Category Archives: Data Mining
Sainsbury’s refutes Which? claims that it is priciest supermarket – Retail Gazette
Sainsburys has slammed Which? after it named the grocer as the priciest supermarket for a big shop if you dont use a loyalty card.
The supermarket overtook Waitrose for the first time according to the consumer choice companys monthly analysis, while Asda remained the cheapest non-discounter supermarket, coming in 33.52 cheaper than Sainsburys.
Which? also compared the price of a basket of 39 items and found thatAldi was cheapest, with a total average cost of 67.72.
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Asda cost the least as the cheapest traditional supermarket. It cost 325.71, on average, for our big trolley shop, beating next-cheapest Morrisons (332.22) by 6.51.
The analysis includes discounts that are available to everyone, but not loyalty prices (where you need to be a member of the supermarkets loyalty scheme to get the discount) or multibuy offers.
In response to the Which? analysis, a Sainsburys spokesperson said: These claims are entirely false and insulting to the millions of savvy customers who choose to shop with us every week. There is an overwhelming amount of independently verified data showing the great value customers get when shopping at Sainsburys. We are disappointed that Which has refused to share its data with us and has instead chosen to mislead customers by choosing to exclude Nectar Prices promotions in its research.
The vast majority of our customers are shopping with Nectar Prices and have saved 400 million on their shopping in the last six months. Customers can be sure they getting great value every time they shop with us.
Sainsburys added that Which? cherry-picked 131 products and has refused to share with us which products are included.
It said it came cheapest including loyalty pricing for the last two weeks in a row in the independent Grocer33 basket, which includes five supermarkets in the survey and stressed that anyone can walk into our shops any day and swipe a Nectar card to enjoy the great value on offer, with an average of 5,000 products on offer via Nectar Prices.
This week Sainsburys revealed it would be doubling the number of in-store screens to provide the largest connected digital supermarket screen network.
The grocerspartnership with Nectar 360 and Clear Channel will place its own channel Sainsburys Live in front of customers, where it can tailor campaigns on display with location, weather, events and competitions.
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Sainsbury's refutes Which? claims that it is priciest supermarket - Retail Gazette
10 Must-Take Online Courses in October 2023 – Analytics Insight
10 must-take online courses in October 2023 offered by Coursera
In the fast-paced world of online education, staying abreast of the latest and most impactful courses is essential for continuous learning and professional development. As we step into October 2023, a myriad of online courses beckons eager learners across various domains. From cutting-edge technologies to personal development, these courses offer a wealth of knowledge and skill enhancement. In this compilation, we present the 10 Must-Take Online Courses in October 2023, curated to cater to diverse interests and career trajectories. Whether youre delving into the realms of technology, honing creative skills, or seeking personal growth, this list aims to guide you towards online learning experiences that promise to be both enriching and relevant in the current landscape.
1. Data Understanding and Visualization: This course offered by the University of Colorado Boulder via Coursera focuses on teaching students essential statistical concepts to effectively comprehend and analyze datasets.
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2. Computer Networking: Offered by the Illinois Institute of Technology via Coursera, this course covers the fundamental concepts of interconnecting devices for communication, including protocols, network hardware, and network services.
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3. Issues in Supply Chain Management: Provided by the University of Illinois at Urbana-Champaign via Coursera, this course explores global trade agreements, policies, barriers, and regulations related to agricultural commodities. It also discusses the implications of world and economic disruptions on the food supply chain.
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4. ChatGPT Advanced Data Analysis: Offered by Vanderbilt University via Coursera, this course aims to enhance productivity and support creativity by leveraging advanced data analysis techniques.
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5. Data Analysis with Python: Provided by the University of Colorado Boulder via Coursera, this course enables students to apply their skills in conducting real-life data analysis projects. It covers topics such as supervised and unsupervised learning, regression, clustering, dimension reduction, association rules, and outlier detection.
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6. Competitive Strategy: Offered by the Illinois Institute of Technology via Coursera, this course explores the factors that contribute to the success of companies in the 21st century, including the challenges posed by competition and the forces that drive it.
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7. Analysis and Interpretation of Large-Scale Programs: Provided by Johns Hopkins University via Coursera, this course is designed for implementers, managers, funders, and evaluators of health programs focused on women and children in low- and middle-income countries. It is also suitable for undergraduate and graduate students in health-related fields.
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8. Digital Business Strategy: Offered by the University of Virginia via Coursera, this course focuses on developing skills to design and implement strategies that will enable companies to thrive in the digital age.
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9. Machine Learning in Healthcare: Fundamentals & Applications: Provided by Northeastern University via Coursera, this course explores data mining methods within the context of healthcare. It covers the theoretical foundations of major data mining techniques and teaches students how to select and utilize the appropriate method for different scenarios.
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10. Cybercrime: Offered by Royal Holloway, University of London via Coursera, this course introduces the fundamental concepts of cybercrime. It covers topics such as the definition of cybercrime, key questions surrounding it, and methods for studying and defining cybercrime.
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10 Must-Take Online Courses in October 2023 - Analytics Insight
Data Mining Tools Market is Expected to Gain USD 2045.79 Million … – Benzinga
The significant Data Mining Tools Market report focuses on specific stock, currency, commodity and geographic region or country. This report is a great source of information for the major happenings and industry insights which is very valuable to thrive in this competitive age. Market research analysis and data lend a hand to businesses for the planning of strategies related to investment, revenue generation, production, product launches, costing, inventory, purchasing and marketing. The market report deeply analyses the potential of the market with respect to current scenario and the future prospects by considering several industry aspects. Thorough and transparent research studies conducted by a team work of experts in their own domain accomplish an international Data Mining Tools Market research report.
