Category Archives: Data Science
Shifting gears: How data science led Madeleine Bonsma-Fisher … – University of Toronto
When Madeleine Bonsma-Fisher bikes through Toronto, she sees where her research meets the road.
Each street she pedals down presents as a series of data points: Shell count 15 people weaving past one another on the sidewalk, while three cars cruise down a road that takes up 80 per cent of the space.
A cycling activist, Bonsma-Fisher is studying traffic patterns as part of her post-doctoral research at the University of Torontos Data Sciences Institute, an institutional strategic initiative that is a tri-campus hub for number crunchers across disciplines. Before that, she modelled evolutionary interactions between microbes.
The common thread? Data and data analysis.
I don't want to say that data science is the answer to everything, but I am finding that there is so much you can do, Bonsma-Fisher says. It gave me a lot of freedom to really just do whatever I wanted.
Her current research focuses on what might seem like a simple question: At any point in Toronto, can you cycle to essential destinations grocery stores, health care and schools within 30 minutes, using only bike lanes and traffic-calmed roads?
The answer, she says, is far from straightforward. It requires sophisticated data analysis to make a map of the entire city and rate each road according to traffic stress, which accounts for factors such as traffic volume, speed limits and physical separation.
The next step, Bonsma-Fisher says, is to pinpoint places where infrastructure could improve access to cycling as a comfortable and convenient mode of transportation, such as dedicated bike lanes and physical separation from car traffic.
As she searches for active transportation solutions, Bonsma-Fisher is working with two advisers at the Data Sciences Institute: Shoshanna Saxe, an associate professor in the department of civil and mineral engineering, and Timothy Chan, a professor of mechanical and industrial engineering both in the Faculty of Applied Science & Engineering.
Whats cool about the Data Sciences Institute is that the vision is to bring people together with different experience and allow people to make that jump to a different field.
The winding road of Bonsma-Fishers research career and the data focus that underpins it began when she arrived at U of Ts School of Graduate Studies in 2014 with a physics degree and an interest in using the fields principles to solve biological problems.
Her supervisor, Sidhartha Goyal, an associate professor in the department of physics in the Faculty of Arts & Science, suggested she look into CRISPR a term she hadnt heard before, but one that would become the subject of both her masters and doctoral studies.
You may have heard of CRISPR in the context of genome editing, but the technology is derived from a bacterial defence mechanism that is analogous to adaptive immunity in humans. Many bacteria have an immune system called CRISPR that allows them to store memories of viruses in their own DNA like a genetic gallery of viral mug shots, Bonsma-Fisher explains.
As part of her PhD research, Bonsma-Fisher built a simple mathematical model to explore how computer-simulated interactions between populations of bacteria and viruses shape CRISPR immune memories.
The paper, published in the journal eLife earlier this year, provides fresh insight into the evolutionary arms race between viruses and bacteria with viruses mutating to evade immune recognition, while CRISPR builds bacterias DNA database of previous attackers. The simplicity of the model helped narrow down the most prominent processes in a complicated system, Bonsma-Fisher says.
Down the road, Bonsma-Fisher says the model could contribute to our understanding of immunity in more complex organisms, including humans.
Some of the conclusions we think are going to apply to any type of immune system-virus interaction.
While she was chipping away at her microbial models, Bonsma-Fisher made another discovery: data analysis skills were in short supply and high demand among her fellow graduate students. So, she co-founded the U of T Coders group to give researchers across all disciplines a chance to learn the basics of programming and teach each other new techniques through hands-on, member-led tutorials.
A lot of people would try to learn by themselves, she says, and there would be a lot of struggle and tears. U of T coders was a place for people to support each other through all of that.
Bonsma-Fisher is interviewed by CBC about cycling infrastructure in Ottawa.
Bonsma-Fishers turn toward sustainability-oriented research around cycling came naturally.
Like many university students, Bonsma-Fisher relied on her bike to commute to campus and was all too familiar with the challenges of being a cyclist in a car-focused Canadian city.
Upon moving to Ottawa, Bonsma-Fisher joined the board of advocacy group Bike Ottawa, where she contributed data analysis to report on how the COVID-19 crisis has influenced cycling trends and advocated for a bike-share program.
The more she learned about transportation infrastructure, the faster the wheels in her head began to turn. What if she could combine her passions cycling and data analysis to make the streets safer and cities more sustainable?
It felt like there were these two parts of me, she says. I [used data analysis] to bring together a lot of things I care about: environmental sustainability and having a more human-scale place to live.
Saxe, who is Canada Research Chair in Sustainable Infrastructure, says Bonsma-Fishers personal investment in the subject is foundational to her work. I find people do better research when they are intrinsically motivated by the topic, she says.
Bonsma-Fisher notes that quantitative data alone cant solve every problem, particularly when it comes to questions of equity and peoples lived experiences. Nevertheless, she says surveys suggest that most adults would be willing to bike if they were physically protected from cars and data can help point policymakers to the places where infrastructure is needed most.
I know from my experience what I want to bike on and what it feels to be on a road that feels unsafe, she says. If the city wants to get people biking and they do they need to make it safe.
