Courses tagged with "Nutrition" (6413)

This subject describes and illustrates computational approaches to solving problems in systems biology. A series of case-studies will be explored that demonstrate how an effective match between the statement of a biological problem and the selection of an appropriate algorithm or computational technique can lead to fundamental advances. The subject will cover several discrete and numerical algorithms used in simulation, feature extraction, and optimization for molecular, network, and systems models in biology.

Learn how to analyze an organization's strategy and make recommendations to improve its value creation by building your strategist's toolkit. This course will be available on demand in February 2015.

Advances in cognitive science have resolved, clarified, and sometimes complicated some of the great questions of Western philosophy: what is the structure of the world and how do we come to know it; does everyone represent the world the same way; what is the best way for us to act in the world. Specific topics include color, objects, number, categories, similarity, inductive inference, space, time, causality, reasoning, decision-making, morality and consciousness. Readings and discussion include a brief philosophical history of each topic and focus on advances in cognitive and developmental psychology, computation, neuroscience, and related fields. At least one subject in cognitive science, psychology, philosophy, linguistics, or artificial intelligence is required. An additional project is required for graduate credit.

This course is an introduction to computational biology emphasizing the fundamentals of nucleic acid and protein sequence and structural analysis; it also includes an introduction to the analysis of complex biological systems. Topics covered in the course include principles and methods used for sequence alignment, motif finding, structural modeling, structure prediction and network modeling, as well as currently emerging research areas.

Serving as an introduction to computational biology, this course emphasizes the fundamentals of nucleic acid and protein sequence analysis, structural analysis, and the analysis of complex biological systems. The principles and methods used for sequence alignment, motif finding, structural modeling, structure prediction, and network modeling are covered. Students are also exposed to currently emerging research areas in the fields of computational and systems biology.

This course is an introduction to computational biology emphasizing the fundamentals of nucleic acid and protein sequence and structural analysis; it also includes an introduction to the analysis of complex biological systems. Topics covered in the course include principles and methods used for sequence alignment, motif finding, structural modeling, structure prediction and network modeling, as well as currently emerging research areas.

This course is an introduction to computational biology emphasizing the fundamentals of nucleic acid and protein sequence and structural analysis; it also includes an introduction to the analysis of complex biological systems. Topics covered in the course include principles and methods used for sequence alignment, motif finding, structural modeling, structure prediction and network modeling, as well as currently emerging research areas.

Thinking about teaching Computer Science, or incorporating CS into a subject you already teach? CS skills are fundamental to student success and you can teach it!

This course is intended for prospective K-12 Computer Science teachers to review key topics in Technology Applications, Programming Languages and CS special topics.

The course provides extensive detailed instruction, examples and practice on everything from introductory programming topics such as variables, functions, loops and logical expressions to more advanced topics such as data structures, recursion, and object oriented programming.

States around the country have adopted the Praxis Computer Science Test as a component of their certification/licensure and this course covers all of the topics in that test and more. In the state of Texas this test is called TExES 141/241 Grades 8-12 Computer Science Certification exam. Hundreds of teachers from around the country have already taken the course and many have gone on to achieve state certification.

Whether you plan to take a certification exam or not, this course will strengthen your understanding of key CS concepts and constructs and increase your confidence in teaching CS.

Note: In addition to the verified certificate, Texas Teachers that successfully complete this course will receive 36 hours of CPE credit.

In this first part of a two part course, we’ll walk through the basics of statistical thinking – starting with an interesting question. Then, we’ll learn the correct statistical tool to help answer our question of interest – using R and hands-on Labs. Finally, we’ll learn how to interpret our findings and develop a meaningful conclusion.

This course will consist of:

• Instructional videos for statistical concepts broken down into manageable topics
• Guided questions to help your understanding of the topic
• Weekly tutorial videos for using R Scaffolded learning with Pre-Labs (using R), followed by Labs where we will answer specific questions using real-world datasets
• Weekly wrap-up questions challenging both topic and application knowledge

We will cover basic Descriptive Statistics – learning about visualizing and summarizing data, followed by a “Modeling” investigation where we’ll learn about linear, exponential, and logistic functions. We will learn how to interpret and use those functions with basic Pre-Calculus. These two “units” will set the learner up nicely for the second part of the course: Inferential Statistics with a multiple regression cap.

Both parts of the course are intended to cover the same material as a typical introductory undergraduate statistics course, with an added twist of modeling. This course is also intentionally devised to be sequential, with each new piece building on the previous topics. Once completed, students should feel comfortable using basic statistical techniques to answer their own questions about their own data, using a widely available statistical software package (R).

With these new skills, learners will leave the course with the ability to use basic statistical techniques to answer their own questions about their own data, using a widely available statistical software package (R). Learners from all walks of life can use this course to better understand their data, to make valuable informed decisions.

Join us in learning how to look at the world around us. What are the questions? How can we answer them? And what do those answers tell us about the world we live in?

In the second part of a two part statistics course, we’ll learn how to take data and use it to make reasonable and useful conclusions. You’ll learn the basics of statistical thinking – starting with an interesting question and some data. Then, we’ll apply the correct statistical tool to help answer our question of interest – using R and hands-on Labs. Finally, we’ll learn how to interpret our findings and develop a meaningful conclusion.

