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If you are a Data Scientist or an aspiring one, you will know the importance of statistics in the sector. Statistics help Data Scientists to collect, analyze, and interpret the data by identifying patterns and trends, to then make future predictions.

A statistical paradox is when a statistical result contradicts expectations. It can be very difficult to pinpoint the exact cause, as it is hard to understand the data without the use of further methods. However, they are an important element for Data Scientists as it gives them a lead on what could possibly be causing the misleading results.

Here is a list of statistical paradoxes relevant to data science:

In this article, we will be focusing on the Berkson-Jekel paradox and its relevance to Data Science.

Berkson-Jekel paradox is when two variables are correlated in data, however, when the data is grouped or subsetted, the correlation is not identified. To put it in layman's terms, the correlation is different in different subgroups of the data.

The Berkson-Jekel paradox is named after the first statisticians who described the paradox, Joseph Berkson and John Jekel. The discovery of the Berkson-Jekel paradox is when the two statisticians were studying the correlation between smoking and lung cancer. During their study, they found a correlation between people who had been hospitalized for pneumonia and lung cancer, in comparison to the general population. However, they conducted further research which showed that the correlation was due to smokers being hospitalized for pneumonia more, in comparison to people who did not smoke.

Based on the statistician's first research on the Berkson-Jekel paradox, you may say that more research was required to figure out the exact reasoning behind the correlation. However, there are also other reasons why the Berkson-Jekel paradox occurs.

Statistical reasoning is very important in Data Science, and the main issue is dealing with misleading results. As a data scientist, you want to ensure that you are producing accurate results that can be used in the decision-making process and for future predictions. Making incorrect predictions or misleading results is the last thing on the cards.

There are a few methods that you can use to avoid the Berkson-Jekel Paradox:

If you are dealing with misleading results due to the sample data not being representative of the population, a solution would be to use data from a variety of sources. This will help you to get a more representative sample of the population, research more on the variables, and get a better understanding.

Misleading outputs can hold a company back. Therefore, when working with data, data professionals need to understand the limitations of the data theyre working with, different variables and the relationship between them, and how to reduce misleading results from happening.

If you would like to know more about Simpsons Paradox, have a read of this: Simpsons Paradox and its Implications in Data Science

If you would like to know more about the other statistical paradoxes, have a read of this: 5 Statistical Paradoxes Data Scientists Should KnowNisha Arya is a Data Scientist, Freelance Technical Writer and Community Manager at KDnuggets. She is particularly interested in providing Data Science career advice or tutorials and theory based knowledge around Data Science. She also wishes to explore the different ways Artificial Intelligence is/can benefit the longevity of human life. A keen learner, seeking to broaden her tech knowledge and writing skills, whilst helping guide others.

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The Berkson-Jekel Paradox and its Importance to Data Science - KDnuggets

The emergence of the Internet of Things (IoT) has led to the proliferation of connected devices and sensors that generate vast amounts of data. This data is a goldmine of insights that can be harnessed to optimize various systems and processes. However, to unlock the full potential of IoT data, organizations need to leverage the power of data science. Data science can help organizations derive valuable insights from IoT data and make data-driven decisions to optimize their operations.

Coherence between IoT and data science is critical to ensure that organizations can maximize the value of their IoT ecosystems. It requires a deep understanding of the interplay between IoT devices, sensors, networks, and data science tools and techniques. Organizations that can effectively integrate IoT and data science can derive significant benefits, such as improved efficiency, reduced costs, and enhanced customer experiences.

An IoT (Internet of Things) ecosystem refers to a network of interconnected devices, sensors, and software applications that work together to collect, analyze, and share data. The ecosystem consists of various components, including devices, communication networks, data storage, and analytics tools, that work together to create an intelligent system that enables automation, monitoring, and control of various processes.

IoT protocols 101: The essential guide to choosing the right option

Some key characteristics of an IoT ecosystem include the following:

An IoT ecosystem diagram is a visual representation of the components and relationships that make up an IoT ecosystem. It typically includes devices, communication networks, data storage, and analytics tools that work together to create an intelligent system.

The diagram provides a high-level overview of the ecosystem and helps to visualize the various components and how they are interconnected. It can also be used to identify potential areas for improvement and optimization within the system.

IoT ecosystem architecture refers to the design and structure of an IoT system, including the various components and how they are connected.

There are several layers to an IoT ecosystem architecture, including:

IoT ecosystems play an important role in data science, as they generate vast amounts of data that can be used to drive insights and optimize processes.

Some ways that IoT ecosystems contribute to data science include:

IoT ecosystems provide a rich source of data that can be used to drive insights and optimize processes, making them a valuable tool in the data science toolkit.

IoT ecosystems are composed of various components that work together to collect, process, and transmit data.

