CMSC/DATA 320 – Summer 2025 (Term I)
Introduction to Data Science
Instructor:
Dr. Fardina Alam (fardina@umd.edu)
The Zoom links for the lectures will be posted on ELMS. Class recordings for both sessions will be posted on Panopto via ELMS.
Course Overview
Data science encapsulates the interdisciplinary activities required to create data-centric products and applications that address specific scientific, socio-political, or business questions. It has drawn tremendous attention from both academia and industry and is making deep inroads in industry, government, health, and journalism.
This course focuses on (i) data management systems, (i) exploratory and statistical data analysis, (ii) data and information visualization, and (iv) the presentation and communication of analysis results. It will be centered around case studies drawing extensively from applications, and will yield a publicly-available final project or tutorial that will strengthen course participants' data science portfolios.
The course will primarily consist of sets of self-contained lectures and assignments that leverage real-world data science platforms when needed. As such, there is no assigned textbook, but there will be some recommended ones. Many lectures will come with links to required readings, which should be completed before or after the lecture (as declared), and, when appropriate, a list of links to other web resources.
Note: This course outline is tentative and subject to modification to meet the specific needs and requirements of the students and the evolving field of data science.
Prerequisites
Students enrolled in the course should be comfortable with programming (for those at UMD, having passed CMSC216 will be good enough!) and be reasonably mathematically mature. The course will heavily utilize the Python scripting language through Google Colab (or sometimes Jupyter Notebook if needed), relying on the Anaconda package manager. Primer lectures on Python for data science will be provided early on, so there's no need to worry if you haven't used Python before. Later lectures will delve into statistics and machine learning, potentially incorporating basic calculus and basic linear algebra. A light mathematical maturity, roughly at the level of a junior CS student, is preferred.
This course is aimed at junior- and senior-level Computer Science majors, but should be accessible to any student of life with some degree of mathematical and statistical maturity, reasonable experience with programming, and an interest in the topic area. If in doubt, e-mail me: fardina@umd.edu!
Tentative Topics:
Introduction to Data Science, Experiment Design, Introduction to Python, Data Types, Data collection, Git, Pandas, Database, SQL, Probability, Summary Statistics, Hypothesis Testing, Data Visualization, Data Exploration, Introduction to Machine Learning, Classifications, Decision Trees, Regression, Feature Engineering, ML Evaluation, Neural Network, Image Processing, Natural Language Processing (NLP) Introduction to Graph(s) Theory, Recommender Systems, Data Science Ethics
Learning Outcomes:
At the completion of this course, students will be able to:
- Develop a foundational understanding of data science, encompassing key concepts, methodologies, and tools.
- Proficiently use Python for data science applications, including data types, collection, and manipulation using libraries like Pandas.
- Explore machine learning concepts, including classifications, decision trees, regression, and feature engineering.
- Evaluate machine learning models using appropriate metrics, ensuring a comprehensive understanding of model performance.
- Understand advanced topics such as neural networks, image processing, natural language processing (NLP) etc.
- Understand the importance of applying ethical frameworks and guidelines to make informed decisions in real-world data scenarios.
Grading
There will be four assignments, one semester long final group project or tutorial, weekly online quizzes, and one final written exams (cumulative).
Final grades will be calculated as:
| Component | Percentages |
|---|---|
| Assignments/Mini Projects | 40% |
| Quizzes | 20% |
| Semester Long Final Group Project/Tutorial | 25% |
| Final Exam | 15% |
Late Policy
Late work gets no credit.
It is recommended to submit homework and projects on time. There will be a 15% penalty for late submissions of homework and a 20% penalty for late submissions of project/tutorial checkpoints 1 and 2 within 24 hours after the deadline. After this 24-hour period, no submissions will be accepted. In ELMS/Gradescope (as instructed in the assignment or project), you can submit multiple times, and only the last submission will be graded. The penalty for late homework will be applied automatically; no request is necessary. This policy applies to both homework and projects, EXCEPT for the final submission/checkpoint 3 of the final project/tutorial and for any kind of BONUS work
See the next section about how to contact us in special circumstances. We aim to help everyone succeed.
Communication & Logistics Request
We are going to use a combination of in-person and online office hours and the Piazza forum (sign up here) for Q&A. This means that it's appropriate to use Piazza for asynchronous communication with the course instructors and other students, and also for short, high-bandwidth discussions that could usually take place before/after class.
