Category Archives: Data Science
Data Scientist: The best Job of 21st Century – – VENTS Magazine
The Harvard Business Review mentioned long back that Data Scientist is the sexiest job of this century. And the reason for that is as the billions of devices are hugely generating data, the need to manage data & infer from it is becoming essential. Furthermore, it is a high-paying field & also there are going to be numerous jobs in the coming future; hence the Harvard Business Review mentioned it so in their edition. So if you are looking to shift your career or begin your career and think of taking a Data Science Course, this article should be a must-read for you. Dont think twice; just grab your drink, sit back for a few minutes & read on!
What is Data?
Data is basically any information. It is a matter written on a piece of paper, calculations made on an article, a memory in the brain. Or it can be an image, a video, or an excel sheet. Basically Data is anything informative. Particularly, since the last two decades, the term Data is referred to as computer information. Specifically, the text files, audio files, video files, software, images & so on. And the study of this is taught in the Data Science course. Further, Data is being referred to as any computer information stored in the hard disk/RAM in the binary format i.e., 0s & 1s. Data in the field of science is broadly classified into two categories.
The Data is in organized form like the one that is produced in the excel sheets. Specifically, the row-column data. And a significantly less percentage of the data in the world is structured.
Basically, this is the unorganized Data. Specifically, the text files, images, audio files, video files, CSV files, etc., come under this category. Specifically, a large percentage of data of the world is unstructured. And a Data Science course mainly deals with collecting, analyzing & inferring from structured & unstructured data.
What is Data Science ?
Basically, Data Science is the study of vast volumes of data collected from text files, audio files, etc. Particularly, the heavy volume of Data is processed using modern techniques & tools, and valuable information is observed. And this information is used to make vital business decisions. Specifically, this study enhances pattern discovery & predictive analysis, which ultimately helps in making better decisions. And this is the core of any Data Science course.
Moreover, this has applications in almost every sector. And it may be Automobiles, Manufacturing, Textile, Software, Sports, Telecommunication, Electronics, Finance, Media, Marketing, Advertising, Entertainment, etc. Furthermore, Modern & high-quality algorithms are employed in inferring valuable information. The main aspects of DS are Analysis, Warehousing, and Visualization.
The primary tasks that Data Science allows are as follows:
Prerequisites to learn Data Science
Basically, to undergo a Data Analytics course , you need to have some specific skills/methodologies. And they are as follows.
Particularly, an adequate level of programming knowledge is necessary to learn this science. Basically, experience in any language is a good sign. And the languages may be C, C++, Java, Javascript, etc. Specifically, Python & R are feasible languages as they are very much used in DS & Machine Learning.
Basically, DS is more about tabulating the data, which are the results of many algorithms used in the study. Moreover, a good level of statistical knowledge gives a good start in this field. And if you are an expert or professional in statistics, then it becomes an added advantage. Furthermore, this allows extracting better intelligence & results from the Data. Lastly, most of the Data Science Course suggest you know at least the basics of Statistics.
Basically, the Database is the foundation of DS. The entire DS process begins with Databases. Moreover knowing any of the SQL or NoSQL is an added advantage. Languages like MySQL, NoSQL, MongoDB, PostgreSQL, Oracle, etc. are should & must to learn DS. Particularly, if you are from a software or computer applications background, it will be easier for you to pick up on them.
Mathematics is a critical factor in DS. And the mathematical models allow you to infer information from the Data which you already know. Moreover, the Mathematical model will enable you to select the appropriate algorithm to be applied to get the desired results. If you come from a mathematical background, then it will be an added advantage.
Basically, Machine Learning (ML) is said to be the backbone of DS. And having a good understanding of ML is a great plus point. And some of the Data Science Course teach ML in their curriculum.
What does a Data Scientist basically do ?
Basically, A Data Scientist analyzes the data & extracts meaningful insights to help make better business decisions. And this will be the core of any Data Science Course.
The basic steps that a Data Scientist takes are:
Conclusion
Finally, after looking at the various aspects of Data Science, we can conclude that Data Science is undoubtedly an exciting field. And DS in the coming years will be an inevitable aspect for any sector. Specifically, it is because DS enhances decision making which thereby accounts for increased profits. And we recommend you to take the suitable Data Science Courses that various institutes offer.
