TTPS4874: Python Fundamentals for Data Science

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About this Course

Geared for scientists and engineers with limited practical programming background or
experience, Python Fundamentals for Data Science 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 with both Python scripts and Jupyter notebooks,
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. 

Objectives
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.

Audience Profile

TThis introductory-level course is designed for technical professionals who are new to Python
and want to use it for data analysis and data science workflows. Typical roles include data
analysts, engineers, developers, and researchers transitioning from tools such as Excel or SQL.

At Course Completion

Outline

Getting Started with the Python Environment
 Starting Python
 Using the interpreter
 Running a Python script
 Editors and IDEs
iPython and Jupyterlab

 iPython features & iPython "magic" commands
 iPython configuration
 Creating Jupyter notebooks
 Managing notebooks with Jupyterlab
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
Intro to Pandas
 Pandas overview
 Series and Dataframes
 Reading and writing data
 Data summaries
 Data alignment and reshaping
 Selecting and indexing
 Basic Data Plotting

Matplotlib
 Creating a basic plot
 Commonly used plots
 Ad hoc data visualization
 Leveraging Seaborn for better plots
 Exporting images

Intro to NumPy
 NumPy basics
 Reading Data
 Creating arrays
 Indexing and slicing
 Large number sets
 Transforming data

Prerequisites

No prior Python experience is required. Familiarity with basic programming or scripting concepts (such
as variables and simple logic) is helpful but not required.