IoT ecosystems consist of both hardware and software components that work together to enable automation, monitoring, and control of various processes. Some of the key hardware and software components of IoT ecosystems include:

Each component in an IoT ecosystem plays a critical role in enabling the system to function effectively. Understanding the role of each component is essential in designing and optimizing IoT ecosystems. Some of the key roles of each component in IoT ecosystems include:

Choosing the right components for IoT ecosystems is essential in ensuring that the system functions effectively and efficiently. Some of the key reasons why choosing the right components is important to include:

Designing and implementing IoT ecosystems can be challenging due to various factors, such as the complexity of the system, the diversity of devices, and the need for interoperability. Some of the common challenges in designing and implementing IoT ecosystems include the following:

Overcoming the challenges of designing and implementing IoT ecosystems requires a combination of technical expertise, strategic planning, and effective execution. Some of the solutions for overcoming IoT ecosystem design and implementation challenges include:

Designing IoT ecosystems for data science requires careful planning and execution. Some of the best practices for designing IoT ecosystems for data science include:

Designing IoT ecosystems for data science requires a combination of technical expertise, strategic planning, and effective execution, and organizations need to adopt best practices to ensure success.

IoT and machine learning: Walking hand in hand towards smarter future

Data science plays a critical role in optimizing IoT ecosystems by enabling organizations to derive insights from the vast amounts of data generated by IoT devices and sensors. Data science can help organizations identify trends and patterns, predict future events, and optimize processes.

Some of the key ways that data science can be used to optimize IoT ecosystems include:

Leveraging data science to optimize IoT ecosystem performance requires a combination of technical expertise, strategic planning, and effective execution. Some of the key steps involved in leveraging data science to optimize IoT ecosystem performance include:

There are several examples of data science applications in IoT ecosystems. Some of the key examples include:

IoT ecosystems pose significant security and privacy challenges due to the sheer volume of data generated by numerous devices and sensors. The data can include highly sensitive information, such as biometric data, personal information, and financial details, making it critical to ensure that it is secured and protected.

One of the significant concerns is device security, where the devices are vulnerable to hacking, compromising their integrity and privacy. Network security is also a concern, where the data transmitted over the networks may be intercepted and compromised. Data privacy is another critical concern where there is a risk of unauthorized access to the vast amounts of sensitive data generated by IoT devices.

Devices and sensors are vulnerable to various types of attacks, including malware, distributed denial-of-service (DDoS) attacks, and phishing scams. These attacks can compromise the security of the devices and data generated, leading to devastating consequences.

Data breaches are another concern where the vast amounts of data generated by IoT devices need to be stored and transmitted securely. Any breach of the data can expose sensitive information, leading to privacy violations, identity theft, and other serious consequences.

Security and privacy concerns can have a significant impact on data science in IoT ecosystems. Data quality can be compromised due to security and privacy concerns, leading to incomplete or inaccurate data that can affect the effectiveness of data science. The volume of data that is available for analysis may also be limited due to security and privacy concerns. Furthermore, security and privacy concerns can make it challenging to store and transmit data securely, increasing the risk of unauthorized access and misuse.

Building trust in IoT ecosystems: A privacy-enhancing approach to cybersecurity

Ensuring security and privacy in IoT ecosystems requires a combination of technical expertise, strategic planning, and effective execution. Some of the best practices for ensuring security and privacy in IoT ecosystems include:

Ensuring security and privacy in IoT ecosystems are essential in enabling organizations to leverage data science to optimize their systems. Implementing best practices can help organizations minimize security and privacy risks and derive maximum value from their IoT ecosystems.

In closing, the combination of IoT and data science offers a world of endless possibilities for organizations looking to optimize their systems and processes. However, it also presents significant challenges, particularly around security and privacy.

To ensure the coherence of IoT and data science, organizations must take a comprehensive approach to data management and security, adopting best practices and adhering to industry standards. By doing so, they can unlock the full potential of their IoT ecosystems, derive valuable insights from their data, and make data-driven decisions that drive growth and success.

As IoT continues to evolve and expand, organizations that can effectively leverage data science to analyze IoT data will be well-positioned to thrive in the digital age.

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What Is An IoT Ecosystem: Examples And Diagram - Dataconomy

A map of the LA food environment shows more nutritious options clustered in more affluent areas. Image/Abigail Horn

Its midweek, and the fridge is bare. How do you stave off the gnawing hunger? Perhaps you turn straight to your favorite local takeout or a trusty delivery app to get that food fix.

For one researcher at USC Viterbi School of Engineering, delivery apps and online menus are an even more valuable resource. The digital menu information offered by online ordering sites represents a big-data goldmine that can give us a detailed map of the nutrition of Angelenoswhere disparities in food access exist and how we can address them.

