Category Archives: Data Science
Nontraditional Student Will Be a Double Hoo, 33 Years Apart – UVA Today
When Maureen OShea heard about UVAs School of Data Science, the youngest of her six children was about to graduate from high school.
OShea, who spent the last two decades living in Charlottesville, decided to attend the Masters in Data Science open house. After listening to faculty members speak, she left feeling inspired and thought, I think I can do this.
In 2021, OShea, who will graduate this spring, began her first semester in the School of Data Science. She took courses that interested her and helped her find a tightknit community. These students are from a variety of fields and ages, something OShea cites as a benefit of the program.
They offer such a broad variety of classes, and its very interdisciplinary, so people are coming from all different backgrounds, she said. Our cohort is only about 50 students, so everybodys familiar, and you can get to know the professor and the teaching assistants.
The School of Data Science offers courses in data ethics, preparing students with decision-making tools. OShea noted that these skills are essential for becoming a data scientist.
In our first semester, we took a class in data ethics, which I found to be really important. As data scientists, we have to take ethics very seriously and question what people are asking you to do with the data, because the way you tell the story with the data can change decision-making.
This isnt OSheas first time as a student on Grounds. After receiving her undergraduate degree from Loyola University in Baltimore, she came to UVA in 1986 for a masters in material sciences and engineering.
The masters program was incredible and also where I met my husband, who was getting his masters in mechanical engineering, she said.
After receiving her UVA degree, she worked at IBM in their failure analysis department. She spent five years there before opting out of the work force to be with her children.
I really enjoyed IBM, and I was fascinated with failure analysis because Im very curious. I always like to know why? and dig into the nitty gritty of things, OShea said. We eventually decided that Id like to stay home and raise our kids. And then, you know, six kids later, I was still home, and I wouldnt trade it for the world.
Now back at UVA, 33 years later, technology has changed the classroom. OShea has noticed a clear difference in how professors teach using technology and how students interact with the class material.
By far, the biggest change between my two UVA experiences has been the advancement in technology. In the 1980s, we didnt have laptops and went to the computer lab to type our papers, she said. I admire and am amazed at how students today are able to take notes on their computers or iPads during class lectures, even though I still take notes the old-school way, by hand.
OShea said a key component of her success in returning to UVA was her preparation prior to the program. She took courses at Piedmont Virginia Community College before starting in the School of Data Science.
I went to PVCC for a semester and brushed up on some skills before joining the Data Science program and I think its huge to familiarize yourself with some material so youre able to hit the ground running, she said. I was sitting in a calculus class at PVCC, and the girl I was next to was someone who graduated with my daughter, which was really funny.
After her second Final Exercises walk down the Lawn, OShea hopes to continue volunteer work with organizations in Charlottesville and East Africa. With new skills from the School of Data Science, she can provide these organizations with critical assistance.
During my time at home, Ive been involved in a number of volunteer organizations. Ive worked with The Haven, a homeless day shelter, to help their guests file taxes. I would like to go back and use the skills Ive learned to help places like The Haven more formally, she said.
Im also involved in an investment angel network that supports entrepreneurs and private sector development in East Africa. Through impact investing, we fund small/medium-sized East African companies with investments and loans. I have a soft spot for the women-owned and educational enterprises.
As OShea prepares to graduate, she is leaving UVA with a desire to help others and gratitude for her time on Grounds.
Just maybe Ill come back for a third graduate degree, OShea said.
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Nontraditional Student Will Be a Double Hoo, 33 Years Apart - UVA Today
For Six Sigma Black Belts: It’s Time To Break Fresh Ground With Sustainable Process Performance – Manufacturing.net
When manufacturing processes are no longer effective, what do you do?
The upheaval suffered by manufacturers since the onset of the COVID-19 pandemic is forcing companies to rethink their processes. Its not surprising that Six Sigma thinkers, as process improvement experts, are high on the go-to list for help.
But for Six Sigma experts, its also time to rethink.
To see why lets take an example: Back in 2012, our process improvement team completed a DMAIC (design, measure, analyze, improve, control) project to improve the process performance of a plants production line. Their recommendations were operationalized and they concluded the project by installing a control system.
The purpose of the control system was to ensure continued performance of the new process at the plant. Any dip would be caught and rectified. This sounds good, but move forward now to 2022: The control system still validates performance based on the data that was collected 10 years ago.
Its highly likely that the data used in 2012 upon which the process was set up is no longer relevant. So why is new data never used by the control system?
In short, becauseDMAIC does not iterate between phases.
The DMAIC control phase is isolated from the analysis phase. In our DMAIC project, we collect the data, define the problem, run our analysis, operationalize our recommendation and define a control system to ensure process performance. But the control phase never revisits the analysis. And our analysis is never re-run based on new data.
We need to merge the analysis and control phases to enable a continuous loop: Analyze Operationalize Control Analyze Operationalize Control.
