Category Archives: Data Science
Clemson U Expands SAS Partnership to Deploy Data Science and Analytics Software for Teaching and Research – Campus Technology
Data Analytics
Clemson University is teaming up with data analytics company SAS to help build students' AI and analytics skills as well as support academic research. Since 2017, SAS has worked with Clemson's Watt Family Innovation Center to provide data analytics training, seminars and technical support; the latest partnership expands on that initiative with a $3.3 million donation that includes access to SAS Viya, the company's flagship artificial intelligence, machine learning, analytics and data management platform.
SAS Viya supports the analysis of large datasets, allowing researchers to transform raw data into powerful insights while significantly reducing data processing time, according to the company. Clemson researchers plan to use the technology to study areas such as inequities in education, wildlife disease, addiction, agriculture and the human genome.
For example, Brian Witrick, a fifth-year PhD student in Clemson's applied health research and evaluation program, is using SAS to analyze an enormous registry of people who suffer from peripheral artery disease and identify disparities in care. Data processing jobs that once lasted seven days now take just 30 hours with SAS.
"The practical implications of using SAS on this project are pretty amazing," said Witrick. "Utilizing SAS allows us to identify the problem and then create potential solutions and interventions."
In addition, SAS will provide Clemson faculty with teaching materials and on-site training to help integrate the technology into coursework and better prepare students for careers where data skills are in high demand.
"By integrating SAS into coursework, we're helping strengthen literacy in data science," explained Cynthia Young, founding dean of Clemson's College of Science, in a statement. "Our students become more competitive, our alumni are more successful, and our state and nation are getting more of a workforce that understands SAS and is better prepared to advance many industries as they harness the power of data."
"Clemson's partnership with SAS has greatly benefitted our faculty, staff and students," said Clemson President James P. Clements. "I am grateful for this gift that will continue to provide them with the best software and hands-on experience."
About the Author
About the author: Rhea Kelly is editor in chief for Campus Technology. She can be reached at [emailprotected].
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Analytics and Data Science News for the Week of April 7; Updates from Atlassian, Databricks, and Grafana Labs – Solutions Review
The editors at Solutions Review have curated this list of the most noteworthy analytics and data science news items for the week of April 7, 2022. In this weeks roundup, news from Atlassian, Databricks, Grafana Labs, and more.
Keeping tabs on all the most relevant data management news can be a time-consuming task. As a result, our editorial team aims to provide a summary of the top headlines from the last month, in this space. Solutions Review editors will curate vendor product news, mergers and acquisitions, venture capital funding, talent acquisition, and other noteworthy data science and analytics news items.
Using the technology from Chartio, a cloud-based data visualization and analytics solution that Atlassian acquired last year, they build Atlassian Analytics. The product offers a flexible hub that lets you connect to the Atlassian Data Lake and allows users to access data in a variety of ways. It touts out-of-the-box interactive dashboards, SQL visualization, no-code visualization, the ability to blend in data from other sources, and more.
Read on for more.
Delta Live Tables is the first ETL framework to use a simple declarative approach to build reliable data pipelines and to automatically manage data infrastructure at scale. It combines both modern engineering practices and automatic management of infrastructure. It simplifies ETL development by allowing engineers to simply describe the outcomes of data transformations.
Read on for more.
With this round of investment, Grafana Labs CEO and Co-founder Raj Dutt said, Our plans are simple: aggressively deliver on our product roadmap and our commitment to embracing the big tent enabling our users to compose and visualize data from any source while continuing to build out modern observability capabilities across metrics, logs, tracing, and more.
Read on for more.
Tinybird helps data teams deliver real-time answers at scale through analytical API endpoints. The product lets you ingest millions of rows per second from data streams, data warehouses, and CSV files. Users can also query and shape data using Pipes, a feature that lets you chain SQL queries. Tinybird is designed to reduce complexity without sacrificing performance as well.
Read on for more.
For consideration in future data analytics news roundups, send your announcements to tking@solutionsreview.com.
