Category Archives: Data Science
StackRoute Announces Data Science Foundation Program and Advanced Post Graduate Program in Data Science and Machine Learning to Meet the Industry…
Business Wire IndiaStackRouteTM, an NIIT venture announced the launch of Data Science Foundation Program and Advanced Post Graduate Program (PGP) in Data Science and Machine Learning.
The Data Science Foundation Program (Full time or Part time) will empower learners with foundational skills to analyze and visualize data using Excel and Python. Upon completion of all assessments, learners acquire the role specific knowledge & skills and gain the experience to meet the respective learning outcomes. The Advanced PGP in Data Science and Machine Learning (Full time or Part time) enables learners to join data science practice team and gain experience to grow up as Data Analyst or ML engineer or Jr. Data Scientist.The programs are delivered virtually to learners who are keen to gain on-demand Data Science skills.Duration for the Data Science Foundation Program is 6 weeks while the Advanced PGP in Data Science has a duration of 18 weeks. For the part time mode, duration for these programs is 20-24 weeks and 40-48 weeks respectively.AnalyticsInsight has forecast that by 2025, there will be 137,630data science jobopenings inIndia,risingfrom 62,793jobsin 2020. As Per LinkedIn, there has been a650%increase in data science jobs since 2012. Today, most Data Science teams are looking for resources with solid foundation skills and with capability to take up any activity across the Data Science wavelength. It is critical to have the necessary skills to join data science team and be able to genuinely contribute as part of the data science practice teams.Speaking on the announcement Abhishek Arora, EVP and Business Head, Skills and Careers Business, NIIT Ltd., said, Having worked with leading Technology Companies, StackRoute has created a niche for itself as the go-to organisationfor disruptive learning solutions and helping professionals build deep skills in full stack technologies. We are happy to launch the data science programs, designed to deliver impactful learning experiences to build all round competent professionals, meeting the talent requirements of the industry.
On successful completion of the immersive programs, the learners will receive certificates by StackRoute and will also be provided with full placement assistance.Over the past 5 years StackRoute has been committed towards delivering disruptive learning solutions, producing highly skilled and deployment ready talent in emerging tech and digital roles. Recently StackRoute was awarded with Brandon Hall Group HCM Excellence award 2020, jointly with a leading independent IT and business consulting services firm. The Gold medal has been awarded under the category - Best Use of Blended Learning for Graduate Transformation Program.Additionally, StackRoute has won Brandon hall Silver award for Best Results of a Learning Program jointly with the worlds leading engineering company in aerospace.For more information about the program visit: https://www.niit.com/india/data-science/advanced-post-graduate-program-data-science-machine-learning-fulltimeAbout StackRoute
StackRoute, an NIIT venture, is a digital transformation partner for corporates to build multi-skilled full stack developers at scale. Established in August 2015, StackRoute provides disruptive IT learning solutions on programming, quality-engineering, data-science and digital architecture. Our immersive and remote programs are practitioner-led and outcome-oriented. Geared towards imparting deep skills in digital technologies, StackRoute works with multiple tier 1 IT companies, product engineering companies, and GICs towards transforming their workforce into full stack developers who can efficiently deliver digital transformation projects with ease.
For more information about StackRoute visit: http://www.stackroute.in
About NIIT Limited
NIIT Limited is a leading Skills and Talent Development Corporation that is building a manpower pool for global industry requirements. The company, which was set up in 1981 to help the nascent IT industry overcome its human resource challenges, today ranks among the world's leading training companies owing to its vast and comprehensive array of talent development programs. With a footprint in over 30 countries, NIIT offers training and development solutions to Individuals, Enterprises and Institutions.For more information about NIIT visit: http://www.niit.com
Prateek ChatterjeeNIIT Limited+91 (124) 4293041,+91-9910201085prateek.chatterjee@niit.comSwati SharmaNIIT Limited+91 (124) 4293042,+91-9999601154swati.sharma@niit.com
View post:
4 AI Trends that will Define the Future of Data Science – Analytics Insight
Prepare your AI ecosystem to match with the data challenges of the future
Companies across the world are increasingly adopting AI for their smooth business operations. The technology unleashed its constructive potential during the onset of COVID-19 in performing a wide range of tasks that are complex and cumbersome for humans, bolstering employee productivity. Right from managing tasks ranging from planning, envisaging, and predictive maintenance to customer service chatbots, aiding data analytics, and more, businesses are extracting the maximum out of this disruptive technology.