Over in the data science world, data scientists take an iterative approach, known as thedata science life cycle. They are collecting the data (in real time) and learning from the data to then improve, describe and predict an outcome/produce an analysis. This outcome is what the data scientists call their model. To ensure that the model continues to perform well when operationalized, it is continually monitored and, whenever necessary, updated. Should there be a dip in performance, they go back to retrain (update) their model on new data, i.e., reanalyze and re-operationalize.
Manufacturers are spending a lot of time, money and effort to improve their processes. In DMAIC, we also need to see how we can improve our process. To be truly sustainable, we need to merge the analysis and control phases and create an iterative cycle. We need to add data science.
As we monitor performance of our production line process, new data is flowing into our database on a daily basis from the sensors fitted to the machines. Typical statistical packages are unable to handle todays high volume of data at high frequency; the effort to collect data also from multiple sources is huge.
Using a data science tool, we have the means to not only easily process huge volumes of data but also do so in real time. We can easily read in the 5 million datasets produced by our production line. Our data science solution literally learns from the data, evaluates the data and produces a model. But it doesnt stop there.
Our data science tool lets us go back to the beginning of the cycle and optimize our process based on the new data.
Now when new people come into the process, e.g., a new supplier is needed for a certain part and new lines are developed, we can feed this new data into our analysis. We can reuse our model: It learns from the new data and produces an optimized result.
A single workflow, for example, enables us to read in the 5 million datasets produced by the machine each day, evaluate this data, show us a visual pre-scan and then run the data through three different models before providing a comparison to tell us which model is performing best. It only takes seconds to run.
The process is now immediately responsive to any changing circumstances reflected in the data. With our DMAIC tool, we would have needed to start an entirely new project to solve the issue.
Fig. 1. KNIME workflow for data preparation, dataset evaluation, visual pre-scan of the data, and model building.Six Sigma
At any stage in the project, we can go into our analysis and check the output of different stages. We can inject our knowledge as process experts, for example, examine the correlation and numeric outliers to get a sense of the quality of the data and tweak as needed. We can use the pre-scan to interactively zoom in to inspect a group of figures in more detail.
If we see that something is wrong, we can immediately go back a step, make an adjustment and rerun the workflow.
Six Sigma
Fig. 2. Two pre-scan visualizations showing sensor data in an interactive parallel coordinates plot and scatter matrix.Six Sigma
In a DMAIC project, we tend to define a single hypothesis, using regression analysis to measure whether results align with what we are expecting. But are we comparing our regression analysis with any other model type? Probably not.
With our workflow, however, we can not only regularly evaluate how our model is performing but also set up multiple models and evaluate how they are all performing.
In our example, a visualized comparison shows us the quality of our three models. The results: Decision tree 0.91-very high, Naive Bayes 0.73-also good, Logistic Regression 0.74 show us that although our regression p value is OK, the decision tree is performing better. In typical Six Sigma tools, analysis techniques such as decision trees or Naive Bayes are not available options.
We can also decide to run each model based on 10 different test and training sets and it takes only a second. It provides us with failure rates and visualization for each scenario.
With our data science solution, we can regularly evaluate our process, it is able to respond quickly to changes in the data and we can compare based on a range of models if performances are changing, check why and deploy the best process.
We can even automate this entire cycle.
By enabling the control system to be automatically monitored, evaluated and (re)deployed, we ensure not only that it gets done reliably but also produces much more accurate results. When you tell a machine to control a process, it just does it. And keeps on doing it.
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Andreas Riess is an expert in Six Sigma and quality engineering. As a certified trainer and supporter, with 15 years atMTS Consulting Partner, he is recognized on both national and international levels and offers consulting services throughout the preparation and ramp-up of new production lines based on a Six Sigma-supported structured approach. His work is backed by 25 years of experience at a global automotive technology company that supplies systems for passenger cars, commercial vehicles and industrial technology. Andreas earned his engineering degree from The University of Applied Sciences Wrzburg-Schweinfurt.
Heather Fyson is a content writer in data science forKNIME, a data analytics software company bridging the worlds of dashboards and advanced analytics through an intuitive interface, appropriate for anybody working with data. KNIME is distinct in its open approach, which ensures easy adoption and future-proof access to new technologies.
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10m Irish project to focus on treating ALS with data science and AI – Siliconrepublic.com
Precision ALS will bring together clinicians, data scientists and AI experts to find new ways to treat motor neurone disease.
A new 10m Irish project is looking to develop new and innovative treatments for patients with motor neurone disease by bringing together medical research, data science and artificial intelligence.
Launched today (1 March) at Trinity College Dublin (TCD), Precision ALS will build tools to enable clinical trials based on precision medicine, where treatments are personalised for individual patients.
The project will be led by two Science Foundation Ireland (SFI) research centres: the Adapt centre for AI-driven digital content technology and the FutureNeuro research centre for neurological diseases.