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Tim is Solutions Review's Editorial Director and leads coverage on big data, business intelligence, and data analytics. A 2017 and 2018 Most Influential Business Journalist and 2021 "Who's Who" in data management and data integration, Tim is a recognized influencer and thought leader in enterprise business software. Reach him via tking at solutionsreview dot com.
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Exposing cyberbullies with data analytics – The Star Online
WHEN he was still schooling, Richard Wijaya (pic) witnessed firsthand the impact of cyberbullying on his classmates.
What seared into his memory was the helplessness of the victims in the face of the attacks due to the online anonymity of the perpetrators, he shared.
That experience inspired the 22-year-old to look for a solution when he wrote his final year paper before graduating with a Bachelor of Science (Hons) in Computer Science with a specialism in Data Analytics from Asia Pacific University of Technology & Innovation (APU).
Titled Expose Cyberbullying Tweets Using Machine Learning: A Data Science Approach, his paper has won a bronze award at the Young Inventors Journal Paper Writing Competition 2021.
Organised by the Association of Science, Technology and Innovation (ASTI), the competition challenged participants to look at social well-being and mental health issues, and think of a viable solution.
Richard, whose paper will be published in the Young Inventors Online Journal soon, beat 46 full paper writers from the Philippines, Thailand and Singapore when the results were announced on Feb 10.
In finding the best approach to identify potential cyberbullies and reduce cyberbullying, Richard, who hails from Jakarta, Indonesia, leveraged his data analytics knowledge by comparing several machine learning and deep learning algorithms, namely, Nave Bayes, Support Vector Machine (SVM) and Long Short-Term Memory (LSTM).
One of the applications of data analytics is called text processing. Sentences can be preprocessed into a form eligible to train the machine learning algorithms and produce a cyberbully detection model, he explained in a press release.
The outcome of his research, he said, could help the relevant authorities to detect cyberbullying content and apply necessary actions such as warning the possible perpetrators.
The end product of this research will not simply be a predictive model, but will evaluate the performance of the predictive model using suitable evaluation measures, he added.
Having gained recognition for his work, Richard said a detailed understanding of a topic needs to be achieved to deliver outstanding performance.
Fundamentally, a deep interest in the domain chosen is important if someone is intrigued to do a certain topic. This will help to dig up critical information to write a better paper.
He also expressed his gratitude to his project supervisor Mafas Raheem from the School of Computing at APU, who encouraged him to submit his paper for the competition.
He helped me a lot, from brainstorming to constructing the paper. He also helped me find a solution if I had any doubt, said Richard, adding that the preparation for the competition took about three weeks.
I needed to prepare a proposal and a journal paper. There were some hurdles along the way as I had to restructure the paper to ensure its legibility with important details in it, he shared.
Currently pursuing a masters degree in big data analytics in Birmingham in the United Kingdom, Richard aspires to elevate his professional knowledge of data analytics, and to be able to generate more innovative ideas that could help to build better societies globally.
Commenting on Richards win, Mafas said: With the right operationalisation of his machine learning predictive model, Richards studies in this domain will support the authorities in performing early detection of cyberbullying attempts, thus contributing to social well-being and mental health.
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UC San Diego and Thermo Fisher Scientific Enter Innovative Strategic Partnership – University of California San Diego
Inside the new Technology Sandbox at UC San Diego: (from left) Peter Silvester (retired Senior Vice President and President, Life Sciences and Laboratory Products Group, Thermo Fisher Scientific); Kit Pogliano (Dean, UC San Diegos School of Biological Sciences); John Sos (Senior Vice President and President, Specialty Diagnostics, Thermo Fisher Scientific); Corinne Peek-Asa (UC San Diego Vice Chancellor for Research); Pradeep K. Khosla (UC San Diego Chancellor). Photo by Erik Jepsen/University Communications.
Two global leaders are joining strengths in a broad new effort to advance the discovery and innovation needed to address complex scientific challenges. The University of California San Diego and Thermo Fisher Scientific have agreed to form a transformational 10-year strategic partnership that leverages the leading expertise of each organization.
The unprecedented public-partnership will focus on achieving four key objectives, including the launch of a Technology Sandbox, establishment of a collaborative research framework, creation of a pipeline of underrepresented, next-generation talent that become tomorrows leaders in science, technology, engineering and math (STEM) and development of a sustainable supply chain.
