Congrats on teaching an interview course with DataCamp! Our team envisions these courses as a unique experience on our platform, different (and more challenging) than a standard course. We aim to prepare learners for the questions, topics, and real-world scenarios they are likely to encounter in a data science, data engineering, or analytics interview. In that interest, we recommend you adhere to the following guidelines and contact your Curriculum Manager or Content Developer if you have any questions.

Course spec considerations


Please do not introduce new topics that have not yet been taught on our platform.


We suggest using at least one dataset per chapter. Since a wide range of topics are being covered in each of these courses, feel free to use a different data set in each lesson to best prepare students for each topic. Please have a look at the list of overused datasets that should be avoided.

Packages and functions

In order to focus on as much content as possible, we suggest teaching in accordance with packages and functions already introduced on our platform. If you want to teach a topic where our choice of package/function is outdated, please adhere to the following:

  • Introduce it briefly.
  • Link students to documentation for the package/function in the slides and the Context of the exercise it is used for.

Course description

Tell students that this course is specifically intended to prepare them for real-world interview questions related to the course topic.

Make sure to warn students that the course is designed to be more challenging than a typical DataCamp course.


You will probably have quite a few, and that is okay. Be transparent about this with your students in your first lesson, and feel free to remind them throughout the course to review the topics in a full course if they are not familiar with it.

Slides and videos

At the beginning of the course (Chapter 1, Lesson 1), remind students of the structure of the exercises they can expect throughout the course.

We will permit you to present students with a multiple-choice question in a slide, pause for 3-5 seconds, and have them consider the answer to the question.

We recommend you show minimal code in the slides. You can discuss packages, functions, and methods, but we want students to be challenged and prepared for interviews. Providing them with a syntax they can copy in the exercises probably won’t prepare them very well!

Suggested approaches to exercises

Exercise types

We suggest starting sequential exercises with a multiple-choice question that leads into a coding problem. This will allow students to approach the ambiguity of the interview question, make a decision, and then solve a coding problem based on the best decision.

Example from an interview question about dimensionality reduction:

Step 1: Asking a multiple-choice question

Steps 2-4: Performing and evaluating PCA on a data set

Exercise anatomy


  • Use the Context to identify (1) the problem being solved, (2) when/where you can expect to encounter it in an interview, and (3) introduce the data set.
  • If you’re not leading with a multiple choice question, we suggest you also (1) highlight some of the ways you can approach/solve the problem, and (4) if applicable, the specific package/function necessary to generate a correct answer. If you’re unsure how specific you need to be in order for us to test correct answers, talk to your curriculum manager or content developer.
  • If you are using a sequential exercise with a multiple choice question in the first step, you can provide some additional context in the instruction section of the second step.

Example context 


  • In our interview courses, we recommend using this field for 1-2 broader interview questions as opposed to a list of tasks to complete. Avoid including syntax as part of the instruction.
  • If students have more than one accepted way to solve a problem (i.e., multiple functions or packages), you will at least need to specify which one you are using (and feel free to remind them in the Context that there is more than one way to achieve their goal here).
  • Where students have more than one way of solving a problem and no clear best practice (i.e., multiple different functions, packages), you will need to specify which one you are using, and that there is more than one acceptable approach

Example instructions


  • Use the Hint section to provide the task-specific instructions (including syntax) that you would put in the instruction section of a standard DataCamp course.
  • You can include more hints than instructions for an interview course!

Example hints

Sample code

  • We recommend including more blanks and less guidance in exercises than in standard DataCamp courses.
  • Include briefer, more general code comments that don’t give away the answer.

Example sample code

Solution code

Solution code across all steps must adhere to the standard Sequential Exercise guidelines. Exercises must include all code written in previous steps.


We understand that many interviews in data science, machine learning, and statistics provide students with open-ended questions in a whiteboard style interview. Since this isn’t fully supported on our platform, please consider these approaches as you develop content, and reach out to us if you have any questions or concerns!

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