Online courses directory (548)

The Coursera course, Data Analysis and Statistical Inference has been revised and is now offered as part of Coursera Specialization “Statistics with R”. This course introduces you to the discipline of statistics as a science of understanding and analyzing data. You will learn how to effectively make use of data in the face of uncertainty: how to collect data, how to analyze data, and how to use data to make inferences and conclusions about real world phenomena.

6.263J / 16.37J focuses on the fundamentals of data communication networks. One goal is to give some insight into the rationale of why networks are structured the way they are today and to understand the issues facing the designers of next-generation data networks. Much of the course focuses on network algorithms and their performance. Students are expected to have a strong mathematical background and an understanding of probability theory. Topics discussed include: layered network architecture, Link Layer protocols, high-speed packet switching, queueing theory, Local Area Networks, and Wide Area Networking issues, including routing and flow control.

Learn critical concepts and practical methods to support research data planning, collection, storage and dissemination.

Data that has relevance for managerial decisions is accumulating at an incredible rate due to a host of technological advances. Electronic data capture has become inexpensive and ubiquitous as a by-product of innovations such as the internet, e-commerce, electronic banking, point-of-sale devices, bar-code readers, and intelligent machines. Such data is often stored in data warehouses and data marts specifically intended for management decision support. Data mining is a rapidly growing field that is concerned with developing techniques to assist managers to make intelligent use of these repositories. A number of successful applications have been reported in areas such as credit rating, fraud detection, database marketing, customer relationship management, and stock market investments. The field of data mining has evolved from the disciplines of statistics and artificial intelligence.

This course will examine methods that have emerged from both fields and proven to be of value in recognizing patterns and making predictions from an applications perspective. We will survey applications and provide an opportunity for hands-on experimentation with algorithms for data mining using easy-to- use software and cases.

Data structures play a central role in computer science and are the cornerstones of efficient algorithms. Knowledge in this area has been at the kernel of related curriculums. This course aims at exploring the principles and methods in the design and implementation of various data structures and providing students with main tools and skills for algorithm design and performance analysis. Topics covered by this course range from fundamental data structures to recent research results.

数据结构是计算机科学的关键内容，也是构建高效算法的必要基础。其覆盖的知识，在相关专业的课程体系中始终处于核心位置。本课程旨在围绕各类数据结构的设计与实现，揭示其中的规律原理与方法技巧；同时针对算法设计及其性能分析，使学生了解并掌握主要的套路与手法。讲授的主题从基础的数据结构，一直延伸至新近的研究成果。

This course is presented in Mandarin.

FAQ

In what language will this course be offered?

Mandarin.

Will the text of the lectures be available?

Yes. All of our lectures will have transcripts synced to the videos.

Do I need to watch the lectures live?

No. You can watch the lectures at your leisure.

Will certificates be awarded?

Yes. Online learners who achieve a passing grade in a course can earn a certificate of  mastery. These certificates will indicate you have successfully completed the course, but will not include a specific grade. Certificates will be issued by edX under the name of DelftX, designating the institution from which the course originated.

Can I contact the Instructor or Teaching Assistants?

Yes, but not directly. The discussion forums are the appropriate venue for questions about the course. The instructors will monitor the discussion forums and try to respond to the most important questions; in many cases response from other students and peers will be adequate and faster.

Is this course related to a campus course at Tsinghua?

Yes. This course corresponds to the campus courses 00240074 (elective for undergraduates of all majors) and 30240184 (required for CS undergraduates), both named Data Structures.

What is the textbook of the course?

Junhui DENG, Data Structures in C++, Sep. 2013, 3rd edn., Tsinghua University Press, ISBN: 7-302-33064-6. (in Chinese)

What is the grading breakdown?

60% - 12 problem sets

40% - 4 programming assignments

This course is a hands-on introduction to statistical data analysis that emphasises fundamental concepts and practical skills.

Learn how to transform information from a format efficient for computation into a format efficient for human perception, cognition, and communication. Explore elements of computer graphics, human-computer interaction, perceptual psychology, and design in addition to data processing and computation.

This course relies on primary readings from the database community to introduce graduate students to the foundations of database systems, focusing on basics such as the relational algebra and data model, schema normalization, query optimization, and transactions. It is designed for students who have taken 6.033 (or equivalent); no prior database experience is assumed, though students who have taken an undergraduate course in databases are encouraged to attend.

Learn about Databases, one of the most prevalent technologies underlying internet and computing applications today.

This is an intermediate algorithms course with an emphasis on teaching techniques for the design and analysis of efficient algorithms, emphasizing methods of application. Topics include divide-and-conquer, randomization, dynamic programming, greedy algorithms, incremental improvement, complexity, and cryptography.

6.777J / 2.372J is an introduction to microsystem design. Topics covered include: material properties, microfabrication technologies, structural behavior, sensing methods, fluid flow, microscale transport, noise, and amplifiers feedback systems. Student teams design microsystems (sensors, actuators, and sensing/control systems) of a variety of types, (e.g., optical MEMS, bioMEMS, inertial sensors) to meet a set of performance specifications (e.g., sensitivity, signal-to-noise) using a realistic microfabrication process. There is an emphasis on modeling and simulation in the design process. Prior fabrication experience is desirable. The course is worth 4 Engineering Design Points.

Understanding how to approach programming problems and devise a solution is an essential skill for any Python developer. In this course, you’ll learn new concepts, patterns, and methods that will expand your coding abilities from programming expert, Peter Norvig.

This course provides you with opportunities to integrate and apply your information security knowledge.

Learn the basics of creating data products using Shiny, R packages, and interactive graphics. This is the ninth course in the Johns Hopkins Data Science Specialization.

This course examines the role of the engineer as patent expert and as technical witness in court and patent interference and related proceedings. It discusses the rights and obligations of engineers in connection with educational institutions, government, and large and small businesses. It compares various manners of transplanting inventions into business operations, including development of New England and other U.S. electronics and biotechnology industries and their different types of institutions. The course also considers American systems of incentive to creativity apart from the patent laws in the atomic energy and space fields.

Acknowledgment

The instructors would like to thank Joanne Rines and Elijah Ercolino for their efforts in preparing this course.

In this course, you'll hone your problem-solving skills through learning to find numerical solutions to systems of differential equations. You'll write code in Python to fight forest fires, rescue the Apollo 13 astronauts, stop the spread of epidemics, and resolve other real-world dilemmas.

Modern society across the world is becoming increasingly dependent on communication networks. We depend on them for the smooth operation of economic and social activities. This course examines networks as the means of interconnecting devices so that two-way communication is possible. It examines protocols like HTTP, TCP/IP and ATM as well as the OSI reference model, and provides an overview of the topic for learners who have no significant prior knowledge of the subject. The main topics covered in this course are: Internet protocol (IP), the Open Systems Interconnection (OSI) model, Transmission Control Protocol (TCP), Hypertext transfer protocol (HTTP), and Asynchronous Transfer Mode (ATM). Areas that are also covered include the layers of communication, vertical and horizontal communication, protocol architecture, Domain name system, the ATM layers, and an analysis of IP over ATM. This course will be of interest to professionals who work in information technology, network design, and network management, and will also be of interest to students who would like a greater understanding of how networks function.<br />

Explore the latest trends in digital marketing. Get beneath the hype. What are the implications? How will you respond?

Digital Media One is a four-week online course designed for students who want to learn digital media. This may include artists, designers, or photographers who would like to build a Web site or Web portfolio to showcase their work, as well as students who would like to learn more about the curriculum of Peninsula College