A Breakdown of Data Science Courses at NYU
Students share reviews and advice for some core data science courses at NYU to make course registration a breeze!
Interested in pursuing a data science major or minor at NYU? Unsure about what classes to take? Curious to know what to expect? Don’t worry, you’ve come to the right place!
This semester, the NYU Data Science Review staff interviewed their friends and classmates taking various data science classes, and have advice that they would like to share with you.
Below, we break down the main components of many core DS courses — we hope you find this article helpful if you are considering taking any of these classes next semester!
Here is the CAS Bulletin Link with a detailed list of CDS Undergraduate Course Offerings.
DATA SCIENCE FOR EVERYONE (DS-UA 111)
Prerequisites: None
Background of Students: Most students in this class have heard about Data Science For Everyone as a popular class to take, are curious about pursuing the major and feel like this is a great starting point into the world of data science.
Skills taught: The class provides a good overarching understanding of the field of data science as a whole without going too in-depth and covers some coding essential to data science.
Resources: The primary learning materials for this course are lecture slides and the textbook, both of which are cohesive and easy to follow. The optional readings make understanding the lecture slides very easy.
Confidence Level: After taking this course, most students are excited to pursue either a Data Science major, joint major or minor!
Advice:
- Do the readings
- Great exposure to DS
- Great refresher if you already have some background
- Having some coding experience (like intro to comp programming) helps
- Practice coding everyday
- Don’t be afraid even if you have limited programming exposure
INTRODUCTION TO DATA SCIENCE (DS-UA 112)
Prerequisites: Data Science for Everyone (DS-UA 111)
- Most students felt that the prerequisite was very helpful, and they would have certainly struggled without it
Skills taught: The class provides a solid foundation for several building blocks of Data Science like Probability & Statistics, Linear Algebra & Coding.
Resources: The primary learning materials for this course are lecture slides and live coding videos which are easy to follow if you attend lectures.
Extracurricular benefits: This course is very helpful for technical interviewing rounds for internships and jobs, and statistical modeling is super helpful even if you’re not recruiting for data science jobs.
Advice:
- Review Data Science For Everyone topics before IDS
- Practice coding once a week
- Don’t attempt the homework assignments without practicing code
CAUSAL INFERENCE (DS-UA 201)
Prerequisites: Introduction to Data Science (DS-UA 112)
- Essential to set a foundation for concepts that are used in this class
Skills taught: This course extensively covers experimental design and quantifies methods to estimate causal effect.
Resources: The primary learning materials for this course are class slides, lab content, and textbook readings which are all easy to follow if you attend lectures.
Extracurricular benefits: Helpful when working on data science research projects or your own personal project.
Advice:
- Participate and discuss during the class
- Revise concepts and code regularly
ADVANCED TOPICS- MACHINE LEARNING & DEEP LEARNING (DS-UA 301)
Prerequisites: Introduction to Data Science (DS-UA 112) and completion of the probability and statistics requirement.
- Many algorithms and function implementations learned in this class require a solid understanding of the math
Skills taught: While the course attempts to dive deep into two areas of data science, the content is fast paced and some concepts are introduced without application. The labs sections help keep up with the work and it is essential to read up before attending lectures.
Resources: The primary learning materials for this course are lecture slides, which are not detailed enough. YouTube and other websites on the internet are certainly helpful while scouting for material to broaden your knowledge on the course topics.
Extracurricular benefits: The content in this course helps with some basic questions and technical interviews. These topics are very useful for personal projects and for full time jobs as well as masters/PhD applications.
Advice:
- While it is not an official prerequisite, taking Fundamentals of Machine Learning (CSCI-UA 473) before taking this class would be incredibly beneficial
- Participate and discuss during the class
- Create study groups to discuss homework and seek out your TA for help
INTRODUCTION TO COMP PROGRAMMING
Background of Students: Most students in this course are interested in picking up a new skill or have an interest in the CS/DS major(s) but do not have prior coding experience.
Skills taught: This course gives a basic overview of how to code in python in a well structured manner.
Resources: The primary learning materials for this course are lecture slides and TA notes, the class website, and assignments plus weekly quizzes.
Confidence Level: Students have mixed reviews — while some are confident in their programming skills to further pursue data science, others feel that they need another class like Data Science for Everyone to understand more data science specific concepts before deciding.
Advice:
- Practice — you can’t just memorize coding
- Don’t be daunted if you are new to the field — this is a good introductory course.
- Put in an effort to understand the material
PROGRAMMING TOOLS FOR THE DATA SCIENTIST
Prerequisites: Data Science for Everyone (DS-UA 111) or equivalent proficiency in Python, and either Introduction to Computer Programming (No Prior Experience) (CSCI-UA 2) or Introduction to Computer Programming (Limited Prior Experience) (CSCI-UA 3)
- Very helpful but the class reviews a lot of the basics of the code used in class!
Skills taught: Students had mixed reviews — while some find catching up with the course content relatively easy, others find the lack of practice hard to improve their skills
Resources: The primary resource materials for this course are lecture slides and in-person classes, both of which are easy to follow.
Extracurricular benefits: Most students taking this class are not pursuing data science related jobs.
Advice:
- Have some understanding of statistics, web development
- Take other computer science/data science classes
- Attend class & pay attention
- Go over examples covered in class
