Courses tagged with "Nutrition" (6413)

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.

Apply the learned algorithms and techniques for data mining from the previous courses in the Data Mining Specialization to solve interesting real-world data mining challenges.

This course is part of the Microsoft Professional Program Certificate in Data Science.

Demand for data science talent is exploding. Develop your career as a data scientist, as you explore essential skills and principles with experts from Duke University and Microsoft.

In this data science course, you will learn key concepts in data acquisition, preparation, exploration, and visualization taught alongside practical application oriented examples such as how to build a cloud data science solution using Microsoft Azure Machine Learning platform, or with R, and Python on Azure stack.

This course is part of the Microsoft Professional Program Certificate in Data Science.

Showcase the knowledge and skills you’ve acquired during the Microsoft Professional Program for Data Science, and solve a real-world data science problem in this program capstone project. The project takes the form of a challenge in which you will explore a dataset and develop a machine learning solution that is tested and scored to determine your grade.

Note: This course assumes you have completed the previous courses in the Microsoft Professional Program for Data Science. For details, go to https://academy.microsoft.com/en-us/professional-program/data-science.

Are you interested in pursuing a degree in Data Science, but unsure whether you have the necessary Math and Programming skills? This assessment will help you identify your current readiness in three core areas required for the study of Data Science; Calculus, Linear Algebra, and Programming.

You can take this assessment at your own pace and receive a private score report that identifies your readiness in each specific area. We will also provide, when necessary, recommendations for additional free online study.

This assessment is free, unproctored, and not offered for credit; it is designed for enrichment and self-assessment for anyone interested in pursuing data science as a career.​

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

Knowing how to code is only part of the skills needed to become a professional software developer.

This course, part of the CS Essentials for Software Development Professional Certificate program, will take your skills to the next level by teaching you how to write “good” software that appropriately represents and organizes data, is easy to maintain, and is of high quality.

As the purpose of most computer programs is to manipulate data, sometimes large quantities of it, the manner in which programs represent and organize data can have an enormous effect on the simplicity and efficiency of the code. In this course, you will learn about important core data structures such as arrays, lists, stacks, queues, sets, maps, trees, and graphs, and learn how to evaluate them and reason about their behavior and efficiency.

Most importantly, you will learn how to determine which data structure is the most appropriate for solving the problem at hand, and see how to use the implementations that are part of the Java library.

However, choosing the right data structure is only part of the challenge of developing high quality software: you must also consider the design of the classes that use those data structures. You will learn about software design principles such as modularity, functional independence, and abstraction, and apply those concepts toward writing programs that are easy to understand, easy to modify, and easy to test.

Although it is important to know how to write high quality code, professional software developers often spend a majority of their time maintaining existing code. You will also learn about software refactoring techniques for improving the design of existing code, and see how to improve code efficiency.

This course will use Java but the concepts you learn can be applied to almost all modern programming languages.

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.

Learn the fundamentals of data visualization and practice communicating with data. This course covers how to apply design principles, human perception, color theory, and effective storytelling to data visualization. If you present data to others, aspire to be an analyst or data scientist, or if you’d like to become more technical with visualization tools, then you can grow your skills with this course. The course does not cover exploratory approaches to discover insights about data. Instead, the course focuses on how to visually encode and present data to an audience once an insight has been found. This course is part of the Data Analyst Nanodegree.

Tell your story and show it with data. In this data visualization course, you will learn how to design interactive charts and customized maps for your website.

We’ll begin with easy-to-learn tools, then gradually work our way up to editing open-source code templates with GitHub. Together, we’ll follow step-by-step tutorials with video screencasts, and share our work for feedback on the web. Real-world examples are drawn from Trinity College students working with community organizations in the City of Hartford, Connecticut.

This course is ideal for non-profit organizations, small business owners, local governments, journalists, academics, or anyone who wants to tell their story and show the data.

This introductory course in data visualization begins with the basics. No prior experience is required.

Learn the fundamentals of data visualization and practice communicating with data. This course covers how to apply design principles, human perception, color theory, and effective storytelling with data. If you present data to others, aspire to be a business analyst or data scientist, or if you’d like to become more effective with visualization tools, then you can grow your skills with this course. This course is part of both the Business Analyst and Data Analyst Nanodegree Programs.