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Shifting gears: How data science led Madeleine Bonsma-Fisher ... - University of Toronto
This Job Role will Still be Relevant When Data Scientists be Gone – Analytics India Magazine
In an era when being a Data Scientist was the epitome of cool, college graduates flocked to the field, drawn by the allure of its potential. The hype was real, and the demand for these professionals was skyrocketing. However, as artificial intelligence (AI) and machine learning (ML) continue to advance at an astonishing pace, doubts have arisen about the very existence of Data Scientists. The rapid adoption of AI and ML has ignited a passionate debate about the future of this once-revered profession.
On one side of the argument, there are those who assert that the recent announcement of OpenAI that the company will be introducing plugins to ChatGPt while teasing the launch of a code interpreter and web browser plugin will render traditional data science roles obsolete. They believe that the plugins may replace many of the common workflows of a data scientist, including visualization, trend analysis, and even data transformation. When looking at the code interpreter in tandem with the other advancements in the data science field, there is a notion that the algorithms and automation offered by AI will replace the need for human intervention in data analysis. Conversely, there are those who staunchly maintain that AI and ML will open up new and exciting opportunities in the field of data science.
One such role is of ML Engineers, where experts believe that the role of Data Scientists will gradually transform into. According to a report by Indeed, the job title machine learning engineer is growing at a rate of 344%, while the job title data scientist is growing at a rate of 25%. While another report by OReilly Media found that 80% of data scientists are planning to learn machine learning in the next year.
Since the age of generative AI is catching up, and the models often involve large-scale data processing and sophisticated algorithmic architectures, ML Engineers will be in more demand than ever. The engineers possess the technical expertise to handle the computational challenges associated with training and deploying these models effectively. They have a deep understanding of distributed computing, parallel processing, and GPU acceleration, allowing them to optimize the performance of generative AI models and scale them to handle vast amounts of data.
Additionally, ML Engineers are skilled in the deployment and productionisation of ML models. Generative AI models are not just research prototypes; they are increasingly being integrated into real-world applications. ML Engineers have the know-how to deploy these models into production environments, ensuring their stability, scalability, and robustness. They are proficient in building end-to-end ML pipelines, handling data preprocessing, model deployment, and monitoring, which are crucial steps in incorporating generative AI into practical use cases.
Furthermore, generative AI models often require fine-tuning and customization to align with specific business objectives and user requirements. ML Engineers possess the expertise to fine-tune and adapt these models, leveraging techniques such as transfer learning and hyperparameter tuning. They can tailor generative AI models to address specific challenges and optimize their performance for the intended application domain. Moreover, ML Engineers have a comprehensive understanding of the ethical implications and considerations associated with generative AI. They are aware of the potential biases, fairness issues, and privacy concerns that can arise when deploying AI models that generate content. ML Engineers are equipped to address these challenges and implement safeguards to ensure the responsible and ethical use of generative AI.
The role of a Data Designer is also becoming increasingly crucial in todays data-driven organizations, particularly in the era of Generative AI. These professionals hold the responsibility of defining the organizations unique norm of data, encompassing aspects such as data literacy, models, topics, and ontology. Moreover, they play a pivotal role in establishing a unified and coherent data vision across the entire organization, ensuring that everyone adopts a common language when dealing with data.
The primary focus of a data designer is to establish a structured framework for data management, ensuring that data is organized, accessible, and usable across the organization. They design and implement data models, which serve as blueprints for how data is structured, stored, and interconnected. These models help in capturing and representing the relationships between different data elements, enabling efficient data analysis and interpretation.
In addition to data modelling, data designers also define data standards and guidelines for data governance. They establish data quality criteria and ensure that data is accurate, consistent, and reliable. Data designers collaborate with various stakeholders, including data engineers, data scientists, and business analysts, to understand their data requirements and translate them into practical data design solutions.
Another important aspect of a data designers role is to create a common language or ontology for data within the organization. They develop a standardized vocabulary and terminology that allows different teams and departments to communicate effectively when working with data. This helps in avoiding confusion, improving collaboration, and promoting data literacy across the organization.
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This Job Role will Still be Relevant When Data Scientists be Gone - Analytics India Magazine
Boise State University wins three NSF Regional Innovation Engines … – Boise State University
Boise State University is included in three $1 million awards from the U.S. National Science Foundations Regional Innovation Engines, or NSF Engines, program. Boise State researchers will be represented among 40 unique teams to receive the first-ever NSF Engines Development Awards, which aim to help partners collaborate to create economic, societal and technological opportunities for their regions.
The three Engines and their points of contact at Boise State include:
Nancy Glenn, Advancing Autonomous Systems Technologies in the Northern Front (North Dakota, South Dakota, Montana, Idaho)
This project will expand Boise States expertise in autonomous vehicles, including unoccupied aerial systems (UAS) or drones, in both training and applications, in partnership with industry and network members. UAS applications range from infrastructure monitoring to natural resource management, geosciences, and beyond, along with expanding data science.
Lan Li, Advancing Quantum and Supporting Technologies in the Northern Intermountain States (Montana, Wyoming, Idaho)
This project is to establish a network of quantum computing and information systems, called the Quantum Capacity, Operational Resilience and Equity (QCORE) in a three-state region (Montana, Wyoming, and Idaho). Boise State will collaborate with Montana State University, University of Wyoming, and local industry partners to enhance economic development, research innovation, and workforce development in the field of quantum computing and information.
