Applied Python for Data Science & Engineering

Essential Python for Analytics, Scientific & Math Computing | With Numpy, Scipy, Pandas & More



4 Days

Course Overview

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 but not required.

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?

Need different skills or topics?  If your team requires different topics or tools, additional skills or custom approach, this course may be further adjusted to accommodate.  We offer additional python, data science, AI, machine learning , web development, data science, machine learning and other related topics that may be blended with this course for a track that best suits your needs. Our team will collaborate with you to understand your needs and will target the course to focus on your specific learning objectives and goals.

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.

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. Please see the Related Courses tab for specific Pre-Requisite courses, Related Courses that offer similar skills or topics, and next-step Learning Path recommendations.

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
  1. Getting Started
  • Using variables
  • Builtin functions
  • Strings
  • Numbers
  • Converting among types
  • Writing to the screen
  • String formatting
  • Command line parameters
  1. Flow Control
  • About flow control
  • White space
  • Conditional expressions (if,else)
  • Relational and Boolean operators
  • While loops
  • Alternate loop exits
  1. 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
  1. Working with files
  • File overview
  • Opening a text file
  • Reading a text file
  • Writing to a text file
  • Raw (binary) data
  1. Dictionaries and Sets
  • Creating dictionaries
  • Iterating through a dictionary
  • Creating sets
  • Working with sets
  1. 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
  1. Errors and Exception Handling
  • Syntax errors
  • Exceptions
  • Using try/catch/else/finally
  • Handling multiple exceptions
  • Ignoring exceptions
  1. 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
  1. Pythonic Programming
  • The Zen of Python
  • Common idioms
  • Named tuples
  • Useful types from collections
  • Sorting
  • Lambda functions
  • List comprehensions
  • Generator expressions
  • String formatting
  1. Introduction to Python Classes
  • Defining classes
  • Constructors
  • Instance methods and data
  • Attributes
  • Inheritance
  • Multiple inheritance
  1. 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
  1. Excel spreadsheets
  • The openpyxl module
  • Reading an existing spreadsheet
  • Creating a spreadsheet from scratch
  • Modifying an existing spreadsheet
  • Setting Styles
  1. 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
  1. iPython and Jupyter
  • iPython features
  • Using Jupyter notebooks
  • Benchmarking
  • External Commands
  • Cells
  • Sharing Notebooks
  1. Introduction to NumPy
  • NumPy basics
  • Creating arrays
  • Shapes
  • Stacking
  • Indexing and slicing
  • Array creation shortcuts
  • Matrices
  • Data Types
  1. Brief intro to SciPy
  • What is SciPy?
  • The Python science ecosystem
  • How to use SciPy
  • Getting Help
  • SciPy subpackages
  1. Intro to Pandas
  • Pandas overview & architecture
  • Series
  • Dataframes
  • Reading and writing data
  • Data alignment and reshaping
  • Basic indexing
  • Broadcasting
  • Removing Entries
  • Timeseries
  • Reading Data
  1. Introduction to Matplotlib
  • Overal architecture
  • Plot terminology
  • Kinds of plots
  • Creating plots
  • Exporting plots
  • Using Matplotlib in Jupyter
  • What else can you do?

Course Materials

Student Materials: Each participant will receive a Student Guide with course notes, code samples, software tutorials, step-by-step written lab instructions, diagrams and related reference materials and resource links. Students will also receive the project files (or code, if applicable) and solutions required for the hands-on work. Any courseware of lab materials provided in a cloud (if applicable) will also be made available to you separately.

Hands-On Setup Made Simple! Our dedicated tech team will work with you to ensure our ‘easy-access’ cloud-based course environment, or local installation, is accessible, fully-tested and verified as ready to go well in advance of the course start date, ensuring a smooth start to class and effective learning experience for all participants. We can also help you install this course locally if preferred. Please inquire for details and options.

Every-Course Extras = High-Value & Long-Term Learning Support! All Public Schedule courses include our unique EveryCourse Extras package (Post-Course Resource Site access with Review Labs & Live Instructor Follow-on Support, access to QuickSkills recorded High-Value lessons, Free *Live* Course Refresh Re-Takes, early access to Special Offers, Free Courses & more). Please inquire for details.

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Please see the current upcoming available open enrollment course dates posted below. Please feel free to Register Online below, or call 844-475-4559 toll free to connect with our Registrar for assistance. If you need additional date options, please contact us for scheduling.

Course Title Days Date Time Price
Applied Python for Data Science & Engineering 4 Days Feb 7 to Feb 10 10:00 AM to 06:00 PM EST $2,495.00 Enroll
Applied Python for Data Science & Engineering 4 Days Mar 21 to Mar 24 10:00 AM to 06:00 PM EST $2,495.00 Enroll
Applied Python for Data Science & Engineering 4 Days May 9 to May 12 10:00 AM to 06:00 PM EST $2,495.00 Enroll
Applied Python for Data Science & Engineering 4 Days Jul 11 to Jul 14 10:00 AM to 06:00 PM EST $2,495.00 Enroll
Applied Python for Data Science & Engineering 4 Days Aug 29 to Sep 1 10:00 AM to 06:00 PM EST $2,495.00 Enroll

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