Programming Languages & Core Development Topics
Programming languages, development fundamentals, coding concepts, and core data structures. Includes syntax, algorithms, memory management at a programming level, asynchronous patterns, and concurrency primitives. Also covers core data manipulation concepts like hashing, collections, error handling, and DOM manipulation for web development. Excludes tool-specific proficiency (see 'Tools, Frameworks & Implementation Proficiency').
Python Data Types and Structures
Practical expertise with Python built in data types and collection types and how to use them idiomatically and efficiently. Topics include lists tuples dictionaries sets and related operations append extend pop sort comprehension and slicing, dictionary lookups and set operations, and utilities from the collections module. Candidates should know time and space characteristics of Python operations, how to manipulate these structures for algorithmic solutions, and how to write clear Pythonic implementations that leverage language features for performance and readability.
Python Programming & ML Libraries
Python programming language fundamentals (syntax, data structures, control flow, error handling) with practical usage of machine learning libraries such as NumPy, pandas, scikit-learn, TensorFlow, and PyTorch for data manipulation, model development, training, evaluation, and lightweight ML tasks.
Basic Data Structures (Objects, Maps, Sets)
Understand how objects work in JavaScript including prototypal inheritance and property descriptors. Know when to use Maps vs Objects and Sets vs Arrays. Understand the performance characteristics of different data structures. Be comfortable with nested data structures and how to manipulate them efficiently.
Error Handling and Code Quality
Focuses on writing production quality code and scripts that are defensive, maintainable, and fail gracefully. Covers anticipating and handling failures such as exceptions, missing files, network errors, and process exit codes; using language specific constructs for error control for example try except blocks in Python or set minus e patterns in shell scripts; validating inputs; producing clear error messages and logs; and avoiding common pitfalls that lead to silent failures. Also includes code quality best practices such as readable naming and code structure, using standard libraries instead of reinventing functionality, writing testable code and unit tests, and designing for maintainability and observability.
Python Fundamentals and Problem Solving
Comprehensive knowledge of the Python programming language, idiomatic usage, and the ability to implement correct, readable, and testable solutions to coding problems. Core language elements include syntax and semantics, primitive and composite data types such as integers, floats, strings, lists, dictionaries, sets, and tuples, sequence and mapping operations, control flow constructs, functions and closures, and object oriented programming basics including classes, instances, inheritance, and special methods. Additional practical topics include error and exception handling, file input and output operations, comprehensions and generator expressions, generator functions and iteration protocols, context managers, lambda functions, unpacking, and common standard library utilities. Candidates should understand algorithmic time and space complexity for common operations, typical performance characteristics of lists and dictionaries, and common pitfalls such as mutable default arguments and shared mutable state. Interview focused expectations include writing clean correct code without editor assistance, sensible variable naming, implementing basic algorithms and data structure manipulations under time constraints, reasoning about tradeoffs and complexity, and demonstrating testability and code quality.
Error Handling and Defensive Programming
Covers designing and implementing defensive, fault tolerant code and system behaviors to prevent and mitigate production failures. Topics include input validation and sanitization, null and missing data handling, overflow and boundary protections, exception handling and propagation patterns, clear error reporting and structured logging for observability, graceful degradation and fallback strategies, retry and backoff policies and idempotency for safe retries. Also address concurrency and synchronization concerns, resource and memory management to avoid exhaustion, security related input checks, and how to document and escalate residual risks. Candidates should discuss pragmatic trade offs between robustness and complexity, show concrete defensive checks and assertions, and describe test strategies for error paths including unit tests and integration tests and how monitoring and operational responses tie into robustness.
Core Data Structures
Fundamental built in data structures used in everyday programming and coding interviews, focusing on lists, strings, and dictionaries. For lists cover indexing, slicing, iteration, common mutation operations such as append and extend, common algorithms such as sorting and reversing, and memory and performance implications. For strings cover indexing, slicing, common methods such as split, join, strip, replace, and approaches to string manipulation and pattern processing. For dictionaries cover key value semantics, insertion and lookup, iteration patterns, methods for safe access, and using dictionaries as hash tables for counting and grouping. Candidates should also know the time complexity of common operations expressed in plain terms such as constant time, linear time, and quadratic time, and be able to choose the appropriate structure for a problem and reason about space and performance tradeoffs. Practice often includes implementation level manipulations, common interview problems such as two sum and frequency counting, and writing clear code using these structures.
Advanced Data Structures and Implementation
Deep and practical expertise in advanced data structures, their implementation details, performance characteristics, and selection for both algorithmic problems and production systems. Topics include arrays and dynamic arrays, strings, linked lists, stacks and queues, hash tables, heaps and priority queues, various tree forms including binary search trees and balanced trees, tries or prefix trees, segment trees and binary indexed trees or fenwick trees, union find or disjoint set union, suffix arrays, and advanced graph representations. Candidates should be able to implement core structures from first principles, demonstrate interfaces and invariants, reason about insertion deletion search traversal and iteration costs including worst case average case and amortized analysis, and discuss memory management and ownership in low level languages such as C and C plus plus as well as safe memory and reference use in managed languages. Evaluation also covers trade offs between contiguous and pointer based layouts, cache friendliness, concurrency considerations, selection of structures based on access patterns update frequency and memory constraints, handling of edge cases, testing and performance tuning for realistic inputs, and applying structures to problems such as top K queries prefix search connectivity range queries caches and union operations.
Backend Language Proficiency
Demonstrate strong practical knowledge of a backend programming language such as JavaScript with Node dot js, Python, Java, Go, or similar. Cover language fundamentals, idiomatic usage, standard library features, package and dependency management, common frameworks and ecosystem tools, testing strategies and tooling, error handling and observability, and patterns for maintainable server side code. Be ready to explain concurrency and asynchronous models in the language, performance considerations, security best practices, deployment and packaging approaches, and examples of backend services or APIs you built including trade offs and chosen libraries.