When does class start/end?
Classes begin promptly at 9:00 am, and typically end at 5:00 pm.
Applied Python for Data Science & Engineering
Geared for scientists and engineers with limited practical programming background or experience, Applied Python for Data Science & Engineering is a hands-on introductory-level course that...
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$2,295 USD
Course Code
TTPS4874
Duration
4 days
Available Formats
Classroom, Virtual
Geared for scientists and engineers with limited practical programming background or experience, Applied Python for Data Science & Engineering 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. Prior scripting experience is helpful.
Skills Gained
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?
Who Can Benefit
This course is geared for data analysts, developers, engineers or anyone tasked with utilizing Python for data analytics tasks.
Prerequisites
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 Details
Course Agenda
The Python Environment
- About Python
- Starting Python
- Using the interpreter
- Running a Python script
- Python scripts on Unix/Windows
- Using the Spyder editor
Getting Started
- Using variables
- Builtin functions
- Strings
- Numbers
- Converting among types
- Writing to the screen
- String formatting
- Command line parameters
Flow Control
- About flow control
- White space
- Conditional expressions (if,else)
- Relational and Boolean operators
- While loops
- Alternate loop exits
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
Working with files
- File overview
- Opening a text file
- Reading a text file
- Writing to a text file
- Raw (binary) data
Dictionaries and Sets
- Creating dictionaries
- Iterating through a dictionary
- Creating sets
- Working with sets
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
Errors and Exception Handling
- Syntax errors
- Exceptions
- Using try/catch/else/finally
- Handling multiple exceptions
- Ignoring exceptions
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
Pythonic Programming
- The Zen of Python
- Common idioms
- Named tuples
- Useful types from collections
- Sorting
- Lambda functions
- List comprehensions
- Generator expressions
- String formatting
Introduction to Python Classes
- Defining classes
- Constructors
- Instance methods and data
- Attributes
- Inheritance
- Multiple inheritance
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
Excel spreadsheets
- The openpyxl module
- Reading an existing spreadsheet
- Creating a spreadsheet from scratch
- Modifying an existing spreadsheet
- Setting Styles
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
iPython and Jupyter
- iPython features
- Using Jupyter notebooks
- Benchmarking
- External Commands
- Cells
- Sharing Notebooks
Introduction to NumPy
- NumPy basics
- Creating arrays
- Shapes
- Stacking
- Indexing and slicing
- Array creation shortcuts
- Matrices
- Data Types
Brief intro to SciPy
- What is SciPy?
- The Python science ecosystem
- How to use SciPy
- Getting Help
- SciPy subpackages
Intro to Pandas
- Pandas overview & architecture
- Series
- Dataframes
- Reading and writing data
- Data alignment and reshaping
- Basic indexing
- Broadcasting
- Removing Entries
- Timeseries
- Reading Data
Introduction to Matplotlib
- Overal architecture
- Plot terminology
- Kinds of plots
- Creating plots
- Exporting plots
- Using Matplotlib in Jupyter
- What else can you do?