AI is one of the most revolutionizing technologies of our time. The current surge in AI research and investment has resulted in an incredible rise in AI applications. These applications do not just promise to yield better business outcomes but enhance the human experience as a whole. The technology is currently being applied for a wide array of industries ranging from healthcare, retail, and banking, to logistics, and transportation. While these industries are using AI to automate their processes and sort out their analytics processes, it is now time to think about the future possibilities with artificial intelligence.
The rate at which technology is developing is beyond measure and the same is the case with how industries are taking advantage of it in terms of managing data. The road AI is heading towards features a vast AI ecosystem with several models and new dependencies. The tech world will witness new approaches to skills, governance, and machine learning engineering where data scientists and software engineers will collaborate to leverage machine learning.
So, what should organizations expect in the future? After all, the success of an organizations AI adoption will depend on how they master the complexity of altering their business processes to accommodate the new change. Here are the four AI trends organizations should bear in mind.
1. Upgrade first, create later.
Instead of being in a hurry to create an AI model, optimize and update the existing models that are put in place. As every industrys challenges and data requirements are different, AI models should be upgraded to suit the domain specifications and for that, data scientists with experience in the specific industry and scientific techniques should be on your radar.
2. Transfer learning will scale NLP
Natural language processing will witness a massive growth in adoption along with increased potential due to transfer learning. Knowledge obtained after solving a problem will be stored and automatically applied to related problems, saving time for newer applications.
3. Governance will come crucial
As newer predictive models will flood the markets, managing them all will become difficult. Only with proper governance, frameworks, and guidelines, organizations can govern the machine-generated data. Proper governance should follow all the ethical standards, which is why organizations should relook the roles and responsibilities of data scientists.
4. Polish Existing Talent
As AI advances, organizations would want to look for greater AI literacy and awareness at all levels. As the business world is getting more data-driven, organizations will only be able to make the most of the technology if all the employees understand at least the basics of AI and data science. Hiring new talent altogether for this purpose will be tedious, hence organizations should train and polish the skills of the existing employees and prepare them with the fundamentals of what is essential, AI and data science.
AI has already made tremendous strides when it comes to leveraging data science and automation. The algorithms will only become more complex and exceed human abilities in the foreseeable future. There to manage these advances, organizations should start preparing and strategizing now before its too late to catch up.
Share This ArticleDo the sharing thingy
Read more here:
4 AI Trends that will Define the Future of Data Science - Analytics Insight
Manchester academic recognised as one of UK’s leading female data science professionals – The University of Manchester
An academic from The University of Manchester has been recognised as one of the nations leading female data science professionals, after being named as one of Women in Data UKs 20 in 20.
Jackie Carter, Professor of Statistical Literacy in the Universitys School of Social Sciences, has been acknowledged for her technical knowledge and experience, and her commitment to encouraging more diverse representation in the data industry.
Previous people recognised by Women in Data have co-created the Oyster card and the algorithms for the Amazon Alexa, and shaped the future for solving the causes of dementia. There are now 80 committed advocates working hard to highlight the fantastic opportunities in STEM, to help girls expand their career choices as well as to encourage current practitioners to set their goals even higher.
Whilst at The University of Manchester Jackie has helped secure 1.3m of funding, and has developed a living-wage paid work-placement programme through the Q-Step Centre. She is Co-Director of the centre, which was developed as a strategic response to the shortage of quantitatively-skilled social science graduates.
Jackies recently published book Work Placements, Internships & Applied Social Research draws on her seven years experience of setting up and running this paid internship programme. Using up-to-date reports from the British Academy, LinkedIn and McKinsey, the book presents frameworks and tools to help learners understand how their degree has relevance to the workplace, and how this can be evidenced. It is full of case studies and narratives based on her former students and others.
She is also on the board of the Urban Big Data Centre, providing advice on the role of social science in understanding how big data can be used to improve lives, and advising on how future data scientists can be trained to develop the necessary skills to equip them in these emerging careers.
Im absolutely delighted to win this award, but I think it reflects the wonderful young women I work with more than anything, and how together we are challenging traditional pipelines into data careers, said Jackie. Women can be data analysts and data scientists, and the award recognises the work I am doing in trying to address that almost 70% of my interns are female.