Data Bridge Market Research analyses that the data mining tools market is expected to reach USD 2045.79 million by 2030, which is USD 832.19 million in 2022, at a CAGR of 11.90% during the forecast period. In addition to the market insights such as market value, growth rate, market segments, geographical coverage, market players, and market scenario, the market report curated by the Data Bridge Market Research team includes in-depth expert analysis, import/export analysis, pricing analysis, production consumption analysis, and pestle analysis.
Get a Sample PDF of Data Mining Tools Market Research Report: https://www.databridgemarketresearch.com/request-a-sample/?dbmr=global-data-mining-tools-market
The growth in data generation, data mining and data storage across different organizations, such as banking, process manufacturing, marketing and ongoing digital transformation, will enhance the market growth during the forecast period. Data mining aids marketing companies to create prototypes based on past data to calculate appropriate results showcasing new marketing operations such as direct mail, online marketing operations, and others and ensuring marketers to have a precise method to provide personalized products to targeted customers.
Top Leading Key Players of Data Mining Tools Market:
Key Opportunities:
Data mining tools bring many profits to retail companies, through data analysis. These devices have a proper production and sales process that highlighting on repeated purchasing products instantaneously by consumers. Furthermore, it also increases retail corporations to provide discounts for products that will attract more and more consumers. Also, a rise in demand for artificial intelligence technologies and machine learning creates numerous opportunities for the growth of the marketcreates numerous opportunities for the market. Moreover, the surge in the need for embedded intelligence and the increasing requirement for generating insights from raw data to gain a competitive benefit are also anticipated to make major advantages in the market.
To Gain More Insights into the Market Analysis, Browse Summary of the Data Mining Tools Market Report@ https://www.databridgemarketresearch.com/reports/global-data-mining-tools-market
Global Data Mining Tools Market Segmentations:
Component
Service Managed Service
Business Function
Industry Vertical
Deployment Type
Organization Size
Data Mining Tools Market Country Level Analysis
The countries covered in the data mining tools market report are U.S., Canada and Mexico in North America, Germany, France, U.K., Netherlands, Switzerland, Belgium, Russia, Italy, Spain, Turkey, Rest of Europe in Europe, China, Japan, India, South Korea, Singapore, Malaysia, Australia, Thailand, Indonesia, Philippines, Rest of Asia-Pacific (APAC) in the Asia-Pacific (APAC), Saudi Arabia, U.A.E, Israel, Egypt, South Africa, Rest of Middle East and Africa (MEA) as a part of Middle East and Africa (MEA), Brazil, Argentina and Rest of South America as part of South America.
The country section of the report also provides individual market impacting factors and changes in market regulation that impact the current and future trends of the market. Data points like down-stream and upstream value chain analysis, technical trends and porter's five forces analysis, case studies are some of the pointers used to forecast the market scenario for individual countries. Also, the presence and availability of Global brands and their challenges faced due to large or scarce competition from local and domestic brands, impact of domestic tariffs and trade routes are considered while providing forecast analysis of the country data.
New Business Strategies, Challenges & Policies are mentioned in Table of Content, Request TOC: https://www.databridgemarketresearch.com/toc/?dbmr=global-data-mining-tools-market
Browse More DBMR Reports:
https://www.databridgemarketresearch.com/reports/global-digital-based-radiography-market
https://www.databridgemarketresearch.com/reports/global-digital-experience-platform-market
https://www.databridgemarketresearch.com/reports/global-discrete-semiconductor-market
https://www.databridgemarketresearch.com/reports/global-disk-encryption-market
About Data Bridge Market Research, Private Ltd
Data Bridge Market Research Pvt Ltd is a multinational management consulting firm with offices in India and Canada. As an innovative and neoteric market analysis and advisory company with unmatched durability level and advanced approaches. We are committed to uncover the best consumer prospects and to foster useful knowledge for your company to succeed in the market.
Data Bridge Market Research is a result of sheer wisdom and practice that was conceived and built-in Pune in the year 2015. The company came into existence from the healthcare department with far fewer employees intending to cover the whole market while providing the best class analysis. Later, the company widened its departments, as well as expands their reach by opening a new office in Gurugram location in the year 2018, where a team of highly qualified personnel joins hands for the growth of the company. "Even in the tough times of COVID-19 where the Virus slowed down everything around the world, the dedicated Team of Data Bridge Market Research worked round the clock to provide quality and support to our client base, which also tells about the excellence in our sleeve."
Data Bridge Market Research has over 500 analysts working in different industries. We have catered more than 40% of the fortune 500 companies globally and have a network of more than 5000+ clientele around the globe.
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COMTEX_441132040/2657/2023-09-28T08:21:40
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Data Mining Tools Market is Expected to Gain USD 2045.79 Million ... - Benzinga
What Is Diagnostic Analytics? (Definition, Examples) – Built In
Diagnostic analytics is a branch of analytics concerned with using data analysis techniques to understand the root causes behind certain data points. We use diagnostic analysis techniques to answer the Why did this happen? question when looking at historical data from a business, practice or process.