It will bring together clinical scientists, data scientists and AI experts to collaborate on data-driven prediction models for progression of the neuromuscular disease and data analysis that will help develop treatments.
Prof Orla Hardiman, who is the director of Precision ALS and a professor of neurology at TCD, said that there is an increasing recognition of the need for precision medicine in developing drugs for motor neurone disease, or ALS, which only affects humans.
ALS is also a heterogeneous disease, she explained, which means that it has many different causes and patterns of progression, and a large amount of data is required to understand these differences.
Using big data analyses, Precision ALS will provide an in-depth understanding of the factors that drive heterogeneity, and in doing so will for the first time allow us to target new and innovative treatments to specific patient subgroups, Hardiman said.
The research is supported by the Irish Government through an SFI investment of 5m, which will be matched by an additional 5m from industry partners.
Speaking at the project launch, Tnaiste and Minister for Enterprise, Trade and Employment Leo Varadkar, TD, said that Precision ALS will combine the best of our technologies, the best of our ideas, and the best of our medical research to change the lives of patients living with the disease.
It will develop tools that facilitate clinical trials based on precision medicine and has the potential to produce benefits for other rare conditions and diseases, supporting job creation and reducing drug costs, he added.
The project will also provide an interactive platform for clinical research in ALS across Europe, which will use AI to analyse large amounts of data gathered at scale and in a timely and cost-effective manner across multiple international sites.
Prof Vinny Wade, director of the SFI Adapt centre, said that Precision ALS brings together a perfect mix of data and technology research skills to trailblaze discoveries in tackling these devastating diseases.
Wade believes that the centres experience in researching data sets for immediate interrogation using AI will help identify contributing factors and help discover changes linked to ALS.
Unlocking this data in an ethical way is the key to achieving the research mission and realising true precision medicine. This pioneering work will lead to transformational change for patients with a ripple effect that will positively impact society, he added.
The Ireland-based researchers will work in partnership with TRICALS, an independent consortium of leading ALS experts, patients and patient advocacy groups across Europe. Companies partnering with Precision ALS include Biogen, Novartis, Takeda, IQVIA, Roche and Accenture.
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10m Irish project to focus on treating ALS with data science and AI - Siliconrepublic.com
Alation Celebrates International Women’s Day with 2nd Annual Women Leading Data Intelligence Event – Business Wire
REDWOOD CITY, Calif.--(BUSINESS WIRE)--Alation Inc., the leader in enterprise data intelligence solutions, today announced that it will host its second annual Women Leading Data Intelligence event. The event will take place on International Womens Day, Tuesday, March 8, 2022 at 8 a.m. PT, and feature a panel of exceptional individuals from Fidelity Investments, Snowflake, and WEX, that are driving data culture in their organizations. To register for the digital event, visit https://events.alation.com/IWD2022.
The women that power data and technology organizations represent a massive talent pool often overlooked or undersupported in male-dominated industries. To support diversity in data careers, Alation created Women Leading Data Intelligence, which provides a forum for networking, education, and career development for women in analytics, data science, machine learning, and artificial intelligence.
The event panel, moderated by Jen Wu, Senior Director of Product Management at Alation, will feature Jennifer Belissent, Principal Data Strategist at Snowflake; Meredith Frinsko, Director, Data Strategy & Governance at WEX; and Susanne O'Neill, Workplace Investing - Data Governance COE Leader at Fidelity Investments. Julie Smith, Director of Data & Analytics at Alation and Tracy Eiler, Alation Chief Marketing Officer and founding member of Women in Revenue, will host an intimate fireside chat following the panel.
Women are a growing force in data and technology, and we are proud to showcase these leaders driving data culture and change within their organizations, said Eiler. The impact of women in our workforce is something that should be recognized throughout the year and we are excited to bring together some of the most influential voices in our industry to highlight their innovative contributions.
The most successful and productive organizations have a diverse employee-base, and gender is a key aspect of diversity. At Alation, 30% of employees are women who lent their work ethic and tenacity to the companys achievements this past year. Alations executive leadership team is nearly 35% women, most recently adding Langley Eide as Chief Financial Officer. The company welcomed Preeti Rathi, General Partner at Icon Ventures, to its board of directors in October 2021. According to the Top Companies for Women Technologists Report that surveyed more than half a million technologists, women represent only 26.7% of the technical workforce on average.
In May 2021, Alation was named one of Inc. Magazines Best Workplaces. To be considered for the list, Alation employees participated in the magazines survey, and 79% of women cited that they saw professional growth and career development opportunities for themselves at Alation. In the last year, Eiler founded a Women at Alation group where women Alationauts participate in quarterly events to discuss topics such as the wage gap, career growth, and mentorship.