See the rest here:
Alteryx Unveils Global SparkED Education Program to Empower Learners of all Levels to Upskill with Data and Analytics – PRNewswire
IRVINE, Calif., May 20, 2021 /PRNewswire/ -- Alteryx, Inc. (NYSE: AYX), the Analytics Automation company, today announced its new education program, SparkED, designed for learners of all skill levels in every discipline to gain hands-on skills and certification in data analytics. SparkED is a global, comprehensive education program that offers free Alteryx software licenses, training and certification, and curriculum for various types of learners: educators and students in a traditional education environment aspiring to achieve data science skills and bolster their resume, and career changers wanting to upskill for a competitive edge in today's job market.
"Businesses and organizations across the globe have an increasing demand for employees with data science and analytics skills, but there remains a major skills gap in these fields," said Libby Duane Adams, co-founder and chief advocacy officer of Alteryx. "As a global leader in data science and analytics automation, it is our responsibility to enable people with the skills to perform in these jobs, no matter which stage of their life they might be in. The Alteryx SparkED program will provide free resources to expand data literacy to data science skills across learners of all ages and levels to close the skills gap."
SparkED's No-Cost Program Benefits
Educators from many universities across the globe such as California State University, Fullerton, University of Richmond and The University of Auckland have already incorporated Alteryx into their curriculum and are seeing their students land interviews and receive job offers from renowned companies because of their experience and certification from using Alteryx in the classroom.
"Incorporating Alteryx into my Emerging Technologies class has been a win-win for everyone," said April Morris, professor of accounting at California State University, Fullerton. "My students consistently tell me that their ability to use an analytics platform like Alteryx has substantially improved their skills and increased their opportunities for advancement, outside of the classroom and in their careers."
"As a business student, it is essential to equip technical and analytical skills to gain a competitive advantage over my peers," said Ringo Cheng, accounting student at California State University, Fullerton. "I was introduced to Alteryx in one of my courses, and the intuitive interface of the platform and friendliness of the Alteryx Community immediately helped me analyze data effectively and efficiently. As a risk consultant for a public accounting firm upon graduation, having these analytical skills will allow me to analyze the company's data and form better recommendations for our clients."
The program is free for qualified learners and educators to access, and by registering, participants will receive an Alteryx Designer software license, interactive learning content and the ability to earn Alteryx certifications that will set them apart from the competition when they are looking for a job or looking to advance in their careers. The program can be accessed anywhere and features responsive support and guidance from other Alteryx users, including other educators, students and customers in our vibrant online community.
To learn more about the SparkED program, tune in to Libby Duane Adams' keynote session at the Alteryx Global Inspire conference today, May 20 at 9 a.m. PDT by registering at inspire.alteryx.com or visit alteryx.com/sparked.
About AlteryxAlteryx, the Analytics Automation company, is focused on enabling every person to transform data into a breakthrough. Alteryx unifies analytics, data science and business process automation in one, end-to-end platform to accelerate digital transformation and shape the future of analytic process automation (APA). Organizations of all sizes, all over the world, rely on Alteryx to deliver high-impact business outcomes and the rapid upskilling of their modern workforce. For more information visit http://www.alteryx.com.
Alteryx is a registered trademark of Alteryx, Inc. All other product and brand names may be trademarks or registered trademarks of their respective owners.
SOURCE Alteryx, Inc.
Read more:
Lawrence of Arabia and Bridging the Data Analytics Gap – JD Supra
T. E. Lawrence, known to the world as Lawrence of Arabia, died as a retired Royal Air Force (RAF) mechanic living under an assumed name. Lawrence is most famous for leading the Arab Revolt in World War I against the Turkish Empire. After the war, he wrote the Seven Pillars of Wisdom, published in 1927. However, by the 1930s, Lawrence retreated out of the public eye in what he termed a mental suicide. The legendary war hero, author and archaeological scholar succumbed to injuries suffered in a motorcycle accident and died on this date in 1935.
