Applied Python for Data Science

Part 1: The Python Environment

1. About Python
2. Starting Python
3. Using the interpreter
4. Running a Python script
5. Python scripts on Unix/Windows
6. Using the Spyder editor

Part 2: Getting Started

1. Using variables
2. Builtin functions
3. Strings
4. Numbers
5. Converting among types
6. Writing to the screen
7. String formatting
8. Command line parameters

Part 3: Flow Control

1. About flow control
2. White space
3. Conditional expressions (if,else)
4. Relational and Boolean operators
5. While loops
6. Alternate loop exits

Part 4: Array Types

1. About sequences
2. Lists
3. Tuples
4. Indexing and slicing
5. Iterating through a sequence
6. Using enumerate()
7. Functions for all sequences
8. Keywords and operators for all sequences
9. The range() function
10. Nested sequences
11. List comprehensions
12. Generator expressions

Part 5: Working with files

1. File overview
2. Opening a text file
3. Reading a text file
4. Writing to a text file
5. Raw (binary) data

Part 6: Dictionaries and Sets

1. Creating dictionaries
2. Iterating through a dictionary
3. Creating sets
4. Working with sets

Part 7: Functions, modules, and packages

1. Four types of function parameters
2. Four levels of name scoping
3. Single/multi dispatch
4. Relative imports
5. Using __init__ effectively
6. Documentation best practices

Part 8: Errors and Exception Handling

1. Syntax errors
2. Exceptions
3. Using try/catch/else/finally
4. Handling multiple exceptions
5. Ignoring exceptions

Part 9: Using the Standard Library

1. The sys module
2. Launching external programs
3. Walking directory trees
4. Grabbing web pages
5. Sending e-mail
6. Paths, directories, and filenames
7. Dates and times
8. Zipped archives

Part 10: Pythonic Programming

1. The Zen of Python
2. Common idioms
3. Named tuples
4. Useful types from collections
5. Sorting
6. Lambda functions
7. List comprehensions
8. Generator expressions
9. String formatting

Part 11: Introduction to Python Classes

1. Defining classes
2. Constructors
3. Instance methods and data
4. Attributes
5. Inheritance
6. Multiple inheritance

Part 12: Developer tools

1. Program development
2. Comments
3. pylint
4. Customizing pylint
5. Using pyreverse
6. The unittest module
7. Fixtures
8. Skipping tests
9. Making a suite of tests
10. Automated test discovery
11. The Python debugger
12. Starting debug mode
13. Stepping through a program
14. Setting breakpoints
15. Profiling
16. Benchmarking

Part 13: Excel spreadsheets

1. The openpyxl module
2. Reading an existing spreadsheet
3. Creating a spreadsheet from scratch
4. Modifying an existing spreadsheet
5. Setting Styles

Part 14: Serializing Data

1. Using ElementTree
2. Creating a new XML document
3. Parsing XML
4. Finding by tags and XPath
5. Parsing JSON into Python
6. Parsing Python into JSON
7. Working with CSV

Part 15: iPython and Jupyter

1. iPython features
2. Using Jupyter notebooks
3. Benchmarking
4. External Commands
5. Cells
6. Sharing Notebooks

Part 16: Introduction to NumPy

1. NumPy basics
2. Creating arrays
3. Shapes
4. Stacking
5. Indexing and slicing
6. Array creation shortcuts
7. Matrices
8. Data Types

Part 17: Brief intro to SciPy

1. What is SciPy?
2. The Python science ecosystem
3. How to use SciPy
4. Getting Help
5. SciPy subpackages

Part 18: Intro to Pandas

1. Pandas overview & architecture
2. Series
3. Dataframes
4. Reading and writing data
5. Data alignment and reshaping
6. Basic indexing
7. Broadcasting
8. Removing Entries
9. Timeseries
10. Reading Data

Part 19: Introduction to Matplotlib

1. Overall architecture
2. Plot terminology
3. Kinds of plots
4. Creating plots
5. Exporting plots
6. Using Matplotlib in Jupyter
7. What else can you do?

This course is geared for:

• Data analysts, developers, engineers, or anyone tasked with utilizing Python for data analytics tasks. 
• Students should be comfortable working with files and folders and should not be afraid of the command line and basic scripting. 

• 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 and JSON
• Manipulate arrays with numpy
• Get a grasp of the diversity of subpackages that make up scipy
• Use iPython notebooks for ad hoc calculations, plots, and what-if?
• Manipulate images with PIL
• Solve equations with sympy