Diagnostic analytics is a form of root cause analysis that explores outliers in our data set and helps us understand why something happened. Organizations use diagnostic analysis techniques for a wide variety of applications including process improvement and equipment maintenance. If our sales dropped 15 percent between February and March, we can use diagnostic analysis methods to help us understand the cause behind the steep decline.
Basic to Basics on Built InWhat Is Data Analytics?
There are multiple ways a company or analyst can conduct an effective diagnostic analytics workflow.Heres an overview of the main methods we associate with diagnostic analytics.
Data drilling consists of performing deeper dives into specific data sets to explore and discover trends that are not immediately visible when looking at aggregated data.
For example, a business looking to understand how many hours its employees spend on manual tasks may start by obtaining a global table of all its people. They might then drill down by region, line of business or type of role to get a more granular (or a drilled down) sense of how manual work is allocated across the employee base.
There are several techniques and modern software available to do this effectively, from simple spreadsheets to more advanced data processing and visualization tools.
Mining data requires a deeper level of processing compared to data drilling, but its goal is the same to understand key patterns and trends. We typically associate data mining with six common groups of tasks through which we can reveal patterns.
Anomaly detection involves tasks targeting the identification of outliers or extreme data points in a vast set of data.
Dependency modeling targets the identification of specific associations between data points that may otherwise go undetected. For example, an electronics company may discover that customer reviews often mention Product A and Product B together and act on that information by placing those products together in a display.
These tasks segment data into similar clusters based on the degree of similarity across data points. Clustering could allow a beauty shop to determine similar groups of customers and advertise to them accordingly.
Classification tasks target the categorization of data points to recognize and classify future data points into specific groups. Classification allows cybersecurity software companies to analyze email data and separate phishing emails from harmless email content.
Regression tasks extract a function that models the relationship between data points according to a specific equation that captures the relationship between different variables at play.
Summarization tasks condense data for easier reporting and consumption while also avoiding the loss of more valuable, granular information we can use for clearer decision making.
More From Built In ExpertsWhat Does Linear Regression Tell You?
Correlation analysis is concerned with understanding and quantifying the strength of the relationship among different data variables in a given set of data points. Correlation is helpful in diagnostic analytics processes concerned with understanding to what degree different trends in the data are usually linked.
Correlation analysis is helpful as a preliminary step in causal analysis, which is a branch of statistics concerned with not only determining the relationship between variables but also the causal process between them.
For example, data may show that sales of pet food are strongly correlated with weather patterns, but it may not be the case that changes in weather cause changes in the level of pet food sales. Wed use causal analysis to answer the latter half of this question.
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Understanding specific processes and leveraging diagnostic analytics techniques to identify root causes is a key use case for this methodology across industries. Lets say were wondering why a particular step in a workflow or manufacturing process is taking longer than average. If we use some of the techniques laid out above, we can map the process from start to finish and gather enough data to answer the question. Diagnostic analytics can help us correct course and improve overall process performance.
The marketing funnel is the sequence of marketing activities that funnel customers, or potential customers, all the way from initial awareness down to product conversion. Understanding the marketing funnel and its data is of critical importance to help companies effectively allocate advertising budgets.
Diagnostic analytics around marketing initiatives are especially important at the early stages of a companys growth. These workflows support frequent iteration and feedback to direct the organizations next best action.
On That Note. . .What Is Marketing Analytics?
Most heavy industrial machinery generates data that informs its functioning and maintenance lifecycle. In this context, diagnostic analytics can help raise alerts regarding the health status of capital-expensive equipment before its too late, thus avoiding costly replacement orders or halting production lines.
We can use diagnostic analytics to study inter-company communication flows and understand whether certain departments are collaborating enough, which communication channels are most used (email, internal chats, video calls) and which employee roles contribute to the bulk of the communication flow. We can perform these analyses on anonymized, aggregated data so individuals are not identifiable. At the same time, the company can derive insights and put them to use to improve internal communication practices.
Descriptive analytics workflows are concerned with providing a historical view or summary of the data. Examples include sales reports and quarterly financial results released periodically by publicly traded companies.
Prescriptive analytics workflows are concerned with providing recommendations and suggesting the next best action to take in a given context. For example, Netflix movie recommendations delivered to the user are derived from prescriptive analytics techniques.
Predictive analytics is concerned with providing insights and forecasts into the future so the organization or data consumer can prepare for the most probable scenario. Time series forecasting and weather predictions are based on prescriptive analytics techniques.
With the above in mind, its easier to appreciate how diagnostic analytics techniques fit into the bigger picture of how we use data to achieve a variety of goals. Where other branches of analytics target what like questions, diagnostic analytics addresses why questions.
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What Is Diagnostic Analytics? (Definition, Examples) - Built In
How To Enable an Effective Business Intelligence Strategy – Software Advice
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In the modern digital age, businesses have access to more data than ever before. However, the data can be overwhelming if not managed properly. To prevent this, every business needs an effective business intelligence (BI) strategy designed to capture, process, and visualize all of the data thats available to them.
In the past, a majority of this analytical data capture and processing was solely the domain of IT departments. However, new tools and platforms are allowing this work to be done throughout an organization.
A Gartner report showed that 67% of the CEOs they polled want this type of technology work to happen within business functions and not just within IT. [1] That means a modern BI strategy must be accessible across departments and by various management and team members at all levels of operations.