Lawrences role of bridging the British war effort with the Arabian goal of independence introduces todays topic of bridging the data analytics gap in compliance by better aligning data teams to compliance. In a October 2020 MIT Sloan Review article, entitled To Succeed With Data Science, First Build the Bridge, authors Roger W. Hoerl, Diego Kuonen, and Thomas C. Redman posited there is a designed-in structural tension between business and data science teams that needs to be recognized and addressed. Structural problems demand structural solutions, and we see a way forward through a data science bridge: an organizational structure and leadership commitment to develop better communication, processes, and trust among all stakeholders. I have taken their concepts and placed them into the compliance context.
What I found perhaps most interesting about the authors approach was identifying the issue as a structure problem and therefore a structural solution would hold sway. Structural solutions do not always come to the forefront of the Chief Compliance Officers (CCO) mind when considering compliance issues. However, they are one more tool which can be used to improve the overall effectiveness of a compliance program and can certainly work to more fully operationalize a compliance program.
Interestingly, the problem began over 100 years ago with Thomas Edison. Edison believed that invention needed to be siloed from the factory. That dynamic is still in play with data scientists usually divorced from both business operations and compliance. The authors believe this is a critical mistake as by separating the [data] lab from the factory, all too often the lab becomes isolated, making it the proverbial ivory tower.
The authors believe that a powerful answer lies in creating a data science bridge, spanning this gap and connecting the data scientists and their work resolving some of the essential strains and enabling the introduction of more, and more useful, data-driven innovations into business operations and compliance. This data science bridge would have four major responsibilities:
The authors go to state that in order to build a sturdy, sustainable bridge, there are several questions that organizations must ask themselves. A starting point is reaching agreement on an operational definition of data science and data analytics. They believe that defining the terms is important for enabling productive dialogue between the data scientists and the compliance function. Some of the questions the authors suggest could include the following:
The authors believe, In many cases, the leader of the data science lab has the most to gain and could be the player to reach out to the factory leader to initiate dialogue. Initially, a footbridge, or informal connector between the lab and factory, can be a good starting point. Data scientists and CCOs can take the initiative to discuss the concept across organizational boundaries and go to senior leadership with specific proposals. (They should abide by the principle of bringing senior leaders a solution rather than a problem.)
Interestingly, the authors believe that compliance professionals should not have to wait for top-down direction either. They believe such personnel can begin a dialogue to discuss how to foster better cooperation. A series of discussions on addressing the tension also constitutes the beginnings of a footbridge. The authors do caution that in order to achieve a sustainable solution, top-down direction needs to intersect with bottom-up, and a structural solution will be required. This means creating the bridge organizationally: that is, naming a senior executive to lead the project, and funding the improvement initiative. Only the CEO can do this.
With the Department of Justice (DOJ) mandate of moving from risk assessment to continuous monitoring to continuous improvement, as laid out in the 2020 Update to the Evaluation of Corporate Compliance Programs, the time for such an approach is now. Only by taking structural steps to address the deeply entrenched frictions can organizations expect to reap the full benefits of their investments in data science. The authors end by intoning Now is certainly the time to act. Senior leaders have a unique opportunity to resolve a previously unrecognized and debilitating tension, thereby putting their data science initiatives on a new and more productive path.
[View source.]
Read this article:
Lawrence of Arabia and Bridging the Data Analytics Gap - JD Supra
Tempus Unveils Its Lens Platform, Offering Unparalleled Access to One of the World’s Largest De-Identified Clinical and Molecular Datasets – Business…
CHICAGO--(BUSINESS WIRE)--Tempus, a leader in artificial intelligence and precision medicine, today announced the launch of its cloud-based data and analytics platform, Lens. The all-in-one platform will provide scientists and researchers across biotechnology and pharmaceutical companies with short term, on demand access to more than 35 petabytes of de-identified clinical and molecular datasets, along with the latest artificial intelligence analytical tools to accelerate drug discovery and development.
The Lens platform enables users to access, analyze, and build upon Tempus library of data in an optimized environment equipped with the tools needed to extract insight in minutes. Lens provides multiple ways for both researchers and physicians to leverage one of the worlds largest libraries of clinical and molecular data - including Tempus tumor/normal matched DNA, RNA, and H&E data - from creating targeted patient cohorts and simulating clinical trials in silico, to renting or licensing the underlying data to test hypotheses. Through Lens, users can also collaborate with Tempus computational biologists and data scientists to analyze datasets to uncover unique insights. Users can analyze data within the Lens platform through a combination of tried-and-true open source and purpose-built Tempus visual analysis tools or a cloud-based data science environment. They can also apply their own proprietary tools to the data.