In this course, we will explore how to wrangle data from diverse sources and shape it to enable data-driven applications. Some data scientists spend the bulk of their time doing this! Students will learn how to gather and extract data from widely used data formats. They will learn how to assess the quality of data and explore best practices for data cleaning. We will also introduce students to MongoDB, covering the essentials of storing data and the MongoDB query language together with exploratory analysis using the MongoDB aggregation framework. This is a great course for those interested in entry-level data science positions as well as current business/data analysts looking to add big data to their repertoire, and managers working with data professionals or looking to leverage big data. This course is also a part of our Data Analyst Nanodegree.

Capturing and analyzing data has changed how decisions are made and resources are allocated in businesses, journalism, government, and military and intelligence fields. Through better use of data, leaders are able to plan and enact strategies with greater clarity and confidence. Data drives increased organizational efficiency and a competitive advantage. Simply, analytics provide new insight and actionable intelligence.

In education, the use of data and analytics to improve learning is referred to as learning analytics. Analytics have not yet made the impact on education that they have made in other fields. That’s starting to change. Software companies, researchers, educators, and university leaders recognize the value of data in improving not only teaching and learning, but the entire education sector. In particular, learning analytics enables universities, schools, and corporate training departments to improve the quality of learning and overall competitiveness. Research communities such as the International Educational Data Mining Society (IEDMS) and the Society for Learning Analytics Research (SoLAR) are developing promising models for improving learner success through predictive analytics, machine learning, recommender systems (content and social), network analysis, tracking the development of concepts through social systems, discourse analysis, and intervention and support strategies. The era of data and analytics in learning is just beginning.

Data, Analytics, and Learning provides an introduction to learning analytics and how it is being deployed in various contexts in education, including to support automated intervention, to inform instructors, and to promote scientific discovery. Additionally, we will discuss tools and methods, what skills data scientists need in education, and how to protect student privacy and other rights. The course will provide a broad overview of the field, suitable for a broad audience. Learners will explore the logic of analytics, the basics of finding, cleaning, and using educational data, predictive models, text analysis, and activity graphs and social networks. We will discuss use of analytics in data domains such as log files and text data.  Tableau Software is partnering with University of Texas Arlington to provide analytics software to course participants as well as technical support and guest lectures. Additional software will be introduced and discussed throughout the course.

How this course works:

This course will experiment with multiple learning pathways. It has been structured to allow learners to take various pathways through learning content - either in the existing edX format or in a social competency-based and self-directed format. Learners will have access to pathways that support both beginners, and more advanced students, with pointers to additional advanced resources. In addition to interactions within the edX platform, learners will be encouraged to engage in distributed conversations on social media such as blogs and Twitter.

In today’s world, managerial decisions are increasingly based on data-driven models and analysis using statistical and optimization methods that have dramatically changed the way businesses operate in most domains including service operations, marketing, transportation, and finance.

The main objectives of this course are the following:

• Introduce fundamental techniques towards a principled approach for data-driven decision-making.
• Quantitative modeling of dynamic nature of decision problems using historical data, and
• Learn various approaches for decision-making in the face of uncertainty

Topics covered include probability, statistics, regression, stochastic modeling, and linear, nonlinear and discrete optimization.

Most of the topics will be presented in the context of practical business applications to illustrate its usefulness in practice. 

This course is designed to introduce first-year Sloan MBA students to the fundamental techniques of using data. In particular, the course focuses on various ways of modeling, or thinking structurally about decision problems in order to make informed management decisions.

It’s like hunting big game in Africa (without the animals, the African Savanna, or the actual hunt). It’s big data and it informs your marketing strategy and opens the door to targeted, customer-aware advertising. So, don’t just start chasing after a herd of gazelles (or whomever makes up your target market). Enroll in this course, take the knowledge and downloadable templates back to your organization, and successfully capture your target market with your own big data-driven marketing campaign. Enrollment cost: $99.00 Students who successfully complete this course will receive a letter of completion. Note: This course is non-refundable.