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Data Scientist: The best Job of 21st Century - - VENTS Magazine
7 Principles To Be Invincible In The Data Science World – Analytics Insight
In every industry today, data science is a hot topic. Rightfully so, because it is bringing industries like artificial intelligence, machine learning, big data, and data visualization to life. To be a successful data scientist, having the will to learn and unlearn is crucial. So if youre about to begin your data science career, these 7 mantras will help you stay steady through the big picture, and if youre an experienced professional, here are some tips you can include in your day-to-day data work.
If you start working on data, create models, and prepare descriptive analysis assuming the data is clean, you can come up with wrong hypotheses. Instead, looking for discrepancies in data can present a lot of important patterns. For example, if a column has more than 50% values missing, an analyst A will think about dropping the column. But if the same error was in a data collection instrument, spotting it would help the business improve. Finding out such errors will open opportunities for questions that might lead to a bigger picture.
In data science, one has to tell stories through data. The companys board of directors and stakeholders will be expecting statistics plans and insights from you which will only be understood by them if you ace effective visualization to show them your datas story and effective communication skills to narrate your opinions. When you spend a lot of time collecting, cleaning, exploring, and modeling data, finding interesting patterns and presenting it will mundane visualization will be ineffective.
Remember this, every business problem is different and it should be optimized differently. For example, if a client wants you to optimize for active users, you should judge better and advise him to optimize the percentage of active users instead to know how the clients product is performing. Having the right metric in place before modeling a data science project is crucial in getting accurate insights.
Embrace the scientific side of data, not just the technological side. According to Colin Melody, senior manager in data science at Deloitte, data science must remember the scientist part of their job. At all times, you are looking to provide evidence which supports an idea. This means, from end-to-end, you must challenge your assumptions, your data, test, and retest, refine, and start again. There is a myriad of tools and technologies available for data scientists and, while it is not necessary to know how to use all of them, try to get a sense of what it might take to grow your toolbox.
While youre mastering all the concepts needed to be a pro data scientist, master when to take your knowledge out. Data science is a field that has new and different advancements every day. There is a possibility that you wont know everything and waiting for it will not accomplish anything. The wait for know enough is not a constant factor. The term is too subjective to risk building a good project or apply for a role. So once your foundation is ready, be out there and apply your knowledge wherever possible.
Data science is not an independent field. That means data science is an interactive interdisciplinary field that depends on other fields like maths, statistics, and scientific learning. Any data science sub-field will require you to tap into machine learning, artificial intelligence, and NLP. So it is advisable to keep yourself up-to-date with everything surrounding the field, not just the bare minimum.
The data part of data science is obvious, but its also about the entire problem and the solution youre trying to find out. Understanding the needs of the end-user will help you solve the problem. It will not be easy at first, but with an inquisitive mind, ponder over the dataset and build the model step by step by understanding how the end result will interact with your model.
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7 Principles To Be Invincible In The Data Science World - Analytics Insight
Why the World Needs More Data Scientists and How Professionals Can Take Advantage of This Opportunity. – Influencive
Who is a Data Scientist?
The word data scientist is a recently familiarized term and refers to professionals who are adept at assimilating massive bulks of data, organizing them and finally analyzing themchunks of structured and unstructured data coming their way to be relieved.
The data scientists retrieve the data from the mass, process them, and model them into decipherable form and finally evaluate them to reproduce results employed by organizations and companies in their strategies and plan to survive in the market.
What is the job of a Data scientist?
The data scientists are professionals working for several organizations and firms who tackle heaves of unprocessed data, condense them into meaningful and applicable data and deliver them to the organization or firm which they implement in their functioning.
One of their chief jobs is to elucidate their technical evaluations and finding to their non-technical employers for this. They must be well versed in their zone of work, alongside being able to elaborate it properly with proper communication skills.
The umbrella of the data scientist houses several designations that functions in different sectors:
Data Scientist: It is the work of a data scientist to seek the pertinent question and the answers for the same all, in the form of data. That is then structured into information by them and communicated to the data Analysts who delineates it to the organization, which further discusses them with the stakeholders of their institutions and makes decisions for their businesss growth and regulation.
Data Analyst: A data analysts functions bridge the gap between that of a data scientist and a business analyst. They receive the question from the firms and organizations as well as the technical findings for the same; their work is to analyze and evaluate the crude technical data and formulate results that can be utilized as business strategies. They are the translators and commuters of the technical findings from the data scientists to practical strategies and actions.
Data Engineer: They are the observers of data the change in it, the fluctuations, evolutions, advancements and alterations in the concerned data. The information regarding the data is engineered and channelized by them to the Data scientists, who further start working with the new data.