Note that Piazza is not appropriate for things like asking for accommodations, extensions or other such issues/concerns. For any logistics-related help, such as extensions or grading issues, please fill out this form: CMSC320 Logistics Request Form. CMSC320- Logistic Request Form However, If you do not receive a response or if your issue is not resolved within 48 hours, then please email:
- cmsc320gradingissues.dr.fardina@gmail.com for any grading, logistics, and extension issues
While sending an email, at the beginning of your email subject line, include relevant tags, such as [hw_extension/req], [project_extension/req], [gradeissue],
etc. More tags will be updated on the website.
If you have a request that fits into one of these categories and you don't email the address given above,
then your request may not reach the right person and may not be answered in a timely manner.
If your correspondence does not fit into either of those two categories, please email an instructor ( professor) with
[CMSC320] in the email subject line. You may also go to an instructor's office hours at the times listed below.
Please note: if you don't include [CMSC320] in your subject line when emailing instructors, your email
may not be filtered correctly.
Office Hours
* All hours are EDT
Instructor Office Hours and TA office hours details can be found on ELMS as well as on Piazza.
Do keep an eye on Piazza, though; TAs will sometimes swap hours, shift hours on Zoom, and so on! Additionally, we have at least one TA explicitly covering Piazza on most weekdays; all course staff will float around Piazza in general, too!
Schedule
ALL THE LECTURE SLIDES WILL BE POSTED HERE
The schedule is subject to change as the semester progresses!
| # | Date | Topic | Sync. (S) / Async. (A) | Reading | Slides | Notes | |
|---|---|---|---|---|---|---|---|
| 1 | Jun 02 | Introduction to Data Science (1.1) and Data Types (1.2) | S: 1.1 A: 1.2 | Self-Study Slide: Git Additional Reading/Helpful slide: Python Demo Python | Lec1.1Intro Lec1.2DataTypes | Sign up on Piazza! HW1 Out Jun 02) Final Project/Tutorial Instruction Out (Jun 02) | |
| 2 | Jun 03 | Experiment Design | S | Experimentation | Lec02ExpDesign | ||
| 3 | Jun 04 | Panda, Database and SQL | A | Lec03Part1_Pandas Lec03Part2_SQL | |||
| 4 | Jun 05 | Hypothesis Testing | S | Hypo Testing Steps And Examples P-Value-Explaination | Lec04HypothesisTesting_Part1 Lec04HypothesisTesting_Part2 Lec04HypothesisTesting_Part3 | ||
| 5 | Jun 06 | Probability, Distributions, and Summary Stats | A | 6. Probability (Bayes thm,Law of tot prob) Central Limit Thm | 5.Concise_Lec5_slides_Part1_Lec05Probability Lec05_DetailedSlide_of_Part1_Probability Lec05_Probability_PArt2_SummaryStatistics | HW2 Out (Jun 06) | |
| 6 | Jun 09 | Data Exploration and Data Visualization | A | Lec6.1DataExploration Lec6.2DataVizualization | HW1 Due (Jun 09) | ||
| 7 | Jun 10 | Data Cleaning | S | Lec7DataCleaning_Part01 | |||
| 8 | Jun 11 | Data Cleaning cont. | S | HandlingMissingData | Lec08DataCleaning_Part02 | Project First Checkpoint Due (Jun 11) | |
| 9 | Jun 12 | Introduction to ML | A | Lec09IntroToML | |||
| 10 | Jun 13 | Discussion Session by TAs | A | A pre-recorded version is available in Discussion Folder in Panepto,Canvas. Discussion_Slides | |||
| 11 | Jun 16 | Feature Engineering | S | Lec10FeatureEngineering | HW3 Out (Jun 16) | ||
| 12 | Jun 17 | ML Evaluation | S |
Confusion Matrix CrossValidationVideo KFoldVideo |
Lec11ML_Evaluation | ||
| 13 | Jun 18 | Decision Trees and Other Classifications | S (Decision Tree) A (Other Classifications) | DecisionTree-Calculate-Entropy-InfoGain Reading materials are given at end of the Classification lecture slide | Lec12DecisionTree Lec13Classifications | HW2 Due (Jun 18) | |
| 14 | Jun 19 | Juneteenth Holiday | No Class | ||||
