Category Archives: Data Science
Oliver Wyman has strengthened its Asian partnership with two new partners: Ilia Dub and Jasper Yip.
Effective the 1st of January 2021, Ilia Dub is a partner in Oliver Wymans Digital practice, based in Singapore. He brings over a decade of experience serving clients at the intersection of business strategy, data and technology to the partnership, with a focus on digital transformations, data management, data science and agile delivery of technology solutions.
Dub also advises CIOs and CTOs on emerging technologies such as artificial intelligence, machine learning, cloud and internet of things. In his partner role, he leads an interdisciplinary teams of consultants, designers, data scientists and software engineers working across Asia.
Based in Oliver Wymans Hong Kong office, Jasper Yip has been promoted to partner in the Financial Services practice. He covers clients across Greater China and Asia Pacific with a focus on institutions in the capital markets, wealth/asset management and fintech space.
Yips consulting experience spans strategy and business model design, digital transformation, organisation and governance design, operating model optimisation and financial management. Most recently, he supported a financial institution with the formulation of its China onshore strategy and operation plans.
In last years annual promotion round, Oliver Wyman promoted seven new partners based in Asia.
Poor data flows hampered governments Covid-19 response, says the Science and Technology Committee – ComputerWeekly.com
Poor data flows and a failure to capitalise on UK strengths in data science have bedevilled the governments response to the Covid-19 pandemic, a House of Commons Science and Technology Committee has found.
The committees 92-page report, The UK response to Covid-19: use of scientific advice, published 8 January, is focused on how the government has obtained and made use of scientific advice during the pandemic.
It notes that the remarkable achievement of developing and being in a position to deploy multiple vaccines against a deadly and virulent virus that was completely unknown a little over a year ago ranks as one of the most outstanding scientific accomplishments of recent years.
It recollects that the first two cases of Covid-19 were confirmed in the UK, in England, on 31 January 2020, less than a year ago. The first death from Covid-19 in the UK, in England, was announced on 5 March. As of 18 December, the total number of deaths since then, where Covid-19 is mentioned on the death certificate, is 82,624. On 06 January, another 1,041 deaths were reported.
The committee, chaired by Conservative MP Greg Clark, said in its report: A fully effective response to the pandemic has been hampered by a lack of data. For a fast-spreading, invisible, but deadly infection, data is the means of understanding and acting upon the course of the virus in the population.
The early shortage of testing capacity restricting testing only to those so ill that they were admitted to hospital had the consequence of limiting knowledge of the whereabouts of Covid-19. The ONS Infection survey did not begin until May, and the fragmentation of data across public organisations has impeded the agility and precision of the response.
The report laments the failures in data management in the governments response to the pandemic, and notes these are all the more damning given a national comparative advantage in the field.
Given the UKs strengths in statistical analysis and data science, it is regrettable that poor data flows, delays in data-sharing agreements and a general lack of structuring and data integration across both the health and social care sectors have throttled timely data sharing and analysis.
For example, it is unacceptable that detailed public health data was only made available to modellers from March. The potential consequences of this will undoubtedly include slower and less effective decision-making.
It finds solace in the establishment of the Joint Biosecurity Centre as an effort to centralise data flows to manage the pandemic, but notes it is unfortunate that no central mechanism to coordinate data was in place at the start of the pandemic.
The committee exhorts the Department of Health and Social Care (DHSC) to set out an action plan that describes what efforts have been made, and will be made, during the pandemic to address the poor data access issues raised by the scientific community and Sage [the Scientific Advisory Group for Emergencies] and its sub-groups.
This plan should, said the report, cover agreements and incentives for data sharing and data integration across the health and social care sectors and across the four nations of the UK.
The early shortage of testing capacity restricting testing only to those so ill that they were admitted to hospital had the consequence of limiting knowledge of the whereabouts of Covid-19 The Science and Technology Committee
The report points out that the line between advice and decision-making was tested on one signally important occasion, when the Prime Minister announced plans for a second stay at home order on 31 October.
Although the chief medical officer and government chief scientific adviser presented modelling data at the press conference alongside the Prime Minister, the data underlying this was only made public three days later and was subject to extensive criticism, including that the data was out of date, it added.