The Technology Sandbox will enhance access to cutting-edge technologies and expertise to accelerate collaboration, discovery, technology transfer, workforce development and data analytics. It will serve students and alumni in tandem with a newly established collaborative research framework, designed to drive innovation in emerging areas of science including CRISPR, next-generation sequencing, mass spectrometry, cryo-electron microscopy (cryo-EM) and data science.
With the aim to build a pipeline of next-generation talent centered on equity, diversity and inclusion, the partnership will focus on training diverse students in advanced research methods and applied skills through education programs and employment opportunities. In alignment with UC San Diegos goal of achieving zero waste and Thermo Fishers ongoing commitment to sustainability, the partnership also will include development of a sustainable supply chain for research supplies and increased use of environmentally friendly alternatives to commonly used materials.
As a global leader in higher education, collaborative research and pioneering innovation, UC San Diego advances scientific discovery to improve the human condition and the planet, said UC San Diego Chancellor Pradeep K. Khosla. Our partnership with Thermo Fisher Scientific will enhance existing pathways to develop novel technologies, reduce our carbon footprint and train a more diverse and equitable workforce.
Partnering with academic institutions of UC San Diegos caliber is critical to the advancement of scientific knowledge and development of tomorrows innovative technologies that drive discovery, said Peter Silvester, retired senior vice president and president, Life Sciences and Laboratory Products Group, Thermo Fisher Scientific. We are excited to partner with UC San Diego in support of our mutual goals, which not only demonstrate our commitment to diversity and inclusion, but are also focused on attracting, inspiring and training an inclusive talent pool from around the world.
Under the agreement, Thermo Fisher will provide support for various research initiatives, scholarships and fellowships. The company also will collaborate with UC San Diego on community outreach programs, curriculum development and career mentorship. The ambitious breadth and scale of the 10-year agreement marks a significant shift from a traditional products-and-services arrangement into a collaborative, complete life-cycle business, research and innovation partnership.
The new UC San Diego-Thermo Fisher Scientific partnership capitalizes on the complementary strengths of each organization, combining UC San Diegos top-ranked biological, environmental and engineering programs with Thermo Fisher Scientifics position as a global leader in serving science, said UC San Diego Division of Biological Sciences Dean Kit Pogliano. The partnership will accelerate discovery, democratize technology, enhance workforce development and boost UC San Diegos position as the destination of choice for the best talent from around the world.
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Domino Data Lab Named One of The Americas’ Fastest Growing Companies 2022 by The Financial Times – PR Newswire
By helping enterprise data science leaders, practitioners, and IT teams transform their companies into model-driven businesses, Domino Data Lab continues to experience global market adoption
SAN FRANCISCO, April 7, 2022 /PRNewswire/ --Domino Data Lab, provider of the leading Enterprise MLOps platform trusted by over 20% of the Fortune 100, has been recognized as part of The Financial Times'list of The Americas' Fastest Growing Companies 2022. This prestigious award is co-presented by The Financial Times and Statista, Inc., the world-leading statistics portal and industry ranking provider.
The FT'slist of The Americas' Fastest Growing Companies is comprised ofenterprises that contribute most heavily to economic growth. The FT, in collaboration with Statista, ranks companies from across the Americas by compound annual growth rate (CAGR) in revenue between 2017 and 2020. Domino Data Lab ranks 108thon that list, which is currently available on the FT website. Out of the millions of active companies in North and South America, only 500 firms earned placement onthe list, and Domino Data Lab is proudto be recognized as the highest-ranking MLOps vendor on the list.
"We're grateful to our customers and our employees they are the fuel for our growth," said Nick Elprin, co-founder and CEO at Domino Data Lab. "Our mission is to unleash data science to address the world's most important challenges, and we have a lot more building and growing to do in pursuit of that mission."
