Course Information

Catalog Focus: In this course, students will understand the fundamental principles of database management systems (DBMS). The catalog emphasizes scalable database design through entity-relationship diagrams, discovery of useful patterns in stored data, SQL-based database analysis and application development, hands-on work with MySQL, and the ability to organize data and construct informative queries.

Course Focus

This Fall 2026 offering connects relational database design, data storage and querying, database management and protection, data analysis, pattern discovery, and evidence-based decision making. Students will use SQL, Python, and modern analytical tools to build and analyze realistic database and data-mining projects.

Prerequisites: ENG 201 and either CSCI 172 or CSCI 373. Students should be comfortable with basic programming and quantitative reasoning. Prior Python experience is helpful but not required; the Python needed for data analysis will be introduced in the course.

The official description and prerequisites are available in the John Jay Undergraduate Bulletin.

Learning Outcomes

Explain core database and DBMS concepts and distinguish conceptual, logical, and physical design.
Translate requirements into an entity-relationship model and relational schema.
Apply keys, constraints, relationships, and normalization to improve relational designs.
Create and modify relational structures and write SQL queries using joins, grouping, aggregation, and subqueries.
Explain database integrity, transactions, access control, security, and performance concepts.
Use Python and data-analysis libraries to load, clean, explore, and visualize datasets.
Implement and interpret introductory regression, classification, and similarity-based data-mining methods.
Recognize overfitting and evaluate models using suitable metrics and validation practices.
Communicate findings through queries, visualizations, written interpretation, and evidence-based recommendations.
Complete applied database and data-mining projects using realistic data.

Weekly Plan

This syllabus provides the official semester course outline and schedule. Brightspace is the primary source for weekly materials, readings, lab instructions, assignments, announcements, due dates, and any subsequently announced changes. The schedule may be adjusted based on class progress, College calendar changes, or instructional needs.

August 31 & September 2 | Module 1: Databases, Data, and DBMS Fundamentals

Why databases exist; data versus information; DBMS responsibilities; users, schemas, and instances; relational and non-relational systems; data independence; and databases in modern applications and analytics.

Database concepts and data-modeling warm-up

September 7 | No Class

College closed. No classes scheduled.

September 9 | Module 2: Entity-Relationship Modeling

Entities, attributes, identifiers, relationships, cardinality, participation, weak entities, and translating application requirements into conceptual database models.

Lab: Build an ER model

September 14 & 16 | Module 3: Relational Modeling and ER-to-Relational Mapping

Relations, tuples, attributes, domains, primary and foreign keys, referential integrity, relationship mapping, and converting conceptual models into relational schemas.

ER-to-relational mapping Quiz / Knowledge Check

September 21 | No Class

No classes scheduled under the Fall 2026 academic calendar.

September 23 | Module 4: Database Design and Normalization

Functional dependencies, redundancy, update anomalies, decomposition, and normalization through the normal forms needed for practical relational design.

Lab: Diagnose and normalize a database

September 28 & 30 | Module 5: SQL Fundamentals

PostgreSQL and relational SQL; database and table creation; data types; constraints; data modification; SELECT queries; filtering, sorting, expressions, aliases, and basic functions.

Lab: Create and query a relational database Assignment 1 Due / Assignment 2 Assigned

October 5 & 7 | Module 6: SQL for Multi-Table and Analytical Queries

Inner and outer joins, grouping, aggregate functions, GROUP BY, HAVING, subqueries, and practical information-retrieval problems from relational data.

Lab: Multi-table SQL analysis Quiz / SQL Check

October 12 | No Class

College closed.

October 13 & 14 | Module 7: Integrity, Transactions, Security, and Performance

Tuesday, October 13 follows a Monday schedule. Transactions and ACID properties; commit and rollback; concurrency; roles and permissions; SQL injection awareness; indexes; performance; and responsible data handling.