What are some of the basic skills necessary for a Data scientist to have?ProgrammingRisk AnalysisEffective communication skillsGood Research abilities
There are a large number of skilled data scientists emerging. Lately, prospective students often have mentions of several courses and diplomas on data science in their CVs Data Science Courses in Delhi, from institutes like Madrid Software, or similar create very good impressions about the students on the employers alongside their skills.
How are some ways in which the data scientist can help professionals with their work?
Detection of which is fraud and which is risky from the associations that an organization or firm plans to indulge in is a vital job that the data scientist masters. They provide their valuable suggestions to the firms, who get careful about their association this saves them from losses, bad reputation, and cheats, thereby uplifting their business. Lately, the data scientists have had major contributions in regions of augmented reality.
The professionals, who are willing to up their names in the market by including evolved and developed technologies in their business and functioning, can benefit from a data scientists works. Another area of expertise of the data scientists have been in the field of gaming. Thereby, the professionals can inherit valuables from the data scientists in the field of gaming too.
The professionals in the industry of drug development can benefit a lot from the data scientists because drug dealing involves a lengthy and complex procedure that can be simplified by a data scientist capable of condensing the work into an easier procedure with their advanced technological functioning and mechanisms.Thereby, the data scientists are a very crucial organ for professional firms, who want more work in lesser time.
Published April 17th, 2021
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Enveda Biosciences Recruits Former Lilly Chief Science Officer as New CSO – Business Wire
BOULDER, Colo.--(BUSINESS WIRE)--Enveda Biosciences, a leading biotechnology company harnessing the power of natures chemistry to develop next-gen therapeutics, has appointed Sotirios Karathanasis, Ph.D. as the companys new Chief Science Officer. Dr. Karathanasis will spearhead the companys small molecule drug discovery portfolio and work closely with Envedas preclinical team to advance the companys lead programs for NASH and Wilson's Disease, along with a number of undisclosed programs.
As former CSO of endocrine and cardiovascular research at Lilly Research Laboratories, Dr. Karathanasis will accelerate Envedas current portfolio in metabolic and cardiovascular diseases and oversee its expansion to other complex disorders. He will collaborate closely with the data science team to investigate the vast untapped potential of naturally derived compounds and build cutting-edge tools to quickly enable their successful translation into therapeutics.
Having generated the largest dataset of medicinally-important plant metabolomes purpose-built for machine learning, Enveda is unlocking a new era of small molecule discovery, said Dr. Karathanasis. I see tremendous potential in Envedas technology to address challenging targets and fundamental disease-causing processes.
Beyond his important work at Lilly, Dr. Karathanasis has held a number of other key positions in the pharmaceutical industry, including Vice President and Head of Biosciences at AstraZeneca and Director of Cardiovascular Pharmacology at Pfizer Global Research & Development. During the course of his career, Dr. Karathanasis has led drug discovery teams with as many as 250 scientists across diverse geographic locations, disciplines, and functional interfaces. He holds 23 patents and has published over 100 original manuscripts, review papers, and book chapters.
Sotirios will bring critical expertise and proven executive leadership to our mission of developing a world-class portfolio in cardiovascular, metabolic, and other complex diseases that have proven refractory to conventional methods, said Viswa Colluru, Ph.D., Envedas Founder and CEO. His experience will help our team of biologists and medicinal chemists deliver validated, first-in-class drug candidates inspired by unique chemical starting points.
Enveda is building the worlds first high-resolution chemical map of the natural world to inspire new medicines for the toughest diseases. The companys platform is unlocking this massive, high-potential chemical space for drug discovery by growing its proprietary metabolomics dataset, rapidly iterating the technical capabilities of its platform, and advancing its portfolio through preclinical development. The company sees great promise in applying its technology to develop molecules that will ultimately have the same transformative impact on medicine as aspirin, metformin, and statins.
About Enveda Biosciences
Enveda Biosciences is a biotechnology company building the first high-resolution chemical map of the natural world to tackle the toughest problems in drug discovery. Envedas platform is the worlds most advanced drug discovery search engine for dark chemical space, building on years of cutting-edge advancements at the intersection of metabolomics and machine learning. Complementing its breakthrough technology, Envedas team includes seasoned drug hunters with decades of experience in the pharmaceutical industry working alongside world-leading data scientists. For more information on Enveda, visit envedabio.com.
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Enveda Biosciences Recruits Former Lilly Chief Science Officer as New CSO - Business Wire
When to Hire a Chief Data Officer and Who – Built In
Quick. What do Coca-Cola and the Centers for Disease Control and Prevention have in common? Yes, both are based in Atlanta, but theres a lesser-known shared trait. Both recently hired their very first chief data officer.
