CSC-PRG7915 - Python for Data Analytics Fundamentals

Course Description

Python Data Analytics Fundamentals introduces learners to the core tools, methods, and workflows used in modern data analysis using Python. The course begins with a concise review of essential Python programming concepts and quickly transitions into practical data-handling skills, including reading, parsing, cleaning, and transforming datasets from multiple sources. 

Students gain hands-on experience working with NumPy and Pandas to manipulate arrays, Series, and DataFrames, perform filtering and aggregation, merge and reshape datasets, and work with date and time data. The course also explores external data sources such as APIs and web scraping, along with best practices for ethical data retrieval. 

Data visualization is emphasized through Matplotlib and Seaborn, enabling students to communicate insights using clear and effective charts and plots. Additional topics include Excel integration, SQL fundamentals, SQLite databases, and building reliable data pipelines and project structures. By the end of the course, students will be able to manage, analyze, visualize, and persist data using industry-standard Python tools commonly used in data analytics roles.

Course Outline

1. Python Foundations Review 

• Python data types, variables, operators, and functions 
• String methods, lists, slicing, and mutability 
• Dictionaries and common iteration patterns 

2. Environment and Tooling 

• Working with Anaconda distributions 
• Package management and virtual environments 
• Jupyter Notebook navigation, configuration, and best practices 

3. Files and Data Sources 

• Reading and writing text, CSV, and JSON files 
• JSON parsing and nested data structures 
• Directory traversal, file systems, and glob patterns 

4. Numerical and Tabular Computing 

• NumPy and Pandas foundations 
• Arrays, Series, and DataFrames 
• Data extraction, filtering, and cleaning 
• Working with text data in Pandas 

5. Grouping, Combining, and Temporal Data 

• Indexing and multi-indexing DataFrames 
• Aggregation and group-by operations 
• Merging, joining, and reshaping data 
• Date and time operations 

6. External Data Sources 

• Consuming APIs using the requests library 
• Web scraping techniques 
• HTML structure and BeautifulSoup 
• Data retrieval strategies and ethics 

7. Data Visualization 

• Matplotlib and Seaborn foundations 
• Pyplot and graphical elements 
• Subplots and layout management 
• Line, bar, pie, histogram, and scatter plots 
• Interactive plots and comparative visualizations 

8. Excel Automation and Integration 

• Exporting Pandas DataFrames to Excel 
• Reading Excel workbooks and worksheets 
• Automating simple Excel-based workflows 

9. Persistence and Databases 

• SQL fundamentals 
• SQLite schemas, tables, and queries 
• Python interaction with SQL databases 

10. Pipelines, Workflows, and Project Architecture 

• Designing simple data pipelines 
• Analytical workflows 
• Project structure and folder organization 
• Naming conventions, versioning, and backups 

Learner Outcomes

In this course, you will gain both theoretical understanding and practical experience in Python-based data analytics, preparing you for further study or career paths that rely on data manipulation, visualization, and analysis using modern Python tools. 

1. Apply Python for Data Analysis: Use Python data types, functions, lists, dictionaries, and text-processing methods to prepare and transform data. 
2. Work with Files and External Data: Read and parse text, CSV, and JSON files; traverse directories; use glob patterns; retrieve data from APIs and web pages. 
3. Use NumPy and Pandas Effectively: Manipulate arrays, Series, and DataFrames; clean, filter, index, group, merge, and reshape datasets. 
4. Analyze Temporal and Structured Data: Work with date and time values, grouped data, and multi-index DataFrames. 
5. Visualize Data Clearly: Create effective data visualizations using Matplotlib and Seaborn, including multi-plot figures. 
6. Integrate and Persist Data: Export analytical results to Excel and interact with SQLite databases using SQL. 
7. Build Analytical Workflows: Design repeatable data pipelines and organize analytics projects using industry-standard structures and practices. 

Notes

Target Audience: 

• This course is designed for: 
• Aspiring data analysts and junior developers 
• IT professionals seeking to add data analytics skills 
• Business professionals who work with data and reports 
• Students preparing for further study in data science or analytics 
• No prior data analytics experience is required. 

Occupational Outlook:

According to the U.S. Bureau of Labor Statistics (BLS), employment for data-focused roles continues to grow rapidly: 

Data Scientists: Projected growth of 35% from 2022–2032, much faster than average 

Computer and Information Analysts: Strong demand across healthcare, finance, technology, and government sectors 

Python is consistently listed by the BLS and industry surveys as one of the most in-demand programming languages for data analysis and analytics-related occupations. 

Information Technology Continuing Education Certificate: 

Certificates are awarded with the successful completion of the final project.  A certificate of completion for the class will be issued to participants with at least 80% attendance, class participation, and completion of hands-on practice and projects. 

Industry Certification:

This course provides partial alignment with the PCAP certification by developing core Python programming skills—such as data structures, file handling, use of libraries, and project organization—within a data analytics context. While there is currently no standalone industry certification specifically for Python Data Analytics, the skills taught in this course support broader Python and analytics certification pathways. 

Method of Instruction: 

Blended learning with lectures, videos, class discussions, live demonstrations, hands-on exercises, and projects. Outside homework is assigned. 

Evaluation: 

Participation and completion of all skill lab practices are expected. Class attendance is required for successful completion. 

• Class participation and attendance (50%)  
• Projects/Competencies/Research (50%)