avatarNaina Chaturvedi

Summary

The web content provides a comprehensive guide to the most popular coding questions categorized by company, system design case studies, and a curated list of resources for tech interviews, data science, machine learning, and other technical projects.

Abstract

The website serves as a valuable resource for individuals preparing for technical interviews, offering a detailed compilation of frequently asked coding questions specific to various tech companies such as Uber, Google, ByteDance, Meta, DoorDash, and Citadel. It also presents in-depth system design case studies for platforms like Instagram, Messenger, Twitter, and YouTube, among others. The content extends beyond coding to include a wide range of topics essential for tech professionals, including advanced SQL, data structures, algorithms, and complete series on data science, machine learning, and data engineering. Additionally, the website provides links to GitHub repositories with relevant code, YouTube channels for video tutorials, and a tech newsletter for ongoing learning and updates in the field.

Opinions

  • The author emphasizes the importance of practicing coding questions and provides a curated list of the most frequently asked ones by tech companies to aid in interview preparation.
  • There is a focus on system design as a critical skill for tech interviews, with a series of case studies that cover both high-level design principles and specific implementation details.
  • The website advocates for a comprehensive approach to learning, offering resources not only in coding but also in data science, machine learning, and data engineering, suggesting that a well-rounded skill set is crucial for success in the tech industry.
  • The inclusion of links to GitHub repositories and YouTube channels indicates a belief in the value of practical, hands-on experience and visual learning aids to complement written content.
  • By offering a tech newsletter, the author seems to encourage continuous learning and staying updated with the latest trends and technologies in the software development and data science domains.

Most Popular Coding Questions — Company Wise List : Part 1

Just for practice/reference…

Burn Freddy Burn (Pic credits : Pinterest)

Welcome back peeps. This post is for the students who are preparing for their tech interviews. While I’m sitting on the other side of the table as an interviewer now; I know how daunting the prep journey can be. Use it just as a reference/practice resource.

System Design Case Studies — In Depth

Design Instagram

Design Netflix

Design Reddit

Design Amazon

Design Messenger App

Design Twitter

Design URL Shortener

Design Dropbox

Design Youtube

Design API Rate Limiter

Design Web Crawler

Design Amazon Prime Video

Design Facebook’s Newsfeed

Design Yelp

Design Uber

Design Tinder

Design Tiktok

Design Whatsapp

Most Popular System Design Questions

Mega Compilation : Solved System Design Case studies

Some of the other best Series —

30 Days of Natural Language Processing ( NLP) Series

How to solve any System Design Question ( approach that you can take)?

Complete System Design Case Studies Series

30 days of Data Engineering with projects Series

60 days of Data Science and ML Series with projects

100 days : Your Data Science and Machine Learning Degree Series with projects

23 Data Science Techniques You Should Know

Tech Interview Series — Curated List of coding questions

Complete System Design with most popular Questions Series

Complete Data Visualization and Pre-processing Series with projects

Complete Python Series with Projects

Complete Advanced Python Series with Projects

Kaggle Best Notebooks that will teach you the most

Complete Developers Guide to Git

All the Data Science and Machine Learning Resources

210 Machine Learning Projects

30 days of Machine Learning Ops

Projects Videos —

All the projects, data structures, SQL, algorithms, system design, Data Science and ML , Data Analytics, Data Engineering, , Implemented Data Science and ML projects, Implemented Data Engineering Projects, Implemented Deep Learning Projects, Implemented Machine Learning Ops Projects, Implemented Time Series Analysis and Forecasting Projects, Implemented Applied Machine Learning Projects, Implemented Tensorflow and Keras Projects, Implemented PyTorch Projects, Implemented Scikit Learn Projects, Implemented Big Data Projects, Implemented Cloud Machine Learning Projects, Implemented Neural Networks Projects, Implemented OpenCV Projects,Complete ML Research Papers Summarized, Implemented Data Analytics projects, Implemented Data Visualization Projects, Implemented Data Mining Projects, Implemented Natural Leaning Processing Projects, MLOps and Deep Learning, Applied Machine Learning with Projects Series, PyTorch with Projects Series, Tensorflow and Keras with Projects Series, Scikit Learn Series with Projects, Time Series Analysis and Forecasting with Projects Series, ML System Design Case Studies Series videos will be published on our youtube channel ( just launched).

