Category Archives: Data Science
What to do to Excel in Data Science as a Beginner – Analytics Insight
There has to be something that makes data science the most sought-after career opportunity all across the globe. Data scientists are entrusted with one of the most crucial roles to play within the organization. A data scientist is hired to aid in the decision-making process that further helps the business in reaching new horizons. The demand for data scientists all over the world is high and it is thus critical to stand out from the rest. How do you do that? You need to work a little harder to gain better knowledge about this field. When you take up initiatives that improve your business knowledge, you are not only in a better position when compared to others but you are also capable enough of putting the knowledge gained to the best use possible and see your organization grow.
Here are a few ways in which you can build your business knowledge to transform yourself into an excellent data scientist
You being a fresher need to have something to impress the recruiter. The best way to do that is to go for an internship. For a fresher, there cannot be a better way to get practical knowledge. Now, lets be honest. Getting hired as an intern isnt that easy as well. And when the subject of interest isdata science, the process gets all the more complicated and tough. However, theres always a way out. Probably the best way to get hired as an intern is to bring your connections into force. Do not rely on just your family and friends to get into the corporate world. Social media is one of the best platforms available. Make full use of this and try to establish as many connections as you can. Yet another aspect that works in your favor is that you can convert your internship offer into the final offer. Just put in all your efforts and seal the opportunity. All that you need to keep in mind is that getting selected as an intern is no less than a golden opportunity to carve a niche for yourself in the field of data science.
The aim of a data scientist is to solve business problems. On that note, if you work on projects that help in solving business problems, you already are a step ahead when compared to others. Yes, technical skills are important for data science but that is not all. Start by researching about the company, its strengths and weakness, challenges faced, articles and research papers published, etc. A deep study of all this makes it easier for you to identify the business problem followed by your approach to dealing with it. This will surely create an impression thatll go in your favor.
This option is for those who are studying, working full-time, or are not having time to take up a full-time opportunity but are willing to do something in the field of data science. Getting a freelancing opportunity is not that easy. As said, connections play a pivotal role. Also, there are a lot of companies that might be in search of freelancers with some relevant skills in place. If you have some technical skills that could help you secure a job as a data scientist then do not hesitate in choosing this option.
No matter which option you choose, ultimately what matters is how much effort you put in to become a data scientist. You should be able to convince the recruiter that there is no one better than you whod be the best fit for the role.
Read more:
What to do to Excel in Data Science as a Beginner - Analytics Insight
Turkcell Democratizes Data Science and Drives Artificial Intelligence Innovation with Red Hat OpenShift – Business Wire
RALEIGH, N.C.--(BUSINESS WIRE)--Red Hat, Inc., the world's leading provider of open source solutions, today announced that Turkcell, a leading converged telecommunication and technology services provider, has built its new artificial intelligence (AI) services architecture and application hub on Red Hat OpenShift, the industrys leading enterprise Kubernetes platform. This has helped Turkcell to transform customer experiences, drive operational efficiencies and bring a greater diversity of consumer and business innovations to market faster.
Operating within Turkey and internationally, Turkcell currently serves close to 48 million customers with a wide range of communications and digital services offerings. AI has been integral to Turkcells strategic vision for many years, covering areas such as computer vision, natural language processing and intelligent automation. As AI technologies become increasingly accessible thanks to advancements in the field and in cloud-native application development, Turkcell set out to build a flexible, more secure cloud platform to accelerate AI and associated intelligent applications delivery.
Already a Red Hat customer of more than ten years, Turkcell chose Red Hat OpenShift as a reliable, resilient and scalable hybrid cloud foundation to power its container-based AI development, including integrations with JupyterLab and Nvidia GPUs. OpenShift gave Turkcell the ability to develop and deploy applications both in the public cloud and on-premises where necessary to comply with data regulation. Red Hat OpenShift provides a more consistent, self-service experience for data scientists and application developers, wherever they are in the Turkcell business, and enables non-technical staff to access AI capabilities. As part of its work with OpenShift, Turkcell launched its AI Hub on the platform, from which it can offer AI innovations as-a-service to enterprises, helping to create new revenue opportunities.
Turkcell is now running around 50 different services on the OpenShift-based platform to support a diverse range of AI-powered use cases. These include:
With its scalable AI platform architecture, use of microservices and adoption of agile practices including DevSecOps, Turkcell has been able to accelerate its application lifecycle, including speeding up development and deployment of AI and machine learning (ML) models. Turkcell can now bring new digital services to market in roughly half the time it could with a monolithic architecture. Turkcell is generating operational efficiencies, helping it achieve cost savings of up to 70% by consolidating AI workloads on containers and Kubernetes. Because OpenShift provides a common platform accessible from anywhere in the organization, Turkcell has been able to empower data scientists across teams to benefit from AI capabilities and spur innovation in diverse areas.