Below, well outline the steps needed to create and implement a modern BI strategy that works for businesses of any size.
An effective business intelligence strategy is a series of methods and protocols for capturing critical data and processing that data to reveal key trends and opportunities. This is done through data mining and data visualization to allow executives and managers access to the data as well as to create their own queries.
One misconception is that BI is a tool mostly for large corporations due to its perceived complexity. However, this is not true, and often small businesses have the most gain from a BI strategy.
Small businesses are more sensitive and more at risk from damage caused by poor decisions, inefficiency, or quickly changing market conditions. Larger corporations can often weather sustained losses due to these events. But a small business has fewer resources to weather such situations. This means that being nimble and mitigating risk is something that a small business needs to prioritize, and a sound BI strategy allows them to do that.
Before implementing a business intelligence strategy, its important to consider the key areas where you need to be successful.
Not all organizations will require the same strategy. Some may be more focused on internal analytics while others may be more interested in competitor analysis to help them find areas where they can match or exceed others in the marketplace.
Setting the scope and goals for your strategy is critical to prevent data collection and analysis from becoming overwhelming and causing more harm than good.
By having a clearly defined scope and goal, targeting data collection can help provide much more accurate forecasting and actionable information.
Its also important for determining the ROI of your BI strategy and spending. Without knowing what areas of your business you want to improve through BI, its impossible to quantify the benefits for the purpose of ROI analysis.
Youll first need to choose an executive sponsor. This will be the person tasked with overseeing the strategy and ensuring that the various components stay on track during the implementation. This executive can also be the Chief Data Officer, or they can appoint one if different departments will be involved in reporting.
Youll also want to bring in managers and other department members who will be accessing the data. For implementation, this will likely be the responsibility of your IT department and assigning roles to key points of contact to various tasks such as platform selection, security, and deployment.
During this process, you want to find the tools and platform that best fit your strategy and goals. Start by comparing BI tools so you can understand any overlapping features, such as data visualization and other common modules, and any budget constraints.
Finally, youll want to determine if you are going to implement a traditional BI strategy or a self-service strategy. This will mostly depend on your internal business structure, such as whether you have an IT department capable of conducting a complex implementation. Youll also base this on your budget and goals for your BI strategy. Keep in mind that simpler goals and a smaller scope may only require a self-service approach.
By now, you should have everything you need to start to map out your implementation of both your strategy and your BI tools of choice. This involves things such as mapping out your data structure and preparing it for your strategy.
Each step in your roadmap should have a reasonable date or timeframe for completion and is accepted by other stakeholders. Make sure every step has a clearly defined milestone attached to it that signifies its completion and overall progress.
Launching your strategy will involve two key phases to ensure all data is being processed correctly and is accurate.
User acceptance testing (UAT): This first phase tests any data processing or transformations to make sure they are accurate and that the proper reports are being produced.
Training end users: This can be the individual managers or executives who will be interacting with the system. The software vendor you chose for your BI tools should offer training to assist with this step. You can also create your own training materials based on your specific environment.
Review your process to ensure your strategy is achieving the goals set out earlier. This can be a measure of ROI on your total BI spending compared to the goals. For example, if the goal was to reduce waste during shipping by 5%, that cost can be calculated and compared against your BI spending.
This final step is vital, especially for small businesses. Additional software expenditures can often be met with skepticism as they can add complexity and cost to your business without knowing if any results have been achieved.
By always measuring your spending and comparing it to the goals that you originally identified, it becomes easy to determine the ROI. This is also why setting your initial goals for your BI strategy is important, so make sure to be as specific as possible.
For most organizations, much of this implementation will be handled by their IT department. Its important that the IT staff understands who they report to, which is usually a member of the BI team.
Since the implementation will require new cooperation across the business, it should be clear how each department and the BI team fit into the overall organizational structure.
Security also needs to be a priority during this process. BI involves capturing and processing large amounts of data, some of which may be sensitive. Security and permissions need to be carefully written so that the flow of data is securely available to only those individuals and applications who need it.
Implementing a BI strategy may seem difficult, but as ABI becomes a core part of business operations, its vital that companies adopt a strategy that allows them to make informed decisions that lead to positive results with reduced risk.
By segmenting the BI implementation process and carefully outlining the goals and scope of your strategy, the entire process becomes easier.
During your BI strategy research, make sure to leverage online resources such as those offered by Software Advice to learn more about BI platforms. These resources help you compare BI software tools as well as look into reviews by other businesses that have had experience with them.
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How To Enable an Effective Business Intelligence Strategy - Software Advice
Learn And Live Easier With This Raleigh-Based Data Management … – GrepBeat
Earlier this year, the Federal Trade Commission (FTC) issued a proposed order against the popular online counseling service BetterHelp, Inc. for its involvement in data mining consumers sensitive health information for advertising. Despite promises that it would not disclose personal health data other than to provide counseling services, the FTC found that BetterHelp still revealed customers email and IP addresses and health questionnaire information to third parties such as Facebook and Snapchat to advertise their platform.
Surreptitious data collection and disclosure are issues that most people struggle with when looking to trust an application that will use their data to help them. According to a 2019 Pew Research study, about 81% of those studied felt that they had little to no control over the data that companies collect.
Thankfully, a Raleigh-based data management nonprofit with a public-facing app can lessen some of these worries and bring the control back to the user.
