Courses tagged with "Nutrition" (6413)

D-Lab Health provides a multidisciplinary approach to global health technology design via guest lectures and a major project based on fieldwork. We will explore the current state of global health challenges and learn how to design medical technologies that address those problems. Students may travel to Nicaragua during spring break to work with health professionals, using medical technology design kits to gain field experience for their device challenge. As a final class deliverable, you will create a product design solution to address challenges observed in the field. The resulting designs are prototyped in the summer for continued evaluation and testing.

This course introduces concepts of supply chain design and operations with a focus on supply chains for products destined to improve quality of life in developing countries. Topics include demand estimation, capacity planning and process analysis, inventory management, and supply chain coordination and performance. We also cover issues specific to emerging markets, such as sustainable supply chains, how to couple product design with supply chain design and operation, and how to account for the value-adding role of a supply chain. A major aspect of class is the student projects on supply chain design or improvement.

This introductory course will provide you with a multidisciplinary approach to managing waste in low- and middle-income countries, with strategies that diminish greenhouse gas emissions and provide enterprise opportunities for marginalized populations. You will focus on understanding some of the multiple dimensions of waste generation and management. Topics are presented in real contexts through case studies, field visits, civic engagement and research, and include consumer culture, waste streams, waste management, entrepreneurship and innovation on waste, technology evaluation, downcycling / upcycling, Life Cycle Analysis and waste assessment. Labs include building low-cost, small scale technology, field trips to waste-related institutions and businesses, art workshops and e-waste scrapping taught by practitioners, artists and waste enthusiasts.

This introductory course will provide you with a multidisciplinary approach to managing waste in low- and middle-income countries, with strategies that diminish greenhouse gas emissions and provide enterprise opportunities for marginalized populations. You will focus on understanding some of the multiple dimensions of waste generation and management. Topics are presented in real contexts through case studies, field visits, civic engagement and research, and include consumer culture, waste streams, waste management, entrepreneurship and innovation on waste, technology evaluation, downcycling / upcycling, Life Cycle Analysis and waste assessment. Labs include building low-cost, small scale technology, field trips to waste-related institutions and businesses, art workshops and e-waste scrapping taught by practitioners, artists and waste enthusiasts.

With the world's first MOOOOO-C, you will gain a broad and comprehensive understanding of all aspects of dairy management such as genetics, nutrition, reproduction, animal health, farm economics, and sustainability of dairy production systems. There's something here for everyone whether you are just looking for the basics or have years of experience in the dairy industry.

This course introduces students to the art and formal ideologies of contemporary dance. We explore the aesthetic and technical underpinnings of contemporary dance composition. Basic compositional techniques are discussed and practiced, with an emphasis on principles such as weight, space, time, effort, and shape. Principles of musicality are considered and developed by each student. Working with each other as the raw material of the dance, students develop short compositions that reveal their understanding of basic techniques. Hopefully, students come to understand a range of compositional possibilities available to artists who work with the medium of the human body.

Humans are social animals; social demands, both cooperative and competitive, structure our development, our brain and our mind. This course covers social development, social behaviour, social cognition and social neuroscience, in both human and non-human social animals. Topics include altruism, empathy, communication, theory of mind, aggression, power, groups, mating, and morality. Methods include evolutionary biology, neuroscience, cognitive science, social psychology and anthropology.

Humans are social animals; social demands, both cooperative and competitive, structure our development, our brain and our mind. This course covers social development, social behaviour, social cognition and social neuroscience, in both human and non-human social animals. Topics include altruism, empathy, communication, theory of mind, aggression, power, groups, mating, and morality. Methods include evolutionary biology, neuroscience, cognitive science, social psychology and anthropology.

Learn about the most effective data analysis methods to solve problems and achieve insight.

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.

Data and visual analytics is an emerging field concerned with analyzing, modeling, and visualizing complex high dimensional data. This course will introduce students to the field by covering state­-of-­the-art modeling, analysis and visualization techniques. It will emphasize practical challenges involving complex real world data and include several case studies and hands-on work with the R programming language.

Want to study for an MBA but unsure of the basic data analysis still required? This online course prepares you for studying in an MBA program.

Data analysis appears throughout any rigorous MBA program and in today’s business environment understanding the fundamentals of collecting, presenting, describing and making inferences from data sets is essential for success.

