This course covers empirical strategies for applied micro research questions. Our agenda includes regression and matching, instrumental variables, differences-in-differences, regression discontinuity designs, standard errors, and a module consisting of 8–9 lectures on the analysis of high-dimensional data sets a.k.a. “Big Data”.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"For an anesthesiologist, medically induced transitions between conscious and unconscious brain states are just part of the daily routine. But precisely how general anesthetics produce a state of unconsciousness isn’t all that clear. That’s because researchers are missing a key piece to the puzzle: no one has been able to definitively pinpoint exactly where consciousness comes from. The prevailing idea is that there’s no single “seat” of consciousness – it’s more the product of multiple interactions occurring throughout the brain. A recent review article published in the journal Anesthesiology argues that, because of this global network property, the field of network science could provide the framework needed to more comprehensively understand the biological basis of consciousness…and by extension the principles underlying anesthetic-induced unconsciousness..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

The IOT services are for customer convenience, control in online booking IOT services such as radio station, smart coffee makers, dim lights and energy programmed lights. Our System will able to recommend the valid customer opinion by analyzing UAE, UK and Oman hotel aspects like services, value, cleanliness and location from customers’ reviews. it include the Big Analytics, Hadoop, HDFS, Sentiment Analytics, Emotion Analytics, ANOVA in Map-Reduce.

This project will recommend a big data analytics tool for the customers, ministry and hotels in Oman to adapt new hotel services after considering together hotel services with customer opinions. The IOT services are for customer convenience, control in online booking IOT services such as radio station, smart coffee makers, dim lights and energy programmed lights.The big data analytics will analyze the hotel information , rating and reviews of UK , Dubai to recomend aspect like services especially IOT services. The coverage of Analysis in R: Big data Analytics with Hadoop/HDFS Sentiment AnalysisEmotion Analysis Machine Learning K-mean , Regression and Neural NetworkAnova version to analyze Big data of 90k reviews 

While big data infiltrates all walks of life, most firms have not changed sufficiently to meet the challenges that come with it. In this course, you will learn how to develop a big data strategy, transform your business model and your organization.

This course will enable professionals to take their organization and their own career to the next level, regardless of their background and position.

Professionals will learn how to be in charge of big data instead of being subject to it. In particular, they will become familiar with tools to:

assess their current situation regarding potential big data-induced changes of a disruptive nature,
identify their options for successfully integrating big data in their strategy, business model and organization, or if not possible, how to exit quickly with as little loss as possible, and
strengthen their own position and that of their organization in our digitalized knowledge economy
The course will build on the concepts of product life cycles, the business model canvas, organizational theory and digitalized management jobs (such as Chief Digital Officer or Chief Informatics Officer) to help you find the best way to deal with and benefit from big data induced changes.

Students act as R&D entrepreneurs, learning ways to research variables affecting the market of their proposed (hypothetical) products. They learn how to obtain numeric data using a variety of Internet tools and resources, sort and analyze the data using Excel and other software, and discover patterns and relationships that influence and guide decisions related to launching their products. First, student pairs research and collect pertinent consumer data, importing the data into spreadsheets. Then they clean, organize, chart and analyze the data to inform their product production and marketing plans. They calculate related statistics and gain proficiency in obtaining and finding relationships between variables, which is important in the work of engineers as well as for general technical literacy and decision-making. They summarize their work by suggesting product launch strategies and reporting their findings and conclusions in class presentations. A finding data tips handout, project/presentation grading rubric and alternative self-guided activity worksheet are provided. This activity is ideal for a high school statistics class.

How would you like to travel to new places while collaborating on a geospatial data challenge with students from around the world? In this class, students collaborate on a global-scale geospatial analysis problem with a focus on data analytics and professional practice in Geographic Information Systems. Penn State MGIS students collaborate with graduate students from ITC - University of Twente in Enschede, Netherlands to develop solutions to analyze spatio-temporal patterns in refugee migration data. Students have the opportunity to present their work and develop new connections with EU geospatial professionals via site visits to European national mapping agencies. Students work in teams to use geospatial analytics to arrive at a solution to visualize patterns over space and time.

As if they are environmental engineers, student pairs are challenged to use Google Earth Pro (free) GIS software to view and examine past data on hurricanes and tornados in order to (hypothetically) advise their state government on how to proceed with its next-year budget—to answer the question: should we reduce funding for natural disaster relief? To do this, students learn about maps, geographic information systems (GIS) and the global positioning system (GPS), and how they are used to deepen the way maps are used to examine and analyze data. Then they put their knowledge to work by using the GIS software to explore historical severe storm (tornado, hurricane) data in depth. Student pairs confer with other teams, conduct Internet research on specific storms and conclude by presenting their recommendations to the class. Students gain practice and perspective on making evidence-based decisions. A slide presentation as well as a student worksheet with instructions and questions are provided.