| 15 | Jun 20 | Discussion Session by TAs | S | Discussion_Slides_Session2 | HW 4 Out (Jun 20) | ||
| 16 | Jun 23 | Regression | S | Simple Linear Regression Lasso&Ridge_Regression_Regularization Detailed Slides from Previous Semester(Optional) | Lec14Regression | ||
| 17 | Jun 24 | Unsupervised Learning - Dimensionality Reduction | A | PCA_overview_slides | Lec15UnsupervisedLearning_DimReduction | ||
| 18 | Jun 25 | Unsupervised Learning - Clustering | S | Lec15UnsupervisedLearning_Clustering | |||
| 19 | Jun 26 | Introduction to Neural Network | S | Lec16.1_NeuralNetwork - | |||
| 20 | Jun 27 | Discussion Session by TAs | S | Project Second Checkpoint Due (Jun 27) | |||
| 21 | Jun 30 | Neural Network cont. | S | Lec16.2_NN_Training | HW3 Due (Jun 30) | ||
| 22 | July 01 | Image Processing with CNN | S | Lec17ImageProcessing | |||
| 23 | July 02 | Intro to Natural Language Processing (NLP) | Cont. | ||||
| 24 | July 03 | Intro to Graph Theory |
NetworkX Intro to GraphQL These two materials are given for self-exploration if someone is interested. ClassNote_Girvan-NewmanAlgorithm Girvan & Newman. "Community structure in social and biological networks," PNAS-02. |
||||
| 25 | July 04 | Independence Day Holiday | NO CLASS | ||||
| 26 | July 07 | Recommender System | HW4 Due (July 07) | ||||
| 27 | July 08 | Recommender System and Data Ethics | S (Lec21) A (Lec 22 - Self Reading for the quiz) | ||||
| 28 | July 09 | Wrap-Up and Exam Review All Together | S | ||||
| 29 | July 10 | No Class | NO CLASS | ||||
| July 11, 11:30 am to 1:30 PM | Hybrid Final Exam (Zoom and IRB) | Project Final Checkpoint Due (July 13, Sunday NOON (11:59 AM)-No extenstion) |
Deliverables
Assignments
All the Assignments will be posted on Piazza/ ELMS. Instructions will appear over the course of the semester. Most assignments get released on one or two days before the lecture material gets presented and are due one or two weeks after that.
| # | Description | Date Released | Date Due |
|---|---|---|---|
| Homework 1 | Git, Pandas, and SQL | June 02 | June 09 |
| Homework 2 | Data Exploration & Data Cleaning & Missing Data | June 06 | June 18 |
| Homework 3 | Machine Learning: Classification and Clustering | June 16 | June 30 |
| Homework 4 | Regression, Gradient Descent and Neural Network | June 20 | July 07 |
Final Project/Tutorial
There will be a group final project/tutorial with a maximum of three/four persons in each group (it will be student’s responsibility to find their project partners, feel free to post in slack if needed). Keep in mind that this is a semester-long project/tutorial for this course, and you should strive to make it your best work possible. It will be graded to a higher standard than the rest of the homework, considering that you have had the chance to practice these skills beforehand. Project details will be posted on Piazza/ELMS. It will be your responsibility to decide project topic as well as project partners.
| # | Description | Date Released | Date Due |
|---|---|---|---|
| Checkpoint 1 | Group Formation and Choosing Dataset | Jun 02 | Jun 11 |
| Checkpoint 2 | Data preprocessing and Exploration | Jun 02 | Jun 27 |
| Checkpoint 3 | Final deliverable of DS Project | Jun 02 | July 13 (noon11:59 AM) - NO EXTENSION/LATE SUBMISSION IS ALLOWED |
Quizzes
All the quizzes will be posted on ELMS. Instructions will appear over the course of the semester. There will be multiple very short quizzes weekly (duration: 5-15 minutes). Topics are based on the weekly topics covered. Most of the quizzes will be released each week on Friday any time after class, and you will need to submit the quizzes by Tuesday at 11:59 pm (except for the last quiz). Remember, you will have just one chance to submit each quiz; you cannot take any quiz twice (No late penalty or submission is allowed). Additionally, the lowest-scored quiz will be dropped automatically during the final grading.