That might seem a surprise for the latter, since the healthcare industry has largely embraced the role. But the former is part of an emerging trend. While the CDO role originated at Capital One and, for years, predominated in finance, then in health, consumer brands and services are increasingly embracing the CDO. Today, SeatGeek, Hulu, Sothebys and Poshmark are just a few that sport a data officer in the C-suite.
It all goes back to digital transformation, the degree to which interactions are digital, said Cindi Howson, chief data strategy officer at ThoughtSpot and host of The Data Chief podcast. You want to get your data house in order.
Consider Arcadia Group, the now-defunct company behind several clothing retailers, most notably Topshop. The pandemic accelerated its demise, but according to retail watchers, it had already been spiraling due to a failure to fully embrace data-driven e-commerce.
It was one of the biggest high-street fashion retailers in Britain in the last decade, [but] because they didnt get their heads around digital and data, its gone bust, said Peter Jackson, chief data and analytics officer at Exasol and co-author of The Chief Data Officers Playbook.
According to Jackson, there are a few broad factors that influence an organizations decision to bring on a CDO. One is data maturity or, sometimes, immaturity. That is, a company recognizes its lagging in data and wants to proactively avoid becoming another Topshop. Related is the realization that data-enabled personalization can provide standout service and boosted revenue in sectors where margins are thin.
Another factor might be regulatory pressure. Thats why financial services embraced it early, Jackson said. You get regulators who start to not trust the data, and organizations get fined for handing over incorrect or inaccurate data.
Lastly, dont discount the keeping-up-with-the-Joneses impulse: Others are doing it, so should we! Not the best rationale, Jackson said, but one that might deliver nonetheless.
In 2021, fewer and fewer companies doubt the primacy of data. But does that necessarily mean an organization needs to take on the expense of installing a dedicated data officer into the upper echelons of the org chart? What about building a robust data team without a chief data officer?
For Doug Laney, the proof is in the numbers. Laney, a data and analytics strategy fellow at West Monroe and an instructor in Carnegie Mellons chief data officer certificate program, conducts an ongoing survey of companies about the impacts of CDOs. So far, hes interviewed some 500 organizations, and a few key statistics have emerged.
According to Laneys research, organizations with a CDO are:
The last figure is notable, as many companies fail to formally value their data, since accounting often doesnt consider it a balance sheet asset the way it does other technologies. Aside from being a bitter irony, the failure to quantify the value of data creates a vicious cycle of not monetizing it.
You cant manage what you dont measure, Laney said, quoting an industry maxim.
While the rise of the CDO also reflects the elevated stature of data in general over the last decade-plus, Howson agrees that the structure of senior data management is itself important. In Howsons opinion, the most impactful CDOs are those who report to the chief operating officer or directly to the CEO.
Its almost like the CDO started out reporting to IT, because data was seen as an IT thing, she said. But over time, they realized this really helps improve customer loyalty, boosts revenue and increases operational efficiencies.
Data leadership is also not necessarily the domain of even the highest-ranking technical officer.
I think if you asked a lot of CTOs what DataOps is, they wouldnt know, whereas I think the CDOs know how to create an operating model that governs the data and leverages the value, Jackson said.
RelatedWhat Kind of Startup Needs a Chief Operating Officer?
Talk to enough CDOs and an inevitable theme arises: offense versus defense. Because the role originated in finance focused on data governance and regulatory compliance it was born defense-minded. But over the years, a shift has occurred.
In the debut episode of her podcast The Data Chief, Howson identified the shift from the data streamlining and safeguarding focus of CDO 1.0 to a focus on leveraging data for digital transformation and business impact as one of the key evolutions in data management.
If the CDO cannot evolve to that, then theres no point in having a CDO, she said.
A poll conducted by Howson showed that her analytics colleagues overwhelmingly measured success through the lens of business KPI improvement, rather than amount of data collected, number of users enabled or response time rates.
That jibes with the most recent NewVantage Partners survey, an annual poll of 85 firms about AI adoption and the role of the CDO. Seventy percent of respondents said that offense-oriented data initiatives are more vital than defense-oriented ones like regulatory and compliance issues for CDOs.
Its not an either/or proposition, of course. CDOs are still expected to mitigate risk related to data. But their jobscant be limited to just security and run-of-the-mill reporting.
Laney recalled working as a consultant with publishers who had reams of valuable data subscriptions, clicks, views but werent using them beyond basic metrics.
