USING DATA SCIENCE TO SOLVE EDUCATION ISSUES

With the adoption of technology in more schools and with a push for more open government data, there are clearly a lot of opportunities for better data gathering and analysis in education. However, education is today at least, a black box. Society invests significantly in primary, secondary, and higher education. Unfortunately, we don’t really know how our inputs influence or produce outputs. We don’t know, precisely, which academic practices need to be curbed and which need to be encouraged. We are essentially swatting flies with a sledgehammer and doing a fair amount of peripheral damage.

Learning analytics are a foundational tool for informed change in education. Over the past decade, calls for educational reform have increased, but very little is understood about how the system of education will be impacted by the proposed reforms. We need a means, a foundation, on which to base reform activities. In the corporate sector, business intelligence serves this “decision foundation” role. In education, data science and learning analytics will serve this role. Once we better understand the learning process — the inputs, the outputs, the factors that contribute to learner success — then we can start to make informed decisions that are supported by evidence.

Learning analytics can provide dramatic, structural change in education. For example, today, our learning content is created in advance of the learners taking a course in the form of curriculum like textbooks. This process is terribly inefficient. Each learner has differing levels of knowledge when they start a course. An intelligent curriculum should adjust and adapt to the needs of each learner. We don’t need one course for 30 learners; each learner should have her own course based on her life experiences, learning pace, and familiarity with the topic. The content in the courses that we take should be as adaptive, flexible, and continually updated. The black box of education needs to be opened and adapted to the requirements of each individual learner.

Programs of study should also include non-school-related learning (prior learning assessment). A student that volunteers with a local charity or a student that plays sports outside of school is acquiring skills and knowledge that is currently ignored by the school system. “Whole-person analytics” is required where we move beyond the micro-focus of exams. For students that return to university mid-career to gain additional qualifications, recognition for non-academic learning is particularly important.

Much of the current focus on analytics relates to reducing attrition or student dropouts. This is the low-hanging fruit of analytics. An analysis of the signals learners generate (or fail to — such as when they don’t login to a course) can provide early indications of which students are at risk for dropping out. By recognizing these students and offering early interventions, schools can reduce dropouts dramatically.

So to summarize, learning analytics serve as a foundation for informed change in education, altering how schools and universities create curriculum, deliver it, assess student learning, provide learning support, and even allocate resources.

USING DATA SCIENCE TO SOLVE ENVIRONMENTAL ISSUES

The proliferation of sensors of all kinds is producing a wealth of data on a wide range of environmental phenomena. The techniques of big data analytics—originally developed for finance and social media—are being applied to this physical data. Extreme weather events and rising sea levels have made it clear that we must focus on adaptation to climate change, even as we continue to work on mitigation.

We believe that research and innovation at the nexus of sensors and big data can lead to new devices, techniques, applications, products, and services for addressing climate change and other important environmental challenges. There are potential entrepreneurial applications in diverse industries such as the environment, agriculture, transportation, infrastructure, energy, consumer technology, mobile apps, finance, health, the military, and governments.

The goal is to develop novel solutions and viable analytics business models around the application of sensors and data analytics to climate change adaptation and other environmental challenges. Sensors encompass low-cost devices like the ones found on smart phones and sophisticated laboratory-grade instruments. Deployments range from smart dust to portable sensors, remote sensing, and fixed installations. Communications technologies include ad hoc wireless networks, high-bandwidth links, data storage for upload at later times, and associated security considerations. Applications of mathematics, computer science, and statistics include computation, analytics, data visualization, and machine learning, often in the context of big data.

The focus on commercially sustainable models is a response to two major ideas. First, environmental problems such as climate change adaptation are problems of huge scale; that is, solutions must be deployed at large scale if they are to have an impact commensurate with the size of the challenge and the opportunity. Second, economic viability is the path by which these solutions can be self-sustaining and deployed without dependence on philanthropic donors. The paying “customer” for such solutions might be corporations, governments, other organizations, or individuals.

USING DATA SCIENCE TO SOLVE ENVIRONMENTAL ISSUES
The proliferation of sensors of all kinds is producing a wealth of data on a wide range of environmental phenomena. The techniques of big data analytics—originally developed for finance and social media—are being applied to this physical data. Extreme weather events and rising sea levels have made it clear that we must focus on adaptation to climate change, even as we continue to work on mitigation.