To continue supporting women in data and technology, Alation is a proud sponsor and supporter of Women in Data and Women in Revenue for the second consecutive year. Women in Data was founded to increase diversity in data careers, and provide awareness, education, and advancement to women in technology, specifically analytics, data science, machine learning, and AI. Women in Revenue was founded in 2018 by 12 revenue-driven women. The organizations mission is to achieve workplace equity through networking opportunities, mentorship, and education.
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About Alation
Alation is the leader in enterprise data intelligence solutions including data search & discovery, data governance, data stewardship, analytics, and digital transformation. Alations initial offering dominates the data catalog market. Thanks to its powerful Behavioral Analysis Engine, inbuilt collaboration capabilities, and open interfaces, Alation combines machine learning with human insight to successfully tackle even the most demanding challenges in data and metadata management. More than 330 enterprises drive data culture, improve decision making, and realize business outcomes with Alation including AbbVie, American Family Insurance, Cisco, Exelon, Fifth Third Bank, Finnair, Munich Re, NASDAQ, New Balance, Parexel, Pfizer, US Foods, and Vistaprint. Headquartered in Silicon Valley, Alation was named to Inc. Magazines Best Workplaces list and is backed by leading venture capitalists including Blackstone, Costanoa, Data Collective, Dell Technologies, Icon, ISAI Cap, Riverwood, Salesforce, Sanabil, Sapphire, and Snowflake Ventures. For more information, visit alation.com.
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Key data analytics trends shaping businesses in 2022 and beyond – Times of India
Over the past decade, data-driven business transformation has inspired enterprise leaders to reimagine business models, new revenue streams, customer experiences, operational models, and processes. Extracting actionable insights from business data often helps organizations bridge the last-mile gaps in analytics and drive faster value realization. In fact, it has grown so popular that market research by Global Newswire confirms the data science market is expected to be valued at nearly $133 billion by 2026.
Considering the impact that data analytics will have in the business world, it is crucial to identify the upcoming trends in this space. The following key trends will definitely play a role in shaping the data analytics industry of tomorrow.
Shifting from a project to a product mindset
Todays forward-thinking clients demand a sustainable, mature product-centric delivery model from their data analytics partners and want them to move from a project mindset to a product mindset. Data science is not limited to just building an accurate algorithm and creating a dashboard around it. Clients today want to go beyond just fancy dashboards; they want the ability to create full-stack applications that can be integrated into an enterprises decision-making process. This requires a mature product mindset. Clients today want a custom product shop, not a project shop. Adopting a product mindset will also pave the way for data analytics solutions that increase revenue and have better business outcomes.
Overhauling the revenue model
Enterprises have dramatically shifted revenue models in the wake of the Covid-19 pandemic. Fixed cost models are a thing of the past and data analytics solution providers are expected to have more at stake with a gain-share model or an outcome-based revenue model. Since the success of a data analytics solution primarily lies in measuring tangible outcomes, clients find it prudent to share both profits and risks equally with solution providers. This also prompts data analytics companies to continuously tweak solutions to ensure that the right business outcomes are met.
Adopting Agile methodologies
The Agile way of working is well established in the software development industry. However, Agile methodologies have only recently begun to gain traction in the world of data science. They allow data scientists to optimize outcomes by prioritizing tasks based on preset benchmarks and performance goals while empowering them to iterate, learn new things, and experiment until the desired result is achieved, strengthening the product mindset.
Operationalizing data science
Clients prefer service providers who operationalize and standardize their data science models and move them towards production environments. The answer to operationalization lies in the three ops MLOps, AIOps and DataOps. MLOps or model ops refers to the collaborative process of building, managing, deploying, and constantly monitoring machine learning models to consistently deliver the right inputs to the organization. AIOps or artificial intelligence ops is the implementation of AI to IT operations with the goal of governing IT structures that are hybrid and distributed. It contributes to smarter, quicker operations within an IT framework by managing the large amount of data generated. DataOps is an innovative methodology that seeks to optimize the time of a full-cycle data analytics solution. Its Agile approach to data analytics ensures that data scientists and users work together to create valuable analytical insights.
Last-mile in data science
Conquering the last mile in data science can be an enormous task. After the entire data collection, preparation, exploration, and modeling lifecycle, the last-mile adoption includes operationalization and translating those data models into actionable insights that drive business impact. This information can further be used to bridge the gap and offer cognizant inputs to drive operational changes in the enterprise. To ensure last-mile adoption, data scientists must start with conceptualizing solutions with the end goal in mind to ensure a clear blueprint to detail the kind of insights required to reach the set goal.
Another roadblock to last-mile adoption is the lack of data interoperability. Leveraging the right data and capitalizing on a robust data strategy hones the focus on execution for data scientists.
In conclusion, enterprise innovation has changed so has its work, methodology, scale, and speed. Data is at the epicenter of this change. Large enterprises are feasting on data, but theyre starved for insights. To drive successful adoption, analytics must be a holistic initiative that solves entire problem spaces. Data analytic strategies are best executed as a long-term vision at scale with incremental short-term wins, with participation from all functions with a stake. These impactful trends will change the shape of data analytics and technology in 2022 and beyond.