The goal of this course is to teach you fundamental data analysis skills so you are prepared for your MBA study and able to focus your efforts on core MBA curriculum, rather than continually playing catch-up with the underlying statistical knowledge needed.

We also hope that learning these data analysis skills will equip you with the ability to understand, to a greater degree, the data you encounter in your working lives and in the world around you - an essential life-skill in today’s data driven environment

This course assumes no prior knowledge of data analysis. Concepts are explained as clearly as possible and regular activities give you the opportunity to practice your skills and improve your confidence.

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.

This statistics and data analysis course will introduce you to the essential notions of probability and statistics. We will cover techniques in modern data analysis: estimation, regression and econometrics, prediction, experimental design, randomized control trials (and A/B testing), machine learning, and data visualization. We will illustrate these concepts with applications drawn from real world examples and frontier research. Finally, we will provide instruction for how to use the statistical package R and opportunities for students to perform self-directed empirical analyses.

This course is designed for anyone who wants to learn how to work with data and communicate data-driven findings effectively, but it is challenging. Students who are uncomfortable with basic calculus and algebra might struggle with the pace of the class.

Exploratory data analysis is an approach for summarizing and visualizing the important characteristics of a data set. Promoted by [John Tukey](http://en.wikipedia.org/wiki/John_Tukey), exploratory data analysis focuses on exploring data to understand the data’s underlying structure and variables, to develop intuition about the data set, to consider how that data set came into existence, and to decide how it can be investigated with more formal statistical methods. If you're interested in supplemental reading material for the course check out the Exploratory Data Analysis book. (Not Required) This course is also a part of our Data Analyst Nanodegree.

Are you ready to leave the sandbox and go for the real deal? Have you followed EX101x Data Analysis: Take It to the MAX() and EX102x Data Analysis: Visualization and Dashboard Design and are ready to carry out more robust data analysis?

In this project-based course you will engage in a real data analysis project that simulates the complexity and challenges of data analysts at work. Testing, data wrangling, Pivot Tables, sparklines? Now that you have mastered them you are ready to apply them all and carry out an independent data analysis.

For your project, you firstly get one raw dataset, which you will turn into a dashboard, step by step. You will begin with a business question, and then touch upon different business domains, such as revenue management, planning, scheduling, management, investment, etc.

The progress of the project will be gradual – in the first weeks you will be guided by quizzes to make sure that you are on track. You will engage with creating first drafts of your work and discuss them with your peers.

In the final week, you will be in charge. You will get a second dataset and a number of questions to answer, and it is up to you to design and build the perfect dashboard.

This course will allow you to practice the skills you’ve gained in previous Data Analysis XSeries courses and prove you can be an indispensable asset in data-driven organizations.

By completing this project and EX101x and EX102x, you can obtain the XSeries Data Analysis Verified Certificate. 

LICENSE

The course materials of this course are Copyright Delft University of Technology and are licensed under a Creative Commons Attribution-NonCommercial-ShareAlike (CC-BY-NC-SA) 4.0 International License.

EX101x is for all of those struggling with data analysis. That crazy data collection from your boss? Megabytes of sensor data to analyze? Looking for a smart way visualize your data in order to make sense out of it? We’ve got you covered!

Using video lectures and hands-on exercises, we will teach you cutting-edge techniques and best practices that will boost your data analysis and visualization skills.

We will take a deep dive into data analysis with spreadsheets: PivotTables, VLOOKUPS, Named ranges, what-if analyses, making great graphs - all those will be covered in the first weeks of the course. After that, we will investigate the quality of the spreadsheet model, and especially how to make sure your spreadsheet remains error-free and robust.

Finally, once we have mastered spreadsheets, we will demonstrate other ways to store and analyze data. We will also look into how Python, a programming language, can help us with analyzing and manipulating data in spreadsheets.

EX101x is created using Excel 2013 and Windows. Most assignments can be made using another spreadsheet program and operating system as well, but we cannot offer full support for all configurations.

The goal of this course is to help you to overcome data analysis challenges in your work, research or studies. Therefore we encourage you to participate actively and to raise real data analysis problems that you face in our discussion forums. 

This course is part of the Data Analysis XSeries.