Supporting QuotesHonor LaBourdette, vice president, Telco, Media & Entertainment, Red HatTurkcell is democratizing data science by opening up artificial intelligence for use across its business with its Red Hat-based platform, helping to improve operations, enliven customer experiences and generate new revenue. Red Hat OpenShift provides greater security, stability, and scalability to support Turkcells expanding AI ecosystem, which is delivering an array of truly exciting offerings to consumers and businesses alike.
nan akrolu, Director, Artificial Intelligence and Analytic SolutionsWe have built a strong collaborative relationship with Red Hat over a number of years, and we value open source for its rapid development model, so Red Hats open hybrid cloud technologies were a natural choice for us. Using Red Hat OpenShift as a flexible, consistent foundation, we have created a playground for data science, making the frameworks and tools available to anyone, so we can invite contributions from all over the Turkcell organization. This has enabled us to create and deliver brand new AI-powered services to market approximately twice as quickly as we could before.
Additional Resources
Connect with Red Hat
About Red Hat, Inc.Red Hat is the worlds leading provider of enterprise open source software solutions, using a community-powered approach to deliver reliable and high-performing Linux, hybrid cloud, container, and Kubernetes technologies. Red Hat helps customers integrate new and existing IT applications, develop cloud-native applications, standardize on our industry-leading operating system, and automate, secure, and manage complex environments. Award-winning support, training, and consulting services make Red Hat a trusted adviser to the Fortune 500. As a strategic partner to cloud providers, system integrators, application vendors, customers, and open source communities, Red Hat can help organizations prepare for the digital future.
Forward-Looking StatementsCertain statements contained in this press release may constitute "forward-looking statements" within the meaning of the Private Securities Litigation Reform Act of 1995. Forward-looking statements provide current expectations of future events based on certain assumptions and include any statement that does not directly relate to any historical or current fact. Actual results may differ materially from those indicated by such forward-looking statements. The forward-looking statements included in this press release represent the Company's views as of the date of this press release and these views could change. However, while the Company or its parent International Business Machines Corporation (NYSE:IBM) may elect to update these forward-looking statements at some point in the future, the Company specifically disclaims any obligation to do so. These forward-looking statements should not be relied upon as representing the Company's views as of any date subsequent to the date of this press release.
Red Hat, the Red Hat logo, and OpenShift are trademarks or registered trademarks of Red Hat, Inc. or its subsidiaries in the U.S. and other countries. Linux is the registered trademark of Linus Torvalds in the U.S. and other countries.
Follow this link:
Beyond formal education, this is where the billion-dollar Data Science & Analytics industry picks its tal – Times of India
Even a glance at the corporate world makes it evident that Data Science & Analytics have become critical cornerstones of business success. In recent years, the unprecedented adoption of Data solutions has relied heavily on accurate Data analysis and insights by businesses worldwide. The global Data Science & Analytics industry is expected to reach a high-watermark valuation of approximately USD 141 Billion by 2024, growing at a remarkable Compound Annual Growth Rate (CAGR) of roughly 30%.
The primary factors propelling this rapid growth are the increasing cross-industry focus on using customer and operational data to boost business and maintain a competitive edge and the pressing need to extract actionable business insights from massive data sets to enhance efficiency brand value.
You have successfully cast your vote
Over the years, Imarticus has forged reliable partnerships with global leaders in Big Data Analytics and premium Data Science education providers, such as KPMG In India and UCLA Extension, to develop industry-approved learning material, deliver world-class experiential training, and offer internationally recognised industry-accredited professional certifications.
As a result of their overall impact and industry influence, Imarticus has received numerous awards and accolades for its unique, state-of-the-art tech-based training methods. The institute believes in experiential learning and goes to great lengths to ensure that our learners comprehensively master the skills employers are looking for in Data Science & Analytics professionals.
Their state-of-the-art Post-Graduate Program in Analytics & Artificial Intelligence (which includes Data Science Fundamentals delivered by UCLA Extension) and Post Graduate Program in Data Analytics not only offer profound industry-endorsed professional education, these programs also come with a job placement guarantee. Successfully complete either program and their in-house Placement Team will help you get an assured job placement in the Data Science & Analytics industry.
Read more:
Making the jump from being a data analyst to a data scientist what skills do you need to learn and improve? – TechBullion
Share
Share
Share
Data plays a huge role in modern business and is something that is extremely valuable to most global organizations. The data that is collected is, of course, only useful when it is interpreted, dissected and explored for future strategic planning. To this end, many companies now employ data analysts and data scientists to help.
While both roles may sound the same, they are actually quite different. Data analysts tend to only look at what current data is saying, while data scientists also look at why and what it may mean for the future. Many people will start off as a data analyst but then decide to move on to the more senior data scientist role in time.