We believe that research and innovation at the nexus of sensors and big data can lead to new devices, techniques, applications, products, and services for addressing climate change and other important environmental challenges. There are potential entrepreneurial applications in diverse industries such as the environment, agriculture, transportation, infrastructure, energy, consumer technology, mobile apps, finance, health, the military, and governments.

The goal is to develop novel solutions and viable analytics business models around the application of sensors and data analytics to climate change adaptation and other environmental challenges. Sensors encompass low-cost devices like the ones found on smart phones and sophisticated laboratory-grade instruments. Deployments range from smart dust to portable sensors, remote sensing, and fixed installations. Communications technologies include ad hoc wireless networks, high-bandwidth links, data storage for upload at later times, and associated security considerations. Applications of mathematics, computer science, and statistics include computation, analytics, data visualization, and machine learning, often in the context of big data.

The focus on commercially sustainable models is a response to two major ideas. First, environmental problems such as climate change adaptation are problems of huge scale; that is, solutions must be deployed at large scale if they are to have an impact commensurate with the size of the challenge and the opportunity. Second, economic viability is the path by which these solutions can be self-sustaining and deployed without dependence on philanthropic donors. The paying “customer” for such solutions might be corporations, governments, other organizations, or individuals.

USING DATA SCIENCE TO TRANSFORM HEALTH CARE

Too often, when doctors order a treatment, whether it’s surgery or an over-the-counter medication, they are applying a “standard of care” treatment or some variation that is based on their own intuition, effectively hoping for the best. The sad truth of medicine is that we don’t always understand the relationship between treatments and outcomes. We have studies to show that various treatments will work more often than placebos; but, like Wanamaker, we know that much of our medicine doesn’t work for half of our patients, we just don’t know which half. At least, not in advance. One of data science’s many promises is that, if we can collect enough data about medical treatments and use that data effectively, we’ll be able to predict more accurately which treatments will be effective for which patient, and which treatments won’t.

Why do we believe that data science has the potential to revolutionize health care? After all, the medical industry has had data for generations: clinical studies, insurance data, hospital records. But the health care industry is now awash in data in a way that it has never been before: from biological data such as gene expression, next-generation DNA sequence data, proteomics, and metabolomics, to clinical data and health outcomes data contained in ever more prevalent electronic health records (EHRs) and longitudinal drug and medical claims. We have entered a new era in which we can work on massive datasets effectively, combining data from clinical trials and direct observation by practicing physicians (the records generated by our $2.6 trillion of medical expense). When we combine data with the resources needed to work on the data, we can start asking the important questions about what treatments work and for whom.

The ability to do large-scale computing on genetic data gives us the ability to understand the origins of disease. If we can understand why an anti-cancer drug is effective (what specific proteins it affects), and if we can understand what genetic factors are causing the cancer to spread, then we’re able to use the tools at our disposal much more effectively. Rather than using imprecise treatments organized around symptoms, we’ll be able to target the actual causes of disease, and design treatments tuned to the biology of the specific patient. Eventually, we’ll be able to treat 100% of the patients 100% of the time, precisely because we realize that each patient presents a unique problem.

DATA SCIENCE TO SOLVE HUMAN RIGHTS ISSUES

In today’s global digital ecosystem, mobile phone cameras can document and distribute images of physical violence. Drones and satellites can assess disasters from afar. Big data collected from social media can provide real-time awareness about political protests. Yet practitioners, researchers, and policymakers face unique challenges and opportunities when assessing technological benefit, risk, and harm. How can these technologies be used responsibly to assist those in need, prevent abuse, and protect people from harm?

For some years now, the humanitarian and development communities have explored new data-driven approaches, innovations, and interventions.1 However, for human rights and human security decision-makers distinct questions emerge, particularly when collecting personal data for case analysis and protection beyond crisis events. Releasing cell phone data might assist first responders to track disease outbreaks. But should the same data be shared for long-term issues such as human trafficking? Collecting aggregate population data can inform economic policies on poverty. Yet what about mining those data sources to personally identify someone who is at immediate risk of genderbased violence?

