MIDS Diagnostic Placement Exam

Dates

Background

Students enter the MIDS program with a range of backgrounds and competencies in foundational data science skills. The Diagnostic Placement Exam serves two purposes: 

1. First, it provides students with a tool to self-assess their foundational skills in programming, statistics, and data engineering. 
2. Second, it serves as a mechanism for students to place out of one or more of these required courses:
   • DATASCI 200. Introduction to Data Science Programming
   • DATASCI 203. Statistics for Data Science
   • DATASCI 205. Fundamentals of Data Engineering

Based on the results of the DPE, we will inform you which of these courses you are required to take and which you may waive or place out of.

Who Should Take the Exam?

We encourage all students, even those who are not interested in waiving one of these 3 courses, to attempt all questions in the diagnostic exam.

Exam Rules

You may not use GenAI tools, GPT, CoPilot, or other assistive generative technologies while completing the Diagnostic Placement Exam. 

This requirement ensures an accurate depiction of your current knowledge and skill set in foundational data science domains and allows us to provide you with a precise recommendation for foundational courses in the MIDS program. Students who use these tools during the exam will not be able to be placed out of the course(s).

Accessing the Diagnostic Placement Exam

The exam is hosted on Gradescope.com. The week before the exam is scheduled to begin, you will receive a welcome email with a link to the Diagnostic Placement Exam and instructions for setting your Gradescope password. 

After you receive that welcome email, you can log into Gradescope at Gradescope.com and find the course titled “MIDS Diagnostic Placement Exam” on your dashboard.

Before the exam’s scheduled start time, you will not see any content; the DPE materials will become visible at the scheduled start time.

Exam Structure

The DPE has six components

Introduction to Data Science Programming, Part I (DATASCI 200)

This component includes multiple choice questions and two short data analysis exercises. It will assess your knowledge of programming basics, git, command line tools, computer science theory, numpy, and pandas. The data analysis exercises require you to have access to a Python environment either running on your computer or on a cloud provider. 

We estimate this part of the exam will take approximately 1.5 hours.

Instructions: Introduction to Data Science Programming, Part II (DATASCI 200)

This component contains instructions for a single object-oriented programming exercise. 

The exercise is estimated to take approximately 1.5 hours.

Submission: Introduction to Data Science Programming, Part II (DATASCI 200)

This component is where you will upload your solution to the object-oriented programming exercise. You must name your file pand.py. Once you upload, an autograder will automatically run. You will see a score, along with details about how your code did against several basic tests.

Statistics, Part I (DATASCI 203)

This component includes multiple choice and true/false questions to assess your knowledge of fundamental statistical concepts including probability, random variables, estimation, quantifying uncertainty, and linear models. 

This component is timed. You will have 90 minutes to complete this component once started, so make sure you are ready to commit to that time frame before beginning.

Statistics, Part II (DATASCI 203)

This component includes mathematical exercises that will require you to submit a proof written in Latex, or scan/upload a handwritten proof. It also includes several short answer questions that will require you to write arguments, evaluations, and explanations. 

This component is expected to take 1.5 hours.

Data Engineering (DATASCI 205)

This component includes true/false questions on foundational data engineering concepts and tools, including: SQL, relational databases, Linux operating oystem, BASH shell and Linux command line interface, virtual machines and containers, Docker container images, data pipeline engineering, CSV files, JSON files, Neo4j NoSQL graph databases, MongoDB NoSQL databases, web servers and web API Servers, big data architecture, and data warehousing

This component is timed. You will have 90 minutes to complete this component once started, so make sure you are ready to commit to that time frame before beginning.

Preparing for the Exam

We recommend that you review the following information about these 3 courses to assist your preparation for the Diagnostic Placement Exam.