In 2019, Domino Data Lab ranked 298thon Inc. Magazine's Inc. 5000list, the most prestigious ranking of the United States' fastest-growing private companies.In 2020, the company was named to Wing Venture Capital's Enterprise Tech 30 list.In 2021, Domino was named to Deloitte's Technology Fast 500 and Forbes' AI50 lists.And earlier this year, Forbes included Domino inits roundup of America's Best Startup Employers 2022. Over this time, Domino has emerged as the MLOps platform that global enterprises count on to scale their data science efforts and accelerate their path to a model-driven future.
Additional Resources
About Domino Data LabDomino Data Lab powers model-driven businesses with its leading Enterprise MLOps platform that accelerates the development and deployment of data science work while increasing collaboration and governance. More than 20 percent of the Fortune 100 count on Domino to help scale data science, turning it into a competitive advantage. Founded in 2013, Domino is backed by Sequoia Capital and other leading investors. For more information, visit dominodatalab.com.
SOURCE Domino Data Lab
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How data and AI are changing the world of education – Microsoft
Why data and AI are the next step in education
Digital systems around the world generate a staggering 2.5 quintillion bytes of new data every day.1 While this information is generally stored in large data silos where it can be easily accessed by users, industries have been harvesting their data for years to make themselves more efficient and effective.
Artificial Intelligence (AI) enables data holders to transform a passive resource into a powerful catalyst for accelerated growth. For instance, when the state of Nebraska realized that it was spending approximately 655,000 staff hours per year to collect data reports from every school in the territoryan effort that yielded surprisingly few benefitsthe Nebraska Department of Education set out to build a Statewide Longitudinal Data System that would allow information to flow in near-real-time from hundreds of sources and deliver actionable insights to state, district, and school leaders; administrators; and educators.
Despite the noticeable positive impact, until recently, the education sector had been relatively slow to embrace digitalization and the use of data and AI to accelerate learning. However, COVID-19 created an urgent need for education systems to use their data to gain visibility into who was engaging in remote learning and where education was taking place. Education leaders the world over were motivated to take decisive action and schools began to make the transition to online learning as quickly as possible.
Throughout the COVID-19 response we understood clearly the importance of having data in order to measure the impact of this unprecedented disruption to education, observed Stefania Giannini, Assistant Director-General for Education at UNESCO.
Today, many institutions have settled into a blended or hybrid learning model and want to see what other benefits their digital framework can offer. Using cloud technology enables them to gain visibility and accelerate the impact of teaching and learning systems.
Education Insights, a feature in Microsoft Teams for Education, is a great example of this. It uses data analytics to keep educators informed of students engagement, learning progress, and well-being. A wide range of built-in digital apps and tools allows teachers to interact with learners on the platform and gain an overview of how well they are progressing, both at the class and individual levels.
One such tool is Reading Progress, a literacy solution that enables students to record themselves while reading aloud. The program makes a note of all the words that are challenging to the reader and provides visual aids and additional reading exercises to help them improve. Best of all, Reading Progress saves teachers hours of time spent evaluating students one at a time. It also allows teachers to take a personalized approach to teaching by addressing each students needs individually.
Given the power and centrality of literacy in conferring future outcomes, we are very proud of Reading Progress, and we are excited to continue to build on it and do even more, said Steve Liffick, Vice President Modern Life and Learning.
Among the key benefits of cloud technology are that it allows institutions to retain full ownership of their student data as well as receive expert support from partner technology companies on how to integrate security protocols and create governing policies around that data. Last year, UNESCOs member states adopted the first-ever global agreement on the ethics of AI. The document outlines a framework for the ethical use of AI including a chapter that is specifically focused on the ethics of AI in education.
UNESCO has been at the forefront of the international response to the global education crisis since the beginning, launching the Global Education Coalition in early 2020. The platform brought together more than 175 members from the UN family, civil society, academia, and the private sector to protect the right to education during the pandemic and beyond. Members are united under the coalitions three flagships: connectivity, teachers, and gender. Weve noticed that in many countries girls have been left behind, said Stefania. Filling the gender gap is something that UNESCO has been focusing on since the beginning of the pandemic.
In order to ensure that all children are able to receive the benefits of education analytics and AI, all children have to participate in digital learning observes Paige Johnson, Vice President, Microsoft Education Marketing. As long as some children are still operating in the analog world, you risk creating Big Data systems that leave those children out of the thinking and the work.