More positively, the report stated: The Office for National Statistics [ONS] is now conducting a very important sampling exercise in which data on the prevalence of Covid-19 in the UK population will be gathered and reported twice-weekly.
It is of great importance in providing data on the spread of diseases, its impact on the different demographic groups and geographies, the incidence of asymptomatic transmission and even the reproduction or R number which the government has made key to easing some social distancing restrictions.
In evidence to the committee, the national statistician, Ian Diamond, gave an impressive account of the speed in which his team had been able to organise and implement a significant testing programme.
The report quotes Diamond as having said: The fact that we came into it on a Thursday and, with the University of Oxford, put together the design and protocoland put it to medical ethics the following Monday and data ethics on Tuesday, with letters out to potential participants on the Wednesday, seems to me to be one of the most rapid surveys I have ever in my life seen go into the field.
However, he also told the committee that the request to put together such a testing programme was made only on 17 April 2020.
It was also drawn to the committees attention that data on the ethnicity of those dying from Covid-19 was not systematically collected.
The committee is recommending that government should consider how ethnicity data on those dying as a result of Covid-19 could be systematically recorded, and it notes that there are significant unexplained differences in the death rates in the UK of Black, Asian and minority ethnic [BAME] groups compared to the population as a whole.
The report also brings out a structural over-emphasis on epidemiological data, as opposed to broader data about the impact of the pandemic on the economy, mental health and other areas.
The report adduces public comments made by Mark Woolhouse, a professor and one of the epidemiologists advising the Scientific Pandemic Influenza Group on Modelling (SPI-M) and the Scottish Government Covid-19 Advisory Group, that he thought scientific advice was driven far too much by epidemiology.
Speaking to the committee in June, Woolhouse said: In the early stages of the epidemic, before we had large amounts of [public health] data, [advice] was largely on the basis of modelling, and that is all right and proper and as it should be, but we are looking literally at only one side of the equation when we do that.
He suggested, according to the report, that the other side of the equation included the harms done by lockdown, including impacts on mental health and social wellbeing, the education of our children, and our economy.
The report noted: While the experience of no country is perfectly comparable with others, it will be important to understand the reasons for [comparatively poor performance in relation to peer nations] to learn lessons for the future.
In this report, there are questions of how quickly scientific analysis could be translated into government decisions; whether full advantage had been taken of learning from the experience of other countries; and the extent to which scientific advice took as a given operational constraints, such as testing capacity, or sought to change them.
For any emergency situation, data systems need to be in place up front to be able to give the information to make the analysis and make the decisions Patrick Vallance, Government Office for Science
Patrick Vallance, the governments chief scientific adviser, told the committee, in registering the importance of data: One lesson that is very important to learn from this pandemic, and for emergencies in general, is that data flows and data systems are incredibly important. You need the information to be able to make the decisions. Therefore, for any emergency situation, those data systems need to be in place up front to be able to give the information to make the analysis and make the decisions.
He told the committee that this was not limited to testing data, but also encompassed basic information flows around patients in hospital, rates of admission and rates of movement.
The report added that Vallance suggested that a principal issue in managing the pandemic was that at the beginning, there were definitely times when we would have liked data that was difficult to getdata flows are getting much better now, but the NHS does not have centralised data flows on everything you need.
As an example, comprehensive data on Covid-19 in care homes were not available to the government in the early months of the pandemic. At a Sage meeting on 15 March, it was noted that because of a 5 to 7 day lag in data provision for modelling, Sage now believes there are more cases in the UK than Sage previously expected at this point, and we may therefore be further ahead on the epidemic curve.
The committee is calling on the government to publish the advice it has received on indirect effects of Covid-19 (including impacts on mental health and social wellbeing, education and the economy) and work to improve transparency around the operation of the Joint Biosecurity Centre.
Measures taken to contain the pandemic [have] had wider and indirect effects, such as on peoples livelihoods, educational progress and mental and emotional wellbeing, said the committee.
The assessment of these wider impacts was and remains much less transparent than the epidemiological analysis; the people conducting the analysis and giving advice are less visible than epidemiological modelling advisers; and its role in decision-making opaque.
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Healthcare Innovations: Predictions for 2021 Based on the Viewpoints of Analytics Thought Leaders and Industry Experts | Quantzig – Business Wire
LONDON--(BUSINESS WIRE)--Quantzig, a leader in delivering scalable analytics solutions and data science services, announced the completion of its recent article that unravels the healthcare innovations set to transform the healthcare industry in 2021.