Abigail Horn, research assistant professor in the Daniel J. Epstein Department of Industrial and Systems Engineering and the USC Information Sciences Institute, studies how data can increase our understanding of eating behavior, diet and health. Her latest work aims to build a map of the nutritional quality of the urban foodscape in Los Angeles, using machine learning to analyze information on digital menus for all restaurants, from top-100 chains to small mom-and-pop operations.

The results will help inform public health research, which generally relies on overly simplistic measures of food environment nutritional quality. The work will ultimately inform decisions with the potential to improve zoning policies to increase access to healthy, nutritional options in disadvantaged communities.

The NIH National Institute of Minority Health recently awarded the project the Top Poster award at the inaugural workshop of the Health Equity in Action Annual Workshop.

The research is centered on identifying where healthy and unhealthy food outlets are located. The work has a particular focus on where unhealthy outlets are clustered in the city, and where there is low access to healthy food. These areas are often known as food swamps neighborhoods with a high concentration of low-quality fast-food outlets, liquor stores and convenience stores and a lower concentration of fresh food outlets and grocery stores.

A cyclist delivers for Uber Eats. Image/Robert Anasch.

Existing public health research frequently characterizes food environment nutritional quality by focusing on the number of food outlets in a neighborhood within each business listing category, for example, using the North American Industry Classification System (NAICS)categorization of food outlets which differentiates limited service from full service restaurants and grocery. outlets. Limited service restaurants are often used as a proxy for fast-food outlets, which are in turn considered an indicator of poor nutritional options. It does not consider a newer generation of limited service restaurants that offer menu alternatives such as salads, grain bowls, and wrapsthink Sweetgreen, Veggie Grill or Chipotle. Harnessing this classification system, a neighborhoods proximity to a grocery store would be the only positive indicator of nutritional outcomes.

Horn said that these types of categorizations make it difficult to gain an accurate overview of the nutrition of the city and which residents faced disparities in access to healthy food.

If its fast food, its unhealthy, Horn said, or if its a grocery store, its healthy, because there is some fresh food there. Were really missing the diversity of nutritional quality within these categories and across categories. There is so much digital menu data available, and so the idea of the project was to use that data to create a more refined indicator of the nutritional quality of the food on offer.

The key hurdle the project faced was that restaurants are not required to publish their nutrient information, aside from chain brands with 20 or more outlets. For non-chains, the available information is limited to how these businesses describe individual dishes on their menus.

In work together with Computer Scientist at the USC Information Sciences Institute Keith Burghardt, Andrs Abeliuk from the University of Chile and USC Viterbi Master of Science in Applied Data Science student Alex Seo, Horn developed a model that estimated the nutritional value of menu items across a broad cross-section of restaurants. The teamhas partnered withEdamam, a food database and provider of nutrition data and analytics, which is sharing data to power the model.

Thats where the machine learning comes in. Edamam has created an impressive database of what we call generic food items, which they developed through their own proprietary analysis of over 50 million recipes online. So we use that to train a neural network to predict the nutrient distribution of a food item, Horn said.

The neural network was also refined with the help of a fastText language embedding model, a simple pre-trained text-based model. Horn said when the model is used together with data from the recipe databases, this gives additional context to food words on menus and how the terms are used together, thereby improving the predictions.

Horn also used data from another company, Nutritionix, to gather detailed nutrition and ingredient content data from large chain restaurant menus.

Abigail Horn Research Assistant Professor in the Daniel J. Epstein Department of Industrial and Systems Engineering and the USC Information Sciences Institute

We are also accessing data from websites like Yelp, Horn said. In addition, Edamam, the nutrition data company that shared the generic meal data with us, had previously done some scraping of restaurant menus from across the country. Next we will be applying the algorithm to restaurants in this database and use it to investigate disparities in the urban foodscape in LA.

The project is funded by the Southern California Center for Latino Health, and Horn said a key focus is to better understand and capture the types of restaurants and food options available in areas with predominantly Latino residents.

Initial findings examined around 1000 restaurants in the region, all within the limited service NAICS category and found a wide distribution in the nutritional quality of the menus.

Within that one category of limited service restaurants, we see a huge diversity of nutritional scores. When we plot those on the map you see there are some pretty clear geographical patterns in where the healthier outlets within that category are located, Horn said. What we see is that the unhealthiest outlets are almost uniformly distributed across the map. They are everywhere. The healthiest outlets are clustered only in the more affluent neighborhoods.

It may seem like theres a food swamp in Venice because there are still a lot of unhealthy outlets but when you examine it, there are also a lot of salad joints there. Thats something we dont see in East or South LA, she said.

As the project expands, Horn said that she hoped it could be a tool for public health researchers to paint a more accurate nutritional picture of the city.