Another aspect to consider is that in order to benefit from big data systems, all students must take part in digital learningotherwise, education leaders run the risk of excluding certain learners from the data and the solutions such learning enables. This is why equipping every student with a digital device is the first step to implementing a successful data and AI strategy.
Helsinki was the first capital city to recognize the importance of having a digitalization strategy for education. In 2016, city officials set out to make Helsinki the most impactful place for learning in the world. Working with Microsoft, the citys Education Division used Azure to build a powerful AI hub capable of enhancing teaching and learning across a wide range of pedagogical use cases. The teams primary focus was to create a personalized learning experience for each of their students, all while improving learning outcomes and placing an emphasis on well-being.
Open Education Analytics is an open source program created by Microsoft to support every education systems unique journey with data and AI. The program was launched as a response to the urgent need for visibility into what was happening with education systems at the onset of the pandemic, especially as those systems moved more and more to digital learning platforms. We realized in that moment that we needed to accelerate our support for our customers data and AI journeys, said Maria Langworthy, Principal Program Manager at Microsoft.
The four components that make up the Open Education Analytics program are a set of open-source technical resources, a comprehensive curriculum on data engineering and data science training, Microsofts principles for responsible AI, and a global community of education systems developing shared use cases.
Each pillar is designed to solve specific challenges to digitalization and to empower education systems to navigate their way forward. As Maria noted, Its not just data and a lot of dashboards. Its about how you use this data to make better decisions, to better utilize your resources to really push learning progress.
Our goal with Microsoft Education Data and AI programs is to meet every education system where they are today and to help them move forward, [in order] to better leverage the data that they have using our modern data and AI services, said Louise Macquet, EMIS Cloud Business Development Lead, Microsoft MEA.
The Education Management Information System (EMIS) is designed to enable education systems to effectively collect, store, manage and report their data.
It does this through an open-source common data model for education that provides systematic consistency for data and supports education systems to develop applications and integrators more quickly to operate across multiple systems more easily. "The common data model was developed by Microsoft and founding partners to eliminate data silos for a connected engaged platform producing efficient and real-time data," explained Louise.
In order to empower education leaders across the world to accelerate the digital transformation of their systems and achieve meaningful impact in education, Microsoft created the Leaders in Digital Transformation of Education program. Join the program today and sign up for the Data and AI Accelerator to harness the power of your student learning data in real time for optimal results.
1https://techjury.net/blog/how-much-data-is-created-every-day/#gref
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How data and AI are changing the world of education - Microsoft
Imperial and France’s CNRS to lead data revolution with joint research centre – Science Business
A joint research centre to tackle some of the worlds biggest challenges has been launched by Imperial College London and Frances National Centre for Scientific Research (CNRS).
Leading British and French scientists will work together at the CNRS-Imperial International Research Centre for Transformational Science and Technology (IRC) on a broad range of research areas and major European-funded projects.
Powered by the two prestigious institutions' world-leading expertise in mathematics and data science, the partnership will work in areas such as artificial intelligence, climate change, physics, material sciences, medicine and chemistry.
The Centre the first of its kind to be launched by the CNRS in the UK or Europe will also see the two institutions collaborate on early-career researcher training such as PhD programmes, research funding and proposals including with Horizon Europe, as well sharing facilities, laboratories, infrastructure and data.
Both institutions have also committed to focus collaborative impact on the UNs Sustainable Development Goals (SDGs) and to new science and technology partnerships across the globe, including linking CNRS and Imperial research units working across Sub-Saharan Africa, Latin America and the Indo-Pacific region.
The IRC will bring together some of the most celebrated academics in mathematics including Fields Medal winner Professor Sir Martin Hairer.
Imperial College London President Professor Alice Gast said: This partnership demonstrates Imperials commitment to strengthening our ties with Europe through improving mobility and providing opportunities for researchers to exchange ideas.
The CNRS-Imperial International Research Centre will bring about the highest level of collaboration between French and British scientists.