Lab: Roles, privileges, transactions, and indexes Database Project Checkpoint

October 19 | Modules 1–7 Review and Database Workshop

Integrated database-design and SQL practice: ER modeling, normalization, query tracing, joins, constraints, transactions, and preparation for the midterm examination.

Midterm Review

October 21 | Midterm Examination

Assessment covering conceptual modeling, relational design, normalization, SQL, and foundational DBMS concepts.

Midterm Exam

October 26 & 28 | Module 8: From Databases to Data Analysis

Turning questions into data tasks; extracting analysis-ready data with SQL; data quality; features and targets; descriptive versus predictive analysis; and the relationship among databases, analytics, and data mining.

Database Project Due Data Mining Project Introduced

November 2 & 4 | Module 9: Python and pandas for Data Analysis

Google Colab; Python essentials; pandas DataFrames; loading tabular data; selecting, filtering, grouping, transforming, and summarizing data.

Lab: Explore a dataset with pandas Assignment 3 Assigned

November 9 & 11 | Module 10: Data Cleaning, Exploration, and Visualization

Missing values, duplicates, data types, outliers, exploratory analysis, distributions, relationships, summary statistics, and effective visualizations.

Lab: Clean, explore, and visualize data Quiz / Knowledge Check

November 16 & 18 | Module 11: Predictive Modeling and Regression

Features and targets; linear regression; fitting and prediction; interpreting output; residuals; assumptions; and distinguishing prediction from explanation.

Lab: Build and interpret a regression model Assignment 3 Due / Assignment 4 Assigned

November 23 | Module 12: Classification and Model Evaluation

Classification; train/test splits; predictions; confusion matrices; accuracy, precision, recall, and selecting evaluation measures based on the problem.

Lab: Train and evaluate a classification model

November 25 | No Class

No classes scheduled for the Thanksgiving recess.

November 30 & December 2 | Module 13: Overfitting, Generalization, and Similarity

Training versus generalization; overfitting and underfitting; validation; nearest neighbors; distance and similarity; feature scaling; and practical model-selection considerations.

Lab: K-nearest neighbors and model comparison Assignment 4 Due

December 7 & 9 | Module 14: Applied Data Mining and Project Presentations

Integrating data preparation, modeling, and evaluation; interpreting results; limitations; responsible claims; visual communication; and presentation of project findings.

Data Mining Project Due Project Presentations

December 14 | Course Synthesis and Final Review

Connecting database design, SQL, analytical thinking, and data mining; cumulative review; applied problem solving; and preparation for the final examination.

Final Review

December 16 | Final Examination

Exam time: 10:30 AM–12:30 PM, following the published Fall 2026 undergraduate final-exam schedule for a Monday/Wednesday class meeting from 12:15 PM–1:30 PM.

Final Exam

Assessments

Performance is evaluated through engagement, attendance, preparation, assignments, quizzes, examinations, and two connected applied projects.

Engagement & Attendance

Class engagement and regular attendance (10%).

Class participation and engagement (5%): database-design exercises, SQL walkthroughs, data-analysis activities, discussion, and collaborative problem solving.
Attendance (5%): regular and punctual attendance under the Departmental Attendance Policy.
Attendance scoring: 0 unexcused absence equivalents earns 5 points; 0.5–1 earns 4 points; 1.5–2 earns 3 points; 2.5–3 earns 2 points; and more than 3 earns 0 points before the departmental letter-grade penalty is applied.

Preparation & Assignments

Independent preparation and applied work (25%).

Homeworks, labs, readings, and knowledge checks (5%): preparation, practice, lab completion, readings, and checks of conceptual understanding.
Assignments (20%): four graded assignments covering database design, SQL, analytical thinking, and introductory data mining.

Quizzes & Examinations

Individual quizzes and examinations (45%).

Quizzes (10%): database concepts, SQL, data-analysis concepts, and assigned material.
Midterm exam (15%): the database portion of the course.
Final exam (20%): cumulative, emphasizing data analysis and data mining while connecting those topics to database and SQL foundations.

Applied Projects

Two projects connecting the major halves of the course (20%).