Never before has real-world data played such a major role in guiding care and accelerating drug discovery and development. Our data library has reached such a significant scale that it was critical we develop Lens to help our partners navigate and utilize our vast dataset in real-time, helping them mine unique insights that can only be found in a library like ours, said Eric Lefkofsky, Founder and CEO of Tempus. The option for scientists and analysts to only pay for data when they need it, on a short term usage basis, is revolutionary and we expect it will not only save the healthcare system money, but will also unblock research and accelerate the very pace of innovation itself.
The platform serves partners drug discovery and development needs by validating targets with one of the markets largest multi-modal datasets, characterizing diseases in more granular ways, analyzing RNA expression signatures, designing trials for precise patient populations, and working with Tempus AI team to launch a multi-scale modeling approach.
"Visual data exploration in Lens is key to exploring ideas in minutes on large datasets. It helps lower the bar to access the data, and having this capability up front makes it easy to then move to deeper analysis, said Markus Bauer, Principal Scientist at Boehringer Ingelheim.
Lens also provides additional utility to administrators and clinical researchers within academic cancer centers by enabling quick, seamless assessment of patient prevalence across key clinical and molecular characteristics. Whether to evaluate and analyze a candidate population for research and publication, identify a subset of patients at the institution who may be considered for a newly approved therapy, or to understand mutation trends within a set of biomarkers relative to the general population, Lens empowers users with the tools to rapidly answer their important questions.
Lens has advanced our research and allowed us to easily leverage Tempus resources in the platform to characterize the genomic landscape of advanced prostate cancer in African American men with the goal to understand the mechanisms behind the disparities seen in clinical outcomes between patients of different ancestries, the findings of which we recently presented at two national meetings, said Dr. Nicholas Mitsiades, Associate Professor of Medicine at Baylor College of Medicine and Oncologist at the Dan L Duncan Comprehensive Cancer Center. The ability to filter patients and mutations in Lens offers a more precise look at patient populations and their genomic landscapes. By adding Lens to an arsenal of other products and services, Tempus has made it easy to interact with large datasets and quickly extract meaningful insights.
To learn more about Lens and how multi-modal data can uncover new personalized insights, please visit https://tempus.co/3uSWHhw.
About Tempus
Tempus is a technology company advancing precision medicine through the practical application of artificial intelligence in healthcare. With one of the worlds largest libraries of clinical and molecular data, and an operating system to make that data accessible and useful, Tempus enables physicians to make real-time, data-driven decisions to deliver personalized patient care and in parallel facilitates discovery, development and delivery of optimal therapeutics. The goal is for each patient to benefit from the treatment of others who came before by providing physicians with tools that learn as the company gathers more data. For more information, visit tempus.com.
Read more here:
Harnessing Data and AI to Crest the Innovation Wave – CDOTrends
Businesses today are collecting more data than at any other time in history but finding the time of day to keep data quality high remains an issue, says Kitman Cheung, chief technology officer at IBM Data, AI, and Automation in the Asia Pacific (APAC). Cheung is referring to some of the roadblocks faced by the banking industry in the APAC region, and how data and AI can play a crucial role to help banks overcome them and crest the innovation wave.
The speed and pace of data pipelines is growing more rapidly than ever, too, with a greater variety of data and disparate database platforms both structured and unstructured data, making their way into data repositories. This burgeoning volume of data must be managed, and doing it well is not a trivial challenge.
Data challenges
Onboarding new data sets and data sources into a traditional data warehouse continues to be a challenge. [The result is that] data sets either remain outside or data get onboarded at a much slower pace, making it difficult for stakeholders to look at the entire spectrum of data in a cohesive way, said Cheung.
In the meantime, the demand for new data continues to increase in both volume and importance. As banks start demanding greater access to data and at a faster pace, the capability gap between business needs and the ability of IT to deliver will only grow.
A dearth of skillsets is another challenge that banks in the region face, says Cheung. It is hard to hire data scientists who understand the banking sector on one hand, even as there are many business users in banks with the smarts and business knowhow but lacking in data science knowledge to collaborate meaningfully. The result is that innovation slows down at a time where banks need it most to stay ahead.