Not only were they not using it internally, but they were also not making it available to partners, suppliers, customers or others in the extended business ecosystem, who might find it valuable and pay for it, he said.
Related4 Steps to Company-Wide Data Literacy
Corporate adoption of the CDO role continues to climb. Sixty-five percent of mainstream companies have now incorporated the role, compared to just 12 percent in 2012, according to the NewVantage Partners survey. And yet, the role is still not completely defined.
One of the most polarizing questionsis whether a company should prize technical expertise or business strategy in a CDO candidate. The answer? It depends on who you ask.
I think its one of the hardest jobs because you have to know the technology, but you also have to know business, Howson said. And you have to be respected by both.
Jackson, of Exasol, said hes seen a slight shift in recent years towardpeople with strongertechnical backgrounds, namely former data scientists. Thats one of four traditional paths hes observed. People migrated to the CDO role either from CTO positions (or aspiring CTOs), from governance positions (especially around the time GDPR was implemented), from business strategy backgrounds or from data science.
A CDO has to be technically credible conversant, if not necessarily expert, in ETL pipelines and different query languages, Jackson said. So coming more from the technical and data science side is probably stronger than from the governance and strategy side.
Laney, however, sees it differently.
I think the most successful CDOs are those with the people skills, more so than the technical skills, and those who report into the business, not into the IT organization, Laney said.
He stressed firm grasps of supply chains, consumption and production models, a dual offense/defense capability, an ability to drive organizational data literacy and a firm background in economics applying concepts like supply and demand, productivity frontiers and price elasticity to data.
Those kinds of economic concepts were never intended to be applied to data, he said, emphasizing the difficulty of that last requirement. They were intended to be applied to guns and butter.
Such difference of opinion is reflective of some larger uncertainties related to the role. The NewVantage Partners survey found that just 49.5 percent of CDOs have primary responsibility for data within their organization, and just a third said the role is successful and established.
That bears out in what respondents consider a profile of a successful CDO. While external change agent/outsider has won out the past four years, the percentage that favored company insider/veteran and data scientist both climbed from 2020 to 2021.
That preference for so-called change agents may help explain the significant turnover rates for CDOs.
Nearly 18 percent of respondents in the NewVantage Partners survey said they struggled with CDO turnover.Few candidates kept a position longer than two years, according to IBM research based on data from 2014 through 2016. Laney said the average tenure is about 2.7 years based on his observations, while Parker noted each of his last three CDO positions were less than two-and-a-half years.
That can be a bad thing in instances where people feel pushed out by change-resistant employers, but its more often a sign that goals are being accomplished, Howson said.
They get in there, do the work, get that company in flight, and are onto the next problem and a bigger impact, she said. That, I think, is a good thing.
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Stateless and PacketFabric Unlock the Value of Automation in Data Transport – StreetInsider.com
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Partnership Gives Businesses the Agility and Reach to Innovate and Grow without Network Limitations
BOULDER, Colo.--(BUSINESS WIRE)--Stateless, Inc., the company putting data back in motion, and PacketFabric, the leading provider of on-demand connectivity for hybrid and multi-cloud IT, today announced a partnership to unlock the value of network automation for data science and warehousing workflows.
As hybrid and multi-cloud workflows become a mainstay for data science applications, agility and control of the data transport between those clouds becomes crucial to ensure data security, performance, and business continuity.
Stateless and PacketFabric have come together to give data science teams more control over hybrid and multi-cloud data workflows. This joint solution provides point-and-click setup and configuration of the secure data transport needed to move data between data centers, application clouds, branch offices, edge data aggregation sites and more.
Building data transport today requires a complicated and time-consuming setup of network devices, connectivity, and cryptic transport configurations between many endpoints, said Eric Keller, Stateless CTO. At Stateless, were changing the paradigm for networking. Companies need routing, not routers. Data science teams shouldnt have to understand the intricacies of BGP just to move data between clouds. Now, they can set up, configure, and change their entire multi-cloud connectivity infrastructure in minutes.
Combining Stateless microservices approach to network functions with the security, performance, and reach of PacketFabrics backboneand automating everythingwill give customers the speed and agility they need without sacrificing security, control and visibility of their networks, said Dave Ward, PacketFabric CEO. Its the easy button for data scientists to get the most out of the network without the long lead times and costly implementations theyve had in the past.
This new joint solution is available directly from Stateless, and through Stateless-enabled service providers. Customers pay only for the creation of the connections, eliminating the need for expensive, unpredictable charges for network devices or virtualized network functions.