The use of technology in healthcare skyrocketed in 2020 as hospitals, health systems, and patients increasingly relied on digital health technologies for care delivery during the pandemic, setting the stage for continued growth and innovation. With several new healthcare innovations paving their way into the health-tech landscape, analytics thought leaders at Quantzig got out their crystal balls to predict and share their views on the most promising healthcare innovations and medical breakthroughs impacting the healthcare industry in 2021 and beyond.
With COVID-19 vaccination trials rolling out this year, next-gen solutions for patient monitoring and virtual healthcare will witness high demand in 2021, says an analytics expert at Quantzig.
Partnering with Quantzig can help you adopt a progressive approach to innovation, with continuous guidance and support from analytics and healthcare industry experts. Request a FREE proposal to get started.
2021 will witness innovations transforming how healthcare researchers aggregate and analyze big data, making data a powerful tool for drug development, lifestyle studies, and research
With the proliferation of advanced technologies, it is now possible for businesses to leverage the power of AI and ML to gain a leading edge
Quantzig is at the forefront of enabling healthcare innovation to drive better healthcare outcomes and improved patient experiences. Contact us to learn more about how you can benefit by focusing on tech-driven innovations.
Innovation is key to drive growth and profitability across sectors, and healthcare is no exception. But implementing new, innovative technologies can be challenging from a technical standpoint. However, the need of the hour is to strengthen your understanding and leverage technology to drive outcomes and offer personalized experiences for patients across the healthcare continuum. Though the benefits of healthcare innovations are widespread, building the necessary skills and capabilities to identify and capitalize on them is not an easy task. At Quantzig, we suggest adopting a progressive approach with guidance and support from big data and analytics experts to test and find loopholes prior to organization-wide implementation. Request more information from our experts to find out how we can help you.
Healthcare Innovations That Will Transform Healthcare in 2021
Few of these healthcare innovations have been a transformative force in reshaping and disrupting the healthcare industry in the past. As such, the new healthcare innovations hold tremendous potential in driving future healthcare outcomes by delivering a personalized, spontaneous, and cohesive experience to both payers and providers in the healthcare ecosystem. Quantzigs team of 550+ seasoned analytics experts and data science professionals have the expertise and skill it takes to design and build systems tailored to the particular needs of your business and equip you with data-driven, actionable insights for prudent decision-making. Request a FREE pilot project to learn more about our proprietary analytics platforms and core capabilities.
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Quantzig is the world's foremost full-service advanced analytics and business intelligence solution provider, turning clients' complex, unstructured data into intelligent, actionable insights that enable them to solve complex business problems and inspire innovation, change, and growth.
Over the past 16 years, our insights have helped over 120 clients spanning across industries and sectors like Pharmaceutical and Life Sciences, Retail and CPG, Food and Beverage, and more. We have successfully delivered 1500 in-depth solutions in areas like Marketing Analytics, Customer Analytics, Supply Chain Analytics, and more. For more information on our engagement policies and pricing plans, visit: https://www.quantzig.com/request-for-proposal.
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AI Update: Provisions in the National Defense Authorization Act Signal the Importance of AI to American Competitiveness – Lexology
The newly enacted National Defense Authorization Act (NDAA) contains important provisions regarding the development and deployment of artificial intelligence (AI) and machine learning technologies, many of which build upon previous legislation introduced in the 116th Congress. The most substantial federal U.S. legislation on AI to date, these provisions will have significant implications in the national security sector and beyond. The measures in the NDAA will coordinate a national strategy on research, development, and deployment of AI, guiding investment and aligning priorities for its use.
President Trump had vetoed the NDAA after its initial passage in December, but the $740 billion NDAA became law over the objection of President Trumps veto with a rare New Years Day Senate vote, 81-13. The House voted to override President Trumps veto on December 28, on a 322-87 vote.
This post highlights some of the key AI provisions included in the NDAA.
