Our curriculum

1.Sorting

• Introduction to Sorting

• Basics of Asymptotic Analysis and Worst Case & Average Case Analysis

• Different Sorting Algorithms and their comparison

• Algorithm paradigms like Divide & Conquer, Decrease & Conquer, Transform & Conquer

2.Presorting

• Extensions of Merge Sort, Quick Sort, Heap Sort

• Common sorting-related coding interview problems

3.Recursion

• Recursion as a Lazy Manager's Strategy

• Recursive Mathematical Functions

• Combinatorial Enumeration

• Backtracking

• Exhaustive Enumeration & General Template

• Common recursion- and backtracking-related coding interview problems

4.Trees

• Dictionaries & Sets, Hash Tables

• Modeling data as Binary Trees and Binary Search Tree and performing different operations over them

• Tree Traversals and Constructions

• BFS Coding Patterns

• DFS Coding Patterns

• Tree Construction from its traversals

• Common trees-related coding interview problems

5.Graphs

• Overview of Graphs

• Problem definition of the 7 Bridges of Konigsberg and its connection with Graph theory

• What is a graph, and when do you model a problem as a Graph?

• How to store a Graph in memory (Adjacency Lists, Adjacency Matrices, Adjacency Maps)

• Graphs traversal: BFS and DFS, BFS Tree, DFS stack-based implementation

• A general template to solve any problems modeled as Graphs

• Graphs in Interviews

• Common graphs-related coding interview problems

6.Dynamic Programming

• Dynamic Programming Introduction

• Modeling problems as recursive mathematical functions

• Detecting overlapping subproblems

• Top-down Memorization

• Bottom-up Tabulation

• Optimizing Bottom-up Tabulation

• Common DP-related coding interview problems

1.Online Processing Systems

• The client-server model of Online processing

• Top-down steps for system design interview

• Depth and breadth analysis

• Cryptographic hash function

• Network Protocols, Web Server, Hash Index

• Scaling

• Performance Metrics of a Scalable System

• SLOs and SLAs

• Proxy: Reverse and Forward

• Load balancing

• CAP Theorem

• Content Distribution Networks

• Cache

• Sharding

• Consistent Hashing

• Storage

• Case Studies: URL Shortener, Instagram, Uber, Twitter, Messaging/Chat Services

2.Batch Processing Systems

• Inverted Index

• External Sort Merge

• K-way External Sort-Merge

• Distributed File System

• Map-reduce Framework

• Distributed Sorting

• Case Studies: Search Engine, Graph Processor, Typeahead Suggestions, Recommendation Systems

3.Stream Processing Systems

• Case Studies: on APM, Social Connections, Netflix, Google Maps, Trending Topics, YouTube

1.SQL Programming

• Derive business insights for a food delivery app by writing SQL queries

• Comprehensive coverage of topics from intermediate-level concepts such as Case Statements and subqueries to advanced SQL functions such as joins and analytical functions

• Application of window functions as lead, lag functions to evaluate day-over-day insight on business performance

• Use rank and dense rank functions to understand merchants’ reach in the market

• Complex SQL problems on customer-merchant pairwise dependence using a variety of functions and operators

• Deep dive into joins, their type, and comparison of left join vs. right join vs. outer join vs. broadcast join

• Thematic coverage of frequently asked interview problems through template problems

• A step-by-step guide to what you can expect in an interview and how to tackle them in a time-constrained environment

2.Data Modeling

• Design Data Warehouse tables for Uber or a similar ride-sharing platform

• Coming up with a conceptual and logical model, define data granularity

• Define the fact and dimension tables with high-level attributes

• Best practices on how to choose keys and constraints for the entities

• Discussion on how to normalize tables

• How to handle cases of Slowly Changing Dimensions

• Thematic discussion on interview problems from Meta, Amazon, Twitter, and Uber

• Learn how to decide your data warehouse schema: Star vs. Snowflake schema design

• A step-by-step guide to approaching atypical interview questions

3.ETL and Pipeline Design

• Create a data pipeline for near-real-time ingestion of Netflix clickstream/playback data. Design for ad-hoc monitoring of certain metrics

• Comprehensive coverage of different stages of design: Upstream, ETL environment, and downstream requirements

• Gain interview perspective on essential ETL design techniques such as handling data ingestion, different file formats, data granularity, landing and storage levels, and reporting metrics

• Detailed outline of performance parameters depending on data granularity, volume, velocity, accepted latency, etc.

• A top-down approach to building a high-level architecture: Identify available technology at each stage

• Follow-up question

  • How often do you update your data in DW?

  • Pipeline has been fine for 6 months; now, certain marketplaces have more aggressively incoming data. How would you handle that? What changes would you make to your design if new data is more unstructured?

  • Discussion on trivial but important questions: What is being monitored? Does everything go into one monitoring dashboard?

  • What would the architecture look like for the ML platform that uses this data?

  • Discussion on the role of DE in large-scale, multi-faceted systems, what you can expect in an interview, and how to tackle them in a time-constrained environment

4.Data Platforms

• Design a data platform for a gaming company. Understand data-driven approach in deciding business metrics

• Breaking down high-level components of Data Platform design: Ingestion, Warehousing, Transformation, Catalog and Governance, Privacy & Access, and Visualization

• Structured discussion on how to define data flow and come up with a DAG

• Learn how to design high-performance platforms at scale

• How do you implement a production-ready design using Kafka and Spark? Orchestrate your pipeline using Airflow (or alternate services)

• How do you define your success metrics? How do you gauge the relevance of your data? At what frequency do we capture and process it?

• How do we ensure data backup, and at what scale?

• Discussion of optimization techniques at scale like partitioning, distributed platform, cloud services, etc.

• An insightful discussion on Product Sense, working with different aspects of data engineering systems, what you can expect in an interview, and how to tackle them in a time-constrained environment