Data Science
Python Software Development and Data Analytics
This program provides candidates with the skills and knowledge to successfully apply data analytics concepts to complement students’ academic fields as well as provide an overview of the different paths to obtain credentials that should be pursued (e.g., Python Institute PCEP™ – Certified Entry-Level Python Programmer and PCED™ – Certified Entry-Level Data Analyst with Python). The expected outcome is for students to understand the data analytics lifecycle from problem definition and data collection to use of various concepts to extract actionable insights from data. Understand the capabilities and limitations of various data analysis approaches including understanding of common tools.
All lectures will follow the following format:
- Exposure to various Python Integrated Development Environments (IDE) by alternating use during class (e.g., Anaconda Prompt, Jupyter Notebook, Jupyter Lab, Google Collab, Visual Studio Code).
- Integrate use of Generative AI assisted coding across lectures using Gen-AI tools like Bing/GitHub Copilot, OpenAI ChatGPT/Codex, Google Gemini, Grok, Replit, and others.
- Lab exercise and assessments at end of most lectures.
- Two Multiple choice assessment tests throughout the class in line with PCEP and PCED Python Institute Certifications.
- 42 Lectures (3 hours per lecture and 126 total contact hours)
AUDIENCE
This program is suitable for individuals seeking to enter the fields of data analytics and data science. This program prepares students for employment as IT professionals with government agencies, government contractors, and commercial enterprises in the Mid-Atlantic region. This Certificate program is offered in response to the Information Technology industry’s need for qualified employees with the right skills and certifications and problem-solving skills to be effective in system support and information security.
Prerequisites
Students taking this program should have good end-user skills with Windows® or Mac OS based computers, a strong interest in computers and technology, and good problem-solving skills. Although no programming experience is required, students will need to complete and pass an assessment on programming concepts (i.e., general math and statistics, control flow, loops, conditional statements, variables, data types, and plotting) before taking the course.
Required Software:
- Anaconda Distribution and/or Google Collab (e.g., Google Account)
- MS Excel
- Generative AI Tools
- Low Code Platform (e.g., MS PowerBI Desktop)
Optional Software:
- Visual Studio Code
- Github
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)
- 2. Other Topics (e.g., review topics, discuss NLP, or other topics as requested by students)
Is there a discount available for current students?
UMBC students and alumni, as well as students who have previously taken a public training course with UMBC Training Centers are eligible for a 10% discount, capped at $250. Please provide a copy of your UMBC student ID or an unofficial transcript or the name of the UMBC Training Centers course you have completed. Asynchronous courses are excluded from this offer.
What is the cancellation and refund policy?
Student will receive a refund of paid registration fees only if UMBC Training Centers receives a notice of cancellation at least 10 business days prior to the class start date for classes or the exam date for exams.