I. Establishment of the National Artificial Intelligence Initiative
Building on concepts set forth in prior legislation, including the National Artificial Intelligence Initiative Act of 2020 (S. 1558, H.R. 6216) introduced in the 116th Congress, Title E of the NDAA mandates the establishment of a National Artificial Initiative, for the purpose of:
In support of those goals, the AI Initiative activities will include:
To implement the AI Initiative, the NDAA mandates the creation of a National Artificial Intelligence Initiative Office under the White House Office of Science and Technology Policy (OSTP) to undertake the AI Initiative activities, as well as an interagency National Artificial Intelligence Advisory Committee to coordinate federal activities pertaining to the AI Initiative. In addition, the Secretary of Commerce, in consultation with other government officials, will establish a National Artificial Intelligence Advisory Committee comprised of members who collectively will provide a broad range of expertise and perspectives. The statute requires the Advisory Committee to establish a subcommittee on AI and law enforcement.
II. Development of Frameworks through the National Institute of Standards
Building on provisions of several pieces of legislation introduced in the 116th Congress, including the National Artificial Intelligence Initiative Act of 2020 (S. 1558, H.R. 6216) and Advancing Artificial Intelligence Research Act of 2020 (S. 3891), the NDAA directs the National Institute of Standards and Technology (NIST) to support the development of relevant standards and best practices pertaining to both artificial intelligence and data sharing. To support these efforts, Congress has appropriated $400 million to NIST through FY 2025.
Specifically, the statute directs NIST to:
In addition, the legislation also grants the Director of NIST the discretion to:
Furthermore, NIST is instructed to (1) develop, in collaboration with public and private organizations, a voluntary risk management framework for trustworthy AI, (2) participate in the development of AI standards and specifications, (3) develop, in collaboration with public and private organizations, guidance to assist with voluntary data sharing among a range of organizations, and (4) develop, in collaboration with public and private sector organizations, best practices for datasets used to train AI, including with respect to documentation.
III. Department of Defense Artificial Intelligence Provisions
The NDAA has several AI-related provisions pertaining to the Department of Defense (DOD). Most notably, in relation to the Joint Artificial Intelligence Center (JAIC), the new law:
Other notable DOD AI provisions include:
IV. Department of Energy AI Research Program
The NDAA authorizes $1.2 billion through FY 2025 for a Department of Energy (DOE) artificial intelligence research program, identifying seven key areas for research grants, including the analysis and development of standardized data sets and development of trustworthy AI systems. To support this program, the Energy Secretary is directed to take certain actions, including making infrastructure, hardware, and software investments and collaborating with many stakeholders. In carrying out the program, DOE is also directed to support technology transfers of artificial intelligence systems in support of society and United States economic competitiveness.
V. Other Provisions Expanding Research, Development and Deployment of AI
The NDAA includes several other provisions pertaining to AI. For example, it allocates $4.8 billion to the National Science Foundation, which, among other things, will form a task force, in coordination with OSTP, to investigate the establishment of a National AI Research Resource. These provisions follow those in last sessions National AI Research Resource Task Force Act / National Cloud Computing Task Force Act (H.R.7096, S.3890) , and contemplate that the National Artificial Intelligence Research Resource, if established, among other things, may create a shared computing infrastructure for researchers throughout the United States. Similar to provisions of last sessions National Artificial Intelligence Initiative Act of 2020 (S. 1558, H.R. 6216), the NDAA also authorizes NSF to support the development of a network of inter-disciplinary AI research hubs or institutes to focus on challenges for AI systems such as trustworthiness or that focus on particular economic or social sectors.
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The University of Miamis Institute for Data Science and Computing will host a lecture series to describe this emerging profession.
Data Science. It is one of the fastest growing professions in the nation, according to the U.S. Bureau of Labor Statistics. And with the sheer increase in technology able to track our every moveboth physically and digitallythe amount of information for these professionals to utilize is growing by the second.
To cultivate more of these minds in the workplace, the University of Miamis Institute for Data Science and Computing(IDSC) recently joined faculty members from across the institution to create a masters degree program that trains aspiring data scientists in four specialty tracks. And now IDSC is looking to demystify the novel profession in the lecture series Meet a Data Scientist.
This exciting new series will introduce the people behind the data, their lives, interests, and career choices, said Nick Tsinoremas, IDSC director and vice provost for research computing and data, as well as professor of biochemistry and molecular biology, computer science, and health informatics. This is a great opportunity to understand how these professionals use data to solve grand challenges in their respective fields.
