avatarNaina Chaturvedi

Summary

The web content provides a comprehensive list of popular coding questions categorized by companies such as Lyft, Airbnb, Snapchat, Paypal, Square, Databricks, Dropbox, Asana, Akuna Capital, Twitter, Twilio, and Facebook, along with resources for system design case studies, advanced SQL series, data structures and algorithms, and machine learning projects.

Abstract

The website article serves as a valuable resource for individuals preparing for tech interviews, particularly in the realms of software development, data science, machine learning, and system design. It offers a curated compilation of frequently asked coding questions from various tech companies, emphasizing the importance of practice with real-world examples. The article also introduces a series of system design case studies, an advanced SQL tutorial series, and a complete guide to data structures and algorithms, all accompanied by practical projects and implementation details. Additionally, the author encourages readers to subscribe to a newly launched YouTube channel, Ignito, for video content on these topics and to follow a tech newsletter for additional insights and updates in the tech industry.

Opinions

  • The author emphasizes the utility of practicing coding questions from LeetCode that are commonly asked in tech company interviews.
  • There is a strong suggestion to subscribe to the Ignito YouTube channel for visual learning and project implementation guidance.
  • The article promotes the idea that readers should leverage the provided resources, such as the complete system design series and the advanced SQL series, to enhance their technical skills.
  • The author believes in the importance of a structured learning approach, as evidenced by the day-by-day breakdown of topics in the data structures and algorithms series.
  • There is an opinion that hands-on projects are crucial for understanding concepts in data science, machine learning, and system design, as they provide practical experience.
  • The author values the sharing of knowledge and resources within the tech community, as shown by the invitation to join a tech newsletter and the provision of GitHub repositories for collaborative learning.

Most Popular Coding Questions — Company Wise List : Part 2

Just for your reference…

Burn Freddy Burn (Pic credits : Pinterest)

Welcome back peeps. This post ( part 2 ) 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.

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30 Days of Natural Language Processing ( NLP) Series

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 :

Where to find part 1 of this series -

Part 3:

Part 4:

Part2: Here we go —

Lyft

Airbnb

Snapchat

Square

Paypal

Databricks

Dropbox

Asana

Akuna Capital

Twitter

Twilio

Part 3 : coming soon!

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 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

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

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Custom Layers in Keras

Machine Learning
Tech
Programming
Software Development
Data Science
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