Courses

Certifications

Instructors

Applied Python for Data Science

Essential Python for Analytics, Scientific & Math Computing | With numpy, scipy, pandas, PIL & More

Geared for scientists and engineers with potentially light practical programming background or experience, Applied Python for Data Scientists is a hands-on Python 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 move to the most important Python modules for working with data, from arrays, to statistics, to plotting results.

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 in 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.

Duration

4 days/32 hours of instruction

Public Classroom Pricing

Starting at: $2395(USD)

GSA Price: $1796.25

Group Rate: $2295

Private Group Pricing

Have a group of 5 or more students? Request special pricing for private group training today.

Download the Course Brochure

Part 1: The Python Environment

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

Part 2: Getting Started

Using variables
Builtin functions
Strings
Numbers
Converting among types
Writing to the screen
String formatting
Command line parameters

Part 3: Flow Control

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

Part 4: 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

Part 5: Working with files

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

Part 6: Dictionaries and Sets

Creating dictionaries
Iterating through a dictionary
Creating sets
Working with sets

Part 7: 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

Part 8: Errors and Exception Handling

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

Part 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

Part 10: Pythonic Programming

The Zen of Python
Common idioms
Named tuples
Useful types from collections
Sorting
Lambda functions
List comprehensions
Generator expressions
String formatting

Part 11: Introduction to Python Classes

Defining classes
Constructors
Instance methods and data
Attributes
Inheritance
Multiple inheritance

Part 12: 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

Part 13: Excel spreadsheets

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

Part 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

Part 15: iPython and Jupyter

iPython features
Using Jupyter notebooks
Benchmarking
External Commands
Cells
Sharing Notebooks

Part 16: Introduction to NumPy

NumPy basics
Creating arrays
Shapes
Stacking
Indexing and slicing
Array creation shortcuts
Matrices
Data Types

Part 17: Brief intro to SciPy

What is SciPy?
The Python science ecosystem
How to use SciPy
Getting Help
SciPy subpackages

Part 18: Intro to Pandas

Pandas overview & architecture
Series
Dataframes
Reading and writing data
Data alignment and reshaping
Basic indexing
Broadcasting
Removing Entries
Timeseries
Reading Data

Part 19: Introduction to Matplotlib

Overall architecture
Plot terminology
Kinds of plots
Creating plots
Exporting plots
Using Matplotlib in Jupyter
What else can you do?

This course is geared for:

Data Scientist
Data Analyst
Data Engineer
Anyone tasked with utilizing Python for data analytics actions.

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

A full refund will be issued for class cancellations made at least 10 business days before the course begins. Payment is nonrefundable for cancellations or reschedules made within 10 business days from the course start date and for No Shows (students who do not attend class). For reschedules made within 10 business days from the course start date, students must reschedule immediately for the same course, up to a maximum of six months from the original date. A student may only reschedule one time.