| # | Description | Date Released | Date Due |
|---|---|---|---|
| Quiz 1 | Week1: Quiz 1. Course Introduction and Introduction to Data Science | Jun 06(Fri) | Jun 10 (Tues) |
| Quiz 2 | Week1: Quiz 2. Data Type, Pandas and SQL | Jun 06(Fri) | Jun 10 (Tues) |
| Quiz 3 | Week1: Quiz 3. Experiment Design and Hypothesis Testing | Jun 07 (Fri) | Jun 10 (Tues) |
| Quiz 4 | Week1: Quiz 1. Probability, Distributions, and Summary Stats | Jun 07 (Fri) | Jun 10 (Tues)) |
| Quiz 5 | Week2: Quiz 5. Data Exploration and Data Visualization | Jun 13 (Fri) | Jun 17 (Tues) |
| Quiz 6 | Week2: Quiz 6. Data Cleaning and Introduction to ML | Jun 13 (Fri) | Jun 17 (Tues) |
| Quiz 7 | Week3: Quiz 7. Feature Engineering and ML Evaluation | Jun 20 (Fri) | Jun 24 (Tues) |
| Quiz 8 | Week3: Quiz 8. Decision Trees | Jun 20 (Fri) | Jun 24 (Tues) |
| Quiz 9 | Week4: Quiz 9. Classifications and Regression | Jun 27 (Fri) | July 01 (Tues) |
| Quiz 10 | Week4: Quiz 10. Unsupervised Learning & Dimensionality Reduction | Jun 27 (Fri) | July 01 (Tues) |
| Quiz 11 | Week5: Quiz 11. Neural Network and Image Processing | July 03 (Fri) | July 08 (Tues) |
| Quiz 13 | Week5: Quiz 12. Intro to Natural Language Processing (NLP) | July 03 (Fri) | July 08 (Tues) |
| Quiz 14 | Week6: Quiz 13. Intro to Graph Theory | July 08 (Tues) | July 10 (Thurs) | Quiz 15 | Week6: Quiz 14. Recommender System and Ethics | July 08 (Tues) | July 10 (Thurs) |
Academic Integrity
Note that academic dishonesty includes not only cheating, fabrication, and plagiarism but also includes helping other students commit acts of academic dishonesty by allowing them to obtain copies of your work. You are allowed to use the Web for reference purposes, but you may not copy code from any website or any other source. In short, all submitted work must be your own. Cases of academic dishonesty will be pursued to the fullest extent possible as stipulated by the Office of Student Conduct. Without exception, every case of suspected academic dishonesty will be referred to the Office. If the student is found to be responsible for academic dishonesty, the typical sanction results in a special grade “XF", indicating that the course was failed due to academic dishonesty. More serious instances can result in expulsion from the university. If you have any doubt as to whether an act of yours might constitute academic dishonesty, please contact your TA or the course coordinator. The University of Maryland, College Park has a nationally recognized Code of Academic Integrity, administered by the Student Honor Council.B This code sets standards for academic integrity at Maryland for all undergraduate and graduate students. As a student, you are responsible for upholding these standards for this course. It is very important for you to be aware of the consequences of cheating, fabrication, facilitation, and plagiarism. For more information on the Code of Academic Integrity or the Student Honor Council, please visit http://www.shc.umd.edu.
Examples of Academic Integrity Violations:
The following are examples of academic integrity violations:
- Hardcoding of results in a project assignment. Hardcoding refers to attempting to make a program appear as if it works correctly (e.g., printing expected results for a test).
- Using any code available on the internet/web or any other source.
- Hiring any online service to complete an assignment for you.
- You may not post the implementation of your assignments, materials related to the class (e.g., project description), or any other material associated with this online course except the final project. Even if the class is over and you have graduated, you may NOT post any material other than what is allowed.
- Sharing your homework solutions or your “test code" with any student.
- Looking at or debugging another student’s code.
- Using online forums to ask for help regarding our assignments.
AI tool disclosure: If a student chooses to use an AI tool to assist in any course work (e.g. assignments, programs, projects, reports, etc), they must disclose this information to the instructor. This disclosure should include the name of the AI tool and explain how it was used.
Consequences for non-compliance: Failure to adhere to this policy may result in a zero on the particular course work where the AI tool is used. In addition, the university honor code is applicable here: violation of the honor code and appropriate action will be enforced.
Class Announcements
You are responsible for reading the class announcements that are posted on both the course webpage and ELMS. Please check them often (at least once a day). Important information about the course (e.g., deadlines, assignment updates, etc.) will be posted on the course webpage.
Additional Administrative Information
University Policies and Resources
Policies relevant to Undergraduate Courses are found here: http://ugst.umd.edu/courserelatedpolicies.html. Topics that are addressed in these various policies include academic integrity, student and instructor conduct, accessibility and accommodations, attendance and excused absences, grades and appeals, copyright and intellectual property.
