Applied Python for Data Science & Engineering

Course Objectives

This course is approximately 50% hands-on, combining expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises.  Our engaging instructors and mentors are highly experienced practitioners who bring years of current "on-the-job" experience into every classroom.  Working in a hands-on learning environment, guided by our expert team, attendees will learn how to:

• Learn essentials Python scripting methods to create and run basic programs
• Design and code modules and classes
• Implement and run unit tests
• Use benchmarks and profiling to speed up programs
• Process XML, JSON, and CSV
• Manipulate arrays with NumPy
• Get a grasp of the diversity of subpackages that make up SciPy
• Use Series and Dataframes with Pandas
• Use Jupyter notebooks for ad hoc calculations, plots, and what-if?

Course Prerequisites

This course is geared for data analysts, developers, engineers or anyone tasked with utilizing Python for data analytics tasks.  While there are no specific programming prerequisites, students should be comfortable working with files and folders and the command line. Prior scripting experience is helpful but not required.

Course Agenda

Please note that this list of topics is based on our standard course offering, evolved from typical industry uses and trends. We’ll work with you to tune this course and level of coverage to target the skills you need most.

1. The Python Environment

• About Python
• Starting Python
• Using the interpreter
• Running a Python script
• Python scripts on Unix/Windows
• Using the Spyder editor

2. Getting Started

• Using variables
• Builtin functions
• Strings
• Numbers
• Converting among types
• Writing to the screen
• String formatting
• Command line parameters

3. Flow Control

• About flow control
• White space
• Conditional expressions (if,else)
• Relational and Boolean operators
• While loops
• Alternate loop exits

4. Array Types

• About sequences
• Lists
• Tuples
• Indexing and slicing
• Iterating through a sequence
• Using enumerate()
• Functions for all sequences
• Keywords and operators for all sequences
• The range() function
• Nested sequences
• List comprehensions
• Generator expressions

5. Working with files

• File overview
• Opening a text file
• Reading a text file
• Writing to a text file
• Raw (binary) data

6. Dictionaries and Sets

• Creating dictionaries
• Iterating through a dictionary
• Creating sets
• Working with sets

7. Functions, modules, and packages

• Four types of function parameters
• Four levels of name scoping
• Single/multi dispatch
• Relative imports
• Using __init__ effectively
• Documentation best practices

8. Errors and Exception Handling

• Syntax errors
• Exceptions
• Using try/catch/else/finally
• Handling multiple exceptions
• Ignoring exceptions

9. Using the Standard Library

• The sys module
• Launching external programs
• Walking directory trees
• Grabbing web pages
• Sending e-mail
• Paths, directories, and filenames
• Dates and times
• Zipped archives

10. Pythonic Programming

• The Zen of Python
• Common idioms
• Named tuples
• Useful types from collections
• Sorting
• Lambda functions
• List comprehensions
• Generator expressions
• String formatting

11. Introduction to Python Classes

• Defining classes
• Constructors
• Instance methods and data
• Attributes
• Inheritance
• Multiple inheritance

12. Developer tools

• Program development
• Comments
• pylint
• Customizing pylint
• Using pyreverse
• The unittest module
• Fixtures
• Skipping tests
• Making a suite of tests
• Automated test discovery
• The Python debugger
• Starting debug mode
• Stepping through a program
• Setting breakpoints
• Profiling
• Benchmarking

13. Excel spreadsheets

• The openpyxl module
• Reading an existing spreadsheet
• Creating a spreadsheet from scratch
• Modifying an existing spreadsheet
• Setting Styles

14. Serializing Data

• Using ElementTree
• Creating a new XML document
• Parsing XML
• Finding by tags and XPath
• Parsing JSON into Python
• Parsing Python into JSON
• Working with CSV

15. iPython and Jupyter

• iPython features
• Using Jupyter notebooks
• Benchmarking
• External Commands
• Cells
• Sharing Notebooks

16. Introduction to NumPy

• NumPy basics
• Creating arrays
• Shapes
• Stacking
• Indexing and slicing
• Array creation shortcuts
• Matrices
• Data Types

17. Brief intro to SciPy

• What is SciPy?
• The Python science ecosystem
• How to use SciPy
• Getting Help
• SciPy subpackages

18. Intro to Pandas

• Pandas overview & architecture
• Series
• Dataframes
• Reading and writing data
• Data alignment and reshaping
• Basic indexing
• Broadcasting
• Removing Entries
• Timeseries
• Reading Data

19. Introduction to Matplotlib

• Overal architecture
• Plot terminology
• Kinds of plots
• Creating plots
• Exporting plots
• Using Matplotlib in Jupyter
• What else can you do?