Certificate in Python Software Development & Data Analytics

This training track is built to give students hands-on, practical command of data analytics — and to prepare them for two well-recognized industry credentials from the Python Institute:

  • PCEP™ (Certified Entry-Level Python Programmer)
  • PCED™ (Certified Entry-Level Data Analyst with Python)

Over the course of the program, students work through the complete data analytics lifecycle — from defining the problem and pulling together data, through to applying the right techniques to turn that data into insights they can act on with confidence. Along the way, the course also covers what different analysis methods and tools are good at, and where their limits are.

A significant portion of the curriculum is also dedicated to getting students comfortable using AI tools for both data analysis and code generation.

Register now for the Certificate in Python Software Development & Data Analytics

 

Each class session follows a consistent format:

  • Students rotate through several Python development environments across the program — including Anaconda Prompt, Jupyter Notebook, JupyterLab, Google Colab, and Visual Studio Code
  • AI-assisted coding is woven into lectures throughout, using tools such as GitHub Copilot, ChatGPT/Codex, Google Gemini, Grok, Replit, and others
  • Most sessions close with a lab exercise or assessment
  • Two multiple-choice exams are given over the course of the program, mirroring the PCEP and PCED certification tests
  • The program runs 42 sessions at 3 hours each, for 126 total hours of instruction

This program is designed for people looking to launch a career in data analytics or data science. It prepares graduates to take on IT and data-analysis positions with government agencies, government contractors, and private employers across the Mid-Atlantic region. The certificate was created specifically to address a gap the IT industry has been facing: too few candidates who combine strong technical skills, up-to-date certifications, and solid problem-solving ability for roles in systems support and information security. Training also opens the door to roles such as AI product manager, AI evangelist, data scientist, and data analyst, and covers much of the ground needed to move toward a career as an AI engineer.

No coding background is required to enroll — but candidates must first complete and pass an aptitude assessment covering core programming fundamentals: general math and statistics, control flow, loops, conditional logic, variables, data types, and plotting. The assessment starts from the assumption that test-takers have no prior exposure to these concepts. It walks candidates through the material first, then checks how well they can apply what they just learned — which is used as a measure of readiness for the Python and data analysis coursework ahead.

Required Software:

  • Anaconda Distribution and/or Google Colab (a Google account is needed for Colab)
  • Microsoft Excel
  • Generative AI tools
  • A low-code platform (e.g., Microsoft Power BI Desktop)

Optional Software:

  • Visual Studio Code
  • GitHub

WIOA Funding

Maryland residents who are unemployed or underemployed may be able to fund their training through the Workforce Innovation and Opportunity Act (WIOA). A number of our programs, including this one, have been approved by the Maryland Higher Education Commission (MHEC) for WIOA funding — support intended to help individuals build new skills and earn credentials on the way to a new career. Applications for this funding are handled through the state’s network of One Stop Career Centers.

Learn More About WIOA Funding

Lecture 1-5: Python Overview

  • Course Overview
  • Overview and References for Python programming language (e.g., PEP, documentation), Anaconda Distribution, Google Collab, and other concepts
  • Generative AI Overview and Coding Generation Use Case
  • Syntax and Python library elements
  • Objects and Variables
  • Conditional Statements
  • Loops (e.g., For, while)
  • Functions (i.e., index/positional vs. keyword positional arguments)
  • Data Collections and Data Types and File Types
  • Assessment (in line with Python Institute PCEP)

Lecture 6-10: Math and Statistics

  • Numpy
  • SciPy
  • Pandas
  • Scikit-learn
  • Descriptive Statistics, and Regressions

Lecture 11-15: Data Analysis with Python

  • Story Telling with Data
  • Data collection (e.g., surveys, databases)
  • Pandas (e.g., data filtering, groupby, crosstab)
  • Data Cleaning and Exploratory Data Analysis

Lecture 16-20: Data Relationship, Plotting, and Dashboarding

  • Matplotlib
  • Seaborn
  • Plotly
  • Jupyter Widgets
  • Other plotting libraries (e.g., Bokeh)

Lecture 21-24: Data Analysis with MS Excel

  • Opening CSV and XLSX Files
  • Working with MS Excel (e.g., filtering data, manipulating data, resolving data issues)
  • Creating Pivot Tables and Dynamic Dashboards

Lecture 24-30: Data Engineering and Python Concepts

  • Cloud Platforms and Data Concept overview
  • Data Access and Manipulation (e.g., Pandas, SQL, API’s)
  • Database and Storage Concepts (e.g., Data Warehousing, OLAP, OLTP, Star vs. Snowflake schema, data lake, data markets, data Lakehouse, etc.)
  • Data Pipelines (e.g., ETL, ELT)
  • Data modeling (e.g., normalization, designing schema)
  • DevOps and CI/CD (e.g., Github, Docker)

Lecture 31-35: Intermediate Pandas and SQL and Big Data Tools

  • Intermediate Pandas
  • Intermediate SQL
  • Python, SQL, and other big data tools

Lecture 36-39: Low Code Data Analytic Platforms

  • Power BI and/or Tableau

Lecture 40-41: Other Data Topics

  • Ethics
  • Machine Learning Overview (e.g., supervised and unsupervised)

Lecture 42: Other Topics and Final Assessment

  • Assessment (in line with Python Institute PCED)
  • Other Topics (e.g., review topics, discuss NLP, or other topics as requested by students)

 

 

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