The Live Learn Innovate Foundation (LLIF) is a 501(c)3 nonprofit organization dedicated to offering unbiased database management for users to regain and maintain control of personal data, gain intuitive insights about their health and environment, benefit from personalized advertising and more. Their mission is to improve personal data management while further improving the general well-being of their users.
Founded in 2018, this nonprofit enables control of access and usage of its data through and by the user to serve them at their best interest, without keeping and/or reselling personal identifiable information (PII). Therefore, LLIF can ensure the data is only used in ways that align with their mission and values instead of relying on third-party services.
LLIF built and hosts the my.llif.org web application and now its Best Life mobile app, both of which provide members with a consolidated personal data pool, rooted in individual data ownership, safety and value. The applications help users track their lives in a private Facebook-like feed including vitals, environment changes, health issues, shopping, entertainment content and diary notes. Think of it as your personal everything journal, which can also help with preventative health measures and life planning.
The intent is for the Best Life app to be a revenue-generating arm to support LLIF, which will continue to be a nonprofit.
Chairman and Founder Jim French found it frustrating that there wasnt a proper way to track health data with or by medical professionals. He felt this frustration the most during and after his mother was diagnosed with Stage 3c colon cancer.
In the summer of 2019, she began showing low-level symptoms, which were mostly dismissed by doctors aside from prescribing an IV and some medication. She had a similar experience at her annual checkup with her primary care doctor, with her concerns being largely ignored rather than explored fully. The cancers wasnt diagnosed until later, after she began to suffer more severe symptoms, but by then the disease had spread.
Since her unproductive annual visit, French began keeping track of his mothers every symptom, heart rate, sleep score, etc. to make sure that such misdiagnoses never happen again. In December 2021, French and LLIF released the first production version of the Best Life app.
French and his son began logging his moms activities and data in the Best Life app using events for things they wanted to track, like medications, bowel movements, pain, food intake and more. With this app, they were able to see the latest real-time treatments and take appropriate action and communication to work together to help Frenchs mother.
Im more frustrated with our treat the symptom, not the problem reactive healthcare approach, French said in this blog post he wrote about his mothers treatment.I vowed that if LLIF could help to provide earlier detection for just one other mom, it would be worth my life savings and decade of my life.
Having something like a symptom diary is important and beneficial for not only the person using them, but also the healthcare providers that can use the data for their own research.
In a blog post written by RiAnn Bradshaw, LLIFs marketing director, a symptom diary or having something to keep track of your symptoms can provide a peace of mind for patients, while also providing a better understanding of the patient for the doctor. With an app like Best Life, patients can track anything from headaches and sleep to food and exercise.
People dont have to exist in pain constantly, Bradshaw said. There is not a doctor that has time to run analytics on your health data but [the data] is present, the capability to run analytics on them is there and you can solve a lot of problems that way.
The application can also be used in more ways than just tracking your health. LLIFs platforms can also be used to keep up with your pets health, the movies youve seen, the weather of the area youre in and more. With artificial intelligence, French said that LLIF will build correlated relationships between health and environment data, so that the user and eventually everyone in the community can benefit and learn from.
For example, if a user logs a migraine, the app can track backlogs to find out what caused it. The platform will check if there is a correlation between the migraine and every other event logged, like lack of sleep, amount of coffee, barometric pressure, change in weather and more, in a stack rank of most- to least-correlated.
I started looking at AI as a way of picking a method based on the data to do the correlation, and that can change over time, French said. I can look at this set of data and the AI can decide the best method to use to figure out patterns in that persons data.
Another part of LLIFs mission is to use the collective data as a direct advertising option for their users, without exposing any PII. The non-identifiable data logged by users can be used to help companies target their products more effectively and specifically. For example, a sunscreen company can target members based on the number of sunny days they experienced last year or a running shoe company can target based on the number of miles a member walks or runs.
With LLIF, your data is not sold to other companies but more so used to help these companies understand your issues and serve your best interest.
All of the data is in the nonprofits database and Best Life is paying LLIF to use that database, where that data is fully encrypted and anonymized before it leaves anything that has to do with that nonprofit, Bradshaw said. We built a profit product on top that allows people to actually access their data, consolidate everything, save it, use it across devices and have insights and analytics given to them that are not sponsored.
Best Life is currently available for free on Android and Apple app stores. The applications will be offered as a freemium, where users can have features for free and then subscribe to get an even more extensive list of features. With this app and more future data, LLIF hopes to build on the health data marketplace for experts, researchers and developers to build and use the extension to conduct clinical studies on more accurate and community-centered data.
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Learn And Live Easier With This Raleigh-Based Data Management ... - GrepBeat
Skidmore College may face potential lawsuits over data breach – The Daily Gazette
SARATOGA SPRINGS Following a data breach, Skidmore College is being investigated by at least two law firms, with at least one ready to file lawsuits against the college if it doesnt take steps to effectively remedy the situation.
Individuals affected by the breach, which occurred in February, were notified by letter dated Sept. 15 from Chief Technology Officer Dwane Sterling, that was obtained by The Daily Gazette Family of Newspapers.
In the letter the college said it was breached on Feb. 17 and upon discovering the breach took steps to contain and remediate the situation, including changing passwords and implementing new threat detection and monitoring software.
The investigation found that an unauthorized actor gained access to the Skidmore network before deploying ransomware that encrypted a small percentage of its faculty and staff file sharing system, the letter states.
