About This Course

You’re about to begin a hands-on, challenge-driven journey into the world of data science and machine learning interviews. Whether you’re pursuing your first role in tech or aiming to transition into a senior data position, this course is designed to strengthen your problem-solving skills, build your confidence, and help you stand out in interviews.

Why take this course?

This isn’t just another passive learning experience. We’ve built this course around real-world interview challenges used by top tech companies to assess candidates—challenges that require more than memorization. You’ll be expected to think, code, and justify your solutions like a data scientist.

Here’s what makes this course unique:

• Real interview prep: Interviews from leading tech companies inspire every challenge and question.

• Hands-on practice: You’ll code in Python and SQL, tackle debugging challenges, and explore machine learning concepts through scenario-based learning.

• Conceptual clarity: Learn to articulate your thought process and make informed design choices—skills interviewers look for.

Here’s an overview of the topics we’ll cover in this course.

Disclaimer

This course includes discussions and examples related to bias, fairness, and representation in machine learning systems. As part of this, certain datasets and case studies may reference topics such as Body Mass Index (BMI), diseases, sex, and ethnicity—including examples involving Black women, Asian men, and older people.

We want to be clear: these examples are presented solely for educational purposes to illustrate real-world challenges in identifying and mitigating algorithmic bias. The inclusion of such content does not reflect any intent to stereotype, exclude, or perpetuate harm against any group. We are mindful of the potential for language-based sensitivity and actively avoid phrasing or framing that could reinforce bias.

Some datasets used in this course are sourced externally and may contain legacy biases or labels that do not align with our values of fairness and inclusivity. We include these datasets not to endorse their framing, but to critically analyze them and equip learners with the tools to build more equitable ML systems.

We appreciate your understanding as we carefully and respectfully explore these necessary but sometimes sensitive topics. We aim to empower learners to design systems that promote fairness, accountability, and inclusivity in AI.

Who should take this course?

The course caters to individuals looking to improve their data science and machine learning skills, whether just starting or looking to advance their existing positions. Learners will master supervised and unsupervised learning algorithms, use advanced machine learning concepts, and explore real-world applications across industries like healthcare, finance, and autonomous vehicles.

This course is ideal for:

• Aspiring data scientists preparing for technical interviews.

• Software engineers pivoting to ML-focused roles.

• Analysts and researchers who want to strengthen their technical skill set.

• Students and recent graduates preparing to land their first role in data science.

• Working professionals who want to build a strong portfolio or advance to the next stage in their careers.

This course is for you if you want to improve your interview performance, apply your knowledge in real-world scenarios, and stand out in a competitive job market.

Prerequisites

Before you begin, you should have:

• A basic understanding of Python programming and SQL.

• Familiarity with core mathematics, including linear algebra, probability, and statistics.

• Prior experience working with data, such as basic data manipulation, handling CSV files, or using data libraries like pandas, is recommended.

Data science interviews are challenging, but with the right preparation, you can confidently approach them. This course offers a clear, structured path to help you build that confidence. Let’s get started.