David Estrada, Advancing Semiconductor Technologies in the Northwest (Oregon, Idaho, Washington)
Aligned to the White House Office of Science and Technologys National Strategy for Advanced Manufacturing, this project includes federal, regional and state government bodies, private industry and public learning institutions to develop a regional innovation ecosystem that expands discovery and entrepreneurship for the semiconductor industry. Partnerships with academic institutions and nonprofit organizations will also advance pathways for careers in semiconductor manufacturing.
All three NSF Engines Development projects represent Boise States expertise in critical and emerging technologies, and will build upon existing workforce training programs and use-inspired research, said Vice President for Research and Economic Development Nancy Glenn. Furthermore, the projects will expand our industry and agency partnerships, ultimately providing new opportunities for students to gain workforce skills and attracting and retaining talent.
One of the greatest challenges facing the information and communications technology ecosystem is the amount of energy required to process and store the tremendous amounts of data we produce, said David Estrada, associate professor of materials science and engineering and site director of Boise States NSF Center for Atomically Thin and Multifunctional Coatings. The emerging Pacific Northwest Semiconductor Ecosystem is very well positioned to solve such semiconductor related challenges, and help reap the economic rewards for Idaho, Oregon, and Washington.
Building a robust quantum innovation ecosystem is crucial for economics and national security, said Lan Li, associate professor of materials science and engineering. Quantum computing and information systems open new market opportunities in cybersecurity, artificial intelligence, financial services, and complex manufacturing. Boise State aims to explore a three-fold plan, including economic development, research innovation, and workforce development, which uniquely fits Boise State and its nationally recognized role in molecular and solid-state quantum materials development and characterization in support of quantum information applications as part of a regional quantum ecosystem.
The NSF Engines program is a transformational investment for the nation, ensuring the U.S. remains in the vanguard of competitiveness for decades to come.
These NSF Engines Development Awards lay the foundation for emerging hubs of innovation and potential future NSF Engines, said NSF Director Sethuraman Panchanathan. These awardees are part of the fabric of NSFs vision to create opportunities everywhere and enable innovation anywhere. They will build robust regional partnerships rooted in scientific and technological innovation in every part of our nation. Through these planning awards, NSF is seeding the future for in-place innovation in communities and to grow their regional economies through research and partnerships. This will unleash ideas, talent, pathways and resources to create vibrant innovation ecosystems all across our nation.
The awardees span a broad range of states and regions, reaching geographic areas that have not fully benefited from the technology boom of the past decades. These NSF Engines Development Awards will help organizations create connections and develop their local innovation ecosystems within two years to prepare strong proposals for becoming future NSF Engines, which will each have the opportunity to receive up to $160 million.
Launched by NSFs new Directorate for Technology, Innovation and Partnerships and authorized by the CHIPS and Science Act of 2022, the NSF Engines program uniquely harnesses the nations science and technology research and development enterprise and regional-level resources. NSF Engines aspire to catalyze robust partnerships to positively impact regional economies, accelerate technology development, address societal challenges, advance national competitiveness and create local, high-wage jobs.
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Boise State University wins three NSF Regional Innovation Engines ... - Boise State University
Building A Data-Driven Organization – Forbes
the Enterprise today.Getty Images
Building data-driven businesses is clearly the topic of most board agendas this year. Increasing technology maturity coupled with the macro-economic environment is driving a sharp pivot to accelerate data monetization and define analytics as a product for many organizations. The reality is that creating a data-driven organization is easier said than done. Research indicates that just 23 percent of executives reported that their companies had created a data-driven organization, down from 31 percent four years ago.
Here are three areas to consider on the journey to building a data-driven business:
Create an enterprise data platform
Building an enterprise data platform (EDP) with truth in data is key to unlocking the value of data across an enterprise. The before and after pictures of companies that started with fragmented data with ownership across distributed groups and moved to a single centralized governed EDP are strikingly clear. The clear and winning model that has evolved is data at the center, analytics at the edge. There is evidence that cloud is actually exploding the total cost of ownership when it comes to data and volume is the single largest problem many in the industry are facing. Breaking down the volume problem by focusing on quality over quantity is key. Leading corporations are setting up data intelligence factories a single place to manage, certify, and publish their data globally. Once in place it can be coupled with a front-end interface for business leaders where they access, and export trusted and reliable business data, directly from the source.
Governance is key in this matter being intentional about the way data is managed, governed, controlled, used, and owned. Were often so focused on the outcome that we forget that the input is really critical. Making data valuable requires the right governance and ownership data strategy, lineage, and clear lines of ownership - with accountability flowing back to the business. In addition, for data to deliver truly meaningful output, data needs to be contextualized at the industry or subindustry level, and building in a foundation for this context is crucial for todays data-driven enterprises.
But even for companies that are truly data-driven, the day-to-day management and governance over data quality and engineering is still an evolutionary discipline. In many ways, this is a journey, not a destination, and there needs to be a fundamental and continued willingness to learn, experiment and innovate, to get to a true data-driven business status.