We see quite a few projects which were delayed due to the unavailability of data. To address that, a lot of clients are now looking at enabling self-service access to data to eliminate some of the bottlenecks in data delivery and IT transformation.
Agility without being bogged down
A fixation on new technologies or solutions for its own sake can often exacerbate the situation. Cheung pointed to how organizations around the world once gravitated towards Hadoop as the Holy Grail to address all their data-centric challenges. Often, they do not solve the [data] problems that we are facing. Instead, it can create a different set of problems, he said.
This can include popular platforms such as the public cloud: You outsource a bunch of capabilities to a third-party vendor. There are economies of scale for sure, but if you are using the cloud for consistent workloads and for a very long time, it starts to get more expensive. You start to think about everything in terms of OPEX (Operating Expenditure), which sounds good in the first place, but it translates into a different set of problems.
Instead of a complete rip and replace strategy with the favored technology of the day, Cheung recommends that banks adopt a more holistic approach and focus on fundamentals such as open-source software, open standards, and interoperability. Pick the right tools for the job. Continue with tools that are working and think deeply about data governance from the start not as an afterthought.
With a holistic IT strategy, banks can create an innovative platform that allows them to stay focused on the business objectives of every project. Leverage technologies such as containerization, microservices, and APIs to plug in new capabilities and evolve the platform, suggests Cheung. When it comes to actual investments in technology, organizations might want to think in small pieces, investing in a variety of interoperable technologies rather than an inflexible, monolithic system.
A fully integrated platform
A holistic approach makes it possible to build a fully integrated platform to support data science and AI. Cheung notes that such a data and AI platform can play a key part in supporting the culture, process, and people within the organization and a meaningful conversation around the same view on data. This is the reason why enterprises are moving to data virtualization, establishing a central place where all datasets can be accessed.
Crucially, such an approach allows a compliance policy to be seamlessly and cohesively applied across the organization instead of a piecemeal approach. Changes or updates to the data policy can hence be easily enforced without having to delve into individual databases or tables, ensuring personally identifiable information (PII) and data assets stay secure.
In closing, Cheung cautioned that data and AI tools are merely enablers to innovation, and not the solution: Successful transformation and becoming more agile and innovative is a cultural and process change. The company needs to say: I want to become more agile in business, and I willleverage data and AI technology to achieve that.
Paul Mah is the editor of DSAITrends. A former system administrator, programmer, and IT lecturer, he enjoys writing both code and prose. You can reach him at paulmah@cdotrends.com.
Image credit: iStockphoto/Jeff_Dotson
Read the rest here:
Harnessing Data and AI to Crest the Innovation Wave - CDOTrends
Ramnik Bajaj joins USAA as Senior Vice President and Chief Data and Analytics Officer – PRNewswire
"Data is critical in serving our membership as we continue to innovate for the future," said Walker. "We conducted an extensive search to find the right leader to continue our data journey; with his strategic mindset and ability to bring simplicity and structure to complex environments, I'm confident that Ramnik will be a tremendous asset in leading USAA's data modernization efforts."
Bajaj joins USAA with impressive data, technology and financial services experience. Most recently, he was the executive vice president and head of data environment and transformation at Wells Fargo & Company. In this capacity, he was accountable for establishing the data management policy and strategy and implementing transformational data programs for the company. Bajaj also oversaw enterprise analytics and data science focused on developing advanced analytics, artificial intelligence and machine learning capabilities.
"Data is, and will continue to be at the forefront of transforming business across industries, even more so in financial services," said Bajaj. "A sound foundation of data management practices and a well designed data environment is essential for safe and efficient operations, innovation and developing differentiated products and experiences. I look forward to making positive contributions to our membership by bringing the power of data, analytics and artificial intelligence to every aspect of running and managing our business."
Prior to Wells Fargo, he was at Deloitte Consulting for 21 years. As a partner in the financial services data practice, Bajaj led large scale data and analytics transformation programs for several domestic and international financial services insitutions.
About USAA
Founded in 1922 by a group of military officers, USAA is among the leading providers of insurance, banking and investment and retirement solutions to 13 million members of the U.S. military, veterans who have honorably served and their families. Headquartered in San Antonio, Tex., USAA has offices in seven U.S. cities and three overseas locations and employs more than 35,000 people worldwide. Each year, the company contributes to national and local nonprofits in support of military families and communities where employees live and work. For more information about USAA, follow us on Facebook or Twitter (@USAA), or visit usaa.com.
