Courses tagged with "Computer Science" (173)
Other topics covered in this software engineering course include:
- How to form, organize and manage small programming teams
- Introduction to design patterns: what they are and how to recognize opportunities to apply them
- Using Rails for more advanced features like third-party authentication and elegantly expressing design patterns that arise frequently in SaaS
There will be four homework assignments: two programming assignments, an open source assignment and one assignment about operations/deployment. There will also be several short quizzes. The videos and homework assignments used in this offering of the course were revised in October 2016.
This intermediate computer programming course uncovers how to code long-lasting software using highly-productive Agile techniques to develop Software as a Service (SaaS) using Ruby on Rails. You will understand the new challenges and opportunities of SaaS versus shrink-wrapped software and learn to apply fundamental Rails programming techniques to the design, development, testing, and public cloud deployment of an Software as a Service (SaaS) application
Using best-of-breed tools that support modern development techniques including Behavior-Driven design, user stories, Test-Driven Development, velocity, and pair programming, learners will discover how modern programming language features in Ruby on Rails can improve productivity and code maintainability.
Weekly coding projects and quizzes will be part of the learning experience in this SaaS course. Those who successfully complete the assignments and earn a passing grade can get a verified certificate from BerkeleyX. The videos and homework assignments have been updated to use Ruby 2, Rails 4 and RSpec 3. The new class also includes embedded live chat with Teaching Assistants and other students and remote pair programming with other students.
Der Kurs führt in das zentrale Gebiet der Informatik ein, auf dem alle anderen Teilgebiete aufbauen: Wie entwickele ich Software? Anhand der Programmiersprache Java werden Algorithmen zum Suchen und Sortieren vorgestellt und die dazu benötigten Datenstrukturen wie Keller, Schlange, Liste, Baum und Graph eingeführt.
Was lerne ich in diesem Kurs?
Die Teilnehmer des Kurses werden in die Lage versetzt, eine Problemstellung auf maschinelle Lösbarkeit hin zu analysieren, dafür einen Algorithmus zu entwerfen, die zugehörigen Datenstrukturen zu wählen, daraus ein Java-Programm zu entwickeln und dieses zur Lösung des Problems einzusetzen.
Welche Vorkenntnisse benötige ich?
Mathematikkenntnisse auf Oberstufenniveau.
|Kapitel 11||Abstrakte Datentypen|
Experienced Computer Scientists analyze and solve computational problems at a level of abstraction that is beyond that of any particular programming language. This class is designed to train students in the mathematical concepts and process of "Algorithmic Thinking", allowing them to build simpler, more efficient solutions to computational problems.
Algorithms power the biggest web companies and the most promising startups. Interviews at tech companies start with questions that probe for good algorithm thinking.
In this computer science course, you will learn how to think about algorithms and create them using sorting techniques such as quick sort and merge sort, and searching algorithms, median finding, and order statistics.
The course progresses with Numerical, String, and Geometric algorithms like Polynomial Multiplication, Matrix Operations, GCD, Pattern Matching, Subsequences, Sweep, and Convex Hull. It concludes with graph algorithms like shortest path and spanning tree.
- Sorting and Searching
- Numerical Algorithms
- String Algorithms
- Geometric Algorithms
- Graph Algorithms
This course is part of the Fundamentals of Computer Science XSeries Program:
This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Part I covers basic iterable data types, sorting, and searching algorithms.
This course teaches a calculus that enables precise quantitative predictions of large combinatorial structures. In addition, this course covers generating functions and real asymptotics and then introduces the symbolic method in the context of applications in the analysis of algorithms and basic structures such as permutations, trees, strings, words, and mappings.
What do self-driving cars, face recognition, web search, industrial robots, missile guidance, and tumor detection have in common?
They are all complex real world problems being solved with applications of intelligence (AI).
This course will provide a broad understanding of the basic techniques for building intelligent computer systems and an understanding of how AI is applied to problems.
You will learn about the history of AI, intelligent agents, state-space problem representations, uninformed and heuristic search, game playing, logical agents, and constraint satisfaction problems.
Hands on experience will be gained by building a basic search agent. Adversarial search will be explored through the creation of a game and an introduction to machine learning includes work on linear regression.