Database Project (10%): design and implement a relational database.
Data Mining Project (10%): prepare, analyze, model, evaluate, and communicate findings from a dataset using Python.

Grading Breakdown

Class Participation & Engagement	5%
Attendance	5%
Homeworks, Labs, Readings & Knowledge Checks	5%
Assignments	20%
Quizzes	10%
Midterm Exam	15%
Database Project	10%
Data Mining Project	10%
Final Exam	20%

College grading rules: Review John Jay's Grades policy for official grade symbols, grade-point values, withdrawals, and Pass/No Credit rules.

Incomplete grades: An INC may be considered only when a student would otherwise be passing and, after consultation with the instructor, there is a reasonable expectation that outstanding work can be completed by the last day of the following semester. An INC is not guaranteed and is assigned at the instructor's discretion. If the work is not successfully completed and no grade change is submitted, the INC becomes a FIN under College policy.

Extra Credit Policy

Extra credit is not guaranteed. If an opportunity is offered, it will be announced to the entire class and governed by the following rules:

Deadline Policy: All extra credit work must be submitted by the posted due dates.
No Extensions: Extra credit deadlines cannot be extended under any circumstances.
No Retroactive Credit: Missed or expired extra credit opportunities cannot be submitted later.

Responsibilities & Policies

Course Expectations

This course meets twice per week in person. Students are expected to keep up with weekly materials, participate in hands-on activities, and monitor Brightspace regularly.

Check Brightspace several times each week for announcements and updates.
Bring a laptop when requested for database or data-analysis labs.
Complete readings, practice, labs, quizzes, and project milestones on time.
Keep backups of SQL scripts, notebooks, datasets, and project files.
Test submitted code and queries and be prepared to explain your work.
Optional module practice is strongly recommended even when it is not graded.

Course Flexibility

To support student learning, the instructor may adapt the weekly plan in response to class progress, demonstrated understanding, instructional needs, College calendar changes, tool availability, or unforeseen circumstances. Adjustments may include the pace or sequence of topics, readings, labs, activities, assignments, project milestones, or due dates. Substantive changes will be announced in class and posted on Brightspace with reasonable notice. Grading changes will not be applied retroactively to work already submitted.

Student Responsibilities

Attend class regularly and arrive on time.
Complete scheduled knowledge checks, labs, quizzes, exams, and in-class activities.
Meet assignment and project deadlines and follow each submission method.
Keep executable SQL and notebooks; screenshots alone do not replace working files unless requested.
Back up work, verify uploads, and be ready to explain database designs, SQL, analyses, and code.

Participation Expectations

Students are expected to engage respectfully and professionally with classmates. Collaboration on concepts is encouraged when permitted; sharing complete solutions to individually graded work is not.

Contribute to database-design discussions and explain modeling decisions.
Trace SQL queries and participate in live coding and lab activities.
Interpret data and visualizations and compare analytical approaches.
Ask thoughtful questions and help identify errors without giving away complete solutions.

Departmental Attendance Policy

Regular attendance is expected and essential to success in the course. You are responsible for all material, announcements, and assignments covered in class, whether present or not.

Unexcused absences: More than 3 unexcused absence equivalents in the semester will result in a one-full-letter-grade penalty, such as B to C or B+ to C+. Additional unexcused absences may result in further letter-grade penalties and may result in a failing grade.
Excused absences: Documented illness, religious observance, family emergency, jury duty, or military service requires notification and, where possible, documentation within 2 class meeting days.
Late arrival or early departure: Arriving more than 15 minutes late or leaving more than 15 minutes early counts as one-half absence. Two partial absences equal one full absence.
Missed material: It is your responsibility to obtain missed notes, materials, and announcements from a classmate and to review Brightspace.
Make-up work: Work missed because of an excused absence must be arranged with the instructor within 1 week of your return to class.

Approved Office of Accessibility Services accommodations will be applied as authorized. The course-level attendance score is described under Assessments. This policy will be reviewed on the first class meeting and posted as a standalone item on Brightspace.

