Applied Python for Data Science

Prerequisites:

While there are no specific programming prerequisites, students should be comfortable working with files and folders and should not be afraid of the command line and basic scripting.

Target Audience:

This course is geared for data analysts, developers, engineers, or anyone tasked with utilizing Python for data analytics tasks. 

Course Objectives:

Skills-Focused, Hands-On Learning: This course is about 50% hands-on lab to 50% lecture ratio, combining engaging instructor presentations, demonstrations and discussions with extensive machine-based student labs and practical project work. Throughout the course, students will learn to write essential Python scripts and apply them within a scientific framework working with the latest technologies listed on the agenda. Although the course is introductory in nature, it will increase in complexity as more sophisticated skills and techniques are introduced. Students can rely on our highly experienced instructors to provide informed, relatable, ‘real-world' answers to their questions.

Working within an engaging, hands-on learning environment, attendees will learn to use Python 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 and JSON
• Manipulate arrays with NumPy
• Get a grasp of the diversity of sub-packages that make up scipy
• Use iPython notebooks for ad hoc calculations, plots, and what-if?
• Manipulate images with PIL
• Solve equations with sympy

Course Outline:

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. Topics, agenda, and labs are subject to change and may adjust during live delivery based on audience needs and skill-level.

Session 1 — The Python Environment

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

Session 2 — Getting Started

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

Session 3 — Flow Control

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

Session 4 — Sequences

• About sequences 
• Lists and tuples
• Indexing and slicing 
• Iterating through a sequence 
• Sequence functions, keywords, and operators 
• List comprehensions 
• Generator expressions
• Nested sequences 

Session 5 — Working with files

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

Session 6 — Dictionaries and Sets

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

Session 7 — Functions

• Defining functions
• Parameters 
• Variable scope 
• Returning values 
• Lambda functions

Session 8 — Errors and Exception Handling

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

Session 9 — OS Services

• The os module
• Environment variables
• Launching external commands
• Walking directory trees
• Paths, directories, and filenames
• Working with file systems
• Dates and times

Session 10 – Pythonic idioms

• Small Pythonisms
• Lambda functions
• Packing and unpacking sequences
• List Comprehensions
• Generator Expressions

Session 11 – Modules and packages

• Initialization code
• Namespaces
• Executing modules as scripts
• Documentation
• Packages and name resolution
• Naming conventions
• Using imports

Session 12 — Classes

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

Session 13 – Developer tools

• Analyzing programs with pylint
• Creating and running unit tests
• Debugging applications
• Benchmarking code
• Profiling applications

Session 14 – XML and JSON

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

Session 15 – iPython

• iiPython basics
• Terminal and GUI shells
• Creating and using notebooks
• Saving and loading notebooks
• Ad hoc data visualization

Session 16 – numpy

• numpy basics
• Creating arrays
• Indexing and slicing
• Large number sets
• Transforming data
• Advanced tricks

Session 17 – scipy

• What can scipy do?
• Most useful functions
• Curve fitting
• Modeling
• Data visualization
• Statistics

Session 18 – A tour of scipy sub-packages

• Clustering
• Physical and Mathematical Constants
• FFTs
• Integral and differential solvers
• Interpolation and smoothing
• Input and Output
• Linear Algebra
• Image Processing
• Distance Regression
• Root-finding
• Signal Processing
• Sparse Matrices
• Spatial data and algorithms
• Statistical distributions and functions
• C/C++ Integration

Session 19 – pandas

• pandas overview
• Dataframes
• Reading and writing data
• Data alignment and reshaping
• Fancy indexing and slicing
• Merging and joining data sets

Session 20 – matplotlib

• Creating a basic plot
• Commonly used plots
• Ad hoc data visualization
• Advanced usage
• Exporting images

Session 21 — The Python Imaging Library (PIL)

• PIL overview
• Core image library
• Image processing
• Displaying images