MIS 370 — Summer 2026
Management Information Systems — Course Schedule & Resources
Required Course Materials
All readings listed in the schedule below correspond to the following sources:
| Code | Author(s) | Title & Access |
|---|---|---|
ES |
Eckstein & Schultz | Introductory Relational Database Design for Business, with Microsoft Access (Wiley, 2018). Free via Rutgers Library → |
H |
Holloway | Excel: Data Analysis in Microsoft Excel. Alex Holloway, 2023. ~$4.99 on Kindle (free for Kindle Unlimited). E-copy recommended. |
G |
Machine Learning online resources. developers.google.com/machine-learning → | |
B |
Univ. of Bucharest | MySQL Workbench Tutorial. Download PDF → |
C |
Cheusheva | Why use $ signs in Excel formulas — absolute and relative references. Read article → |
Supplementary materials (lecture slides, datasets, handouts, assignment instructions) are posted on Canvas. Check Canvas announcements daily.
Course Schedule
Tentative schedule — subject to change. Because this is an accelerated summer session, each meeting combines multiple regular-semester class sessions. Changes in assignment due dates, quiz dates, and exam logistics will be posted on Canvas announcements.
| Meeting | Date | Topics & Activities | Readings & Assignments |
|---|---|---|---|
| 1 Classes 1–2 |
Mon 7/6 |
Course Introduction & Database Basics
|
ES pp. 7–8
B §§1,2,4
MySQL datatypes handout (Canvas)
Installation videos by Prof. Ordille
|
| 2 Classes 3–5 |
Wed 7/8 |
Data Management & Multi-Table Design
|
ES Ch. 3–4 |
| 3 Classes 6–8 |
Mon 7/13 |
Many-to-Many Relationships & SQL Workbench
|
B §3
ES Ch. 7
HW1 Window Opens — Database Design/MySQL
|
| 4 Classes 9–10 |
Wed 7/15 |
Midterm 1 Review & SQL Foundations
|
ES Ch. 10 |
| 5 ★ Midterm 1 + Class 11 |
Mon 7/20 |
Midterm Exam 1 + SQL Aggregation
|
ES Ch. 10
HW2 Window Opens — SQL
|
| 6 Classes 12–13 |
Wed 7/22 |
SQL Capstone & Data Analysis Pipeline
|
H pp. 17–53 |
| 7 Classes 14–16 |
Mon 7/27 |
Excel Basics & Hotel Business Analysis
|
H pp. 55–143 (before Ex. 10) C (absolute & relative refs) |
| 8 Classes 17–19 |
Wed 7/29 |
Midterm 2 Review & Excel Analysis
|
H pp. 143–200
HW3 Window Opens — Excel Analysis
|
| 9 ★ Midterm 2 + Class 21 |
Mon 8/3 |
Midterm Exam 2 + AI/ML Introduction
|
G: intro-to-ml |
| 10 Classes 22–24 |
Wed 8/5 |
AI/ML Models & Data Preparation
|
G: crash-course modules
linear-regression, logistic-regression, classification, neural-networks, llm, numerical-data, categorical-data, overfitting
|
| 11 Classes 25–26 |
Mon 8/10 |
Hands-On AI/ML Capstone & Final Review
|
HW4 Window Opens — AI/ML Concepts & Capstone
|
| 12 ★ Final Exam |
Wed 8/12 |
Final Exam — Cumulative
|
Check Canvas for final exam logistics and any updates.
|
Grading Breakdown
No Rounding / No Curve: Grades are calculated to the hundredth place and used as-is for midterm and final grade assignments. Submit any regrading requests in writing within one week of receiving the graded item.
Key Policies
Attendance
A sign-in sheet is used every class. Absences are unexcused unless covered under official Rutgers policies (religious observance, varsity activities, illness, family emergency). Contact Dean of Students: (848) 932‑2300.
Academic Integrity
Cheating is not tolerated. All deliverables require the RU Honor Pledge. Written work may be screened through plagiarism detection. See academicintegrity.rutgers.edu and business.rutgers.edu/ai.
Submissions
All assignments submitted on Canvas. Accepted formats: Word, PDF, Excel, or Access — depending on the assignment. No pictures or scanned images. Presentations must be well-organized and easy to follow.
Grade Inquiries
No grade information via email — in-person appointments only. Regrade requests must be submitted in writing within one week of item return. Final grades are not negotiable.