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Keith Alexander Ashe spent his career using data and technology to transform processes and systems. Although he took a couple programming courses in high school and college; he ultimately used weekend projects, self-paced learning, and on-the-job experience to become a full stack software developer. Mr. Ashe previously worked at Booz Allen, Gartner, Citibank, Happy Money, and Rollbar before launching his own consulting firm, Ordinal Prime.
His last project was working as a contractor with the Bill & Melinda Gates Foundation's Institute for Disease Modeling to prepare Starsim for a public beta release. Starsim is an open-source, stochastic disease simulation modeling tool built using Python.
He created the PyForward training series because he wanted to make it easier for others to start coding in Python. He is a graduate of Florida A&M University and Columbia University with a BS and MS in Industrial Engineering, respectively. He also earned a Lean Six Sigma Blackbelt from Villanova University.
Course Outline
Getting Started
A Brief History of Computing
Number Systems
Bits and Bytes
Data Analysis Best Practices
Data in the Enterprise
Process Improvement
Thinking Like a Programmer
Intro to AI, ML, DS
Python Basics
Getting Started
Data Structures
Pandas and NumPy Libraries
Self-Paced Worksheets
Data Analysis with Python
Creating a Python Cheat Sheet
Landing a Job
Next Steps & Resources
As I mentioned, learning can be difficult but I've designed this course to make it easier to tackle complex topics. This session provides a few tricks and tips that I've picked up throughout my career to make sure that I can effectively learn and apply new knowledge.
This lesson briefly delves into the history of data storage, analytics, and computing from early human history to the printing press and now social media and cloud technology.
This lecture helps demonstrate the importance of number systems in computing including binary (base 2), decimal (base 10), and hexadecimal (base 16). This also covers the types of data including discrete (ball bouncing) and continuous (ball rolling).
Binary or 0s and 1s are the fundamental units in computer science.
Data analysis best practices. Never rush the process.
Data in the Enterprise covers how data is stored and transferred in large organizations. Topics include the Data Dictionary or Target Record Layout (TRL), Agile and the software development lifecycle, and tools for storing structured and unstructured data,
This session covers process improvement and problem solving frameworks including process mapping, Lean Six Sigma, and Value Stream Mapping and more.
Think like a programmer. Objects, libraries, time and space complexity.
Introduction to Artificial Intelligence, Machine Learning with an illustrative demonstration, and Data Science.
Introduction to Python
Facts, data, and statistics on why Python is popular and useful to learn as your first coding language.
Video instructions to download the Anaconda to write, debug, and execute Python code.
Video instructions to download the Anaconda to write, debug, and execute Python code. Interactive coding exercise included as a ZIP file.
This session covers variables, data types, and assignment of data types in Python.
Data structures are data types and collections.
Python Pandas Library. Interactive coding exercise included as a ZIP file.
Python NumPy Library. Interactive coding exercise included as a ZIP file.
The Lambda Function. Interactive coding exercise included as a ZIP file.
Loops in Python. Python utilizes collections rather than arrays. Collections are data structures that can be iterated through using the foreach loop (for i in my_data_structure = for every element represented as i in my collection, my_data_structure).
Advanced topics in data analysis, statistics, complexity and problem-solving.
Never remember what you can look up.
Interactive coding exercise included as a ZIP file.
Tips on showing your work, creating an on profile & presence, networking, and updating your resume to land a job that utilizes your newfound skills.
This is a journey and not a destination. Here a few more tips as you continue your journey.
Start Your Journey in Coding and Data Analysis
PyForward gives you the tools to build freedom, resilience, and career sustainability in today’s uncertain economy. You’ll write and understand your first lines of code, learn Python fundamentals, and complete your first data analysis project. With Python, tasks that take hours in Microsoft Excel can be done in minutes or seconds, saving you valuable time and boosting efficiency. You’ll also discover next steps: finding projects, showcasing your work, improving your resume, networking, and landing a job in finance, technology, or data science.
Learn How and Where to Run Python Code with Runsheets
In this course, you’ll get step-by-step video guides to the most popular Python coding environments, including Anaconda Navigator, Anaconda Cloud, and Jupyter Notebook. To make learning easier, we provide Runsheets—Jupyter Notebook files preloaded with code, documentation links, key explanations, and helpful coding tips. These Runsheets let you learn by doing: you can run the code, see the results instantly, and experiment at your own pace. They’re included for:
Python Input and Output methods
Python Basics
Pandas and NumPy
Lambda functions
A complete Python Data Analysis project
Runsheets are a powerful companion to the video lessons, helping new and aspiring coders build confidence in real coding environments. You’ll practice writing, editing, and executing Python code while reinforcing key concepts and workflows. By the end, you’ll feel comfortable navigating professional Python tools and completing hands-on projects.
Why Python for Data Analysis and Finance?
Python is one of the fastest-growing programming languages—integrated with Microsoft Excel, central to AI and machine learning, and used in orchestration tools including AWS Lambda, Apache Airflow, and KNIME. In finance, it drives automation, reduces reporting and reconciliation times, eliminates End User Computing (EUCs), and helps firms meet regulatory requirements. Learning Python for finance and data analysis now means being ahead of the curve in your team and your industry.
What You’ll Learn:
Write and understand Python code confidently
Complete your first real-world Python data analysis project
Automate Excel tasks and streamline financial reporting
Apply Python in finance for compliance, reconciliation, and process automation
Build a foundation for AI, machine learning, and modern data visualization tools
Who This Course Is For:
Finance professionals looking to future-proof their careers with Python for finance
Students or career changers exploring data analysis with Python
Anyone who wants to gain a high-demand skill with practical business impact
Your Career Advantage:
Freedom – Work on your own terms: in-office, hybrid, or remote
Resilience – High-demand skills make you more valuable and secure
Sustainability – Future-proof your career as AI and data transform industries
By the end of this course, you’ll have completed your first coding project in Python, mastered core Python for data analysis skills, and positioned yourself to thrive in a fast-changing professional world.