DATASCI 200. Introduction to Data Science Programming

We have designed DATASCI 200 to cover foundational skills that will be essential for courses in machine learning and data engineering. The course focuses on the Python programming language, but also provides experience with command line tools, git, and some computer science theory. It is a fast-paced course; we designed the lectures assuming that you have taken at least one general-purpose programming course in the past. 
Course details | Syllabus | List of fundamentals covered

DATASCI 203. Statistics for Data Science

DATASCI 203 is designed to cover core fundamentals of statistics, such as the mechanics of hypothesis testing, operational definitions, data preparation and graphics in R, inferential bivariate statistics, principles of multiple regression, and regression diagnostics using R.
Course details | Learning objectives

DATASCI 205. Fundamentals of Data Engineering

This course introduces the fundamental knowledge and skills of data engineering that are required to be effective as a data scientist. This course focuses on the basics of data pipelines, data pipeline flows and associated business use cases, and how organizations derive value from data and data engineering.
Course details | Syllabus | List of fundamentals covered

Support and Questions

If you encounter problems accessing the Diagnostic Placement Exam, or have questions or issues with specific parts of the exam, please send an email to mids-placement@ischool.berkeley.edu for support. Please specify which part of the exam you need support for in the Subject of the email (e.g., Statistics, Part I). You should expect a response within 24–48 hours, although the response may be delayed on weekends.

Waiving Course Requirements

In order to waive out of DATASCI 200, 203, or 205, students must:

1. Successfully complete all components of the specific placement exam for the course and receive a grade high enough to place out of the course. (Note that using any shortcuts such as online search tools or GenAI tools such as ChatGPT will result in a non-satisfactory grade.)
2. Receive an email confirmation explicitly stating the recommendation to waive out of that course.

Please note that any student who does not receive an email with the recommendation to waive out of the DATASCI 200 exam components will be required to enroll in DATASCI 200. Introduction to Data Science Programming in their first term (along with DATASCI 201, the other required course for all first term MIDS students). Similarly, students who do not place out of DATASCI 203 or DATASCI 205 will be required to take these courses as part of their MIDS degree program. 

In the event that you do successfully place out of any of these three courses, you are still required to complete 27 units as part of the program, and you are required to take two courses in your first term. Students who waive out of all three courses are not necessarily guaranteed space in other courses in any given semester.

Specific course sequencing questions should be directed to your Student Success Advisor.

Frequently Asked Questions

If I take the DPE and do poorly, does that mean that I’m going to have a hard time in the course? 

No, not at all! The DPE tests core competencies that are taught in the course. If you’re not familiar with the concepts now, we’re sure you will be by the completion of the term!

If I take the exam and do well, does that mean I am required to be placed out of the course? 

No, but if you’re an expert in the topic, we would encourage you to use your time developing deep expertise in a challenging topic (i.e., an advanced elective), rather than reviewing materials you already know.

How long will these exams take? 

For students with a solid mastery of each course’s required skills and knowledge, we expect each course section to take 1–3 hours, or 9 hours total for the whole exam.

If you have a passing familiarity with a section’s concepts, we encourage you to work out each question as far as you can, but don't spend much more than three hours on any section.

What will be the result of taking this DPE? Does it affect my grades for the program? 

These DPEs have no bearing on grades or marks for the program. After you complete the DPEs, you will get an email with our recommendation for the courses you should enroll in. If you’ve demonstrated competency, we will also set the enrollment system so that you can take courses that have prerequisites you have passed out of. Enrollment into all courses is on a space available basis and not guaranteed in any given semester.

I have no education, experience, or training in these domains, or I have a great deal of ‘rust’ based on experience long ago. Should I attempt the DPE? 

Yes! If you have no background or are “rusty” in the materials covered in the courses, the DPE will seem hard and might use terms or concepts that are entirely unfamiliar to you. That’s okay! This is a chance to start to understand what you are familiar with, what you aren’t, and what you’ll be learning about in these 3 courses. Over the span of the semester in these courses, you will learn all of these concepts. We anticipate that at the end of the semester all students who work hard in the associated course would do very well on an exam of this form.  If you have an extensive background in these topics, then parts of the DPE might seem quite easy. However, we would encourage you to review your understanding before beginning and ensure that you’re still working hard to answer every question correctly, because this is the signal that you’re sending about the most appropriate course placement.

Please reach out to your admissions counselor if you have any questions. Good luck! Once again, welcome, and we look forward to having you in class!