Projects/Labs: On any graded project or lab, you are NOT allowed to exchange code. We compare each student's code with every other student's code to check for similarities. Every semester, we catch an embarrassingly high number of students that engage in cheating and we have to take them to the Honor Council.
GroupMe/Other Group Chats: We encourage students to talk about course material and help each other out in group chats. However, this does NOT include graded assignments. There have been a couple instances in the past where students have posted pictures/source files of their code, or earlier sections have given away exam questions to later sections. Not only did this lower the curve for the earlier section because the later one will do better, the WHOLE group chat had to pay a visit to the Honor Council. It was an extremely ugly business. Remember that in a group of 200+, someone or the other will blow the whistle. If you happen to be an innocent person in an innocent groupchat and someone starts cheating out of the blue, leave it immediately (and better yet, say you are leaving and say you will report it).
Github: You may post your project code to private Github (or similar service) repos only. As a student, you can make a private repo for free. Just remember that your free premium subscription has an expiration date, and your code becomes public once it expires. The Honor Council can retroactively give an XF (even to students who have already graduated) if your code is then used by another student to cheat. So just be careful. Posting graded code to a public repo will give you a free ticket to the Honor Council(Unless the instructor has given you permission with some strict conditions).
Excused Absences
Missing an exam for reasons such as illness, religious observance, participation in required university activities, or family or personal emergency (such as a serious automobile accident or close relative’s funeral) will be excused so long as the absence is requested in writing at least 2 days in advance and the student includes documentation that shows the absence qualifies as excused; a self-signed note is not sufficient as exams are Major Scheduled Grading Events. For this class, such events are the final project assessment and midterms, which will be due on the dates listed in the schedule above. The final exam is scheduled according to the University Registrar.
For medical absences, you must furnish documentation from the health care professional who treated you. This documentation must verify dates of treatment and indicate the timeframe that the student was unable to meet academic responsibilities. In addition, it must contain the name and phone number of the medical service provider to be used if verification is needed. No diagnostic information will ever be requested. Note that simply being seen by a health care professional does not constitute an excused absence; it must be clear that you were unable to perform your academic duties.
It is the University's policy to provide accommodations for students with religious observances conflicting with exams, but it is the your responsibility to inform the instructor in advance of intended religious observances. If you have a conflict with a planned exam, you must inform the instructor prior to the end of the first two weeks of the class.
The policies for excused absences do not apply to project assignments. Projects will be assigned with sufficient time to be completed by students who have a reasonable understanding of the necessary material and begin promptly. In cases of extremely serious documented illness of lengthy duration or other protracted, severe emergency situations, the instructor may consider extensions on project assignments, depending upon the specific circumstances.
Besides the policies in this syllabus, the University's policies apply during the semester. Various policies that may be relevant appear in the Undergraduate Catalog.
If you experience difficulty during the semester keeping up with the academic demands of your courses, you may consider contacting the Learning Assistance Service in 2201 Shoemaker Building at (301) 314-7693. Their educational counselors can help with time management issues, reading, note-taking, and exam preparation skills.
Information regarding official University closing and delays
Students can find information about official university closings and delays on the campus website or by contacting the weather emergency phone line at 301-405-7669. If any exam or class assignment is rescheduled or a class is canceled due to inclement weather, we will provide announcements via ELMS. We will ensure to follow the university campus rules
Right to Change Information
Although every effort has been made to be complete and accurate, unforeseen circumstances arising during the semester could require the adjustment of any material given here. Consequently, given due notice to students, the instructors reserve the right to change any information on this syllabus or in other course materials. Such changes will be announced and prominently displayed.
Course Evaluations
Course evaluations are important and the department and faculty take student feedback seriously. Near the end of the semester, students can go to http://www.courseevalum.umd.edu to complete their evaluations.
Miscellaneous Resources
As we go through the course sometimes I will mention additional resources or next steps. None of this is required for the course, but students have asked for me to keep a record of which texts/websites I mention.
- Python for Data Analysis covers some of the same topics that we cover in this class, but in textbook form.
- Data Science From Scratch this book is meant to give you a grounding in how some of the statistics and math could be implemented. Mostly, data scientists use off-the-shelf libraries for their mathematical routines/functionality, this books works through how you would implement some of those libraries. The idea is that this might give you a deeper understanding of what is going on. The implementations in this book are not meant to be high-performance, or industrial strength, but illustrative.
- Web Scraping 101 is a good overview to how web scraping works.