Develop a clear strategy for data science teams
With the proliferation of data, there is a need to prioritize efforts of data science teams along a portfolio approach, keeping a strong focus on key stakeholders and learning when to follow, partner or lead, as discussed in a recent interview with Murli Buluswar, head of analytics for Citi.
The portfolio approach is tried and tested in the venture capital world, but it applies to data science as well. The data science organization needs to build relevance in the short, medium and long term. You have to be tackling a set of problems that satisfy the business in the near term while ensuring that you're driving step change intelligence in the medium term and then more fundamentally, transformation in the long term. Having that portfolio approach allows you to be strategic and relevant. That is true for capital allocation as well.
Putting yourself in the shoes of your key stakeholders is also a key success factor for a data-driven strategy. If you are thinking like a CEO, you're thinking of materiality. How is this bending the curve on the future of that business unit or the larger enterprise in a way that is meaningful and appreciable at scale? If you are thinking like a CFO, the measurement manifests in either the P&L or the balance sheet. And if you are thinking like a Head of Audit, you have the mindset of assessing whether the decision is having the impact that it intended to. Some examples of outcome metrics include 1. financials realized 2. financials identified, but not yet realized. 3. adoption and non-financial change, for instance the speed of decision-making in some particular area, or more clarity with which decisions are made. 4. new frontiers of innovation, new questions that we are asking that are more early stage and will hopefully manifest themselves in outcomes and financial metrics. 5. the beta of what we do, delivering on your operating commitments a critical part of ensuring that the entire ecosystem is operating effectively.
Looking at analytics and data science end to end is key. The risk for many data science functions is to measure success through a simple functional lens of delivering a set of insights. But those measures are an intermediate step, they are not necessarily the end metrics. A CEO might care about the fact that his or her decision sciences or analytics team is coming out with super useful insights, they are more focused on this is driving financial outcomes, the breadth or the speed or the depth of decision-making in some important area of their business. That requires the data science capability to not just think vertically as a function but think horizontally and understand the end-to-end process.
Establish a data-driven culture
The need to create the right culture within the business is key and the right culture is one that understands the value of data. Data is only valuable if you do something with it. And the best technology leaders play a large role in helping the business really understand how they can use data to achieve better outcomes, asking the questions the business does not know to ask. There is a need to move from data science to decision science within the enterprise. With this new focus, the organization is not just chartered with delivering analytics or insight, it is chartered with delivering clarity on the decisions being made. It therefore needs to have an acute sense of the outcome youre trying to drive.
One great example value of data-driven superior outcomes is digital twins instead of bringing a manufacturing line down and re-laying the parts and pieces, the ability to assemble, run and test a digital twin, before even touching the assembly line, presents significant cost savings for businesses and reimagines how enhancements are delivered.
The other critical success factor is culling ideas quickly and decisively and only developing and driving initiatives that will have an impact at scale and within a reasonable timeframe. Along with the data democratization we want to see, new issues emerge, especially around curiosity, accountability, ownership, and change management often clubbed together as data literacy. A framework can accelerate data literacy programs across the corporation, and the first piece is new tools that are designed for businesspeople instead of just data scientists. The second is driving agile programs across the company that demonstrate the journey from ideation to visualization to outcomes. The third is affecting the mindset of making data a first-class citizen. And the best practice here is driving mindsets top-down, not bottom-up, starting with the CEOs office.
I am Chief Digital Strategist at Genpact, Venture Partner at Masa Group, and Chair of the Executive Technology Board. I advise F500 technology CXOs on digital transformation at the intersection of people, process, data and technology, and I am board advisor to AI startups, limited partner in digital-focused venture funds, and mentor to startup CEOs.
Previously I served as CDO at Genpact, and earlier as CEO for a SaaS firm. Before that I was an entrepreneur and built four startups - in edge networking, data center automation, predictive applications, and enterprise SaaS (and sold to Akamai, BMC, FIS, and Genpact) and well before that I managed compute servers at HP.
I earned my graduate degree at the University of Minnesota, and my undergraduate degree at the Indian Institute of Technology, and studied in the executive education programs at Northwestern and Stanford Universities
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You can get into these online master’s degree programs by … – Fortune
BY Sarah Thomas-OxtobyMay 12, 2023, 12:57 PM
General view of the Beneficence statue on the campus of Ball State University, as seen in November 2016 in Muncie, Indiana. (Photo by Michael Hickey/Getty Images)
Ball State University recently announced plans to re-envision the admissions process for two online masters degree programs in computer science and data science that will launch in fall 2023. The university is partnering with online learning platform Coursera to offer three introductory-level online courses in both fieldsand after students successfully complete the courses and earn an average grade of B or higher, they earn a spot in one of the masters degree programs.
This new admissions approach to these programs is an effort to attract students who may not have been drawn to these fields previously. Both of these programs are built with the person in mind that doesnt necessarily have a background in computer science or any type of data analysis, or programming or math, Jill Coleman, associate dean of the College of Sciences and Humanities and program executive director at Ball State University, tells Fortune. Both masters degree programs are housed in the universitys College of Sciences and Humanities.
Whats more, the online introductory courses are offered at the same cost of $1,314 per three-credit course, regardless of a students state of residency. Once students enroll in either of the masters degree programs, they will continue paying the in-state tuition price of $438 per credit for the degree programs, as well.
