Applied Python for Data Science [TTPS4874]

OVERVIEW

Geared for scientists and engineers with limited practical programming background or experience, Applied Python for Data Science is a hands-on introductory-level course that provides a ramp-up to using Python for scientific and mathematical computing. Students will explore basic Python scripting skills and concepts, and then explore the most important Python modules for working with data, from arrays, to statistics, to plotting results.

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:

• 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 Series and Dataframes with Pandas
• Create plots with Matplotlib
• Optional / Upon Request: Use Jupyter notebooks for ad hoc calculations, plots, and what-if?

AUDIENCE

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

NEXT STEP

Our core Python and data science training courses provide students with a solid foundation for continued learning based on role, goals, or their areas of specialty.  Our learning paths offer a wide variety of related follow-on courses such as:

- TTPS4876 Next Level Python in Data Science (Intermediate) | Numpy, Pandas, Spark, TensorFlow & More (5 days)

- TTML5506-P Machine Learning Essentials with Python (3 days)

- Please see the attached Python Training Suite list for additional topics and titles.

CONTENT

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.
 
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.Sequences

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

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

• Returning values
• Types of function parameters
• Variable scoping
• Documentation best practices
• Creating and importing modules
• Organizing modules into packages

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

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

13.Excel spreadsheets

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

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
• Terminal and GUI shells
• Creating and using notebooks

16.Intro to NumPy

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

17.Brief intro to SciPy

• What is SciPy do?
• Some useful functions
• SciPy subpackages

18.Intro to Pandas

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

19.Time-Permitting: Matplotlib

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