Professor Gast added: We hosted the UKs first joint mathematics laboratory with the CNRS and are now even prouder to be partnering in their first International Research Centre in the UK and Europe.
Working together we will make an impact globally for the public good in sustainability, health and resilience.
Antoine Petit, Chairman and Chief Executive Officer of the CNRS, said: We celebrate the launch of this International Research Center (IRC), created jointly with Imperial College London. It is the first of its kind in the UK and in Europe, and only the second one in the world.
It will be a creative hub of cross-disciplinary collaboration and a solid platform for building new scientific projects between the CNRS and Imperial, addressing major challenges for the benefit of society. The Centre will also give even greater momentum to the long-term strategic partnership between our two institutions, acting as a bridge of knowledge and learning between France and the UK.
The CNRS is Europes top institution in terms of receiving European Union funding and its number of European Research Council grant awards.
New joint PhDs announced. Imperial and the CNRS also announced the latest joint PhD projects to be funded by the two institutions.
Professor Richard Craster, Dean of the Faculty of Natural Sciences at Imperial and Director of the Abraham de Moivre International Research Laboratory (IRL), said: Since we launched our joint PhD programme with the CNRS, our students have contributed to advancing our research understanding in areas such as mathematics, physics, computing and aeronautics.
"We see great opportunities to further grow our research connections and develop the next generation of outstanding academics. These new PhD collaborations will focus on digital transformation and finding solutions to some of the most pressing global challenges.
Alain Schuhl, Chief Science Officer of the CNRS, said: PhD candidates will be, of course, major players in the IRC, which will provide a great environment for them. The PhD Joint Programme with Imperial reflects the excellence of our partnership. Once again, we were impressed by the quality and diversity of the proposals we received. The five projects that were selected this year span chemistry and physics. The 10 PhD candidates (five at CNRS and five at Imperial) will reinforce the collaboration and develop new axes of cooperation within the IRC.
The CNRS-Imperial PhD joint programme will soon have a cohort of over 30 PhD student and 30 Academic Co-Leads working on major collaborative projects that will benefit UK, French and European science.
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Imperial and France's CNRS to lead data revolution with joint research centre - Science Business
5 reasons no-code platforms are the new realities in data science and AI – ESBO – Bizcommunity.com
With the rapid advancements in data science and artificial intelligence (AI), businesses are finding new and innovative ways to optimise their operations and improve customer experience. However, many companies are struggling to keep up with these changes due to the lack of technical expertise within their workforce.
1. Increased productivity and efficiency
No-code platforms allow business users to quickly create and deploy custom applications without any help from developers. This leads to increased productivity and efficiency as business users are able to get their applications up and running in a fraction of the time it would take for them to code it themselves.
2. Increased innovation
With no-code platforms, businesses can quickly create and deploy custom applications that address specific needs and requirements. This leads to increased innovation as businesses are able to quickly respond to changes in the market and create new applications that meet the ever-changing needs of their customers.
3. Cost-efficiency
No-code platforms are typically more cost-effective than traditional coding solutions as they require less investment in terms of time and money. This is because no-code platforms often come with pre-built templates and modules that can be quickly deployed without the need for costly development resources.
4. Increased accuracy
No-code platforms often come with built-in validation checks that help ensure the accuracy of data entry. This leads to increased accuracy as businesses are able to avoid errors that can occur when manually coding applications.
5. Increased flexibility
No-code platforms offer increased flexibility as they can be easily modified to meet the changing needs of businesses. This is in contrast to traditional coding solutions which can be difficult and time-consuming to change.
Overall, no-code platforms offer a number of advantages over traditional coding solutions. They are more productive, efficient, cost-effective, and accurate. They also offer increased flexibility, which makes them well-suited for businesses that are looking to quickly create and deploy custom applications.
The limitations of no-code platforms
Although no-code platforms offer a number of advantages, there are also some limitations to consider.
Limited to its use-case
You can do anything with code. With no-code systems, you can only build solutions and applications that it was built for. So make sure a no-code you pick is suited for the use case you need.
Integrations
Another limitation of some no-code platforms is that they can be difficult to integrate with other systems. This is because certain no-code platforms often use proprietary formats and data structures which can make it difficult to exchange data with other systems. When selecting a no-code platform make sure to consider how well it integrates with other systems.
