CHE 696: On-Ramp to Data Science

Contents:

• Syllabus
  • Course description
  • Topics
  • Texts
• 0) Welcome to ChE 696, On-Ramp to Data Science!
  • Topics for today:
• 1) Intro to basic text-based computer interfaces: bash and vim
  • Shell overview
  • Some basic, extremely useful bash commands
  • Some of my favorite bash time-saving tips
  • Customizing bash
  • Recommended text editor: vim
  • Reusable bash scripts
  • A note on file names
  • Other great utilities and functions
  • Summary
• HW1, practicing bash and vim
  • 1. For each part below, type the bash commands to complete the following tasks:
  • 2. Type the vim commands needed to perform the following tasks.
• 2) Jupyter notebooks
  • Why Jupyter notebooks?
  • A note on organization
  • Creating a new jupyter notebook
  • Edit and command modes
  • What is Markdown?
  • Extensions to jupyter notebooks
  • One last cool note about ipynbs, for those who love LaTex
• 3) Intro to python using ipynb
  • Some general notes about python
  • On to some simple python
• HW2, practicing with jupyter notebooks
  • 1. Checking installation
  • 2. Setting up jupyter notebooks
  • 3. Python in jupyter notebooks
• 4) Data types in python
  • Comments, Integers and floats
  • Other collections in python: lists, tuples, and dicts
• 5) More data types in python
  • Past and Present Topics
  • Identity and Membership Operators
  • Strings
  • A note on printing and floating-point precision
  • The None Type
  • More types of collections: sets and dicts
  • Some material I intentionally skipped
• HW3, practice with python data types and syntax
  • 1. Data types
  • 2. Simple functions and logic
• 6) Git for good programming practices
  • Version control with git
  • Some visuals of what git can do for you:
  • When first using Git on a machine
  • Let’s make a Git repo!
  • Tracking Changes
  • Exploring History
• 7) Git for good programming practices, cont.
  • Ignoring Things
  • Branches!
  • Remotes in GitHub
  • Collaborating
  • Conflicts
  • Open Science
  • Licensing
  • Citation
  • Hosting
  • Summary of key points from this notebook
• HW4, practice with Git
  • 1. Git
• 8) Cookie cutter projects and IDES for organized, delightful programming
  • Plan for today: Good programming workflow using python, git, and IDEs
  • Start with a plan
  • Rough plan:
• 9) Cookie cutter projects and IDES for organized, delightful programming, cont.
  • Sharing on Github
  • Merging conflicts with IntelliJ
• HW5, practice material from the “Whirlwind Tour of Python”
  • 8. Control Flow Statements
  • 9. Defining Functions
  • 10. Errors and Exceptions
  • 11. Iterators
  • 12. List Comprehensions
  • 13. Generators and Generator Expressions
• 10) Writing unit tests and running from anywhere on your machine
  • What is meant by unit testing?
  • Examples of types of errors to look for
  • Some best practices in writing tests
  • Next steps
• 11) Automated testing and packaging your project
  • FYI: Continuous integration
  • Setting up your package to run anywhere
  • Next steps
• 12) Intro to NumPy (prodounced Num-Pie), Numerical Python
• Libraries commonly used for Data Science
• The Case for NumPy
• Some Useful NumPy Array Attributes
• Some Ways ndarrays Are Like Python Lists: Slicing and indexing
  • The general format for slicing is:
• And some differences
  • Subparts of an array will change the parent array
• Array Concatenation and Splitting
• HW6, Practice Working with Projects
  • 1. Extending arthritis_proj_demo
  • 2. Working with NumPy Arrays
• 13) More NumPy Plus Linear Algebra Fundamentals
• First, reminder to submit evaluations
• Second, let’s discuss the individual project
• The simplicity of NumPy math
  • Broadcasting: How NumPy will make compatible arrays
  • More examples of NumPy’s math knowledge
  • What about math with NaNs?
• Linear algebra
  • Overview
  • Fundamentals
• Matrices
  • Matrix Addition
  • Scalar Multiplication
  • Matrix Multiplication
  • Inner product of vectors
  • Special Matrices
  • Calculating determinants
• Next up: Vectors and Linear Transformations!
• 14) Vectors and Linear Transformations
• Vectors
  • Overview
  • Linear Dependence and Bases
  • Dimension
  • What is a subspace?
• Linear Transformations
  • Image
  • Rank
• Solutions of Linear Equations
  • Fundamental theorem for linear systems
  • The homogeneous linear system
  • The nonhomogeneous linear system
  • Cramer’s Theorem
• 15) Eigenvectors and Eigenvalues
• One of the most important problems in linear algebra:
  • But how do we solve these equations?
  • Can real matrices get complex?
• 16) Data Manipulation with Pandas
  • First, let’s check that we have Pandas installed
  • The Pandas Series Object
  • The Pandas DataFrame Object
  • The Pandas Index Object
  • Some Index methods and properties
  • Reindexing
  • Dropping Entries from an Axis
  • Selection with loc and iloc
• HW7, Practice with NumPy and Linear Algebra
  • 1. Probability fundamentals
  • 2. Matrices and Linear Algebra
  • 1. Pandas
• Databases
  • Why databases?
  • Types of databases
  • Examples:
  • Sources
• Databases Pt. 2
  • Example, continued: IMBD, Index of Marvel data with Bradley Dice
  • JOINing [STRIKEOUT:forces] tables
  • Types of JOINs
  • More Derived Columns
  • Visualizing Data with Pandas
  • Common problems in designing relational databases
  • Normal Form
  • Chinook Sample Database
• Individual Project
  • Resources to help you
  • Due date and submission instructions