Data scientists often organize massive amounts of data collected by an institution or company to find connections that could help solve problems, make decisions, or improve efficiency. For instance, if a hospital wanted to increase its productivity, a data scientist could look at the times of year when it typically has a high volume of patients, and make sure they have extra staff on hand during those weeks.
Essentially, a data scientist is someone who gathers chaos into something organized to allow others to understand it, said Alberto Cairo, the Knight Chair in Visual Communication at the School of Communication, as well as an associate professor of journalism and media management, and the director of IDSCs Center for Visualization, Data Communication, and Information Design.
In the first session of Meet a Data Scientist, Cairo described some of his experience crafting informational graphics at media companies in Brazil and Spain, as well as his current consulting role at places including Google and the Congressional Budget Office, where he takes data, structures it logically, and uses it to create visual representations of complex information in ways that are easy for the public to understand. Often, these come in the form of maps, charts, and graphs, he pointed out, but not always.
In many cases, Cairo said, he has worked with research scientists to communicate their findings more easily to a widespread audience, which helps to propel their career. He said the job of a data scientist is not only to be able to analyze the data, but to explain it in many formats.
I dont collect the data, but I help to present the data, he said.
Cairo emphasized the importance of working as a team to create the best data visualizations and mentioned that publications like The New York Times have nearly 50 data experts, including developers, graphic designers, and programmers who work together on projects. In addition, he has worked with programmers and visual artists to create Waves of Interest, an interactive illustration of the most popular Google searches during election years. And Cairo praised the work some of his former students did for The Washington Post, which localized the COVID-19 pandemic in numbers, and in The New York Times, where a survey and color coded map can help people understand the range of American dialects.
Getting your data right and your questions right is a huge amount of time spent for data scientists, he said, explaining that he often collaborates with clients extensively about their target audience and what they want to convey before getting the data and crafting ideas on how to illustrate it. I also ask scientists to explain their research to me. This helps me to organize the data, understand the relevant information, and plot it correctly, he added.
The next Meet a Data Scientist session will feature Ben Kirtman, professor of atmospheric sciences and deputy director of IDSC, as well as director of its Atmosphere, Ocean, and Earth Science division, on Wednesday, Nov. 18, from 4 to 5 p.m.
To learn more about being a data scientist, register for upcoming sessions here.
Endowed Chair of Data Science job with Baylor University | 299439 – The Chronicle of Higher Education
The McCollum Family Endowed Chair in Data Science is aresearch-focused position in the Baylor University Computer Scienceand Informatics Department. Data Science is one of the fiveSignature Academic Initiatives in Baylors strategic planIlluminate (Illuminate- Data Science) and is involved in key research for theUniversity (Data ScienceResearch). This transformative, endowed position is a visionaryinvestment in the future of Data Science research and educationacross the university (EndowmentDetails).
Qualifications: The University invitesapplications for this tenure-track position at the rank of fullProfessor beginning in the Fall 2021 semester. An ideal candidatewill help shape a comprehensive, university-wide strategic plan forData Science. This will be done through leadership,collaboration, and growth of infrastructure and interdisciplinaryresearch. Applicants should have a Ph.D. in Data Science or arelated discipline; Baylor is recruiting new faculty with a deepcommitment to excellence in teaching, research, and scholarship.Other qualifications include an established history of extramuralfunding, high impact academic artifacts, and graduate studentmentorship. A viable applicant should demonstrate excellentpotential as an individual researcher and collaborator acrossmultiple disciplines.
The Department: Computer Science andInformatics is one of three departments in the School ofEngineering and Computer Science. It offers a B.S. in Informaticswith majors in Data Science and Bioinformatics, B.S. and B.A.degrees in Computer Science, and a B.S. in Computing with a majorin Computer Science Fellows. On location M.S. and Ph.D. degrees inComputer Science are offered, as well as an online M.S. programwhich started Fall 2020. The Department has 17 full-time faculty,over 280 undergraduates, and over 25 graduate students.Departmental website: Informatics
The University: Baylor University is a privateChristian university and a nationally ranked research institution,consistently listed with highest honors among The Chronicle ofHigher Education's "Great Colleges to Work For." Baylor seeksfaculty who share in our aspiration to become a tier-one researchinstitution while strengthening our distinctive Christian mission.As the worlds largest Baptist University, Baylor offers over 40doctoral programs and has over 17,000 students from all 50 statesand more than 85 countries.