Customisation
Finally, no-code platforms can be inflexible when it comes to customizations. This is because certain no-code platforms use rigid templates and modules which can be difficult to modify. When selecting a no-code platform make sure to consider how well it can be customized to meet the specific needs of your business.
In conclusion, no-code platforms offer a number of advantages over traditional coding solutions. They are more productive, efficient, cost-effective, and accurate. They also offer increased flexibility, which makes them well-suited for businesses that are looking to quickly create and deploy custom applications. However, no-code platforms do have some limitations including being limited to its use-case, difficulty with integrations, and inflexibility when it comes to customisation. Despite these limitations, no-code platforms are a powerful tool that can be used to quickly create and deploy custom applications.
The advantages of no-code platforms have made them increasingly popular in recent years as businesses look for more efficient and cost-effective ways to create custom applications. However, there are also some limitations to consider when selecting a no-code platform.
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5 reasons no-code platforms are the new realities in data science and AI - ESBO - Bizcommunity.com
"We’re just at the start of AI" | ING – ING
For many, the field of analytics, which includes futuristic technologies like artificial intelligence (AI), may sound a world away, but the reality is organisations are busily adopting it for their business to become faster and smarter. So, what are the big trends in analytics in 2022 and how are these going to impact our banking experience? Kerem Tomak, INGs chief analytics officer, looks at the five biggest trends at the moment and what they mean to you.
Lets face it. An AI-enabled future will look much different to the world we know now. Within five years, automated driving will probably be the norm and the first smart cities will have been built. Were just at the start of AI. The biggest challenge now for organisations is finding people, like data scientists, that are capable of using the available technology. Were in the middle of a war on talent. It started 10 years ago and should go on for the next 10 years.
Data scientists want to join companies where they can work on innovations and solutions that make an impact. ING is no different. We have about 250 data scientists working on advanced analytics, and this number is growing quickly.
This year, well see the continuation of citizen data science, where AI is no longer the exclusive domain of mathematicians or statisticians. More and more people are able to use analytics without having to apply complex techniques to the data. Algorithms are being automated, so you dont have to build or code them yourself. Instead, you need to understand and interpret the results to help businesses make better decisions.
ING, for example, offers data science training to all of employees, helping them use analytic insights while catching up with emerging trends.
Companies are now quick to realise that you cannot be digital and data-driven without experimentation. Running algorithms to make certain predictions is not enough. You need to be bold, experiment often, and not be afraid of failing. The Big Tech have strongly embedded this in their culture. For instance, Amazon runs thousands of online experiments, so their pricing algorithms are tuned towards how people interact with their website.
At ING, we use experimentation by involving the end user in all stages of product development to gather insights and adjust products if necessary. The closer we are to customers the better we understand their needs.
Most of us have heard about the cloud, and how its used for storing mobile device data. Our mobiles would simply be too slow if all information was stored on the device itself. The same goes for organisations that deal with huge quantities of data and require significant computing power to meet customer needs. Using cloud technology is necessary to become faster and more agile.
At ING, weve adopted a hybrid cloud setup for analytics and AI. Connecting our private cloud to public cloud technology allows us to scale up and improve our digital capabilities in a safe and secure way. We will be able to offer customers better, faster and more personalised experiences.
With many customers now doing banking on their mobile device, banks are increasingly using edge cloud technology to respond more quickly. Here, data processing happens close to the device, which allows applications to run in real time.
Customers rely on organisations more than ever to keep their data safe and protected. Since cybersecurity is an increasing area of concern, there is a need to develop solutions to anticipate cybercriminals attempts to obtain sensitive information. AI can be used to develop models and products that fight financial economic crime. For instance, applying machine learning techniques to transactions data can effectively detect potential risks, putting banks one step ahead of money launderers and safeguarding customer accounts from malicious acts.
In addition, theres an increased emphasis on using data and analytics models in an ethical way. Modelers, for example, need to be aware that there are certain biases in the data. At ING, we provide guidance on the use of models, ensuring they are used in the right way.
