UMD CMSC 320: Introduction to Data Science
CMSC 320 is UMD's data science course: the Python data pipeline from collection and cleaning through exploratory analysis, visualization, basic machine learning, and communication of results, typically culminating in an open-ended final project.
Fennie is independent and not affiliated with University of Maryland. This is an unofficial study guide.
What makes it hard
The breadth is the challenge: pandas idioms, statistical reasoning, and ML concepts arrive faster than any one of them can be mastered, and messy-data assignments resist the tidy examples lecture used. The open-ended final project rewards students who managed scope and timeline like a real analysis, and punishes week-before starts.
What you'll cover
- • Python data tools and pandas
- • Data collection and cleaning
- • Exploratory analysis and visualization
- • Hypothesis testing basics
- • Intro machine learning
- • Communicating results
The CMSC 320 study guide
How to study for UMD CMSC 320, step by step.
- 1
Get fluent in pandas early
Every assignment routes through dataframe manipulation, and fighting the library doubles every task. Short daily practice with indexing, grouping, and merging pays off for the entire semester.
- 2
Embrace messy data as the curriculum
Real datasets (missing values, broken types, weird encodings) are the point, not an obstacle. Practice the cleaning steps deliberately, because assignments and the project both start there.
- 3
Learn the statistics as reasoning, not ritual
Know what a hypothesis test or a model actually claims and when it's misleading. The conceptual questions separate grades more than the code does.
- 4
Scope the final project early and small
Pick a dataset and question weeks before the deadline, and prefer a modest analysis done well to an ambitious one done at 3 a.m. Project grading rewards completeness and clarity over ambition.
Today's CMSC 320 plan
What a Fennie Daily Plan looks like for CMSC 320. Yours is built from your syllabus and adapts every day to your deadlines and progress.
- Review: Intro machine learningReview · 25 min
Work back through the CMSC 320 material on intro machine learning. In the app, Fennie builds this from your own notes and syllabus.
- Practice: Communicating resultsPractice · 20 min
Targeted problems on communicating results, the kind CMSC 320 actually tests.
- Quick quiz: Python data tools and pandasQuiz · 10 min
Five generated questions to expose weak spots on python data tools and pandas before the exam does.
- Preview: Data collection and cleaningPreview · 10 min
A first pass over data collection and cleaning so the next session starts from familiar ground.
First plan free, no card required. Fennie is independent and unaffiliated with your school.
FAQ
Is CMSC 320 at UMD hard?
It's broad rather than deep: pandas, statistics, and ML basics arriving quickly, plus an open-ended project. Students with steady weekly engagement find it one of the more enjoyable CMSC courses; procrastinators find the project brutal.
What do you actually do in CMSC 320?
Work the full data pipeline in Python: collect and clean real datasets, explore and visualize them, apply basic statistical tests and ML models, and communicate findings, culminating in a self-directed final project or tutorial.
How do I do well on the CMSC 320 final project?
Choose a dataset and question early, scope modestly, and execute the full pipeline cleanly: cleaning, analysis, visualization, interpretation. Graders reward a complete, clear analysis far more than an ambitious, half-finished one.
More UMD courses
CMSC 131: Object-Oriented Programming I
CMSC 131 is UMD's first programming course for CS majors, taught in Java: objects, control flow, methods, arrays, and intro design, with weekly projects graded by an autograder against test cases you can't see all of. It sets the tone for the entire CMSC sequence.
CMSC 132: Object-Oriented Programming II
CMSC 132 continues UMD's Java sequence into data structures and design: inheritance, recursion, linked lists, trees, hash tables, and intro threads, with bigger autograded projects and the same handwritten-exam format as 131.
CMSC 216: Introduction to Computer Systems
CMSC 216 drops UMD CS majors below the Java abstraction: C programming, pointers, dynamic memory, the UNIX environment, and assembly-level concepts, with substantial autograded projects. It's taken alongside or near CMSC 250 in the sequence.
CMSC 250: Discrete Structures
CMSC 250 is UMD's discrete math course for CS majors: logic, proof techniques, induction, sets, functions, combinatorics, and probability basics. It's the course where computer science becomes mathematics for a semester.