By creating a new pathway to enrollment in a masters degree program, Ball State wants to offer a low-cost and inclusive approach to train graduates for jobs in these fast-growing fields. By 2031, the number of jobs is projected to grow 36% for data scientists and 21% for computer scientists, according to the U.S. Bureau of Labor Statistics. These jobs often pay in excess of six-figure salaries.
Heres what you need to know about the changes to Ball States admissions process for these masters degree programs.
Offering an equalizer for people new to these fields
Removing the traditional admittance requirements creates an equalizer for people who want to move into the fields of computer science or data science, Nancy Prater, executive director of market development for online and strategic learning at Ball State University, tells Fortune. Whereas applicants to other masters degree programs typically need to demonstrate their relevant experience in computer science or data science, that will no longer be the case at Ball State.
By first funneling prospective students through the introductory courses, the university can attract a broader cohort to these masters degree programsincluding students who may not have felt qualified to apply previously. Theres not a big involved process or wondering, Will I get in or not get into the program? Coleman says.
Students need only complete a form to register and pay tuition fees before starting the introductory courseswhich can be applied to either of the two degrees.
Streamlining the admissions process
Several elements typically go into an admissions process for an advanced degree program, including completing an application, securing recommendation letters, and preparing for and completing a standardized entrance exam. Theres also generally a minimum GPA requirement for acceptance into these programs.
For those people who are working full-time jobs with family commitments, finding a way to carve out time to complete the admissions process can feel dauntingand particularly for people looking to switch fields. Both the online computer science and online data science programs at Ball State University are designed with these types of student in mind.
The master of computer science has been redesigned and reimagined for this new audience, Coleman says. Both of these programs are looking for the working adult that is seeking a career change and new types of skill sets.
This effort to attract a new type of student is also why the university was receptive to a partnership with Coursera, Coleman notes. We saw this as an opportunity to have our Ball State faculty be spread out to the world, and to enable many more people to have the Ball State experience.
A humanities approach to computer science, data science
Both of the introductory courses, along with the program curriculum for the online masters in computer science and data science, were developed byand will be taught byfaculty from Ball States College of Sciences and Humanities. The colleges involvement is a natural framework that will benefit students, Coleman says.
Where humanities comes into play in both of these fieldsand especially in data scienceis that you have to communicate the information, be a decent writer, and a decent presenter. Because otherwise its just data, she adds.
The faculty intentionally designed the two online programs with a bit of overlap, says Prater. If youre not sure if you want to take computer science or data science, you can figure that out, she continues.
To start, students can take one to two courses which can be applied to either degree. That way students have a little time to test what path suits them best, says Prater.
How to apply to Ball States introductory courses
Beginning in August, students can complete a form to register and pay for the introductory courses. Students who successfully pass the three courses with a combined GPA of 3.0 or better will then be admitted into one of the two masters degree programs, according to Prater.
Each of the masters degree programs will take 24 months to complete. The computer science program requires 36 credits to graduate, with a total tuition price tag just short of $16,000. Meanwhile, the data science program requires 33 credits, and will cost just shy of $15,000.
For the three introductory courses, students can opt to take them at their own paceone at a time or all three at once. The courses each run for 16 weeks, meaning it will take students who are new to these fields anywhere from about four months to nearly a year to complete these prerequisites.
For students where this is a completely new field, I would recommend taking one to two courses to start, says Prater.
Check out all of Fortunes rankings of degree programs, and learn more about specific career paths.
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You can get into these online master's degree programs by ... - Fortune
Cyber Security vs. Data Science Which Is the Right Career Path? – Analytics Insight
Here is the comparison between the most in-demand fields Cyber Security vs. Data Science
Todays IT-intensive environment has taught us two important lessons: we need solutions to transform tidal surges of data into something that organizations can utilize to make educated decisions. We must safeguard that data and the networks on which it is stored.
As a result, we have the fields of data science and cyber security. So, which is the better job path? You wont get far if you approach the debate between cyber security vs. data science in terms of which field is more in demand. Both fields are in desperate need of a workforce.
Cyber security is the discipline of securing data, devices, and networks against unauthorized use or access while assuring and maintaining information availability, confidentiality, and integrity. A career in cybersecurity entails entering a thriving industry with more available positions than qualified applicants.
Data science combines domain knowledge, programming abilities, and mathematical and statistical knowledge to generate usable, relevant insights from massive amounts of unstructured data, often known as Big Data.
A career in data science includes carrying out data processing responsibilities, data scientists often use algorithms, processes, tools, scientific methods, techniques, and systems, and then apply the derived insights across multiple domains.
Data science and cyber security are inextricably linked since the latter demands the defences and protection that the former supplies. To obtain their conclusions and assure the security of the resultant processed information, data scientists require clean, uncompromised data. As a result, the area of data science looks to cyber security to assist protect the information in any form.
For someone interested in a career in one of the more intriguing and busy IT disciplines, cyber security and data science present fantastic chances. The career trajectories in both fields are comparable.
Experts in cyber security often begin their careers with a bachelors degree in computer science, information technology, cyber security, or a related profession. Aspirants in the field of cyber security should also be proficient in fundamental subjects like programming, cloud computing, and network and system administration.