New technologies enable awareness (and potential intervention) into the physical, social, and political environment more quickly than ever before. Analytic tools can filter through huge amounts of data to find signals of potential threats and identify individuals in need.3 However, big data analysis deals in possible correlations, not causation nor objectivity. Serious concerns about sampling, representation, and population estimates call into question the utility of big data in policy making.4 Moreover, all big data sets give a biased view of reality as individuals and attributes will be excluded. New requirements for disclosing biases may be needed to alert decision-makers to avoid hasty generalizations.

The human rights, human security, and technology sectors are remarkably dynamic. As new data techniques and tools emerge, specialists in these fields must ask which technologies have the potential to bolster their work, and what opportunities and risks they pose to vulnerable populations. While most of the challenges and opportunities discussed above apply to a broad range of technologies, delving more deeply into specific case studies of data on human rights and human security can highlight the actual benefits and risks. Examples of case studies range from data mining to combat human trafficking,42 mobile data and unsafe migration, unmanned aerial vehicles and political violence, human rights and cyber-warfare, and private sector data sharing.43 In addition, more work needs to be done on beneficiaries, including the disparate impact on “users at risk,” which includes minority groups, dissidents, and journalists.

Developing a strategic path forward includes field building, conceptual work, mapping, stakeholder analyses, case studies, workshops, education, multisector convenings, policy briefings, and cutting edge research and development. Through these efforts, guidelines and guiding principles can be developed for multiple stakeholders in different contexts. While some of the issues raised in this primer can be resolved, others will remain as important social, political, and technical challenges.

USING DATA SCIENCE TO SOLVE POVERTY ISSUES

The limited availability of data on poverty and inequality poses major challenges to the monitoring of the World Bank Group’s twin goals – ending extreme poverty and boosting shared prosperity. According to a recently completed study, for nearly one hundred countries at most two poverty estimates are available over the past decade.Worse still, for around half of them there was either one or no poverty estimate available.* Increasing the frequency of data on poverty is critical to effectively monitoring the Bank’s twin goals.

Against this background, the science of “Big Data” is often looked to as providing a potential solution. A famous example of this science is “Google Flu Trends (GFT)”, which uses search outcomes of Google to predict flu outbreaks. This technology has proven extremely quick to produce predictions and is also very cost-effective. The rapidly increasing volumes of raw data and the accompanying improvement of computer science have enabled us to fill other kinds of data gaps in ways that we could not even have dreamt of in the past.

The World Bank Group is also now proposing various initiatives that blend “Big Data” with “Small Data” for poverty estimation. SWIFT (Survey of Well-being via Instant and Frequent Tracking) is one such initiative. Like typical “Small Data” efforts, SWIFT collects data from samples that are representative of underlying populations of interest. Like typical “Big Data” approaches, SWIFT applies a series of formulas/algorithms, as well as the latest ITS technology, to cut the time and cost of data collection and poverty estimation. For example, SWIFT does not estimate poverty from consumption or income data, which is time-consuming to collect, but uses formulas to estimate poverty from poverty correlates, which can be easily collected. Furthermore, by embedding the formulas into the SWIFT data management system, the correlates will be converted to poverty statistics instantly. To further cut the time for data collection and processing, SWIFT uses Computer Assisted Personal Interview (CAPI) linked to data clouds, and if possible, adopts a cell phone data collection approach.

USING DATA SCIENCE TO SOLVE PUBLIC SAFETY CONCERNS

You cannot fix what you cannot measure. NYC government realized this fact during a spike in fatal accidents due to slow 911 response times during the summer of 2013. Despite the city’s $88 million upgrade of its computer-aided dispatch system, the source of the delay remained elusive. Theories included delays associated with the new software, operator error, or indeed something else entirely.

The Mayor’s Office of Data Analytics (MODA), in collaboration with the NYPD, FDNY, EMS, and Verizon, developed a method to measure every stage of the emergency-response process—from the second that a resident dials 911, to the exact moment that emergency responders arrive on the scene. The analytical model stitches together data from decades-old agency systems into a comprehensive picture of the city’s emergency response. The city can now track the speed with which 911 operators interface with emergency medical-service responders, isolating these transactions from dispatch and travel time.

Integrating these data sets across legacy platforms presented challenges: each public safety agency has a different unique ID for each incident and records these data in systems that do not communicate with one another. To connect the systems, MODA developed an analytical matching technique based on time, type, and location of call. This software script automates the process of linking together complex data sets into a comprehensible whole. Rather than overhauling each agency’s unique and functional system, the mayor’s team developed a creative work-around to link isolated information. By isolating each facet of the emergency response, city leaders can detect inefficiencies and blockages, respond to inquiries about what exactly caused each individual delay, and make strategic decisions about where to invest future resources.