It would be wonderful if it could help inform policy around rezoning in neighborhoods, Horn said, and help to create quantified metrics of where we have disparities and the true nutritional quality of food outlets.

Horn said there are federal food financing initiatives that invest money into improving food environments and incentivizing new healthy outlet options and grocery stores.

In those kinds of financing schemes, that could be a scenario where we can help identify neighborhoods that truly have extreme disparities, instead of relying on business listing categories to tell us what might be unhealthy and needs reforming, she said.

Published on April 3rd, 2023

Last updated on April 3rd, 2023

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What Delivery Apps Reveal About Food Disparities in Los Angeles ... - USC Viterbi School of Engineering

UNC Charlottes upward trajectory toward top-tier research university status is supported by a recent national report of research expenditures for U.S. colleges and universities.

In the latest National Science Foundation Higher Education Research and Development Survey, Charlotte moved into the top third of all U.S. colleges and universities for fiscal research expenditures.

During the past decade, UNC Charlotte was among the fastest-growing universities in the nation, and with increased investment from federal and state sources, our research has flourished exponentially, said Chancellor Sharon L. Gaber.

According to the report, Charlottes research expenditures have risen 87% since 2012.The Universitys number of doctoral graduates has grown 62%, too, according to the NSF Earned Doctorate rankings.

CCI faculty conducting innovative research

Leading the Universitys efforts is the College of Computing and Informatics, which, according to the recently released 2021 NSF HERD report, now ranks 39th in the U.S. for research expenditures in the area of computer and information sciences and second among North Carolina universities.

CCI Dean Bojan Cukic said the college has seen considerable growth in research funding during the past decade.

Our faculty expertise in cybersecurity, bioinformatics and genomics, and artificial intelligence is attracting higher levels of federal funding, he noted. For the college, it aids in attracting new faculty in these areas of research excellence, and for students, it ensures they are receiving a high-quality education that will prepare them to excel in their professional careers or advanced studies.

Charlotte is the No. 1 producer of computer science bachelors degrees in North Carolina, and the Queen City has become a top city for startups and a top 10 Tech Town, according to CompTIA. CCI is providing the talented, diverse tech workforce that will drive continued economic development for the region.

Key focus areas: CIPHER, cybersecurity and AI

Through investment from the General Assemblys Engineering a Smart and Secure Future for North Carolina, Charlotte will grow enrollments in computer science, data science and engineering by more than 2,000 students during the next five years. The Universitys School of Data Science was the first of its kind in the Carolinas.

Special state legislative funding for the Center for Computational Intelligence to Predict Health and Environmental Risks is powering its efforts to understand, prevent and combat future outbreaks of known and unknown infectious diseases. CIPHER, as it is known, features 20-plus experts from bioinformatics, engineering and data science to biology, mathematics and computer science, and more who are combining forces to identify vital connections among the worlds natural environment, interactions with animals and human health.

Cybersecurity is an area in which Charlotte has been a national leader for more than 20 years. CCI houses a Center of Academic Excellence in Cyber Defense Education and Research as designated by the National Security Agency and Department of Homeland Security. Researchers are leading the investigation not only into how to keep governments and industries safe from unwarranted electronic intrusion but private homes as well.

Artificial intelligence, increasingly in the news, has wide-ranging applications from autonomous modes of transportation to affecting the boundaries of machine intelligence and human creativity. CCI faculty and students have created unmanned aerial vehicles that can survey and report a scene of a road accident, developed automated chatbots and conversational agents, and designed computer vision and analytics applications that improve customer experience at major retail stores.

Shaping What's Next, UNC Charlotte's 10-year strategic plan, lays out a new vision as an emerging top-tier global research university to support the fast-growing Charlotte region.

UNC Charlottes upward trajectory is a win-win for the region, said Gaber. As we have seen in cities across the U.S., top-tier public research universities drive innovation and supercharge businesses, helping to advance economic mobility in their regions.

Commercializing research

Charlotte is among national leaders in commercializing its research. Since 2018, University researchers have been awarded 87 new patents. According to the Association of University Technology Managers, Charlotte ranks in the top 10 nationally per research dollar spent for the number of new patents filed, new patents approved and new startup ventures formed.

Gaber noted greater support for Charlottes burgeoning research enterprise will enable the region to better compete against other metropolitan areas when it comes to attracting new industry jobs and business relocations and other investments.

About the NSF Higher Education Research and Development Survey

The Higher Education Research and Development Survey is the primary source of information on research and development expenditures at U.S. colleges and universities. The survey collects information on R&D expenditures by field of research and source of funds and also gathers information on types of research, expenses, and headcounts of R&D personnel. The survey is an annual census of institutions that spent at least $150,000 in separately accounted for R&D in the fiscal year.

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Charlotte research expenditures among top third in U.S., led by ... - Inside UNC Charlotte