Business Analytics and Data Intelligence

Course introduction

This course teaches practical techniques to collect, clean, analyze, visualize, and interpret data so you can make evidence-based business decisions. We'll blend hands-on tools (spreadsheet and desktop data tools), foundational statistics, modern analytics workflows, and an introduction to machine learning and data ethics. Expect case studies across marketing, operations, finance, and product teams — plus a final project based on a real business question.

What you'll learn

Design data workflows to gather, clean, and validate business data.
Apply descriptive and inferential statistics to extract actionable insights.
Build dashboards and visual stories that communicate findings to stakeholders.
Use basic predictive models and evaluate their business impact.
Consider ethical, privacy, and bias concerns when working with data.

Who this course is for

Professionals and students who want to use data to solve business problems — product managers, marketers, operations analysts, small business owners, and aspiring data analysts. No advanced math or programming required.

Prerequisites

Comfort with spreadsheets (Excel, Google Sheets) for formulas and pivot tables.
Basic algebra (percentages, ratios) and a curiosity about data-driven decision making.
Willingness to learn a simple analytics tool (optional: familiarity with SQL or Python is helpful but not required).

Accessibility note: If you need accommodations for any prerequisite expectations, please contact the program office.

Course format & assessment

The course combines short lectures, hands-on labs, weekly problem sets, group activities, and a capstone project. Assessment typically includes:

Weekly practical assignments (40%)
Midterm applied case or quiz (20%)
Capstone project & presentation (35%)
Participation & peer review (5%)

All assignments emphasize reproducible workflows and clear communication of results.

Sample module breakdown

Intro to data-driven decision making & data literacy
Data collection, cleaning, and quality checks
Exploratory data analysis & visualization fundamentals
Descriptive statistics & hypothesis testing for business
Dashboards & storytelling with data
Intro to regression and basic predictive models
Model evaluation, A/B testing, and causal thinking
Ethics, privacy, and bias in data
Capstone project: from question to insight

Resources & tools

Core tools used in the course may include Excel/Sheets, a visualization tool (Tableau, Power BI, or open-source alternatives), and optional introductions to SQL and Python (pandas). Readings combine short applied articles, pieces from industry blogs, and selected textbook chapters.

Frequently asked questions

Do I need to know programming?

No. The course is designed for learners with no programming background. Programming examples are optional, and practical spreadsheet workflows are taught in parallel. If you already know SQL or Python, you'll be able to dive deeper in certain labs.

What is the capstone project like?

Teams (or individuals) pick a real business question, gather or are provided with data, apply analytics, and deliver a short written report plus a presentation and dashboard. Projects are graded on rigor, clarity, and potential business impact.

How much time should I expect to spend each week?

Plan for ~6–8 hours per week for a standard-term offering: watching short lectures, completing labs, and preparing assignments. Time can vary by module and project work.

Are there group work and peer reviews?

Yes — group activities and peer feedback are included to mirror real-world collaboration. Participation and contribution are part of the assessment.

Can I apply this course directly to my job?

Absolutely. The curriculum prioritizes transferable skills, reproducible workflows, and business-focused case studies so you can apply techniques immediately in marketing, operations, finance, product, or strategy roles.

Who do I contact with questions about enrollment or accommodations?

Contact the program office at program-office@example.edu or visit the student services desk for assistance.