project #2, which spans four chapters. Assemble into groups of 3 or 4 students. A group of three may not have the same members as the group for Project #1. A group of four may have no more than two students repeating from the group from the Project #1. This group will do the entire project together.
* Replicate Galileo's "inclined plane" experiment. Start by designing the research and write down your plan. List materials needed, specify methods to be used, identify variables to be measured, create data recording sheets, etc.
* Conduct the experiment according to the design. Take pictures. Record your data results.
* Enter the data into R. Use R to produce tables and draw plots of your data. See if you can draw the theoretical curve Galileo was trying to discover on your data plots.
* Prepare a slide presentation that includes a description of your methods, pictures of your apparatus, a table of your raw data, a table of your analyzed results, plots of your results, a list of several things the group learned on its own about data science during the course of this project.
Note: Your group can specialize on tasks, but everyone needs to participate in all phases of the assignment. Also, the chapters covered to this point do not teach you everything you need to know to do this assignment. Please do the best you can with what you know. This assignment is not just to show the instructor how much of the previous chapters you have learned, but the assignment is a learning experience in and of itself. The assignment is designed for the students to discover knowledge not contained in the chapters.

## More Reading

* Huff, Darrell (1991) [1954]. How to Lie with Statistics (New Ed ed.). New York: Penguin. ISBN 0-14-013629-0. {{cite book}}: |edition= has extra text (help)
* Best, Joel (2001). Damned Lies and Statistics: Untangling Numbers from the Media, Politicians, and Activists. Berkeley, CA: University of California Press. ISBN 0520219783.

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