The prospective cyber security specialist joins a corporation as an entry-level employee after graduating. After a few years of work experience, its time to apply for a senior position, which normally calls for a masters degree and certification in a variety of cybersecurity-related fields.
Cyber security experts choose career paths like security analyst, ethical hacker, chief information security officer, penetration tester, security architect, and IT security consultant.
Data scientists demand more formal education than cyber security specialists. A masters or even a bachelors degree isnt required for cybersecurity professionals, though having those resources helps. A bachelors degree in data science, computer science, or a similar branch of study is required for most data science professions. After a few years in an entry-level role, the ambitious data scientist should seek a masters degree in Data Science, reinforced by a few relevant certifications, and apply for a position as a senior data analyst.
Data science experts choose career paths like data engineer, marketing manager, data leader, product manager, and machine learning leader.
According to Glassdoor, the average yearly salary for cyber security specialists in the United States is US$94,794, whereas this figure is 110,597 in India.
In the field of data science, Indeed reports that US-based data scientists make an average salary of US$124,074 annually, while their Indian counterparts earn an average salary of US$830,319 annually.
Depending on demand, the hiring of certain individuals, and the location, these numbers frequently change.
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Cyber Security vs. Data Science Which Is the Right Career Path? - Analytics Insight
Justin Yee: A Poised Personality Highly Evolved and Impacted by … – Analytics Insight
The marketing industry has undergone a significant transformation in recent years, and the rise of data in marketing has played a major role in this transformation. While traditional media analytics, reporting, and dashboarding focused on front-end media metrics, there is now a greater emphasis on classical media analytics topics such as attribution, media mix, and optimization. As someone who has worked in the marketing industry for several years, Justin Yee has witnessed firsthand the evolution of the industry and the impact that data science has had on it.
Justin says My career in data science and marketing began at RAPP, an advertising agency that focuses on personalized customer experiences. At RAPP, I was part of a team that was responsible for analyzing data to inform our clients marketing strategies. Working at RAPP gave Justin a strong foundation in data analysis, and he learned how to use data to identify customer behaviors and preferences.
From there, Justin moved on to Initiative, a global media agency that is part of IPG Mediabrands. At Initiative, he was able to apply his skills in data analysis to a broader range of marketing channels, including digital, social, and traditional media. Justin was also able to work on campaigns for a variety of clients across multiple industries, which gave him a deeper understanding of how different businesses approach marketing.
Justin says My most recent role has been at Dentsu, a global advertising agency that is part of Dentsu Group, one of the largest holding companies in the advertising industry. At Dentsu, he continued to expand his skill set in data science and marketing. Justin has been able to work on some of the largest and most complex marketing campaigns in the industry, and he was challenged to find new and innovative ways to use data to drive better results for their clients.
Throughout his career journey, Justin says I have been fortunate to work with great mentors and coworkers who have helped me grow both personally and professionally. These individuals have provided me with guidance, feedback, and support, and they have challenged me to take on new and exciting opportunities. They have helped me develop my skills in data analysis and marketing strategy, and they have taught me the importance of collaboration and teamwork.
The importance of mentorship and good coworkers cannot be overstated. In a fast-paced and ever-changing industry like marketing, having a supportive network of colleagues can make all the difference. They can help you navigate complex challenges, provide feedback on your work, and offer insights that you may not have considered. They can also provide you with a sense of community and belonging, which is essential for anyone working in a high-pressure environment.
Justin says My career journey has paralleled the growth of the industry. In the early days of my career, data was viewed as a luxury rather than a necessity. However, as the industry evolved, the importance of data became increasingly apparent. Today, the ability to extract value from the vast amount of digital data available is essential to any successful marketing campaign. As a data scientist in the marketing industry, I have been able to use my skills to analyze data and provide insights that drive better decision-making.
One of the most significant changes in the industry has been the shift from traditional media analytics to more complex analytics techniques. Attribution modeling, media mix modeling, and optimization are now key components of any marketing campaign. By using these techniques, marketers can gain a more complete understanding of the effectiveness of their campaigns and make data-driven decisions.
Moreover, marketers can now look at data aggregations and split them by different attributes such as channels, creativity, and more. Additionally, it is now possible to delve deeper into audience dimensions, including demographics such as age and position. This level of granularity provides marketers with a more complete picture of their target audience and helps them develop more effective campaigns.
While the opportunities presented by data science in marketing are vast, they also raise ethical concerns. With the rise of hyper-targeting in marketing, issues surrounding data privacy have become more prevalent. Recent scandals such as the Facebook and Cambridge Analytica scandals have highlighted the importance of data privacy and the need for greater regulation. The General Data Protection Regulation (GDPR) and Apples tracking permission prompts are just two examples of how the industry is trying to address these concerns.
In a cookie-less world, the role of data science in marketing is only going to become more important. With the impending loss of third-party cookies, marketers will need to rely more heavily on first-party data. This shift will require marketers to be more strategic in their use of data and to develop a more complete understanding of their customers. It will also require marketers to develop new methods for targeting customers and measuring the effectiveness of their campaigns.