Appointment Date: Fall 2021. For fullconsideration, applications must be received by December 31,2020.
Application Procedure: To apply, please submita letter of application, a 1-2 page research plan, a 1-2 pageteaching philosophy, a copy of an official transcript showing thehighest degree conferred (if the Ph.D. is in progress, a copy ofthe official transcript of completed Ph.D. hours should also besubmitted), and the names and email addresses of three personswilling to provide letters of recommendation as a single PDF filethrough this Interfolio link: Application Link Finalistsfor this position will be required to submit official transcriptsfor the doctoral degree in advance of a campus visit. Inquiriesabout the position can be sent toCSSearch@Baylor.edu.
Baylor University is a private not-for-profit universityaffiliated with the Baptist General Convention of Texas. As anAffirmative Action/Equal Opportunity employer, Baylor is committedto compliance with all applicable anti-discrimination laws,including those regarding age, race, color, sex, national origin,marital status, pregnancy status, military service, geneticinformation, and disability. As a religious educationalinstitution, Baylor is lawfully permitted to consider anapplicants religion as a selection criterion. Baylor encourageswomen, minorities, veterans and individuals with disabilities toapply.
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2020 AI and Data Science in Retail Industry Ongoing Market Situation with Manufacturing Opportunities: Amazon Web Services, Baidu Inc., BloomReach…
GlobalAI and Data Science in Retail MarketResearch Report 2019-2026:This comprehensiveAI and Data Science in Retail Marketresearch report includes a brief on these trends that can help the businesses operating in the industry to understand the market and strategize for their business expansion accordingly. The research report analyzes the market size, industry share, growth, key segments, CAGR and key drivers.
New vendors in the market are facing tough competition from established international vendors as they struggle with technological innovations, reliability and quality issues. The report will answer questions about the current market developments and the scope of competition, opportunity cost and more.
The AI and Data Science in Retail market is a comprehensive report which offers a meticulous overview of the market share, size, trends, demand, product analysis, application analysis, regional outlook, competitive strategies, forecasts, and strategies impacting the AI and Data Science in Retail Industry. The report includes a detailed analysis of the market competitive landscape, with the help of detailed business profiles, SWOT analysis, project feasibility analysis, and several other details about the key companies operating in the market.
This report studies the AI and Data Science in Retail market status and outlook of Global and major regions, from angles of players, countries, product types and end industries; this report analyzes the top players in global market, and splits the AI and Data Science in Retail market by product type and applications/end industries.
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AI and Data Science in Retail Marketin its database, which provides an expert and in-depth analysis of key business trends and future market development prospects, key drivers and restraints, profiles of major market players, segmentation and forecasting. A AI and Data Science in Retail Market provides an extensive view of size; trends and shape have been developed in this report to identify factors that will exhibit a significant impact in boosting the sales of AI and Data Science in Retail Market in the near future.
Company Coverage (Sales Revenue, Price, Gross Margin, Main Products, etc.):Amazon Web Services, Baidu Inc., BloomReach Inc., CognitiveScale Inc., GoogleInc., IBM Corporation, Inbenta Technologies, IntelCorporation, Interactions LLC, Lexalytics Inc., MicrosoftCorporation, NEXT IT Corp., NvidiaCorporation, OracleCorporation, RetailNext Inc., Salesforce.com Inc., SAPSE, Sentient Technologies, Visenze.
Scope and Segmentation of the Report:
The segment analysis is one of the significant sections of this report. Our expert analyst has categorized the market into product type, application/end-user, and geography. All the segments are analyzed based on their market share, growth rate, and growth potential. In the geographical classification, the report highlights the regional markets having high growth potential. This thorough evaluation of the segments would help the players to focus on revenue-generating areas of the AI and Data Science in Retail market.
Our analysts are experts in covering all types of geographical markets from developing to mature ones. You can expect a comprehensive research analysis of key regional and country-level markets such as Europe, North America, South America, Asia-Pacific, and the Middle East & Africa. With accurate statistical patterns and regional classification, our domain experts provide you one of the most detailed and easily understandable regional analyses of the AI and Data Science in Retail market.