We will cover basic Inferential Statistics – integrating ideas of Part 1. If you have a basic knowledge of Descriptive Statistics, this course is for you. We will learn how to sample data, examine both quantitative and categorical data with statistical techniques such as t-tests, chi-square, ANOVA, and Regression.

Both parts of the course are intended to cover the same material as a typical introductory undergraduate statistics course, with an added twist of modeling. This course is also intentionally devised to be sequential, with each new piece building on the previous topics. Once completed, students should feel comfortable using basic statistical techniques to answer their own questions about their own data, using a widely available statistical software package (R).

This course will consist of:

• Instructional videos for statistical concepts broken down into manageable topics
• Guided questions to help your understanding of the topic
• Weekly tutorial videos for using R
• Scaffolded learning with Pre-Labs (using R), followed by Labs where we will answer specific questions using real-world datasets
• Weekly wrap-up questions challenging both topic and application knowledge

With these new skills, learners will leave the course with the ability to use basic statistical techniques to answer their own questions about their own data, using a widely available statistical software package (R). Learners from all walks of life can use this course to better understand their data, to make valuable informed decisions.

Join us in learning how to look at the world around us. What are the questions? How can we answer them? And what do those answers tell us about the world we live in?

Data structures provide a means to manage large amounts of data for use in databases and internet indexing services. Efficient data structures are key for designing efficient algorithms and obtaining maintainable software design.

In this Computer Science course, you will start by learning basic data types, such as numbers, and gradually build a conceptual framework for organizing and managing efficient structures.

Topics covered:

• Basic Data Types, Notion of an Abstract Data Type
• Mathematical Properties of Sequences
• Special Types of Sequences: Stacks, Queues, Strings
• Implementation of Sequence Type: Arrays and Linked Lists
• Trees
• Sets and Maps
• Graphs

Preliminary understanding of implementing sequence structures such as stacks, queues, and linked lists, will also be covered.

This course is part of the Fundamentals of Computer Science XSeries Program: 

• Programming Basics
• Object-Oriented Programming
• Implementation of Data Structures
• Algorithms

This course explores the foundations of policy making in developing countries. The goal is to spell out various policy options and to quantify the trade-offs between them. We will study the different facets of human development: education, health, gender, the family, land relations, risk, informal and formal norms and institutions. This is an empirical class. For each topic, we will study several concrete examples chosen from around the world. While studying each of these topics, we will ask: What determines the decisions of poor households in developing countries? What constraints are they subject to? Is there a scope for policy (by government, international organizations, or non-governmental organizations (NGOs))? What policies have been tried out? Have they been successful?

This course is part of the MITx MicroMasters program in Data, Economics, and Development Policy (DEDP). To audit this course, click “Enroll Now” in the green button at the top of this page.

To enroll in the MicroMasters track or to learn more about this program and how it integrates with MIT’s new blended Master’s degree, go to MITx’s MicroMasters portal. 

In this course, we will study the different facets of human development in topics such as education, health, gender, the family, land relations, risk, informal and formal norms, public policy, and institutions. While studying each of these topics, we will delve into the following questions:

• What determines the decisions of poor households in developing countries?
• What constraints are poor households subject to?
• What is the scope for policy interventions (implemented by the government, international organizations, or NGOs)?
• What policies have been tried out? Have they been successful?

At the same time, you will discover modern empirical methods in economics, in particular Randomized Control Trials (RCTs). Throughout the course, we will expose you to all facets of empirical projects, from experimental design and ethical issues, to data collection and data analysis.You will have the chance to gain experience working with real data using software for statistical analysis during weekly assignments.

The aim of this course is to understand issues that companies need to address when moving from being offline to online and learn how companies can craft business models where IT is embedded as an integral part of products, processes and customer interactions. You would get to appreciate how IT is changing the way companies create value through networks and coopetition.

This course will provide learners with an overview of the seven steps of evidence-based practice (EBP) in nursing and health sciences.

This course continues from the fall semester. The course introduces students to the fundamental theories and methods of modern political science through the study of a small number of major books and articles that have been influential in the field. This semester, the course focuses on American and comparative politics.

This is a foundation subject in modern software development techniques for engineering and information technology. The design and development of component-based software (using C# and .NET) is covered; data structures and algorithms for modeling, analysis, and visualization; basic problem-solving techniques; web services; and the management and maintenance of software. Includes a treatment of topics such as sorting and searching algorithms; and numerical simulation techniques. Foundation for in-depth exploration of image processing, computational geometry, finite element methods, network methods and e-business applications. This course is a core requirement for the Information Technology M. Eng. program.

This class was also offered in Course 13 (Department of Ocean Engineering) as 13.470J. In 2005, ocean engineering subjects became part of Course 2 (Department of Mechanical Engineering), and the 13.470J designation was dropped in lieu of 2.159J.

Foundations of Teaching for Learning is a program of study primarily for people who are currently teaching but have had no formal teacher education. This course is an introductory one that considers the three domains of being a teacher: Professional Knowledge and Understanding; Professional Practice; and Professional Values, Relationships and Engagement.

How we teach has its foundation in how we were taught and how we learned. This course provides an opportunity for you to reflect on your personal and professional development as a teacher. Through reflection and portfolio development you will enhance your knowledge and understanding of how to promote learning.

The teacher’s work becomes meaningful when it is informed by research and theories of learning, and their relationship to actual practice. This course provides an opportunity for you to identify and understand students’ expectations and prior learning.