This class addresses important, current debates in media with in-depth discussion of popular perceptions and policy implications. Students will engage in the critical study of the economic, political, social, and cultural significance of media, and learn to identify, analyze, and understand the complex relations among media texts, policies, institutions, industries, and infrastructures. This class offers the opportunity to discuss, in stimulating and challenging ways, topics such as ideology, propaganda, net neutrality, big data, digital hacktivism, digital rebellion, media violence, gamification, collective intelligence, participatory culture, intellectual property, artificial intelligence, etc., from historical, transcultural, and multiple methodological perspectives.

Data Science and AI in Psychology is an interactive eTextbook that provides an introduction to data science, big data, and machine learning in psychology. It covers current trends in data science and big data in the field of psychology (Chapter 1), applications of AI in the field of psychology (Chapter 2), the psychology of data visualization (Chapter 3), data ethics (Chapter 4), an introduction to how machines learn (Chapter 5), a hands-on guide for reading and critiquing machine learning research articles that are relevant to psychological topics (Chapters 6 and 7), and an introduction to coding in Python (Chapter 8). This eTextbook also includes an introduction to ChatGPT and tips for using ChatGPT to assist with writing and coding without plagiarizing (Chapters 6 and 8). This is an interactive resource that provides students with opportunities to engage with their peers and develop critical thinking skills through problem-based, active learning.

Our final database video. This one looks at some odds and ends. We examine: Data Warehouse, Data Mining, Big Data. I also talk about the ethics of data mining from the NSA and CDC, and how they are different.

We also give out top picks for the lesson.

Links from Video:
•http://www.w3schools.com/sql/
•What is Database & SQL by Guru99 http://youtu.be/FR4QIeZaPeM
•What is a database http://youtu.be/t8jgX1f8kc4
•MySQL Database For Beginners https://www.udemy.com/mysql-database-for-beginners2/

Although there is clear potential to improve science and innovation systems through big data and open science, barriers still remain with respect to data sharing efforts. How can the available massive and diverse data collections be used and shared more efficiently to boost global research and innovation and improve care? What actions are needed to facilitate open access to research data generated with public funding?

The OECD is bringing together policy makers, funding agencies and researchers to tackle the issue of open access to data, focused around developing good practice and principles on data governance. Four case studies highlight best practice and identify barriers to progress.

Following an OECD-hosted consultation with the Ontario Brain Institute (OBI), the United Kingdom Medical Research Council (MRC), and the US Alzheimer’s Association, two concrete examples of global data sharing have been created. The first, focused on providing a wealth of open-source biomedical data for the community (deep data), builds upon GAAIN, the Global Alzheimer’s Association Interactive Network, and links eleven international partners through a federated network of data resources. The capability of this network is being extended significantly through connections with the French National Alzheimer’s Database (BNA), the European Medicines Informatics Framework (EMIF), and the Canadian based Longitudinal Online Research and Imaging System (LORIS). The second focused on linking big data approaches at the population level (broad data), is a complementary collaboration between the Canadian Consortium on Neurodegeneration in Ageing and the Dementias Platform UK to share and analyse large-scale complex population-wide datasets from up to 2 million individuals, including imaging, genomics and health data.

As a result, these collaborations will enable the aggregation of an unprecedented volume of individual and population-level data, offering an open science solution to help research to more efficiently tackle Alzheimer’s disease and related disorders.

This links to a Slideshare presentation by Ian Brown which gives an overview of Big Data

This course is an introduction to data cleaning, analysis and visualization. We will teach the basics of data analysis through concrete examples. You will learn how to take raw data, extract meaningful information, use statistical tools, and make visualizations.
This was offered as a non-credit course during the Independent Activities Period (IAP), which is a special 4-week term at MIT that runs from the first week of January until the end of the month.

Typical machine learning classes teach techniques to produce effective models for a given dataset. In real-world applications, data is messy and improving models is not the only way to get better performance. You can also improve the dataset itself rather than treating it as fixed. Data-Centric AI (DCAI) is an emerging science that studies techniques to improve datasets, which is often the best way to improve performance in practical ML applications. While good data scientists have long practiced this manually via ad hoc trial/error and intuition, DCAI considers the improvement of data as a systematic engineering discipline.

This is the first-ever course on DCAI. This class covers algorithms to find and fix common issues in ML data and to construct better datasets, concentrating on data used in supervised learning tasks like classification. All material taught in this course is highly practical, focused on impactful aspects of real-world ML applications, rather than mathematical details of how particular models work. You can take this course to learn practical techniques not covered in most ML classes, which will help mitigate the “garbage in, garbage out” problem that plagues many real-world ML applications.

Infrastructures for energy, water, transport, information and communications services create the conditions for livability and economic development. They are the backbone of our society. Similar to our arteries and neural systems that sustain our human bodies, most people however take infrastructures for granted. That is, until they break down or service levels go down.

In many countries around the globe infrastructures are ageing. They require substantial investments to meet the challenges of increasing population, urbanization, resource scarcity, congestion, pollution, and so on. Infrastructures are vulnerable to extreme weather events, and therewith to climate change.
Technological innovations, such as new technologies to harvest renewable energy, are one part of the solution. The other part comes from infrastructure restructuring. Market design and regulation, for example, have a high impact on the functioning and performance of infrastructures.