Catalog Course Description

Overview of data management and analytical techniques used incorporate environments and their practical implementation using a state-of-the-art Business Intelligence software.

Expanded Course Description

Business Intelligence (BI) combines analytics, data mining, data visualization, and statistical methods to

enable large organizations to make improved data-driven decisions by extracting important information

from complex enterprise systems. Modern BI platforms bring all the components of a project lifecycle from data preparation through discovery and modeling to assessment, deployment, and governance together in  a single, integrated environment the requires minimal to no low-level coding (“low-code/no-code”).

The primary objectives of this course are to provide the student a theoretical overview of the entire

lifecycle of a data science initiative in commercial settings and to provide the opportunity to get experience implementing these techniques using advanced Business Intelligence software currently used by large corporate and public sector entities.

Prerequisite(s):  None.

Recommended Preparation:   It is recommended that students have an undergraduate-level familiarization

with statistics.  Previously or concurrently taking ISE-529 is helpful but not required.  While this course will not involve significant amounts of programming,a basic familiarization and comfort with Python will be helpful.

Learning Objectives and Outcomes

The overall course objective is to learn the latest technologies and methodologies used in large enterprises to perform a broad range of business intelligence and systems analytics activities.

•    The latest generation of business intelligence and analytics software is moving towards a cloud-based "no- code/low-code" data and analytics platform.   We will be using leading commercial tools including the

Tableau data visualization/dashboard development environment a state-of-the-art cloud-based analytics platform.

•    Techniques covered will include data preparation and management, data exploration and

reporting/dashboarding, advanced analytical modeling (descriptive and predictive), free text analytics, forecasting, and deployment of analytical products in a large enterprise.

•    The course will focus developing an advanced understanding of setting model hyperparameters,

interpreting and assessing model results, and deploying and managing data and analytics products in an enterprise environment.

Course Overview and Schedule

The course is structured into modules that correspond to many of the components of atypical analytics project.  In   general, each module will consist of lecture material to introduce (or refresh for the student) the theoretical basis of the technique being covered, a case study with sample data for classroom discussion, and instruction in the use of     the corresponding BI software to perform the techniques.  Readings will be assigned prior to each module and there will generally be a hands-on assignment.

Course Notes

All course materials (PowerPoints, assigned readings, etc) will be distributed via Blackboard.

Technological Proficiency and Hardware/Software Required

An advanced BI Analytics software platform will be used.  The hardware/software required is a laptop or other personal computer and a browser. Accounts will be established for each student and a basic orientation to the software will be provided in the first session with each platform.

Required Readings and Supplementary Materials

There are no mandatory texts for this class.  Required readings and supplementary materials will be assigned for each module and distributed via Blackboard and will include the following:

•     Mastering Tableau, Meier and Baldwin, 2021, O’Reilly

•     Engineering MLOps, Raj, 2021, Packt Publishing

•     Selected journal papers of significance in the field of data science

Grading Breakdown

Grading Scale

Course final grades will be determined using the following scale:

A   95-100

A-   90-94.9

B+   87-89.9

B   83-86.9

B-   80-82.9

C+   75-79.9

C   70-74.9

C-   50-69.0

F   Below 50

Assignment Submission Policy

Assignments will be posted on Blackboard and submitted for grading on GradeScope (student instructions will be provided)

Assignments turned in after the due date will be penalized 10%. Assignments not turned in within 48 hours of the due date will not be accepted.

If you feel that your submission was not graded correctly, use the GradeScope “ Request

Regrade” button and type an explanation of why you feel the grade should be changed. I will accept requests for one week after grades published, after which the request regrade function will be disabled.

Course Schedule: Weekly Breakdown

Notes:

•   The “W/E” column above means “weekending” and the date listed is the Friday of that week and the corresponding row refers to topics and assignments covered on one or

both class days (Tuesdays or Thursdays) of that week.

•   This schedule is subject to changes throughout the semester.  This syllabus will not be updated, but the latest schedule will always be available on Piazza.

•   The current correct due dates for the homework assignments can be found in GradeScope.