Organizations are struggling to build their data pipeline as their infrastructure expands across different clouds, said Murad Kablan, Stateless CEO. Because of this complexity, data becomes siloed within independent networks. At Stateless, we help customers eliminate the network as a barrier to effectively create value from the ever-increasing volume of data that is being produced. Our partnership with PacketFabric allows us to deliver on that goal one step further by leveraging the vast and highly automated network of PacketFabric.
Supporting ResourcesLearn more about the PacketFabric partnership.
About PacketFabricPacketFabric redefines enterprise hybrid and multi-cloud connectivity. PacketFabrics Network-as-a-Service (NaaS) platform leverages end-to-end automation, a private optical network, and the latest in packet switching technology. PacketFabric delivers on-demand, private, and secure connectivity services between hundreds of premier colocation facilities and cloud providers across the globe. IT, network, and DevOps teams can deploy cloud-scale connectivity in minutes via an advanced Application Program Interface (API) and web portal. PacketFabric was named the 2020 Fierce Telecom Innovation Award for Cloud Services, one of the "10 Hottest Networking Startups of 2020" by CRN, and a 2020 Cool Vendor in Enhanced Internet Services and Cloud Connectivity by Gartner. PacketFabric investors include NantWorks and Digital Alpha Advisors. For more information, visit http://www.packetfabric.com.
About StatelessStateless is software that puts data back in motion. The hybrid, multi-cloud data ecosystem has exploded. Data has become scattered and siloed and now is anywhere but where it needs to be. Your data infrastructure needs a new network. We free you to move data without sacrificing visibility, security, or control. Because we separate state from function, Stateless makes it possible for applications to talk to the network and for the network to talk right back. Easily replicate network configurations across users and endpoints, transition to network automation, and drive better data pipeline observability. Unlock a new level of network segmentation based on data taps and not just users or tenants. Stateless. Data in motion. Stateless is proudly based in Boulder, Colorado. Learn more at http://www.stateless.net.
2021 All rights reserved. Stateless is a trademark owned by Stateless, Inc. All other trademarks are the property of their respective owners.
View source version on businesswire.com: https://www.businesswire.com/news/home/20210420005344/en/
Media Contacts:IGNITE Consulting, on behalf of StatelessKathleen Sullivan, 303-439-9365Linda Dellett, 303-439-9398stateless@igniteconsultinginc.com
Source: Stateless, Inc.
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Stateless and PacketFabric Unlock the Value of Automation in Data Transport - StreetInsider.com
Data scientists and technology working in tandem – TechNative
In this new age of knowledge automation, successful business strategies often depend on using skilled data scientists to enhance the advances provided by new technology
This technology will not replace people. In fact, it will serve to enhance talent by unlocking the potential for the digital workforce of the future.
For data scientists working across asset-intensive industries, and for the manufacturers and operators they serve, its therefore vital that streamlined data processes and increasingly self-sufficient technology are not seen as a threat but rather, as an opportunity to raise the value skilled workers deliver to data science applications and large-scale projects.
Enabling teams to fulfil their potential
As the roll-out of AI and advanced automation accelerates, organisations need to use these technologies both to better define the roles of employees and to add value to them. Asset-intensive manufacturers and operators need to ensure that engineers have clear job functions that play to their strengths and that internal data scientists have the freedom and flexibility to add value to the business.
In line with this, data scientists must be allowed to evolve their role in delivering success to organisations through the use of advanced technology, helping them streamline engineering and maintenance processes alike.
Todays data scientists need to understand that technology can act as an ally. Industrial AI solutions can capture knowledge from engineers and analysts in a way not previously achievable. That is especially key for industries that are digitally transforming while vast amounts of knowledge exit the workforce as workers retire. This knowledge capture will bolster the platform for learning that data science can build on, complementing the role of analysts within the organisation rather than threatening them.
Raising the value of the data scientists role
Ultimately, knowledge automation technologies should enable data scientists to focus on more strategic initiatives, positively enriching the data scientists role. For example, built-in sensors can flag anomalies or potential errors in software with pinpoint accuracy that can provide clarity to data scientists to make large-scale improvements.
Senior managers must change their mindset and start giving data scientists the opportunity to work on projects where they are likely to have a wider, more profound impact. Creating a distinction between the smaller issues that engineers should tackle and the overarching challenges that are the responsibility of data scientists also helps define job roles.
