Module Description¶
In this module, students gain an introduction to the foundational concepts in data science. This includes teaching students how to code in Python as well as an introduction to the relevant statistical skills from measures of central tendency to visualizations. Students begin to ask questions about ethics and data science and investigate the role of data science in society through applied examples.
Throughout the module students will learn the following content topics:
Introduction to Computer Science¶
Python Syntax
Operators, built in functions and print()
Variables and Name Assignments/Conventions
Data Types and Data Casting
Interpreting Error Messages
Call Expressions and Functions
Introduction to Hardware and File types
History of Programming Languages
Lists
Objects and Object Oriented Programming (introduction)
Introduction to Data Science¶
History of Data Science as a field
Features of data sets and table attributes
Arrays
NumPy functions
Data cleaning
Measures of central tendency (mean, mode, median, range)
Categorical and Quantitative Variables
Dot Plots, Histograms, Box and whisker plots
Interpreting Distribution Shape (tails, skew, distribution, symmetry)
Data Sensemaking¶
Interrogating what is being measured, what was left out, who collected the data and potential sources of bias
Implications of data analysis on society through applied examples
How data has changed the job market, the economy and the environment Introduction to bias and data science ethical dilemmas (privacy etc)
Statistics¶
5 Number Summary
Frequencies and distributions
Variance and Standard Deviation