A third-party data mining team was used to find out which individuals and information was affected, according to the letter. Peoples name, address and social security number were all impacted, the letter said.
There is currently no evidence that any information has been misused for identity theft or fraud in connection with the incident, the letter states.
The college said Wednesday part of the analysis they did included scanning the dark web and the college found that no data was there.
Shortly after the incident, Skidmore replaced both our security services vendor and the software that manages security on our environment on a 24/7 basis, the college said Wednesday.
William Federman, the lead attorney with Federman & Sherwood which is investigating the breach, said hes spoken to over a dozen people himself who were victims of the breach.
We know just by interviewing people that there were some employees, some students and former students, but we dont know the mix of the two yet, but were looking for people to reach out and talk to us about any problems theyve had, he said.
He said its still unclear exactly how many people were impacted by the breach, noting hes seen anywhere from 12,100 to 121,000, although he said the 121,000 could just be a typo in and that the college has not been forthcoming with additional information he has been seeking.
If it does shape up that Skidmore was negligent, they need to do more to remedy the problem, give an explanation of whose information theyre holding and why, he said. For instance, are they holding former students information from 1972? If so, why? Are they holding applicants information that never worked at the university? If so, why?
He said the college also needs to pay for damages.
A lot of people are having to take up a lot of their time to now protect themselves from the negligence of Skidmore, he said.
He said if the college doesnt want to try and remedy the situation it could face lawsuits.
The college said data security is one of their top priorities. Following the breach the college offered two years worth of an identity monitoring service to those affected.
Skidmore has and continues to encourage our community to leverage the security software that the school offers, including dual-factor authentication, to avoid sharing their accounts with anyone, and to be aware of and report potential phishing attacks, the college said. The College continues to review our security on a regular basis and is committed to making improvements as available technology permits.
Federman said people should lock their credit reports, monitor all of their credit cards, bank cards, bank statements, security brokerage accounts and get in touch with the IRS.
We would encourage everybody to get supplemental ID theft insurance, he said.
He also said people need to be careful with anyone trying to solicit stuff from them.
People may contact them pretending to be somebody they know and gather additional information on them which could lead to some significant problem, he said. Theres no easy way to say it, theyre going to have to spend the time to protect themselves because Skimore failed to do that.
Console & Associates also indicated on its website it is investigating the college.
Categories: Email Newsletter, News, Saratoga County, Saratoga Springs
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Skidmore College may face potential lawsuits over data breach - The Daily Gazette
Big Data Market to be Driven by the Increasing Demand for Data … – Digital Journal
The new report by Expert Market Research titled, GlobalBig Data Market Size, Share, Price, Trends, Growth, Report and Forecast 2023-2028, gives an in-depth analysis of the global big data market, assessing the market based on its segments like components, hardware, deployment mode, organisation size, application, end uses, and major regions.
The report tracks the latest trends in the industry and studies their impact on the overall market. It also assesses the market dynamics, covering the key demand and price indicators, along with analyzing the market based on the SWOT and Porters Five Forces models.
Get a Free Sample Report with Table of Contents https://www.expertmarketresearch.com/reports/big-data-market/requestsample
The key highlights of the report include:
Market Overview (2018-2028)
Historical Market Size (2020): USD 208 billion (Big Data and Business Analytics Market) Forecast CAGR (2023-2028): 10% Forecast Market Size (2026): USD 450 billion
Multiple businesses are switching to managing big data and exploiting information into developing business strategies and is driving profits on a global scale. Allied with technological advancements such as easily accessible internet and various interconnected devices, the demand for big data services has increased massively through these past years. Introduction of cloud computing has eased the storage of data market in the forecast period.
Industry Definition and Major Segments
Big data refers to large, diverse sets of data that are growing at an exponential rate. The volume of data, the velocity or speed with which it is created and collected, and the variety or scope of the data points covered are all factors to consider. Big data is frequently derived from data mining and is available in a variety of formats. Big data has three Vs: volume, variety, and velocity.
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The market is divided on the basis of component into:
SolutionServices
By hardware, the market is segmented into:
Storage Network Equipment Server Others
The market is bifurcated in terms of deployment mode into:
On-Premises Cloud Hybrid
The market is divided on the basis of organization size into:
Large Enterprises Small and Medium-Sized Enterprises
The market is segregated on the basis of application into:
Customer Analytics Operational Analytics Fraud Detection Compliance Data Warehouse Optimisation Others
The market is segmented on the basis of end use into:
Manufacturing Retail Media and Entertainment Healthcare IT and Telecommunication Government Gaming Energy and Power Engineering and Construction Others
The regional markets for the product include:
North America Europe The Asia Pacific Latin America The Middle East and Africa
Market Trends
As volume of data generated through various devices is growing at an exponential rate, the requirement for extracting value out of this data is the need of the hour. Introduction of cloud computing has eased the storage of data, making it more cost effective, flexible, and secure. Rising usage and penetration of internet in the developing countries is driving the market for big data in these regions at a fast pace. The emergence and adoption of IoT is also pushing the market forward.
Managing vast volumes of data and extracting value and business insights from the same is pushing business in unprecedented ways. The market for big data is expected to grow at a fast rate in emerging economies such as China in the forecast period.
