Coding interview preparation

• Titel: Coding interview preparation
• Titelzusatz: learn to solve algorithmic problems to land your next software role
• Mitwirkende: Heineman, George T. [MitwirkendeR]
• Institutionen: O'Reilly (Firm), [Verlag]
• Ausgabe: [First edition].
• Verlagsort: [Sebastopol, California]
• Verlag: O'Reilly Media, Inc.
• E-Jahr: 2024
• Jahr: [2024]
• Umfang: 1 online resource (1 video file (2 hr., 56 min.))
• Illustrationen: sound, color.
• Fussnoten: Online resource; title from title details screen (O’Reilly, viewed July 30, 2024)
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Génie logiciel ; Orientation professionnelle
• Sach-SW: Résolution de problème
• Sach-SW: Instructional films
• Sach-SW: Nonfiction films
• Sach-SW: Internet videos
• Sach-SW: Films de formation
• Sach-SW: Films autres que de fiction
• Sach-SW: Vidéos sur Internet
• K10plus-PPN: 1900848120
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m4573899480
• Lokale URL Inst.: Zum Volltext

Abstract

To demonstrate your problem-solving skills “in real time” during a coding interview, you need to practice how you break down a problem to be solved. Being able to explain how you will solve a problem is often more important than being able to fully code a solution to that problem in a limited amount of time. Problem solving is a skill that can be learned. In this course you will apply knowledge of algorithms and data types to solve problems. Through repetition, you will practice common strategies that work to decompose a problem into its smaller sub-problems, and see algorithms in action. Often a problem appears to give no information about how it can be solved, but by paying attention to all known details, you will see that eventually each problem provides hints as to how it can be solved. Gaining the skill to solve problems is essential to become a capable programmer and software engineer. To solve computational problems you need to refresh your knowledge of fundamental data types that are essential to efficient software. These data types include: stacks, queues and priority queues. Common data structures used to implement these types include arrays (or Python lists), binary heaps and linked lists.