TTPS4874: Applied Python for Data Science & Engineering

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About this Course

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 you with a ramp-up to using Python for scientific and mathematical computing. Working in a hands-on learning environment, you’ll learn basic Python scripting skills and concepts, as well as the most important Python modules for working with data, from arrays, to statistics, to plotting results.

Throughout the course, guided by our expert instructor, you’ll gain a robust skill set that will equip you to make data-driven decisions and elevate operational efficiencies within your organization. You’ll explore data manipulation with Pandas, advanced data visualization using Matplotlib, and numerical analysis with NumPy. You’ll also delve into best practices for error and exception handling, modular programming techniques, and automated workflow development, equipping you with the skill set to enhance both the effectiveness and efficiency of your data-driven projects.

NOTE: For those interested in Leveraging AI with Python for Data Science and Analytics, optional chapters are available to extend the course to dive into some of the core innovative skills. Please inquire for details.

Audience Profile

This introductory-level course is geared for technical professionals new to Python. Roles include data analysts, developers, engineers or anyone tasked with utilizing Python for data analytics tasks. Familiarity with basic scripting skills is recommended, as this course does not teach general scripting basics.

At Course Completion

Working in a hands-on learning environment, guided by our expert team, attendees will learn about and explore:

Core Python Proficiency: By the close of the course, participants will have a firm grasp on the foundational elements of Python, such as variables, data types, and flow control, empowering them to write scripts and build simple programs with confidence.
Analytical Problem-Solving: Utilizing libraries such as NumPy and SciPy, students will develop the ability to perform complex mathematical operations and statistical analyses, significantly amplifying their analytical capabilities for tasks such as data modeling or optimization problems.
Data Manipulation Mastery: By the end of the course, participants will be proficient in employing Pandas to clean, transform, and analyze data sets, enabling them to make data-driven decisions effectively.
Automated Workflow Development: Students will acquire the ability to construct automated scripts using Python's Standard Library, optimizing repetitive tasks and thereby enhancing operational efficiency in their organizations.
Advanced Data Visualization: Upon course completion, learners will be equipped to utilize Matplotlib and other Python libraries to craft intricate visual representations of data, facilitating clearer and more impactful reporting and presentations.
Error-Resilient Coding: Attendees will learn best practices for implementing robust error and exception handling techniques, leading to the creation of more stable and secure Python applications.
Modular Programming Proficiency: By mastering Python functions, modules, and packages, students will be adept at developing modular and maintainable code, a key skill for scalability and collaborative programming projects.

Outline

Please note that this list of topics is based on our standard course offering, evolved from typical industry uses and trends. We will work with you to tune this course and level of coverage to target the skills you need most. Course agenda, topics and labs are subject to adjust during live delivery in response to student skill level, interests and participation.

Getting Started with the Python Environment

Starting Python
Using the interpreter
Running a Python script
Editors and IDEs

Variables and Values

Using variables
Builtin functions
String data
Numeric data
Converting types

Basic input and output

Writing to the screen
String formatting
Command line arguments
Reading the keyboard

Flow Control

About flow control
The if statement
Relational and Boolean values
while loops
Exiting from loops

Array types

Sequence types in general
Lists and list methods
Tuples
Indexing and slicing
Iterating through a sequence
Sequence functions, keywords, and operators
List comprehensions and generators

Working with files

File I/O overview
Opening a text file
Reading a text file
Writing to a text file

Dictionaries and Sets

About dictionaries
Creating dictionaries
Getting values
Iterating through a dictionary
About sets
Creating sets
Working with sets

Functions, modules, and packages

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

Virtual Environments

Why do we need virtual environments
Creating an environment
Activating and deactivating
Replicating an environment
Tools for environments

Exception handling and logging

About exceptions
Using try/catch/else/finally
Handling multiple exceptions
Logging setup
Basic logging

Introduction to Python Classes

Defining classes
Constructors
Instance methods and data
Attributes
Inheritance
Multiple inheritance

Excel spreadsheets

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

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 Jupyterlab

iPython features & iPython "magic" commands
iPython configuration
Creating Jupyter notebooks
Managing notebooks with Jupyterlab

Intro to NumPy

NumPy basics
Creating arrays
Indexing and slicing
Large number sets
Transforming data
SciPy overview

Intro to Pandas

Pandas overview
Series and Dataframes
Reading and writing data
Data summaries
Data alignment and reshaping
Selecting and indexing
Merging and joining data sets
Plotting data

Matplotlib

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

Optional Topics or Day Five:

For Dedicated / Private Classes:

Introduction to AI with Python for Data Analysis

Overview of AI Libraries
Setting Up Your Environment:
Understanding AI Models
Creating Your First Model
Evaluating Model Performance

Practical AI Projects in Python

Set up a Python project for AI applications.
Data Handling
Model Development
Test and validate your AI model's effectiveness.
Applying Your Model

Using GPT Tools for Record Analysis in Data Science

Introduction to GPT
Setting Up GPT Tools
Analyzing Text Data
Generating Insights
Practical Applications

Prerequisites

Familiarity with basic scripting skills is recommended, as this course does not teach general scripting basics.