Key Market Players
The major players in the market are IBM Corporation, Oracle Corporation, Microsoft Corporation, Hewlett Packard Enterprise Development LP, SAS Institute Inc., Amazon Web Services, and Accenture Plc, among others. The report covers the market shares, capacities, plant turnarounds, expansions, investments and mergers and acquisitions, among other latest developments of these market players.
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Big Data Market to be Driven by the Increasing Demand for Data ... - Digital Journal
William Woods to offer first STEM-based graduate program – Fulton Sun
William Woods Universityin Fulton will begin offering its first STEM-based graduate program.
The master of business analytics will teach graduate students skills such as processing and analyzing data, data mining and utilizing artificial intelligence to justify decision-making.
Miriam O'Callaghan, associate dean of research and scholarship at William Woods, said skills in artificial intelligence and data analytics "will be some of the most in-demand skills by employers in the very near future."
"As data and technology disruptions are transforming businesses at an exponential rate, the need for workers well-versed in data analytics and related fields will rise significantly," O'Callaghan said in a press release.
O'Callaghan also stated a skills-based program similar to the new master's program will "help graduates to 'future-proof' their careers."
The addition of the program comes a year after the launch of William Woods Global, an initiative "designed to help the University to better serve working adults by increasing overall access to online programs," a release states.
The new master's program will be offered fully online in an eight week term beginning this fall.
See more here:
William Woods to offer first STEM-based graduate program - Fulton Sun
30 Years of Data Science: A Review From a Data Science Practitioner – KDnuggets
30 years of KDnuggets and 30 years of data science. More or less 30 years of my professional life. One of the privileges that comes with working in the same field for a long time - aka experience - is the chance to write about its evolution, as a direct eye witness.
I started working at the beginning of the 90s on what was then called Artificial Intelligence, referring to a new paradigm that was self-learning, mimicking organizations of nervous cells, and that did not require any statistical hypothesis to be verified: yes, neural networks! An efficient usage of the Back-Propagation algorithm had been published just a few years earlier [1], solving the problem of training hidden layers in multilayer neural networks, enabling armies of enthusiastic students to tackle new solutions to a number of old use cases. Nothing could have stopped us just the machine power.
Training a multilayer neural network requires quite some computational power, especially if the number of network parameters is high and the dataset is large. Computational power, that the machines at the time did not have. Theoretical frameworks were developed, like Back-Propagation Through Time (BPTT) in 1988 [2] for time series or Long Short Term Memories (LSTM) [3] in 1997 for selective memory learning. However, computational power remained an issue and neural networks were parked by most data analytics practitioners, waiting for better times.
In the meantime, leaner and often equally performing algorithms appeared. Decision trees in the form of C4.5 [4] became popular in 1993, even though in the CART [5] form had already been around since 1984. Decision trees were lighter to train, more intuitive to understand, and often performed well enough on the datasets of the time. Soon, we also learned to combine many decision trees together as a forest [6], in the random forest algorithm, or as a cascade [7] [8], in the gradient boosted trees algorithm. Even though those models are quite large, that is with a large number of parameters to train, they were still manageable in a reasonable time. Especially the gradient boosted trees, with its cascade of trees trained in sequence, diluted the required computational power over time, making it a very affordable and very successful algorithm for data science.
Till the end of the 90s, all datasets were classic datasets of reasonable size: customer data, patient data, transactions, chemistry data, and so on. Basically, classic business operations data. With the expansion of social media, ecommerce, and streaming platforms, data started to grow at a much faster pace, posing completely new challenges. First of all, the challenge of storage and fast access for such large amounts of structured and unstructured data. Secondly, the need for faster algorithms for their analysis. Big data platforms took care of storage and fast access. Traditional relational databases hosting structured data left space to new data lakes hosting all kinds of data. In addition, the expansion of ecommerce businesses propelled the popularity of recommendation engines. Either used for market basket analysis or for video streaming recommendations, two of such algorithms became commonly used: the apriori algorithm [9] and the collaborative filtering algorithm [10].
In the meantime, performance of computer hardware improved reaching unimaginable speed and we are back to the neural networks. GPUs started being used as accelerators for the execution of specific operations in neural network training, allowing for more and more complex neural algorithms and neural architectures to be created, trained, and deployed. This second youth of neural networks took on the name of deep learning [11] [12]. The term Artificial Intelligence (AI) started resurfacing.
A side branch of deep learning, generative AI [13], focused on generating new data: numbers, texts, images, and even music. Models and datasets kept growing in size and complexity to attain the generation of more realistic images, texts, and human-machine interactions.
New models and new data were quickly substituted by new models and new data in a continuous cycle. It became more and more an engineering problem rather than a data science problem. Recently, due to an admirable effort in data and machine learning engineering, automatic frameworks have been developed for continuous data collection, model training, testing, human in the loop actions, and finally deployment of very large machine learning models. All this engineering infrastructure is at the basis of the current Large Language Models (LLMs), trained to provide answers to a variety of problems while simulating a human to human interaction.
More than around the algorithms, the biggest change in data science in the last years, in my opinion, has taken place in the underlying infrastructure: from frequent data acquisition to continuous smooth retraining and redeployment of models. That is, there has been a shift in data science from a research discipline into an engineering effort.
The life cycle of a machine learning model has changed from a single cycle of pure creation, training, testing, and deployment, like CRISP-DM [14] and other similar paradigms, to a double cycle covering creation on one side and productionisation - deployment, validation, consumption, and maintenance - on the other side [15].