Views expressed above are the author's own.
END OF ARTICLE
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Key data analytics trends shaping businesses in 2022 and beyond - Times of India
Dying glaciers, rising oceans, sparse data, uncalibrated models and the beauty of Gaussian Processes Regression | Science and Technology – Science and…
Abstract: This talk provides an in-depth exploration of a single science question, How much have glaciers contributed to sea level rise over the past 60 years?, for which data science techniques are applied to help bring together disparate observations and modeling to advance our understanding of the Earth System. The talk takes a utilitarian perspective to advancing scientific understanding through embracing of data science.
Author Details: Dr. Gardner is a Research Scientist in JPLs Sea Level And Ice Group since 2014. He studies the Earth's cryosphere (frozen Earth) with a particular focus on glaciers and ice sheets and their impacts on sea level rise and water resources. He is most interested in how glaciers respond to natural and human induced forcing and the implications for our future. Alex is a member of NASA's ICESat-2, NISAR, GRACE, Surface Topography and Vegetation, Surface Deformation and Change, and Sea Level Change Science Teams. He is also involved with many novel initiatives to measure ice on Earth, and elsewhere, including the use of snakelike robots (EELS) to look for life under Enceladus icy shell. Alex is also PI of the ITS_LIVE NASA MEaSUREs initiative.
WebEx (https://jpl.webex.com/jpl/j.php?MTID=m67864565d359f465f890770c2a039347)
Meeting number (access code): 2763 831 4152
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Global Healthcare Analytics Market is expected to grow at a CAGR of >20% to cross $70 billion by 2025 – GlobeNewswire
BRUSSELS, Belgium, March 03, 2022 (GLOBE NEWSWIRE) --
Summary:
Medi-Tech Insights: The global Healthcare Analytics market growth is driven by growing complexity and volumes of data, supportive government initiatives, accelerated digital health adoption post Covid & rising VC/PE investments.
Description:
Healthcare data analytics combines real-time and historical data to predict trends, reveal actionable insights, and improve clinical, financial, and operational performance.
Shift towards Evidence-based Care Model
Data and analytics have been central to healthcare for decades. However, a major shift in how data is generated, aggregated and utilized is being witnessed as the healthcare industry moves from a fee-for-service to value-based care model.
Key Growth Drivers: Healthcare Analytics Market
The adoption of healthcare analytics is anticipated to grow rapidly worldwide as it can potentially reduce the cost of treatment, predict disease outbreaks, avoid preventable illnesses and improve the overall quality of care and life of patients.
Key Challenges: Healthcare Analytics Market
Healthcare data security and patient privacy issues, unstructured/fragmented data, ever-changing data, interoperability and data science skills gap are some of the key challenges to be addressed to witness exponential growth.
US Leads in terms of Adoption of Healthcare Analytics Market
The US is the largest market for healthcare analytics with >60% share, followed by Europe. In 2020, the US healthcare expenditure reached an all-time high accounting for 19.7% of its GDP. With skyrocketing costs, rising demand for value-based care and growing regulatory reporting requirements, the healthcare stakeholders are increasingly adopting data-driven solutions like analytics. This trend is evident by significant VC funding/PE investments in this field. Healthcare analytics companies raised $1.5 billion VC funding in the 1H of 2021, ~90% increase YOY.
Growing focus on Advanced Analytics
Based on our research & interviews with industry experts, currently, healthcare analytics companies derive most of their revenues from descriptive analytics however, increasingly the healthcare providers and payers are implementing predictive and prescriptive analytics.
On-premise Model still Prevailing
Cloud offers a flexible, scalable environment at a lower cost than on-premise deployments but still majority of healthcare analytics solutions are currently implemented on-premise due to security, control and privacy reasons. However, the trend is towards hybrid cloud storage model.- Founder, Executive Chairman, Healthcare Analytics Company, US
Competitive Landscape: Healthcare Analytics Market
The healthcare analytics market is fragmented with no clear leader. Key industry players having a strong foothold in the global market include IBM, Optum, Cerner, SAS Institute, Allscripts, Change Healthcare, MedeAnalytics, Inovalon, Oracle, Health Catalyst, SCIO Health Analytics, Cotiviti, VitreosHealth, Dedalus, LOGEX, Clanwilliam Group, Evolucare and Clinithink.
Explore Detailed Insights on Healthcare Analytics @ https://meditechinsights.com/healthcare-analytics-market/
About Us:
Medi-Tech Insights is a healthcare-focused business research & insights firm. Our clients include Fortune 500 companies, blue-chip investors & hyper-growth start-ups. We have successfully completed 100+ projects in the areas of market assessments, due diligence, competitive intelligence, market sizing and forecasting, pricing analysis & go-to-market strategy.
