A portion of these materials have been classroom-tested: the Kaggle classroom assignment.
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.
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
Week 1¶
Lecture 1: What is Data Science¶
This lecture introduces the field of data science through historical and modern case studies, exploring its societal impact and ethical considerations.
Lecture 2: Data, Statistics and Ethics¶
This lecture defines fundamental concepts of data, Big Data, and statistics, covering sampling methods, data storage basics, and career roles. A significant focus is placed on data ethics, including privacy, bias, and responsible data handling throughout its lifecycle.
Lab 1: Reading and Exploring Datasets¶
In this lab, students will inspect a dataset’s characteristics and practice preliminary data cleaning and then analyze the story of a data set.
Week 2¶
Lecture 3: Introduction to Programming and Measures of Central Tendency¶
This lecture introduces basic Python programming concepts alongside statistical measures of central tendency. Students will learn how to calculate and interpret mean, median, and mode, and understand when to use each measure.
Lecture 4: Graphing and Python Fundamentals¶
This session expands on Python programming by covering data types, variables, and lists for organizing data. Students will also learn to visualize quantitative data using dot plots and interpret the shape and distribution of datasets.
Lab 2: Computer Programming Introduction¶
Students use a Jupyter Notebook to explore data, interpret dot plots, and use variables and lists to perform calculations. This lab focuses on an understanding of data types, functions, and introductory data analysis.
Homework 1: Python Programming Practice¶
This assignment gives students the opportunity to practice foundational Python programming skills, requiring students to engage with variables, data types, arithmetic operators, and list manipulation.