Late Work & Make-Up Policy

Labs, homework, readings, and knowledge checks: Complete these by the posted deadline. Some activities cannot be made up after solutions or feedback have been released, except when an excused absence qualifies under the attendance policy.
Assignments and project milestones: Late work may be accepted for up to 3 days when the student contacts the instructor before the deadline. If a documented emergency makes advance contact impossible, the student must contact the instructor as soon as reasonably possible. Approved late work may receive up to a 20% deduction.
Quizzes: Make-up quizzes are generally limited to documented or approved absences.
Midterm and final examinations: Exams must be completed on the scheduled dates unless a documented emergency or approved accommodation applies.

Specific Brightspace instructions take precedence when they establish a different submission rule.

Use of AI Tools

AI tools may be used to learn, explore, debug, and receive feedback when permitted, but they may not substitute for the student's own understanding or authorship.

Clearly disclose permitted AI assistance used in graded work.
Do not submit AI-generated SQL, code, analysis, visualizations, or explanations as your own work.
Preserve prompts and responses used for graded work in case they are requested.
Verify generated code, queries, facts, and analytical interpretations independently.
Do not upload private, confidential, restricted, or personally identifiable datasets to public AI systems.
Be able to explain submitted designs, SQL, notebooks, models, and conclusions in person.

Example disclosure: “Used ChatGPT on November 18, 2026, to help explain why my pandas merge produced duplicate rows. I then corrected and tested the code independently.” The instructor may designate work as AI-free.

Academic Integrity

Academic dishonesty is prohibited. Penalties may include academic sanctions, such as a failing or reduced grade, and disciplinary sanctions, including suspension or expulsion.

Cheating is the unauthorized use or attempted use of materials, information, notes, study aids, devices, collaboration, or communication during an academic exercise. Examples include:

Copying or sharing answers on an assignment, quiz, or exam.
Collaborating when collaboration is not authorized.
Having another person complete work, completing work for another student, or submitting someone else's work as your own.
Fabricating data, results, or citations, or using unauthorized notes, websites, devices, AI tools, or services.

Plagiarism is presenting another person's ideas, research, writing, code, media, or computer-generated content as your own without appropriate attribution. This includes copying or paraphrasing online material without citation, failing to acknowledge collaborators, or submitting AI-generated content as original work.

Credit all permitted external help, including peers, documentation, forums, snippets, and AI tools.
Be prepared to orally explain any database design, query, notebook, model, or result you submit.
If you are unsure whether a resource or collaboration is allowed, ask before submitting.

These examples are not exhaustive. Review the John Jay Academic Integrity Office for definitions, College policies, and information about the review process.

Accessibility Services

Students who believe they may need an accommodation for a temporary or permanent disability are encouraged to contact the Office of Accessibility Services (OAS) at [email protected]. OAS handles disability documentation and coordinates approved academic adjustments.

You are welcome, but not required, to discuss course-specific needs with the instructor privately. You do not need to disclose your diagnosis or the specific nature of your disability to the instructor. Any disability-related information you choose to share will be treated privately. Please contact OAS and the instructor early so approved accommodations can be implemented.

Student Support Resources

John Jay offers academic, health, counseling, accessibility, emergency, and basic-needs support. Seeking support early can help you stay healthy and engaged in the course.

Wellness Center — health, counseling, crisis services, and other student-wellness programs.
Emergency Funding and Basic-Needs Support — emergency grants, food pantry, book and meal vouchers, transportation assistance, and other urgent support.
Office of Accessibility Services — registration, exam requests, assistive technology, and academic adjustments.
Academic Advisement Center — academic planning and advisement.

Tools & Setup

Core Database Workflow

PostgreSQL — primary relational DBMS for hands-on SQL work.
DBeaver Community — recommended desktop database client for students who prefer a GUI.
A browser-accessible managed PostgreSQL environment may be used for selected labs.
Save executable .sql files; screenshots alone are not a substitute unless requested.