Another change of mindset that can reap significant business benefits is an organisations approach to projects. Implementations based solely on proof of concept or project scale, rarely deliver maximum impact. Larger-scale rollouts often bring faster return on investment, especially when compared with limited in-house data science projects which may not deliver value for months. The time it takes to build, tune, and deploy a data science model is often the biggest challenge for organisations that pursue an in-house approach, and scaling is not easy.
Greater clarity provides greater opportunity
This is where great advantage can be found by working side by side with a packaged outsourced solution delivered by a third-party provider or partner, as it can often bring faster time to value through ease of use, scalability and deployment speed. With greater clarity about their role and responsibilities, data scientists can harness this technology to make enhancements to processes and complex workflows that help to transform the whole organisations working model.
In other words, with technology having built the firm foundation, data scientists have the freedom to be more creative, and explore the full potential of their knowledge and expertise to deliver added value to manufacturers and operators, and to the industry as a whole.
About the Author
Matt Holland is VP EURA at Aspen Technology. AspenTech is a global leader in asset optimization software helping the worlds leading industrial companies run their operations more safely, efficiently and reliably enabling innovation while reducing waste and impact on the environment. AspenTech software accelerates and maximizes value gained from digital transformation initiatives with a holistic approach to the asset lifecycle and supply chain.
Featured image: 2020
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Data scientists and technology working in tandem - TechNative
Harvard Data Science Initiative Upcoming Events | Department of Biostatistics | Harvard TH Chan School of Public Health – HSPH News
Industry Seminar: Juan M. Lavista Ferres, MicrosoftThursday, April 15, 2021 | 1:30pm to 2:30pm ET |Registration
Everything you always wanted to know about AI and AI For Good* but were afraid to ask
The Microsoft AI for Good Research Lab is a philanthropic team of data scientists and applied researchers dedicated to using AI, Machine Learning, and statistical modeling to tackle some of humanitys most significant challenges. We partner with leading nonprofits, research institutions, NGOs, and governments to accelerate work across the AI for Good program portfolioEarth, Accessibility, Humanitarian Action, Cultural Heritage, Healthas well as other pressing issues such as affordable housing, broadband access, digital skills, justice reform, legal compliance, etc. This talk will describe the type of work we do, the lessons we learned, the impact, and the pitfalls.
Juan M, Lavista Ferres is currently the General Manager and Lab Director of the Microsoft AI For Good Research Lab, where he works with a team of data scientists and researchers in AI, Machine Learning and statistical modeling, working across Microsoft AI For Good efforts. These efforts includes projects in AI For Earth, AI for Humanitarian Action, AI For Accessibility and AI For Health.
Bias^2 Seminar:Yeshimabeit MilnerThursday, April 22, 2021 | 1:30pm to 2:45pm ET |Registration
Yeshimabeit Milner is the Founder & Executive Director of Data for Black Lives. She has worked since she was 17 behind the scenes as a movement builder, technologist and data scientist on a number of campaigns. She started Data for Black Lives because for too long she straddled the worlds of data and organizing and was determined to break down the silos to harness the power of data to make change in the lives of Black people. In two years, Data for Black Lives has raised over $3 million, hosted two sold out conferences at the MIT Media Lab and has changed the conversation around big data & technology across the US and globally.
As the founder of Data for Black Lives, her work has received much acclaim. Yeshimabeit is an Echoing Green Black Male Achievement Fellow, an Ashoka Fellow and joins the founders of Black Lives Matter and Occupy Wall Street in the distinguished inaugural class of Roddenberry Foundation Fellows. In 2020, Yeshimabeit was honored as a Forbes 30 under 30 social entrepreneur.
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Data Science Institute virtual event April 16 to share data used to predict elections – Vanderbilt University News
How do television networks predict election outcomes? The virtual event A Peek Inside the NBC Decision Desk: Election 2020 scheduled for Friday, April 16, at 2 p.m. CT will provide an overview. Registration is required.
Josh Clinton, Abby and Jon Winkelried Chair and professor of political science, will discuss the data networks use to predict elections and the ways that information is used to determine which candidates will emerge victorious. The discussion is hosted by the Data Science Institute.
While the precise details of the data models are confidential trade secrets, participants will learn about some of the challenges involved in projecting races brought about by the decentralized administration of elections across the United States.
The Vanderbilt Data Science Institute accelerates data-driven research, promotes collaboration and trains future leaders. The institute brings together experts in data science methodologies and leaders in all academic disciplines to spark discoveries and to study the impact of big data on society. The institute is educating students in computational and statistical data science techniques to become future leaders in industry, government, academia and the nonprofit sector. This is the third and final discussion in the spring speaker series.
