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Graduate course

MIS 769: Big Data Analytics for Business

Large-scale data processing, modern text and AI workflows, and practical analytics for business decisions.

Course overview

What we’ll work on

MIS 769 examines how organizations work with data that is too large, fast, or complex for traditional workflows. Students connect scalable data processing with modern analytics, natural language processing, and AI-supported business problem solving.

01

Scalable data-processing concepts and workflows

02

Text analytics, embeddings, and language-model applications

03

Practical evaluation and communication of business results

Learning outcomes

What you will be able to do

Who it is for. Graduate students in MIS or Data Analytics who want hands-on experience with large-scale data, text, and language-model workflows.

Prerequisites. MIS 740 or MIS 768, or DA 621 and DA 651, or equivalent. Enrollment requires admission to a graduate program in MIS or Data Analytics and Applied Economics, or consent of the Director of MIS Graduate Programs.

Tools. Python, MapReduce, NLP libraries, large language model APIs, WebCampus. Format. Online, asynchronous, Fall 2026 (August 24 to December 12).

01

Big data fundamentals: processing techniques and the complexities of managing very large datasets

02

Scalable analytics with MapReduce in Python and abstracted MapReduce patterns

03

Natural language processing: tokens, stems, preprocessing, vectorization, classification, and clustering

04

Large language models: core concepts and practical business applications

05

Applying these methods to real business scenarios and communicating results clearly

Student checklist

Start here

  1. 01
    Confirm your section

    Check the course code, format, and enrollment details in MyUNLV.

  2. 02
    Open WebCampus

    Read the current syllabus, announcements, and first module.

  3. 03
    Set up your Python environment

    Install Python and the packages listed in the syllabus before week one; the first lab depends on it. Need more compute? See student resources for GPU access.

  4. 04
    Ask early

    Email ryoung@unlv.edu with the course code and section format in the subject line.

Watch

Lectures and walkthroughs

Selected recordings from this course and related work are on my YouTube channel. WebCampus remains the official home for required course videos.

Open the channel on YouTube

Course support

Everything official stays together.

Fall 2026 office hours are Mondays and Wednesdays from 3:00 to 4:00 p.m. in BEH 328. Friday meetings are available by appointment on Zoom.

The current syllabus, announcements, assignments, submissions, and grades are maintained in WebCampus.