The catalog historically references MySQL. PostgreSQL provides a modern, standards-oriented relational environment. Core SQL and relational concepts transfer across major systems; relevant MySQL differences may be discussed when useful.

Data Analysis & Data Mining

Google Colab — primary notebook environment for Python-based labs.
Python 3, pandas, NumPy as needed, Matplotlib as needed, and scikit-learn.
No paid software is required for the core course workflow.

Course Files & Cloud Database Exposure

A public repository may distribute SQL scripts, starter files, notebooks, sample datasets, labs, and technical examples. GitHub is not required for submission unless specifically instructed. Brightspace remains the official location for graded submissions.

Managed-database or optional AWS Academy/Amazon RDS activities may demonstrate cloud concepts. Cloud administration is supplementary; the primary goals remain database design, SQL, data management, and analysis.

Brightspace & Submission

Brightspace is the official course hub for announcements, weekly modules, lecture slides, lab instructions, assignments, quizzes, project requirements, due dates, submissions, grades, and feedback. Unless an assignment explicitly says otherwise, graded work should be submitted through Brightspace.

Depending on the assignment, submissions may include:

.sql files, .ipynb notebooks, or .py files.
Diagrams or PDFs, project documentation, data files, or ZIP archives.

Students are responsible for verifying that uploaded files open correctly and contain the intended work.

Books & References

Required textbook: None.

Assigned readings and course materials will be provided or identified through Brightspace. Students are not required to purchase a textbook for this course.

Recommended Database Reference

Fundamentals of Database Systems, Ramez Elmasri and Shamkant B. Navathe, 7th Edition.

A reference for database fundamentals, ER modeling, relational design, normalization, SQL, transactions, and related DBMS topics.

Recommended Data Mining / Analytical Thinking Reference

Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking, Foster Provost and Tom Fawcett.

This text provides conceptual grounding for analytical thinking, predictive modeling, evaluation, and data-driven decision making.

Important Websites

Brightspace — official modules, assignments, submissions, announcements, and grades.
CSCI 362 Undergraduate Bulletin entry.
John Jay Academic Calendar.
Fall 2026 Undergraduate Final Exam Schedule.
PostgreSQL Documentation.
pandas Documentation.
scikit-learn Documentation.

How to Practice Effectively

Draw the database design before creating tables; state keys and relationships explicitly.
Write SQL by hand and predict query results before executing queries.
Test SQL using small, understandable datasets and keep executable files rather than screenshots.
Inspect data and visualize distributions and relationships before modeling.
Separate training data from evaluation data and compare model performance.
Explain results in plain language and document assumptions, limitations, and data-quality concerns.

Quick FAQs

Where do I submit assignments?

Brightspace is the default submission system. Follow the instructions attached to each assignment.

Do I need GitHub or AWS?

No. A repository may distribute resources, and a cloud-database activity may provide exposure to managed systems, but neither GitHub nor AWS is required for the core workflow unless a specific activity says otherwise.

Do I need to install PostgreSQL?

Instructions will be provided. Some labs may use a browser-accessible PostgreSQL environment, while local PostgreSQL and DBeaver may be recommended for longer-term work.

Do I already need to know Python?

No. The Python and pandas concepts needed for the data-analysis portion will be introduced.

Can I use AI tools?

Only under the course AI policy and the instructions for the specific activity. Any permitted use must be disclosed, and you must be able to explain submitted work.

Which source should I follow if dates change?

Brightspace. This syllabus establishes the semester framework; Brightspace contains current operational details and announcements.

Course Updates

August 27, 2026 — Fall 2026 syllabus published.

Contact & Office Hours

You can reach me at ARoy [AT] jjay [DOT] cuny [DOT] edu

Office: NB 6.63.29. Course-specific announcements, office hours, and appointment details will be maintained on Brightspace.

Questions about course content, assignments, or your progress are welcome.

CSCI 362: Databases and Data Mining