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Ruta HaldeAssociate, Medi-Tech Insights+32 498 86 80 79info@meditechinsights.com
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What is Data Science? A Complete Guide. | Built In
Data science has been proven useful in about every industry.Data Science Uses
Data science helps us achieve some major goals that either were notpossible or required a great deal more time and energy just a few years ago, such as:
Additionally, here are a few examples of how businesses are using data science to innovate in their sectors, create new products and make the world around them even more efficient.
Data science has led to a number of breakthroughs in the healthcare industry. With a vast network of data now available via everything from EMRs to clinical databases to personal fitness trackers, medical professionals are finding new ways to understand disease, practice preventive medicine, diagnose diseases fasterand explore new treatment options.
Tesla, Ford and Volkswagen are all implementing predictive analytics in their new wave of autonomous vehicles. These cars use thousands of tiny cameras and sensors to relay information in real-time. Using machine learning, predictive analytics and data science, self-driving cars canadjust to speed limits, avoid dangerous lane changes and even take passengers on the quickest route.
UPS turns to data science to maximize efficiency, both internally and along its delivery routes. The companys On-road Integrated Optimization and Navigation (ORION) tool uses data science-backed statistical modeling and algorithms that create optimal routes for delivery drivers based on weather, traffic, construction, etc. Its estimated that data science is saving the logistics company up to 39 million gallons of fuel and more than 100 million delivery miles each year.
Do you ever wonder how Spotify just seems to recommend that perfect song you're in the mood for? Or how Netflix knows just what shows youll love to binge? Using data science, the music streaming giant can carefully curate lists of songs based onthe music genre or band youre currently into. Really into cooking lately? Netflixs data aggregator will recognize your need for culinary inspiration and recommend pertinent shows from its vast collection.
Machine learning and data science have saved the financial industry millions of dollars, and unquantifiable amounts of time. For example, JP Morgans Contract Intelligence (COiN) platform uses Natural Language Processing (NLP) to process and extract vital data from about 12,000 commercial credit agreements a year. Thanks to data science, what would take around 360,000 manual labor hours to complete is now finished in a few hours. Additionally, fintech companies like Stripe and Paypal are investing heavily in data science to create machine learning tools that quickly detect and prevent fraudulent activities.
Data science is useful in every industry, but it may be the most important in cybersecurity. International cybersecurity firm Kaspersky is using data science and machine learning to detect over 360,000 new samples of malware on a daily basis. Being able to instantaneously detect and learn new methods of cybercrime, through data science, is essential to our safety and security in the future.
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What is a Data Scientist? – Master’s in Data Science
Data scientists are big data wranglers, gathering and analyzing large sets of structured and unstructured data. A data scientists role combines computer science, statistics, and mathematics. They analyze, process, and model data then interpret the results to create actionable plans for companies and other organizations.
Data scientists are analytical experts who utilize their skills in both technology and social science to find trends and manage data. They use industry knowledge, contextual understanding, skepticism of existing assumptions to uncover solutions to business challenges.
A data scientists work typically involves making sense of messy, unstructured data, from sources such as smart devices, social media feeds, and emails that dont neatly fit into a database.
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Experienced data scientists and data managers are tasked with developing a companys best practices, from cleaning to processing and storing data. They work cross functionally with other teams throughout their organization, such as marketing, customer success, and operations. They are highly sought after in todays data and tech heavy economy, and their salaries and job growth clearly reflect that.
Here are six common steps to consider if youre interested in pursuing a career in data science:
You will need at least a bachelors degree in data science or computer-related field to get your foot in the door as an entry level data scientist, although most data science careers will require a masters degree. Degrees also add structure, internships, networking and recognized academic qualifications for your rsum. However, if youve received a bachelors degree in a different field, you may need to focus on developing skills needed for the job through online short courses or bootcamps.
1QS World University Rankings (2020)
Data scientists may specialize in a particular industry or develop strong skills in areas such as artificial intelligence, machine learning, research, or database management. Specialization is a good way to increase your earning potential and do work that is meaningful to you.
Once youve acquired the right skills and/or specialization, you should be ready for your first data science role! It may be useful to create an online portfolio to display a few projects and showcase your accomplishments to potential employers. You also may want to consider a company where theres room for growth since your first data science job may not have the title data scientist, but could be more of an analytical role. Youll quickly learn how to work on a team and best practices that will prepare you for more senior positions.
Here are a few certifications that focus on useful skills:
Certified Analytics Professional (CAP)
CAP was created by the Institute for Operations Research and the Management Sciences (INFORMS) and is targeted towards data scientists. During the certification exam, candidates must demonstrate their expertise of the end-to-end analytics process. This includes the framing of business and analytics problems, data and methodology, model building, deployment and life cycle management.
SAS Certified Predictive Modeler using SAS Enterprise Miner 14
This certification is designed for SAS Enterprise Miner users who perform predictive analytics. Candidates must have a deep, practical understanding of the functionalities for predictive modeling available in SAS Enterprise Miner 14.