As marketers, we must continue to embrace the potential that data science offers while also being mindful of the ethical considerations surrounding its use. Data can provide invaluable insights into customer behavior, but it is important to use that data responsibly. Ultimately, Justins career journey has taught him that while the marketing industry may change, the importance of ethical considerations and the need to evolve with the industry remain constant.
In conclusion, data science has had a profound impact on the marketing industry, and Justins career journey has been closely intertwined with the industrys evolution. As a data scientist, he has seen the industry shift from traditional media analytics to more complex analytics techniques. Justin also witnessed the growing importance of data privacy and the need for greater regulation. While the challenges facing the industry are significant, he is confident that by embracing data science and remaining mindful of ethical considerations, we can continue to drive innovation and create more effective marketing campaigns.
Quote: As the popularity and attractiveness of data science solutions grows, companies will focus on the accessibility and favorability of these solutions, as the user experience and perception of these solutions will determine widespread adoption.
Management: Justin Yee, Senior Manager, Data Science at DentsuWebsite: https://www.dentsu.com/
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Justin Yee: A Poised Personality Highly Evolved and Impacted by ... - Analytics Insight
St. Jude scientist M. Madan Babu elected to the Royal Society of … – St. Jude Children’s Research Hospital
St. Jude Childrens Research Hospital scientist Madan Babu Mohan, Ph.D., Center of Excellence in Data-Driven Discovery director and member of the Department of Structural Biology, has been elected a Fellow of the Royal Society of London. The Royal Society is the oldest scientific academy in continuous existence.
Babu was selected to join the Royal Society for his pioneering data science-based strategies to reveal fundamental principles in biological systems. His scientific accomplishments include determining the molecular mechanisms governing G-protein-coupled receptor (GPCR) signaling, uncovering the roles of disordered protein regions in biology and disease, and establishing genome-scale principles of gene regulation.
One-third of all Food and Drug Administration-approved drugs target GPCRs, membrane proteins found on the surface of cells. Babus work has shown how genetic and isoform variability of GPCRs can influence drug responses. His most recent work investigated how GPCR selectivity for G-proteins is determined. Understanding this family of proteins is of tremendous interest to the development of novel therapeutics.
I am honored for our work to receive this recognition, Babu said. The science we have achieved is possible because of long-term support for fundamental research and the collaborative environment at St. Jude and the MRC Laboratory of Molecular Biology in Cambridge, England. I am grateful for the many contributions of my past and current colleagues, as well as my mentors and family.
Dr. Babus election to the Royal Society is well-earned, and we are all honored to call him a colleague, said James R. Downing, M.D., president and CEO of St. Jude. His investigations of GPCRs have the potential to have profound implications for pharmaceutical development. Through these discoveries, we can advance cures for pediatric cancer and other catastrophic diseases.
I am delighted to welcome our newest cohort of Fellows, said Sir Adrian Smith, President of the Royal Society. They are pioneering scientists and innovators from around the world who have confounded expectations and transformed our thinking.
Founded in the 1660s, the Royal Society is an independent scientific academy of the U.K. and the Commonwealth. Its Fellows have included many of the worlds most eminent scientists and technologists, representing a range of personalities, from Sir Isaac Newton and Benjamin Franklin to Dorothy Hodgkin and Robert Webster (St. Jude Infectious Diseases, emeritus).
This year sees 59 Fellows, 19 Foreign Members and two Honorary Fellows elected. Babus fellow U.S.-based new Fellows and Foreign Members include researchers at Google DeepMind, Harvard University, the Howard Hughes Medical Institute, the Institute for Advanced Study, Stanford University and the University of Chicago.
Babu joined the faculty of St. Jude in 2020, following a 14-year tenure as a program leader at the MRC Laboratory of Molecular Biology in Cambridge, England. He earned his Ph.D. in computational genomics from Cambridge University and his undergraduate degree from Anna University, Chennai, India. Babu completed a postdoctoral fellowship with the National Institutes of Health, Bethesda, Maryland.
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WVU Today | WVU tapped to transform neuroscience research in the … – WVU Today
West Virginia University will play a pivotal role in a $20 million, National Science Foundation-funded project that will strengthen neuroscience research, the workforce and infrastructure in the state. (WVU Illustration/Michelle McGettigan)
West Virginia University will help elevate neuroscience throughout the Mountain State by ramping up academic scientific research, making strategic faculty and staff hires, and adding state-of-the-art infrastructure to address the fields most perplexing challenges.
The National Science Foundation-funded project will also benefit students by providing them with opportunities in neuroscience and data research and will contribute overall to the science education of K-12 schools in West Virginia.
Randy Nelson, chair and professor of the WVU Department of Neuroscience, part of the Rockefeller Neuroscience Institute, is leading the Universitys efforts for the proposed West Virginia Network for Functional Neuroscience and Transcriptomics, a collaboration of neuroscientists and bioinformaticists working to position the state as an epicenter for neuroscience research. Joining Nelson on the project are WVU researchers Eric Horstick, of the Department of Biology; Aric Agmon, Morgan Bridi, Michelle Bridi, Martin Hruska, Charles Anderson, all of Neuroscience; and Michael Hu, of Microbiology, Immunology & Cell Biology.
The WV-NFNT will aim to expand and diversify the neuroscience and data science workforce in the state through implementing education and development activities for students, especially those who are rural, first-generation college students, and from other underrepresented groups.