Contact: USAA Media Relations[emailprotected](210) 498-0940
SOURCE USAA
More here:
Ramnik Bajaj joins USAA as Senior Vice President and Chief Data and Analytics Officer - PRNewswire
Alert: Apply to These Top Data Science Government Jobs in India – Analytics Insight
As a growing number ofgovernment sectoragencies shift from the traditional paper-pen method of storing data to electronic databases, there is a mounting need fordata scientistsacross many mediums to make sense of the information. Especially, in a country like India where the population is high and manual maintenance of data is time-consuming and labor-intensive,data scienceintervention is becoming increasingly necessary.Data science in Indiais already seeing a drastic surge with emerging tech companies and education institutions. This has resulted in the spike of moredata sciencejob openings. Remarkably, as thegovernment sectorhas moved to the digital mode,data science government jobsare also gaining momentum in the country. Henceforth, Analytics Insight has listed topdata science government jobsthat aspirants should apply for in May 2021.
Location: Haryana
No. Of Vacancies: 6
Recruitment process: THSTI Recruitment Applications are invited from THSTI prescribed form through online mode for various assistant posts. THSTI Andra Pradesh will recruit the data scientist on a central government basis. An interview will be conducted preliminarily. Those who crack that will be shortlisted and pointed towards THSTI. Applicants can track THSTI vacancy, upcoming notices, syllabus, answer key, merit list, selection list, admit card, result, upcoming notification, etc at the departments official website.
Other qualifications:
Applyherefor the job.
Location: Not specified
Salary: Rs.3,75,000 per month
Recruitment process: NABARD will shortlist candidates in the ratio of 1:10 on the basis of qualification, experience, etc. Following the selection, the roll no of the selected candidates will be displayed on the banks website. The shortlisted candidates will undergo an interview and subsequent selection process. The final appointment will be based on the decision of the selection committee constituted for the purpose.
Other qualification:
Applyherefor the job.
Location: Mumbai
Roles and responsibilities: ReBIT is looking for a senior data scientist to analyze large amounts of raw information to find patterns, choose algorithms/models, build AI/ML products, extract insights, and optimize algorithms/models in iterations, preferably using python, scikit-learn, TensorFlow/Keras, and PyTorch. The candidate should identify valuable data sources and automate the collection process. They are also expected to perform statistical analysis, and fine-tuning using text results. The candidate should have hands-on experience in Python, or R, SQL and /or NoSQL databases, familiarity with Scala, Java or C++.
Other qualification:
Applyherefor the job.
Location: Bihar
No of vacancies: 10
Recruitment process: ICMR-NRCL Bihar will recruit the candidate on a central government basis. An interview will be conducted following which the eligible shortlisted candidates will be appointed at ICMR-NRCL. Before the confirmation, candidates should check their education qualification, age limit, experience, etc. for the application.
Other qualification:
Applyherefor the job.
Location: New Delhi, Delhi
Qualifications:
Applyherefor the job.
Share This ArticleDo the sharing thingy
View original post here:
Alert: Apply to These Top Data Science Government Jobs in India - Analytics Insight
Data Scientists, This is the History Behind your Data Science Jobs – Analytics Insight
Ask a graduate about their first step in the tech world and data science is the term that echos. The interesting thing about data science is that the fundamental role of the job existed much before the term was coined. The history dates back to 1962 when researchers, statisticians, computer scientists had initial discussions about this field.
1962 seems like a long time ago, right? Sit back as you are going to read through the timeline of the evolution of data science and its applications.
1962: John Wilder Turkey, an American mathematician, widely known for the development of the Fast Fourier Transform algorithm and box plot, wrote in The Annals Of Mathematical Statistics journal some articles about data science titled The Future Of Data Analysis. As a mathematician, he talked about how his interest grew in data analysis and how the statistical component of data analysis should be characterized as a science rather than mathematics. The article described data analysis as an empirical science.
1974: Peter Naur, a Danish computer science pioneer and the recipient of the Turing award published a book as a survey of contemporary data processing methods with a wide range of applications. Named the Concise Survey of Computer Methods, Naurs book was published in Sweden and the United States and talked about the concept of data according to the definition of IFIP Guide to Concepts and Terms in Data Processing.