Academic qualifications may be more important than you imagine. When it comes to most data science jobs, is a masters required? It depends on the job and some working data scientists have a bachelors or have graduated from a data science bootcamp. According to Burtch Works data from 2019, over 90% of data scientists hold a graduate degree.
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Earn a masters in data science online from SMU. Statistics refresher course offered.
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Earn a Masters in Data Science online from UC Berkeley. Learn through a project-based curriculum.
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On any given day, a data scientists responsibilities may include:
Every company will have a different take on data science job tasks. Some treat their data scientists as data analysts or combine their duties with data engineers; others need top-level analytics experts skilled in intense machine learning and data visualizations.
As data scientists achieve new levels of experience or change jobs, their responsibilities invariably change. For example, a person working alone in a mid-size company may spend a good portion of the day in data cleaning and munging. A high-level employee in a business that offers data-based services may be asked to structure big data projects or create new products.
Data scientists dont need to just understand programming languages, management of databases and how to transpose data into visualizations they should be naturally curious about their surrounding world, but through an analytical lens. Possessing personality traits that resemble quality assurance departments, data scientists may be meticulous as they review large amounts of data and seek out patterns and answers. They are also creative in making new algorithms to crawl data or devising organized database warehouses.
Generally, professionals in the data science field must know how to communicate in several different modes, i.e to their team, stakeholders and clients. There may be a lot of dead ends, wrong turns, or bumpy roads, but data scientists should possess drive and grit to stay afloat with patience in their research.
Successful data scientists have a strong technical background, but the best data scientists also have great intuition about data. Are the features meaningful, and do they reflect what you think they should mean? Given the way your data is distributed, which model should you be using? What does it mean if a value is missing, and what should you do with it? The best data scientists are also great at communicating, both to other data scientists and non-technical people. In order to be effective at Airbnb, our analyses have to be both technically rigorous and presented in a clear and actionable way to other members of the company.
Lisa Qian, Data Scientist at Airbnb
Programming: Python, SQL, Scala, Java, R, MATLAB
Machine Learning: Natural Language Processing, Classification, Clustering,Ensemble methods, Deep Learning
Data Visualization: Tableau, SAS, D3.js, Python, Java, R libraries
Big data platforms: MongoDB, Oracle, Microsoft Azure, Cloudera
According to the Bureau of Labor and Statistics (BLS), employment growth of computer information and research scientists, which include data scientists, from 2019 to 2029 is 15%. Demand for experienced data scientists is high, but you have to start somewhere. Some data scientists get their foot in the door working as entry-level data analysts, extracting structured data from MySQL databases or CRM systems, developing basic visualizations in Tableau or analyzing A/B test results. If youd like to push beyond your analytical role think about what you could do with a career in data science:
Companies of every size and industry from Google, LinkedIn and Amazon to the humble retail store are looking for experts to help them wrestle big data into submission. In certain companies, new look data scientists may find themselves responsible for financial planning, ROI assessment, budgets and a host of other duties related to the management of an organization.
A data scientists salary depends on years of experience, skillset, education, and location. According to The Burtchworks Study, employers place greater value on data scientists with specialized skills, such as Natural Language Processing or Artificial Intelligence. The BLS claims skilled computer research and information scientists, which include data scientists, enjoy excellent job prospects because of high demand. Salary data below comes from 2019 data from the Bureau of Labor Statistics.
Data ScientistAverage Data Scientist Salary: $122,840 per yearLowest 10%: $69,990Highest 10%: $189,780
Senior Data ScientistMedian Sr. Data Scientist Salary: $171,755Total Pay Range: $147,000 $200,000
The first step to becoming a data scientist is typically earning a bachelors degree in data science or a related field, but there are other ways to learn data science skills such as a bootcamp or through the military. You may also consider pursuing a specialization or certification or earning a masters degree in data science before getting your first entry-level data scientist job.
Data scientists use a variety of skills depending on the industry they work in and their job responsibilities. Most data scientists are familiar with programming languages such as R and Python, as well as statistical analysis, data visualization, machine learning techniques, data cleaning, research and data warehouses and structures.
The time it takes to become a data scientist depends on your career goals and the amount of money and time you prefer to spend on your education. There are four-year bachelors degrees in data science available, as well as three-month bootcamps. If youve already earned a bachelors degree or completed a bootcamp, you may want to consider earning a masters degree, which can take as little as one year to complete. As shown in the aforementioned Burtch Works study, most data scientists do hold an advanced degree.
Tech bootcamps are a quick way to gain experience with data science and become knowledgeable in programming languages such as Python, R and SQL. Data science bootcamps are typically short programs offered in a variety of formats including part time, full time, online or on campus. Some bootcamps may take a couple of weeks to complete while others may take up to a couple of months. Bootcamps may help you expand your network and could offer dedicated career services to help with job placements after graduation.
