Description

Outline

The Data Analytics Programming course provides a comprehensive introduction to Python programming workflows and foundational statistical concepts essential for exploratory data analysis in business operations. Learners will dive into the practical application of statistical methods, learning how to summarize distributions, compare groups and identify meaningful patterns or anomalies within datasets. By leveraging Python to inspect, transform and analyze tabular data, learners will develop the ability to interpret relationships between variables using descriptive statistics and simple models, while critically distinguishing between association and causation in real-world contexts.

Beyond core analytical techniques, the curriculum emphasizes the importance of building well-documented analytical workflows. This ensures that all code and derived insights can be seamlessly validated, reused and integrated into downstream reporting or visualization tools to support data-informed decision-making. Additionally, learners will gain hands-on experience with emerging AI-assisted tools, employing emerging AI tools to troubleshoot complex code and refine analytical workflows to enhance operational efficiency while adhering to strict data governance guardrails.

Course Outcomes

By the end of this course, participants will be able to:

• apply statistical analysis concepts to explore data, summarize distributions, compare groups and identify patterns or anomalies in data.
• interpret relationships between variables using descriptive statistics and simple models, distinguishing association from causation in analytical contexts.
• use Python to inspect, transform and summarize data by using tabular data structures to create derived variables.
• implement organized and reproducible analytical workflows in Python to support reuse and downstream reporting.
• employ AI-assisted tools to diagnose coding challenges and improve workflows to enhance operational efficiency.

Course Format

This six-week course blends elements of asynchronous and synchronous components to provide learners with flexibility in project work, while also having face-to-face time with instructors and fellow learners via Zoom. 
Each week, learners will:

• attend one 90-minute Zoom, during which students will participate in discussions and workshop their project with the instructor and peers
• complete three to four hours of assignments or project work

Learners will be graded based on completion of assessed assignments/projects.

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Applies Toward the Following:

• Data Analytics Certificate : Required Courses