Table of Contents:-
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Data Science Platform Market Research Growth by Manufacturers, Regions, Type and Application, Forecast Analysis to 2026 – Eurowire
Global Data Science Platform market provides a detailed report which covers market analyses before COVID19 & opportunities after this pandemic. With COVID-19 pandemic, many industries are transforming rapidly. The Global Data Science Platform Market is one of the major industries undergoing changes. This year many industries have vanished entirely from the market and many industries have risen.
Moreover, the government-backed schemes throughout the globe are offering many advantages to businesses. As the governing bodies are supporting the industries, it be a strong pillar to support the market growth of Data Science Platform in the upcoming decade (2020-2026). Organizations planning to move into new market segments can take the help of market indicators to draw a business plan. With the technological boom, new markets are blossoming across the globe, making it a breeding ground for new businesses.
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Global Data Science Platform Market 2020: Covering both the industrial and the commercial aspects of the Global Data Science Platform Market, the report encircles several crucial chapters that give the report an extra edge. The Global Data Science Platform Market report deep dives into several parts of the report that plays a crucial role in getting the holistic view of the report. The list of such crucial aspects of the report includes company profile, industry analysis, competitive dashboard, comparative analysis of the key players, regional analysis with further analysis country wise.
Global Data Science Platform Market Analysis by Key Players:
Moreover, one of the uniqueness in the report is that it also covers the country-level analysis of the regulatory scenario, technology penetration, predictive trends, and prescriptive trends. This not only gives the readers of the report the actual real-time insights but also gives country-wise analysis, that plays a vital role in decision making. The inclusion of the report is not limited to the above mention key pointers. The report also emphasizes on the market opportunities, porters five forces, and analysis of the different types of products and application of the Global Data Science Platform Market.
The report splits by major applications:
Then report analyzed by types:
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Global Data Science Platform Market Report is a professional and in-depth research report on the worlds major regional market conditions of the Data Science Platform industry, focusing on the main regions and the main countries as Follows:
COVID-19 Impact on Data Science Platform Market:
The outbreak of COVID-19 has brought along a global recession, which has impacted several industries. Along with this impact COVID Pandemic has also generated few new business opportunities for Data Science Platform Market. Overall competitive landscape and market dynamics of Data Science Platform has been disrupted due to this pandemic. All these disruptions and impacts has been analysed quantifiably in this report, which is backed by market trends, events and revenue shift analysis. COVID impact analysis also covers strategic adjustments for Tier 1, 2 and 3 players of Data Science Platform Market.
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Table of Contents Includes Major Pointes as follows:
Fuelled by digital expansion and the use of new-age technologies across all industries, the demand for data science skills has grown rapidly.
By Emmanuel Osanga, head: data office, Africa regions at Standard Bank Group
The supply of skilled applicants, however, is increasing at a pace that fails to match demand, and which has accelerated under the new normal.
In financial services, where significant investments are being made to enhance customer experience and engagement through new digital capabilities, more data than ever is being generated. In this context, data scientists are crucial to turning significant datasets into useful insights.
This type of future skill has been in high demand over the past few years as enterprises expand their digital footprints. Now, almost everyone has upped the ante on their digital transformation efforts in the current environment, and this means an even greater gap to be filled.
A sooner-than-expected reality
If we rewind 10 or 20 years, few would have thought about technological advancements such as Artificial Intelligence and the volume of data that could be achieved at the scale we are seeing today.
Many legacy organisations lacked planning intensity because it did not seem like a potential reality in a scenario outcome at the time. The speed at which digital advancement has taken place in recent years, and in the past few months specifically, caught many by surprise.
This left companies in a position of unpreparedness and fast-tracked the demand for the type of skills that can turn datasets into action. IBM projected in 2019 that there would be around 30% growth in demand for the data science capability in 2020 itself. This prediction is expected to escalate due to Covid-19 leading digital transformation.
While skills supply in the Science Technology Engineering and Maths has been on a heavy uptick as alternative ways of producing data scientists become available, the world is battling an under-supply and over-demand of data science human capital.
It is, however, one of the key skillsets of the future that companies will have to prepare for. But for organisations to yield appropriate quality skills, there must be investment in mastery programmes that cover the full scope of what is required to close the gap.