LICENSE
The course materials of this course are Copyright Delft University of Technology and are licensed under a Creative Commons Attribution-NonCommercial-ShareAlike (CC-BY-NC-SA) 4.0 International License.

Struggling with data at work? Wasting valuable time working in multiple spreadsheets to gain an overview of your business? Find it hard to gain sharp insights from piles of data on your desktop?

If you are looking to enhance your efficiency in the office and improve your performance by making sense of data faster and smarter, then this advanced data analysis course is for you.

If you have already sharpened your spreadsheet skills in EX101x Data Analysis: Take It to the MAX(), this course will help you dig deeper. You will learn advanced techniques for robust data analysis in a business environment. This course covers the main tasks required from data analysts today, including importing, summarizing, interpreting, analyzing and visualizing data. It aims to equip you with the tools that will enable you to be an independent data analyst. Most techniques will be taught in Excel with add-ons and free tools available online. We encourage you to use your own data in this course but if not available, the course team can provide.

This course is part of the Data Analysis XSeries. 

LICENSE

The course materials of this course are Copyright Delft University of Technology and are licensed under a Creative Commons Attribution-NonCommercial-ShareAlike (CC-BY-NC-SA) 4.0 International License.

Please note that the verified certificate option is not currently open for this course. Please enroll in the audit track and you will be emailed when the verified certificate option is open for enrollment.

Businesses, consumers, and societies leave behind massive amounts of data as a by-product of their activities. Leading-edge companies in every industry are using analytics to replace intuition and guesswork in their decision-making. As a result, managers are collecting and analyzing enormous data sets to discover new patterns and insights and running controlled experiments to test hypotheses.

This course, part of the Analytics: Essential Tools and Methods MicroMasters program, prepares you to understand data and business analytics and become a leader in these areas in business organizations.

It covers the methodologies, algorithms, issues, and challenges related to analyzing business data. It will illustrate the processes of analytics by allowing you to apply business analytics algorithms and methodologies to real-world business datasets from finance, marketing, and operations. The use of real-world examples and cases places business analytics techniques in context and teaches you how to avoid the common pitfalls, emphasizing theimportance of applying proper business analytics techniques.

In addition to cases, this course features hands-on experiences with data collection, analysis, and visualization using Python programs and analytics software such as SAS.

This course includes a significant analytics project.

Many people talk about the promise of “big data” to health care. But how can the application of data analytics to big data actually improve health and health care? We will show that novel data analytics based solutions can result in better diagnosis, better care and better curing. This provides fertile ground for entrepreneurship and the development of new businesses.

In our course we’ll start from the very basics of data analytics, look at different real world approaches and help you to see entrepreneurial opportunities and develop a business plan.

We will cover three important fields:

• Health care expertise: We will present medical approaches to data and give an overview of challenges where big data based solutions have been developed to improve the efficiency and effectiveness in medicine.
• Data analytics: We’ll explain the basics of data mining within the context of a wide variety of health care settings, and the types of data and data analysis challenges that you will likely encounter in each. We’ll start with gathering the data, move on to classifying, analyzing and finally visualizing it.
• Entrepreneurship: You will learn how to assess when data sciences based improvements in health care represent entrepreneurial opportunities. The development of a rigorous business plan is used to help you make that assessment.

Participants with prior experience in the medical field will learn how novel data science applications can improve healthcare, create societal value and how to spot entrepreneurial opportunities.

Participants with experience in data science or mathematics will learn about medical approaches to data and why healthcare is an exciting area to apply and develop data analytics.

Participants interested in launching their startup will learn how big data solutions in health care can provide a solid basis to build great ventures.

Whatever your motivation to enrol in this course, we care about your project and your success - that’s why we will guide you through all parts of this learning journey step by step!

Enter now to see how you can engage in data driven innovation and make an impact on improving care, outcomes and the quality of life.

Is your data messy? Do you need to learn how to clean it up? In this computer science course, we will discuss the discipline of Data Quality Assurance and Data Quality Services (DQS). You will learn why your data needs cleansing, the capabilities and features of DQS, what a DQS solution looks likes and how data cleansing integrates with an Integration Services (SSIS) data flow. We will demonstrate a variety of critical data quality activities such as knowledge discovery, domain management, matching policies for the de-duplication of data, and administration topics covering installation, configuration and security.