Subscribe today!

Tech Newsletter —

If you are interested, you can join my newsletter through which I send tech interview tips, techniques, patterns, hacks — Software Development, ML, Data Science, Startups and Technology projects to more than 30K readers. You can subscribe to Tech Brew :

Part 2 of this series :

Part 3:

Part 4:

Here we go —

Uber

Most Popular Dynamic Programming and Memoization Questions

Google

Goldman Sachs

Byte Dance

Doordash

Meta

Citadel

Part 2 of this series :

All the Complete System Design Series Parts —

1. System design basics

2. Horizontal and vertical scaling

3. Load balancing and Message queues

4. High level design and low level design, Consistent Hashing, Monolithic and Microservices architecture

5. Caching, Indexing, Proxies

6. Networking, How Browsers work, Content Network Delivery ( CDN)

7. Database Sharding, CAP Theorem, Database schema Design

8. Concurrency, API, Components + OOP + Abstraction

9. Estimation and Planning, Performance

10. Map Reduce, Patterns and Microservices

11. SQL vs NoSQL and Cloud

12. Most Popular System Design Questions

Github —

For Python Projects —

For complete 60 days of Data Science and ML : Day 1 — Day 60 : Quick Recap of 60 days of Data Science and ML

Follow for more updates. Stay tuned and keep coding!

Advanced SQL Series

Day 1 : SQL Basics and Kick start of Advanced SQL Series

Day 2 : SQL Basics, Query Structure, Built In functions Conditions

Day 3 : Most Important Commands, Joins and Filters

Day 4 : Set Theory Operations, Stored Procedures and CASE statements in SQL

Day 5 : Wildcards, Aggregation and Sequences in SQL

Day 6 : Subqueries, Group by, order by and Having clauses in SQL and Analytical Functions

Day 7 : Window Functions, Grouping Sets and Constraints in SQL

Day 8 : BigQuery Basics, SELECT, FROM, WHERE and Date and Extract in BigQuery

Day 9 : Common Expression Table, UNNEST Clause, SQL vs NoSQL Databases

Day 10 : Triggers, Pivot and Cursors in SQL

Day 11 : Views, Indexes and Auto Increment in SQL

Day 12 : Query optimizations, Performance tuning in SQL

Day 13 : Introduction to MySQL, PostgreSQL and Mongo DB, Comparison between MySQL and PostgreSQL and Mongo DB, Introduction to SQL and NoSQL Databases

Day 14 : MySQL in Depth

Day 15 : PostgreSQL inDepth

Anyways, For Day 15 of 15 days of Advanced SQL, we will cover —

PostgreSQL inDepth

Github for Advanced SQL that you can follow —

All the projects, data structures, algorithms, system design, Data Science and ML, Data Engineering, MLOps and Deep Learning videos will be published on our youtube channel ( just launched).

Subscribe today!

System Design Case Studies — In Depth

Design Instagram

Design Messenger App

Design Twitter

Design URL Shortener

Design Dropbox

Design Youtube

Design API Rate Limiter

Design Web Crawler

Design Facebook’s Newsfeed

Design Yelp

Design Uber

Design Tinder

Design Tiktok

Design Whatsapp

Most Popular System Design Questions

Mega Compilation : Solved System Design Case studies

Complete Data Structures and Algorithm Series

Complexity Analysis

Backtracking

Sliding Window

Greedy Technique

Two pointer Technique

Arrays

Linked List

Strings

Stack

Queues

Hash Table/Hashing

Binary Search

1- D Dynamic Programming

Divide and Conquer Technique

Recursion

Github —

For other projects, tune to —

Build Machine Learning Pipelines( With Code)

Recurrent Neural Network with Keras

Clustering Geolocation Data in Python using DBSCAN and K-Means

Facial Expression Recognition using Keras

Hyperparameter Tuning with Keras Tuner

Custom Layers in Keras

Tech
Programming
Software Development
Coding
Data Science
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