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
• Presorting
• Extensions of Merge Sort, Quick Sort, Heap Sort
• Common sorting-related coding interview problems

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

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

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

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

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

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

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

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 questions:
• 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

15 mock interviews

Take classes you missed/retake classes/tests

1:1 technical/career coaching