Skip to content

Latest commit

 

History

History
380 lines (300 loc) · 35.7 KB

README.md

File metadata and controls

380 lines (300 loc) · 35.7 KB
Raw

HitCount Star this repository

Data-Science-Interview-Resources

Update : Drawing from extensive experience in interviews over the past few years, I recently decided to launch a dedicated channel to help individuals excel in Data Science. My goal is to create a comprehensive resource for anyone looking to revisit the basics before an upcoming interview or master the skills and in-depth knowledge required for both succeeding in Data Science interviews and applying Data Science in practice. This channel aims to provide a clear understanding of various techniques used on a day-to-day basis, covering a vast range of Machine Learning topics. Feel free to explore it here :

First of all, thanks for visiting this repo, congratulations on making a great career choice, I aim to help you land an amazing Data Science job that you have been dreaming for, by sharing my experience, interviewing heavily at both large product-based companies and fast-growing startups, hope you find it useful.

With an increase in demand for so many Data Scientists, it's really hard to successfully get screened and accepted for an interview. In this repo, I include everything from getting successfully screened and rocking that interview to land that amazing position, make sure to nail it with the following resources.

Every Resource I list here is personally verified by me and most of them I have used personally, which have helped me a lot.

Word of Caution: Data Science/Machine Learning has a very big domain and there are a lot of things to learn. This by no means is an exhaustive list and is just for helping you out if you are struggling to find some good resources to start your preparation. However, I try to cover and update this frequently and my goal is to cover and unify everything into one resource that you can use to rock those interviews!

Please leave a star if you appreciate the effort.

Note: For contribution, refer Contribution.md

How to get an interview ?

  • First and foremost, develop the necessary skills and be sound with the fundamentals, these are some of the horizons you should be extremely comfortable with -

    • Business Understanding(this is extremely critical across all seniority levels, but specifically for people with more than 3 years of experience)
    • SQL and Databases(very crucial)
    • Programming Skills(preferably in Python, if you know Scala, extra brownie points for some specific roles)
    • Mathematics(Probability, Statistics, Linear Algebra and Calculus) - https://medium.com/@rbhatia46/essential-probability-statistics-concepts-before-data-science-bb787b7a5aef
    • Machine Learning(this includes Deep Learning) and Model building
    • Data Structures and Algorithms(must and mandatory for top product based companies like FAANG)
    • Domain Understanding(Optional for most openings, though very critical for some roles based on company's requirement)
    • Literature Review(must for Research based roles) : Being able to read and understand a new research paper is one of the most essential and demanding skills needed in the industry today, as the culture of Research and Development, and innovation grows across most good organizations.
    • Communication Skills - Being able to explain the analysis and results to business stakeholders and executives is becoming a really important skill for Data Scientists these days
    • Some Engineering knowledge(Not mandatory, but good to have) - Being able to develop a RESTful API, writing clean and elegant code, Object Oriented programming are some of the things you can focus on for some extra brownie points.
    • Big data knowledge(not mandatory for most openings, but good to have) - Spark, Hive, Hadoop, Sqoop.

  • Build a personal Brand

    • Develop a good GitHub/portfolio of use-cases you have solved, always strive for solving end-to-end use cases, which demonstrate the entire Data Science lifecycle, from business understanding to model deployment.
    • Write blogs, start a YouTube channel if you enjoy teaching, write a book.
    • Work on a digital, easy-to-open, easy-to-read, clean, concise and easily customizable Resume/CV, always include your demo links and source code of every use-case you have solved.
    • Participate in Kaggle competitions, build a good Kaggle profile and send them to potential employers for increasing the chances of getting an interview call real-quick.

  • Develop good connections, through LinkedIn, by attending conferences, and doing everything you can, it's very important to land referrals and get yourself started with the interview process through good connections. Connect regularly with Data Scientists working at top product-based organizations, fast-growing startups, build a network, slowly and steadily, it's very important.`

Some Tips on Resume/CV:

  • Describe past roles and an impact you made in a quantifiable way, be concise and I repeat, quantify the impact, rather than talking with facts that have no relevance. According to Google Recruiters, use the XYZ formula - Accomplished [X] as measured by [Y], by doing [Z]

  • Keep it short, ideally not more than 2 pages, as you might know, an average recruiter scans your resume only for 6 seconds, and makes a decision based on that.

  • If you are a fresher and don't have experience, try to solve end-to-end use-cases and mention them in your CV, preferably with the demo link(makes it easy for the recruiter) and the link to source code on GitHub.

  • Avoid too much technical jargon, and this goes without saying, do not mention anything you are not confident about, this might become a major bottleneck during your interview.

  • Some helpful links :

Probability, Statistics and Linear Algebra

SQL and Data Acquisition

This is probably the entry point of your Data Science project, SQL is one of the most important skills for any Data Scientist.

Data Preparation and Visualization

Classic Machine Learning Algorithms

1. Logistic Regression

2. Linear Regression

3. Tree Based/Ensemble Algorithms

4. K-Nearest-Neighbors

5. Support Vector Machines

6. Naive Bayes

Time Series

Unsupervised Learning

Recommender Systems

Deep Learning

GenAI and LLMs

Machine Learning System Design

Machine Learning Interpretability

Case Studies

Case studies are extremely important for interviews, below are some resources to practice, think first before looking at the solutions.

NLP

Data Science Interviews at FAANG and Similar Companies

Becoming a Rockstar Data Scientist(read if you have extra time)

Going through these will definately add extra brownie points, so don't miss these if you got time.

Data Structures and Algorithms(Optional)

Although this might be optional, but do not miss this if the Job Description explicitly asks for this, and especially never miss this if you are interviewing at FAANG and similar organizations, or if you have a CS Background. You don't have to be as good as an SDE at this, but at least know the basics.

Engineering and Deployment

Big Data and Spark

Some amazing stuff on Python and Spark

You can't afford to miss this if you are interviewing for a Big data role.

General Interview Questions across the Spectrum (Video)

General Interview Questions across the Spectrum (Reading)

Interesting Reads