The use of the terms analytics and AI can be confusing at times. So, what do they mean and how do they relate? At ING, we define data analytics as the process of analysing and making sense of data. By using analytics, we can get insights that allow us to offer customers better personalised experiences always taking their privacy into account. It can also make banking more efficient, reducing repetitive tasks, time and costs, as well as mitigating risk and preventing fraud. AI is software that is used widely in analytics and enables computers to simulate human intelligence. AI is automated, so the software learns by itself from vast amounts of data. It enables us to make smarter decisions and do things more efficiently and effectively.
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Top 10 Deep Learning Python Courses to Take Up in 2022 – Analytics Insight
Deep learning Python is trending in the global tech market to transform a business in 2022
Deep learning and Python are gaining huge popularity among aspiring techies as well as working professionals. Deep learning (DL) is known as a class of machine learning algorithms for feature extraction and transformation. Meanwhile, Python is one of the popular and trending programming languages across the world for developers. Thus, the combination, deep learning Python, is thriving in the global tech market in recent times with different best deep learning courses in Python. There are multiple courses on deep learning Python to gain a deep understanding of the concepts before entering a professional career. Machine learning in Python and programming language courses are available on multiple educational platforms in recent times. Machine learning in Python is becoming important to transform businesses with digital transformation. Thus, lets explore some of the top ten deep learning Python courses in 2022 to enroll.
Duration: 4 hours
Datacamp offers one of the top deep learning Python courses to learn the fundamentals of neural networks and build models with Keras 2.0. This course on deep learning Python consists of 17 videos and 50 exercises with hands-on knowledge through a cutting-edge library.
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Duration: 25 hours
One of the best deep learning Python courses is set to offer a deep knowledge of multiple features regarding debugging, software programmers, language skills, pattern designing, and many more. This course in deep learning Python is focused on providing optimizing a simple model in pure Theano, enhancing generalization with data augmentation, and many more with 20 hours of lab.
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Duration: 57 hours 17 mins
One of the udemy deep learning Python courses helps with an experimental scientific approach through architectures of feedforward and convolutional networks, calculus and code of gradient descent, fine-tuning deep network models, programming language Python, and many more. there are 265 lectures with 32 sections for students who have sufficient knowledge of a programming language.
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Duration: 15 hours 36 mins
This course on deep learning Python offers a complete hands-on machine learning tutorial with data science, artificial intelligence, and neural networks. The curriculum includes building artificial neural networks with TensorFlow and Keras, classifying data, programming languages such as Python, and many more. There are 115 lectures with 13 sections including machine learning with Python, neural networks, and so on.
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Duration: 14 hours 9 mins
Udemy offers a complete guide to TensorFlow for Deep Learning with Python to learn how to solve complex problems with cutting-edge techniques. Students can have a deep understanding of how neural networks work, and the process of building their own neural network from scratch with the programming language, Python, and many more. There are 96 lectures with 13 sections covering all the concepts and mechanisms with hands-on practical knowledge.
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Duration: 2 hours
Coursera offers one of the top deep learning Python courses to make students understand the concepts behind convolutional neural networks with TensorFlow 2.0. This is a project-based course with eight different tasks including building a model with a trending programming language, Python.
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Duration: 8 hours
Great Learning provides a course on deep learning Python with explanations and an introduction to the TensorFlow library of the Python programming language. This course includes the hands-on session on regression with TensorFlow and the Keras framework.
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Duration: 35 hours
This machine learning in Python course is known for focusing on techniques and methods of statistics. The course starts with the discussion of how machine learning is different from descriptive statistics, more advanced techniques, scikit learn predictive models and many more.
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Duration: 2-4 weeks
This machine learning in Python course provides hands-on Python tutorials with machine learning applications. This programming language is needed to build machine learning systems with hands-on tutorials including code and real-world datasets.
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Duration: 40 hours
The machine learning in Python training course is focused on covering the curriculum consisting of 16 modules. These modules include convolutional neural networks, reinforcement learning, programming language, training models, and many more.
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Top 10 Deep Learning Python Courses to Take Up in 2022 - Analytics Insight