A significant portion of the research component of this project will be conducted by an undergraduate workforce, Nelson said. Immersive teaching of K-12 educators, formalized training and mentorship of undergraduates in both neuroscience and data science research, coupled with access to internships, will contribute to the goal of placing West Virginians in competitive post-graduate programs or employment in STEM industries. Broadening participation will be supported at the K-12 level through the equity focus of CodeWV and other programs such as summer brain and data sciences camps.
CodeWV, housed at WVU, helps bring computer science to every K-12 student in West Virginia schools. WV-NFNT will partner with CodeWV to expand offerings by working with research faculty to identify the content and skills students need for data science and bioinformatics fields. The focus will be data literacy in early grades, and data science introduction in advanced courses.
Among the top research goals, according to Nelson, is studying synaptic and circuit plasticity, which involve changes in neurons and the connections between them as the result of developmental or environmental changes. This work will provide the foundational knowledge of how the brain typically develops and ages; thus, subsequent research could provide insights into brain function that is atypical, such as in autism, Alzheimers disease or schizophrenia.
The underlying flexibility in neuronal structure and function to cope with changing environments, broadly known as neuronal plasticity, is the basis for how organisms can adapt and survive when confronted by change, Nelson explained.
Under different conditions, developmental stages, stimuli or environmental exposure, brain plasticity can be influenced, he said. Neural plasticity can be achieved through adding or removing nerve cells (neurons) or by remodeling existing neurons at different spatial, molecular or physiological scales.
For example, connections between neurons might be strengthened during memory formation or neurons might be recruited or deleted from a circuit that helps process sounds.
Despite the importance of plasticity, the mechanisms underlying how these changes are made in the brain remain unspecified. Nelson said the grant will provide the tools and personnel to examine these changes at a microstructural and single-cell genetic level.
The project was awarded a highly competitive, five-year $20 million grant from the NSFs Established Program to Stimulate Competitive Research, which is facilitated by the West Virginia Higher Education Policy Commissions Division of Science, Technology & Research. WVU will receive $9.3 million as part of the project, which includes Marshall University, West Virginia State University, Shepherd University and WVHEPC heading up the initiative.
WVU, in collaboration with its colleagues, Nelson said, will play a central role in developing and deploying new technologies such as stimulated emission depletion microscopy and single-cell or spatial transcriptomics gene transcription to understand the plasticity of synapses, glia, neurons and circuits in animal models.
A new STED microscope will be placed in the Biology Department to examine ultrastructural changes in the brain. New equipment will also be obtained for conducting spatial transcriptomics analyses. Spatial transcriptomics is a relatively new molecular profiling method that allows neuroscientists to assess all the gene activity in a tissue sample and map where the activity is occurring. WVU also operates Imaging and Genomics Core facilities, which will expand under the initiative.
WV-NFNT hopes to capitalize from the strength of the WVU Center for Foundational Neuroscience Research and Education and the RNI. The WVU Neuroscience Department launched in 2018, and the neuroscience program, includes more than 100 undergraduate majors, 20-plus doctoral students and a new masters program beginning later this year.
University leaders said they believe the project helps bolster the Universitys standing as an R1 institution and strengthen its collaborative efforts, not just across campus but beyond.
One of the most important ways we can change the trajectory of our state is to fund initiatives that educate our future workforce and provide hands-on training and research opportunities, said Dr. Clay B. Marsh, WVU Health Sciences chancellor and executive dean. The collaborative nature of this project not only raises the profile of WVU but also that of our partner institutions, and we are grateful to the National Science Foundation and the West Virginia Higher Education Policy Commission for recognizing the importance of education and scientific research in the field of neuroscience.
Its great to see the continued growth in fundamental neuroscience at WVU, said Sheena Murphy, associate vice president for research development at the WVU Research Office. This is a cross-disciplinary effort engaging researchers from both Health Sciences and the Eberly College. Its also exciting to see that there are so many assistant professors who are key to this collaboration.
-WVU-
js/05/08/23
MEDIA CONTACT: Jake StumpDirectorWVU Research Communications304-293-5507; jake.stump@mail.wvu.edu
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Journal of Data Science and Intelligent Systems To Sponsor UC … – Rebellion Research
Journal Of Data Science And Intelligent Systems Becomes a Free Media Partner with UC Berkeleys Financial Innovation Conference
TheJournal of Data Science and Intelligent Systems(JDSIS)is an international, peer-reviewed, interdisciplinary journal that provides in-depth coverage of the latest advances in the closely related fields of data science and intelligent systems.
Conference Agenda
JDSISconsiders researches that focus on data integration, data information and knowledge extraction, and data application in a wide range of fields, including health, education, agriculture, biology, medicine, finance, environment, engineering, commerce, and industry.
By integrating of data with computer science, artificial intelligence, and other appropriate methods, the scope ofJDSIScovers the entire process of areas of Data Science and Intelligent Systems.
The journal is aGold Open Accessjournal, online readers dont have to pay any fee.
All Article Processing Charges (APCs) waived until the end of 2023.
UC Berkeleys Financial Innovation Conference
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Journal of Data Science and Intelligent Systems To Sponsor UC ... - Rebellion Research