Consequently, data science tools had to adapt. They had to start supporting not only the creation phase but also the productionization phase of a machine learning model. There had to be two products or two separate parts within the same product: one to support the user in the creation and training of a data science model and one to allow for a smooth and error-free productionisation of the final result. While the creation part is still an exercise of the intellect, the productionisation part is a structured repetitive task.
Obviously for the creation phase, data scientists need a platform with extensive coverage of machine learning algorithms, from the basic ones to the most advanced and sophisticated ones. You never know which algorithm you will need to solve which problem. Of course, the most powerful models have a higher chance of success, that comes at the price of a higher risk of overfitting and slower execution. Data scientists in the end are like artisans who need a box full of different tools for the many challenges of their work.
Low code based platforms have also gained popularity, since low code enables programmers and even non-programmers to create and quickly update all sorts of data science applications.
As an exercise of the intellect, the creation of machine learning models should be accessible to everybody. This is why, though not strictly necessary, an open source platform for data science would be desirable. Open-source allows free access to data operations and machine learning algorithms to all aspiring data scientists and at the same time allows the community to investigate and contribute to the source code.
On the other side of the cycle, productionization requires a platform that provides a reliable IT framework for deployment, execution, and monitoring of the ready-to-go data science application.
Summarizing 30 years of data science evolution in less than 2000 words is of course impossible. In addition, I quoted the most popular publications at the time, even though they might not have been the absolute first ones on the topic. I apologize already for the many algorithms that played an important role in this process and that I did not mention here. Nevertheless, I hope that this short summary gives you a deeper understanding of where and why we are now in the space of data science 30 years later!
[1] Rumelhart, D.E.; Hinton, G.E.; Williams, R.J. (1986). Learning representations by back-propagating errors. Nature, 323, p. 533-536.
[2] Werbos, P.J. (1988). "Generalization of backpropagation with application to a recurrent gas market model". Neural Networks. 1 (4): 339356. doi:10.1016/0893-6080(88)90007
[3] Hochreiter, S.; Schmidhuber, J. (1997). "Long Short-Term Memory". Neural Computation. 9 (8): 17351780.
[4] Quinlan, J. R. (1993). C4.5: Programs for Machine Learning Morgan Kaufmann Publishers.
[5] Breiman, L. ; Friedman, J.; Stone, C.J.; Olshen, R.A. (1984) Classification and Regression Trees, Routledge. https://doi.org/10.1201/9781315139470
[6] Ho, T.K. (1995). Random Decision Forests. Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, QC, 1416 August 1995. pp. 278282
[7] Friedman, J. H. (1999). "Greedy Function Approximation: A Gradient Boosting Machine, Reitz Lecture
[8] Mason, L.; Baxter, J.; Bartlett, P. L.; Frean, Marcus (1999). "Boosting Algorithms as Gradient Descent". In S.A. Solla and T.K. Leen and K. Mller (ed.). Advances in Neural Information Processing Systems 12. MIT Press. pp. 512518
[9] Agrawal, R.; Srikant, R (1994) Fast algorithms for mining association rules. Proceedings of the 20th International Conference on Very Large Data Bases, VLDB, pages 487-499, Santiago, Chile, September 1994.
[10] Breese, J.S.; Heckerman, D,; Kadie C. (1998) Empirical Analysis of Predictive Algorithms for Collaborative Filtering, Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (UAI1998)
[11] Ciresan, D.; Meier, U.; Schmidhuber, J. (2012). "Multi-column deep neural networks for image classification". 2012 IEEE Conference on Computer Vision and Pattern Recognition. pp. 36423649. arXiv:1202.2745. doi:10.1109/cvpr.2012.6248110. ISBN 978-1-4673-1228-8. S2CID 2161592.
[12] Krizhevsky, A.; Sutskever, I.; Hinton, G. (2012). "ImageNet Classification with Deep Convolutional Neural Networks". NIPS 2012: Neural Information Processing Systems, Lake Tahoe, Nevada.
[13] Hinton, G.E.; Osindero, S.; Teh, Y.W. (2006) A Fast Learning Algorithm for Deep Belief Nets. Neural Comput 2006; 18 (7): 15271554. doi: https://doi.org/10.1162/neco.2006.18.7.1527
[14] Wirth, R.; Jochen, H.. (2000) CRISP-DM: Towards a Standard Process Model for Data Mining. Proceedings of the 4th international conference on the practical applications of knowledge discovery and data mining (4), pp. 2939.
[15] Berthold, R.M. (2021) How to move data science into production, KNIME BlogRosaria Silipo is not only an expert in data mining, machine learning, reporting, and data warehousing, she has become a recognized expert on the KNIME data mining engine, about which she has published three books: KNIME Beginners Luck, The KNIME Cookbook, and The KNIME Booklet for SAS Users. Previously Rosaria worked as a freelance data analyst for many companies throughout Europe. She has also led the SAS development group at Viseca (Zrich), implemented the speech-to-text and text-to-speech interfaces in C# at Spoken Translation (Berkeley, California), and developed a number of speech recognition engines in different languages at Nuance Communications (Menlo Park, California). Rosaria gained her doctorate in biomedical engineering in 1996 from the University of Florence, Italy.
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30 Years of Data Science: A Review From a Data Science Practitioner - KDnuggets