Turning to data to drive operations is not just about delivering better service; it also saves agencies time, money, and other resources. These applications of predictive analytics in public safety were all implemented during a fiscal crisis. By making upfront investments in technology, public safety organizations lower costs and increase capacity for targeted interventions.

It is apparent, once again, that police and other emergency-response agencies are leading local government in using sophisticated data-mining tools to target resources, solve problems, and pre-position in times of crisis. The breakthroughs for these agencies will again set a pattern for government, as predictive analytics power the advance of ever more effective government.

USING DATA SCIENCE IN THE SUCCESSFUL OPERATION OF SMART CITIES

A Smart City is a city that performs well in the following characteristics: economy, people, governance, mobility, environment and living [1]. The high degree of datification and connectivity embedded in a Smart City demand tools and mechanisms for data manipulation and representation that facilitate the extraction of meaningful insight.

Smart Cities demand a precise set of data skills. In addition to the ability to identify, extract and format data, a data scientist should have enough expertise and domain knowledge to achieve meaningful integration of data, as several different services (online and offline), which speak different protocols, need to be consolidated. Additionally, the data scientist must design data models and delivery mechanisms to allow those applications to enable citizens to make informed decisions

The Internet of Things may be giving over to the Internet of Everything as more and more uses are dreamed up for the new wave of Smart Cities. In the Internet of Things, objects have their own IP address, meaning that sensors connected to the web can send data to the cloud on just about anything: how much traffic is rolling through a stoplight, how much water you’re using, or how full a trash dumpster is.

Cities are discovering how they can use these new technologies — and the data they generate — to be more efficient and cost effective in many different ways. And it’s a good thing, too; some estimates suggest that 66 percent of the world’s population will live in urban areas by the year 2050. These are cutting edge ideas, but here are some of the most fascinating ways Smart Cities are using big data and the Internet of Things to improve quality of life for their residents:

The city of Long Beach, California is using smart water meters to detect illegal watering in real time and have been used to help some homeowners cut their water usage by as much as 80 percent. That’s vital when the state is going through its worst drought in recorded history and the governor has enacted the first-ever state-wide water restrictions.

Los Angeles uses data from magnetic road sensors and traffic cameras to control traffic lights and thus the flow (or congestion) of traffic around the city. The computerized system controls 4,500 traffic signals around the city and has reduced traffic congestion by an estimated 16 percent.

Xcel Energy initiated one of the first ever tests of a “smart grid” in Boulder, Colorado, installing smart meters on customers’ homes that would allow them to log into a website and see their energy usage in real time. The smart grid would also theoretically allow power companies to predict usage in order to plan for future infrastructure needs and prevent brown out scenarios.

A tech startup called Veniam is testing a new way to create mobile wi-fi hotspots all over the city in Porto, Portugal. More than 600 city buses and taxis have been equipped with wifi transmitters, creating the largest free wi-fi hotspot in the world. Veniam sells the routers and service to the city, which in turn provides the wi-fi free to citizens, like a public utility. In exchange, the city gets an enormous amount of data — with the idea being that the data can be used to offset the cost of the wi-fi in other areas. For example, in Porto, sensors tell the city’s waste management department when dumpsters are full, so they don’t waste time, man hours, or fuel emptying containers that are only partly full.

New York City is creating the world’s first “quantified community” where nearly everything about the environment and residents will be tracked. The community will be able to monitor pedestrian traffic flow, how much of the solid waste collected is recyclable or food waste, and air quality. The project will even collect data on residents’ health and activity levels through an opt-in mobile app.

Songdo, South Korea has been conceived and built as the ultimate Smart City — a city of the future. Trash collection in the city is completely automated, through pipes connected to every building. The solid waste is sorted then recycled, buried, or burned for fuel. The city is partnering with Cisco to test other technologies, including home appliances and utilities controlled by your smartphone, and even a tracking system for children (using microchips implanted in bracelets).

This is just the beginning of the integration of big data and the Internet of Things into daily life, but it is by no means the end. As our cities get smarter and begin collecting and sending more and more data, new uses will emerge that may revolutionize the way we live in urban areas.