During the bootcamp, youll work on projects and create a portfolio to demonstrate your abilities to potential employers. Data science bootcamps typically cover a variety of topics such as machine learning, natural language processing, different types of data analytics, data visualization and more.
When researching bootcamps, it is important to consider your career goals and what youd like to get out of the program. Some bootcamps are geared toward beginners, while others are better suited for those with some programming or computer science experience. You may also want to consider the background of the instructors teaching the bootcamp as well as cost. Are you able to take time off and commit to a full-time immersive experience? Does the bootcamp offer scholarships or discounts? Make sure to ask about all of your financing options.
Last updated: March 2021
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5 Top Trends in the Data Analytics Job Market – Datamation
Data analytics jobs have been well paid and in high demand for some time.
The IT Skills and Certifications Pay Index by Foote Partners shows that such skills often merit a pay premium, and the average salary of these specialists has been steadily rising. Among the high-paying areas currently are risk analytics, big data analytics, data science, prescriptive analytics, predictive analytics, modeling, Apache Hadoop, and business analytics.
But data analytics is a broad term. It encompasses business intelligence (BI) and visualization as well as the application of analytics to other functions, such as IT and cybersecurity.
Here are some of the five top trends in data analytics jobs:
See more: The Data Analytics Job Market
Experience or certification in a specific programming language or analytics discipline used to be a passport to good jobs. It will still gain people some positions, but they need more if they hope to move up the pay scale.
For analytics professionals, listing proficiency in SAS, Python, or R may get someone past the initial HR screening, but thats about it, said Sean OBrien, SVP of education at SAS.
Data analytics candidates need experience, certification, and other human skills to succeed in todays market.
It used to be enough to crunch some numbers and then tell the business an outcome or prediction using regular language.
These days, executives demand more. A top trend for data analytics jobs is the increasing importance of communication skills and storytelling. The rise of chief data officers and chief analytics officers is the clearest indication that analytics has moved from the backroom to the boardroom, and more often, its data experts that are setting strategy.
The ability to make analytics outputs relatable to stakeholders across the business will set them apart, said OBrien with SAS.
Its not enough to be able to clean, integrate, and analyze huge amounts of data. Analytics pros have to understand how data and analytics directly support business goals and be able to communicate the story the data is telling. They need to be able to not just present trends and reports but communicate their meaning.
Cybersecurity trends apply to data analytics in two ways: Analysts need to be aware of and possess some security skills if they are to keep their platforms and models secure. But perhaps even more importantly, analytics jobs are becoming available in greater frequency in security. Analysts are needed who can unlock the vast troves of data available in system logs, alerts, and organizational data to find the potential incursions and isolate threats.
Flexibly and securely viewing trusted data in context through shared applications across an industry ecosystem also enables process and governance improvement, said Jeffrey Hojlo, an analyst at IDC.
Storage, too, has transitioned into the analytics arena. Storage administrators are spending less time managing storage devices and more time managing data. This entails being more strategic about data mobility, data management, data services, and delivering the foundation for generating value from unstructured data.
Storage administrators must leverage analytics about files, such as types of files, access times, owners, and other attributes, said Randy Hopkins, VP of global systems engineering and enablement at Komprise.
This knowledge will allow them to manage data throughout its life cycle from creation to deletion and in a way that looks across storage and clouds to deliver the best bang for the buck as well as the best performance for distinct workflows and departmental needs.
See more: Top Data Analytics Certifications
Risk is a hot area across the business world. And it is up to risk management and risk analysts to identify, analyze, and accept or mitigate any uncertainty that may exist in business or investment decisions.
A variety of tactics are used to determine risk. For example, a common tool is known as standard deviation, which is a statistical measure where data is plotted around a central tendency. Management can then see how much risk might be involved and how to minimize that risk.
Those skilled in modern risk analytics are now in greater demand, as the risk management field transitions from manual or traditional methods. Accordingly, risk analytics and risk assessment jobs rose by 5.3% in value over a six-month period, according to surveys by Foote Partners. This form of business intelligence exploits structured and unstructured data as a way to model scenarios and outcomes and provide insight into potential fraud, market risk, credit risk, financial risk, supply chain risk, and other areas of risk.
As a sign that there was definite substance to the hype around big data, Foote Partners notes that big data analytics jobs continue to be in demand. They have risen in value by 13.3% over a six-month period.
Big data analytics sits at the crossroads of many BI and analytics disciplines. Traditionally, analysts would attack small subsets of structured data and then present their findings. Gradually, larger data sets were added as compute and memory resources became more available.
Today, analytics needs to be applied not just to structured but to unstructured data. Big data analysts, then, can use advanced analytics techniques on huge data sets that include structured, semi-structured, and unstructured data from many sources. Their insights fuels faster decision making, make it possible to create more accurate models, and offer prediction of future trends with a higher degree of accuracy.
See more: 10 Top Companies Hiring for Data Analytics Jobs
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