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Artificial Intelligence and Data Science Are Top of Mind as These Two Grantmakers Join Forces Inside Philanthropy – Inside Philanthropy
For some, artificial intelligence and data science are fantastic technologies that will benefit people and society. For others, theyre terrifying assaults on individual privacy and dire threats to human existence. The point is, these latest innovations are evolving fast in the hotbeds of business, science and government, and it can be difficult for regular citizens and civil society to keep up, particularly nonprofits and others involved in addressing the spectrum of societys needs. What is philanthropys role in the growth, use and regulation of these powerful and protean technologies?
These are some of the questions Vilas Dhar considers in his role as president of the Patrick J. McGovern Foundation, a relatively young grantmaker whose late founder built a fortune in publishing and industry research, tracking the expanding computer industry in the late 20th and early 21st centuries. McGoverns International Data Group published a number of popular computer industry magazines such as Computerworld, PC World and InfoWorld.
McGovern the person was a believer in technologys potential to improve society and the human condition. He is remembered for some notable philanthropic moves involving science and technology, including a $350 million pledge in 2000 that established the McGovern Institute for Brain Research at MIT.
McGovern the foundationestablished in 2015, a year after the death of its namesakeis also active in the information technology world. The foundation has so far made about $295 million in grants, in areas like tech education, climate change, digital health and pandemic response, as well as data science and AI ethics.
Now, in a move common in business but rare in the philanthropy and nonprofit world, the McGovern Foundation has augmented its powers through a high-profile merger. It recently announced that the Cloudera Foundationa philanthropy created by Silicon Valley data and AI software company Cloudera Inc. to bring data analytics technology to the nonprofit sectorhas merged its $9 million endowment, staff and operations into McGovern.
Dhar says the merger with Cloudera creates an organization thats neither exclusively a philanthropic foundation nor a technology company. Its a hybrid that says were an impact-driven organization that will pull from the private sector when we need to, will pull from technology companies when we need to, and will pull from the long history of philanthropy in this country to build something that actually drives outcomes for people, he said. To me, thats the direction of where the field is already going and should be going.
Most often, grantseekers just require cash to maintain or expand services, pay employees and to keep the lights on. But when it comes to a novel and developing field like data science, it can pay to have a funding partner with the experience to envision potential solutions and the hands-on expertise to design those solutions. Toward that end, the newly expanded McGovern Foundation plans to be something of a technology consulting group for philanthropy and nonprofits.
Claudia Juech, the now-former CEO of Cloudera Foundation, will have a central role in the new hybrid organization, directing activities around data enablement for nonprofits as the head of its new Data and Society program. According to Juech, McGoverns approach will involve resourcing the field as nonprofits seek new ways to apply data science to their work. While creating solutions for specific nonprofits will be part of the job, more central to the mission going forward will be creating tools to let nonprofits everywhere access new technology. We can only work with so many organizations, she said.
McGoverns Data and Society team will create and share a portfolio of solutions to serve as practical examples of whats possible in the field of data and AI for social change, guided by equity principles and the ethical use of data. The bigger question, Juech said, is how can we make this accessible to the broader sector?
What are some possibilities for nonprofits as they delve into these new data and AI applications? As in business, one potential area is predictive tools that let organizations better plan and prepare for future problems and needs. Its evolving, Juech said. A lot of nonprofits are using data science to look backward, to understand what happened. But what is possible these days is to see more of what could happen. For example, the Cloudera Foundation helped Womens World Banking create tools to predict the future of womens financial inclusion and empowerment in emerging markets. Another grantee is using data to forecast malaria outbreaks in West Africa.
Of course, artificial intelligence and data science are hot-button issues these days, with many observers voicing unease about potential dangers, including racial and algorithmic biases or the loss of privacy. This is no theoretical worry. One of the most widespread applications of AI affects nearly everyone in the U.S. and billions around the worldthat is, social media companies use of algorithms to populate individual newsfeeds, which has contributed to political polarization, volatility and even violence in the U.S. and abroad.
Those concerns have not escaped Dhar. Though hes a self-described tech optimist, he nevertheless believes philanthropy must keep potential pitfalls front and center, and that nonprofits and the people they serve must be part of the conversationrather than leaving it all up to tech companies and government. The answer isnt to get rid of one or get rid of the other, he said. Its to let civil society be the ones who are coming into that conversation and promoting all of our best interests.
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