Data is a representation of facts or ideas in a formalized manner capable of being communicated or manipulated by some process.
1977: Lets divide this year into two, the John W Turkey year and IASC year. 1977 saw Exploratory Data Analysis, a book published by Turkey arguing about the need for emphasis on using data to suggest hypotheses for necessary tests.
As a section of the ISI, The International Association for Statistical Computing (IASC) established itself with an aim to link traditional statistical methodology, modern computer technology, and the knowledge of domain experts in order to convert data into information and knowledge.
1989: The first Knowledge Discovery In Databases workshop was organized and chaired by Gregory Piatetsky-Shapiro, which became the annual event on KDD in 1995.
1994: The September edition of the Business Week published a cover story on Database Marketing as a first. It read, Companies are collecting mountains of information about you, crunching it to predict how likely you are to buy a product, and using that knowledge to craft a marketing message precisely calibrated to get you to do so. It further added that when the world witnessed the concept of checkout scanners for the first time, the result was a collective disappointment as companies were too overwhelmed by the flood of data and didnt know what to do with it.
1996: The trio Usama Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyth publish From Data Mining to Knowledge Discovery in Databases, which talked about the various names given to the process of finding useful patterns in data like data mining, knowledge extraction, information discovery, information harvesting, data archeology, data pattern processing, etc. They further added that according to the KDD, the overall process of discovering useful knowledge from data and data mining refers to a specific step in the process. Data mining is the application of specific algorithms for extracting patterns from data with additional steps like data preparation, data selection, data cleaning, incorporation of appropriate prior knowledge, and proper interpretation of the results of mining, which are essential to ensure that useful knowledge is derived from the data. The publishing also critiqued the blind application of these methods as they would result in the discovery of meaningless and invalid data patterns.
1997: Professor C.F. Jeff Wu, currently a faculty member at the Georgia Institute of Technology gave his inaugural lecture for the H.C. Carver Chair in Statistics at the University of Michigan. He called for statistics to be renamed as data science and statisticians to be renamed as data scientists.
1999: In a Journal for Knowledge@Wharton, Jacob Zahavi quoted, Conventional statistical methods work well with small data sets. Todays databases, however, can involve millions of rows and columns of data which makes scalability a huge issue in data mining.
Known as Mining Data for Nuggets of Knowledge, the journal also addressed another technical challenge that developing models that can do a better job at analyzing data, detecting non-linear relationships and interactions between elements, and special data mining tools should be developed to solve website decisions.
2001: In a plan to enlarge the major areas of technical work of the field of statistics, Willian S. Cleveland published Data Science: An Action Plan for Expanding the Technical Areas of the Field of Statistics. It talked about data science as a field in the context of computer science and the applications in data mining.
2002: April of that year saw the launch of Data Science Journal that published papers on the management of data and databases in Science and Technology. The Journal contained descriptions of data systems, their publication on the internist, applications, and legal issues as published by the Committee on Data for Science and Technology of the International Council for Science.
2005: The National Science Board published Long-Lived Digital Data Collections: Enabling Research and Education in the 21st Century. The report stated the need to develop the career path for data scientists and make sure that research enterprises have a sufficient amount of professional data scientists. The report further defined data scientists as the information and computer scientists, database and software engineers and programmers, disciplinary experts, curators, and expert annotators, librarians, archivists, and others, who are crucial to the successful management of a digital data collection.
2010: The mention of a new kind of profession as the data scientist emerges in a report written by Kenneth Cukier, The Economist. The role is defined as a professional who combines the skills of a software programmer, statistician, and storyteller/artist to extract the gold hidden under mountains of data.
2012: September 2012 was the time when Harvard Business Review published Data scientist: The Sexiest Job of the 21st Century written by Tom Davenport and D.J. Patil.
The work that started in 1962 to recognize data analysis as a science first and then data science as a profession required in every enterprise, started taking shape in the early 2000s. After 59 years, we now know data science as a booming career option in the tech world. Not just research enterprises, data science is transforming every major industry and small businesses, and refining their business processes to dig out insightful information from floods of data which is more than ever.
To read more about data science and its applications in the post-pandemic era, the one were living and surviving, click here.
Excerpt from:
Data Scientists, This is the History Behind your Data Science Jobs - Analytics Insight