| Week | Start Date | Topic |
|---|---|---|
| 01 | January 22 | Introduction |
| 02 | January 29 | Python Review |
| 03 | Feb 05 | Numpy & Pandas |
| 04 | February 12 | Data Wrangling |
| 05 | February 19 | Supervised Learning – Linear Regression vs. Logistic Regression (statsmodels) |
| 06 | February 26 | Supervised Learning – Regression and Classification with sklearn |
| 07 | March 05 | Supervised Learning – Suport Vector Machines, and multiclass classification |
| 08 | March 17 | Unsupervised Learning – clustering (Kmeans) |
| 09 | March 24 | Neural Networks |
| 10 | March 31 | Neural Networks and NLP |
| 11 | April 07 | Data Visualization and Recommender Systems |
| 12 | April 14 | Reinforcement Learning |
| 13 | April 21 | Other Advanced Techniques |
| 14 | April 28 | Final Project Presentations |
CSIT-360-557 Syllabus
Course Information
- Monday and Wednesday, 12:00 – 1:25pm
- Mode: in-person, at School of Business 011
Course Description
This course provides the advanced techniques in Data Science. After learning data manipulating, processing, cleaning, and visualization, the major goals of this course are to learn how to make data-driven inferences and decisions by using fundamental techniques in machine learning, in Python language.
Instructor
- Adriana Picoral, PhD
- Office: Schmitt 374
- email: picorala@montclair.edu
- Office Hours (open door/drop in):
- Tuesdays and Thursdays, 1:45pm to 3:30pm
Course Outcomes
Upon completion of the course, students should be able to:
- CO1: understand the concepts and various phases of Data Science
- CO2: understand how to use Python data science libraries to demonstrate the data science skills and visualize data
- CO3: understand the concepts of fundamental techniques in machine learning and neural networks
- CO4: implement techniques of machine learning algorithms on data analysis using advanced libraries such as sci-kit learn and TensorFlow in Python
- CO5: apply data science concepts and skills to solve problems with real-world data sets
Course Schedule
Grading Breakdown
| Assessment Element | Percentage of Final Grade |
|---|---|
| In-class Activities | 10% |
| Projects | 30% |
| Quizzes (5) | 30% |
| Group Project | 20% |
| Project Presentation | 10% |
Late Work Policy
Students are expected to complete work on schedule. The late policy for all assignments is as follows:
- 20% points off if submitted within 24 hours after the due date
- 30% off if submitted 24-48 hours after the due date
- No credit if submitted two days or more days after the due date unless prior arrangements are made with the instructor with acceptable reasons.
Note: This is a firm policy and it will be automatically applied
Attendance Policy
Attendance is mandatory. All students must sign an attendance sheet at the beginning of class. Excused absences include illness or a serious personal crisis (a letter from the Dean of students is required). If you are missing class and have a reasonable excuse, contact me before lecture if you want to make up missed assessments.
Grading Scale
| Letter Grade | Grade Percentage |
|---|---|
| A | 94-100% |
| A- | 90-93% |
| B+ | 87-89% |
| B | 84-86% |
| B- | 80-83% |
| C+ | 77-79% |
| C | 74-76% |
| C- | 70-73% |
| D+ (undergraduate only) | 67-69% |
| D (undergraduate only) | 64-66% |
| D- (undergraduate only) | 60-63% |
| F | 0-59% |
Academic Honesty and Integrity
Academic Honesty is a core University value. Take time to understand the University’s policy. Your questions about academic honesty are always welcome.
All work you submit for grading in this course must be your own. Submitting work that includes (minor and/or major) components that are not your own work is considered plagiarism. I recommend that when talking with others about the assignment, do not write anything down.
Keep in mind that all assignments and practice problems provided in this course are meant to help you practice the skills that you will need to do well in all assessments (including on paper quizzes), so it is generally in your best interest to avoid taking shortcuts even on practice problems (which are ungraded).
Sharing your code with others (in addition to copying code from others) is considered a break of the academic integrity code (unauthorized assistance) as well.
Land Acknowledgement
We respectfully acknowledge that Montclair State University occupies land in Lenapehoking, the traditional and expropriated territory of the Lenape. As a state institution, we recognize and support the sovereignty of New Jersey’s three state-recognized tribes: the Ramapough Lenape, Nanticoke Lenni-Lenape, and Powhatan Renape nations.
We recognize the sovereign nations of the Lenape diaspora elsewhere in North America, as well as other Indigenous individuals and communities now residing in New Jersey. By offering this land acknowledgement, we commit to addressing the historical legacies of Indigenous dispossession and dismantling practices of erasure that persist today. We recognize the resilience and persistence of contemporary Indigenous communities and their role in educating all of us about justice, equity, and the stewardship of the land throughout the generations.
Subject to Change
Changes may be made to this syllabus as needed to support student learning. Any updates will be announced in class or through course materials with advance notice.