Data science: A multi-faceted skill
Data science is a multi-faceted skill that is not learnt overnight. Few universities, academies and online courses are bringing it into application. While they cover the theoretical aspects, it is the business domain knowledge across multiple industries that will produce the desired output.
This is a key component of what is required to be a true data scientist. No amount of technical or theoretical training in data science will solve business challenges. It is the business acumen and competence, combined with access to mentorship and on-the-ground experience, that creates the magic.
It is also about matching individuals within academies with the right type of mentality, curiosity, and inspiration and showing them what they can achieve by leveraging data. Many tend to think about it as statistics, maths, and complexity. The truth of the matter is that those who are most successful in this area are inspired to make a difference and want to solve real problems using data science.
Standard Bank data science mastery programme
Standard Banks Data Science Mastery programme, launched in 2016, fuses the fresh thinking of greenfield graduates with the competence of experienced staff members. The programme is designed not to teach theory but to provide practical experience, nurturing and mentorship.
Participants are exposed to a diverse set of problems in different fields of business areas where unique problems exist relating to that area and data-driven type solutions differ.
The ability to combine technical or theory with practical experience by working alongside internal employees is proving invaluable. The experience of rotating individuals across business areas cannot be replaced.
Standard Bank has worked to improve the overall data literacy of the organisation to support its activities now and in the future. When exposed to the skill and its possibilities, individuals become interested in mastering data language.
This is expected to place further demand on the programme, which has already received an overwhelming response. It has, however, been structured to scale over the next two to three years to meet the demand both internally and externally.
This investment is one of the significant strategic decisions that we, as Standard Bank, have made to build a foundation for the platform journey we are now on. Our extended agreement with CRM Salesforce is a major step towards transforming the Standard Bank Group into a client-centred platform business that delivers a range of individualised, instantly available solutions, services and opportunities, enabled by modern digital technologies and delivered in whatever way a client prefers.
The Salesforce investment requires every staff member to reflect on their current skill set, and whether it represents that of the future. If not, there is an expectation that everyone undergoes reskilling to cope and fundamentally evolve and adapt.
Africa can accelerate the solve
There is a massive opportunity for Africa as a continent, given its young population, to skill its people appropriately for the future. There is great demand for the data science capability yet significant unemployment plagues the continent. Africas youth are, however, ripe for these capabilities; they are more inclined to quickly comprehend technology because that is their immediate experience.
The 4IR presents a golden opportunity for Africa. The rapid evolution of digital technology has caught the world off guard. Legacy organisations in well-established economies are undergoing transformation processes to digitally adopt. The continent, meanwhile, is a blank canvas unhindered by legacy that can leapfrog the worlds delay into the adoption if connectivity is enabled.
Investing in a data science mastery programme reinforces Standard Banks commitment to driving Africas growth. We are preparing Africans to be relevant in the future. Africa is our home, we are contributing to the future of the continent by laying these foundations. We will continue to expand on that and be part of the African story of preparing to meet demand for future skills in the region and globally as well.
UTSA to break ground on $90 million School of Data Science and National Security Collaboration Center – Construction Review
The University of Texas at San Antonio will commence construction of a $90 million School of Data Science and National Security Collaboration Center in the coming few days. The 167,000-square-foot building is located along Dolorosa Street east of Interstate 35 and is a major component of the universitys downtown campus expansion plan. The project is expected to spur business in the area and attract private investment.
Corrina Green, director of major capital projects and real estate for UTSA while giving an update on the proposed School of Data Science and National Security Collaboration Center said they have already finished developing the project design.
UTSA officials are awaiting design approvals from the UT System Board of Regents before commencing construction in mid-December. The project is expected to be complete by July 2022.
The building design has tried to draw more attention to a reimagined San Pedro Creek. The centers ground level will be reserved for a caf plus a large mixed-use space that will be used by students and tenants and also available for public events.
We really want to draw people into the building and up from the creek so that we are interacting with the other development thats happening around us, Green said. Were working hand in hand with all of these developers to make sure that this is very engaging with what they are doing.
UTSA also announced plans to construct a 250,000-square-foot Innovation, Entrepreneurship, and Careers Building at the site where the countys old prison is being demolished. The building will start west of the new structure that is being constructed.
The University is yet to release the timeline for the Innovation, Entrepreneurship, and Careers Building whose construction is expected to cost $161.2 million. This building will house an expanded College business.