Many people will achieve this by first getting the right education under their belt. A data science online degree from Kettering University is one course that certainly helps with this. By studying on this course at Kettering, you not only learn the skills needed to move up to data scientist, but also get to study at a truly world-class institution.
What are the key skills to transition from data analyst to data scientist?
Brush up on problem solving and critical thinking
If you already work as a data analyst but want to move up to being a data scientist, then both of these soft skills are key. Whereas analyzing data might be focused on interpreting already presented data, this is not always so in data science. A data scientist will often need to think critically beforehand to decide on what data to collect and how to go about it. Problem solving naturally comes in when things do not go to plan and you have to find solutions.
Coding needs to be on point
Being able to collect large data sets, work out why something has happened, and then map how that may play out in future is achieved with computer algorithms. As a data scientist, you will often be responsible for deciding which algorithms to use and also creating your own. The net result is that data scientists need strong coding skills in programming languages such as Python and R.
Data visualization skills are a must
If you do not know about data visualization, then you need to find out more to work in data science. Data visualization is simply using the latest tech to represent data in a visual format. The visual nature of this approach is very useful for presentations and helping people to understand your findings.
It is a big leap from data analysis to data science
There is no doubt that data analysis is a key role in modern business and has its own merits. Data science is a step up from this though, and as a result, you may need to learn new skills to succeed. After this, it is just a case of updating your data science skills regularly to stay on point.
More:
DOT Appoints Chief Science Officer for the First Time in Over 40 Years Homeland Security Today – HSToday
The U.S. Department of Transportation is appointing a Chief Science Officer for the entire Department for the first time in over four decades and has taken several additional steps to act on the Biden-Harris Administrations commitment to address the climate emergency.
The Department also announced that it has begun work to reestablish its Climate Change Center and has moved to restore public access to climate-related reports, program information, and other scientific and technical information.
The Departments Chief Science Officer has been named as Dr. Robert C. Hampshire, PhD. In this role, he will serve as the principal advisor to Secretary Pete Buttigieg on science and technology issues. He is charged with ensuring that DOTs research, development and technology programs are scientifically and technologically well-founded and conducted with integrity. He was previously associate professor at the University of Michigans Gerald R. Ford School of Public Policy and at both the U-M Transportation Research Institutes (UMTRI) Human Factors group and Michigan Institute for Data Science (MIDAS), and holds his PhD from Princeton University.
Climate resilience and environmental justice are at the heart of this Administrations mission to build back betterand that effort must be grounded in scientific expertise, said Buttigieg. Were thrilled to officially name Dr. Hampshire as our Chief Science Officer, and look forward to his contributions to this historic effort.
The re-introduction of a Chief Science Officer underscores transportations key role in addressing the complexity and criticality of our dynamically changing climate. I look forward to working across all modes of transportation to address the immediate concerns, and to ensure our future transportation system is sustainable, said the Acting Assistant Secretary for Research and Technology Robert Hampshire. It is important that USDOT incorporate scientific research to advance climate change initiatives that are fair and equitable to all.
The Departments actions stem from the Presidents Executive Order on Protecting Public Health and the Environment and Restoring Science to Tackle the Climate Crisis and the Presidential Memorandum on Restoring Trust in Government Through Scientific Integrity and Evidence-Based Policymaking.
The Climate Change Center will help coordinate the Departments related research, policies, and actions and support the transportation sector in moving toward a net-zero carbon emissions. The DOT Center for Climate Change and Environmental Forecasting was established during the Clinton Administration to serve as the multi-modal focal point for information and technical expertise on transportation and climate change, coordinating climate-related research, policies, and actions. The Center has been dormant since early 2017.
The Department has assessed public websites and information repositories, including the National Transportation Library, and identified 24 websites and 33 reports and other publications which had been de-published after January 21, 2017. All of these materials have been restored to public access.
The Department will also re-designate a Scientific Integrity Officer, responsible for research policy implementation, who reports directly to the Chief Science Officer.
The transportation sector is the number one producer of greenhouse gases in the U.S., which underscores the ability of the transportation industry and the Department to quickly and meaningfully reduce greenhouse gases and address the climate crisis. These actions are the first steps in returning the Department to its position as a leader in addressing climate change and environmental justice.
Read the announcement at the Department of Transportation
(Visited 26 times, 1 visits today)
More here:
Ring in the 2021 summer semester with these virtual events – News@Northeastern
Learn about the sustainability of cycling. Hear from trailblazing Northeastern graduates. Ponder the future of nursing. These events and more are on tap for May.
Cycling is the most sustainable means of urban travel, and promotes physical, social, and mental agility. Join the Department of Civil and Environmental Engineering for a seminar to explore how cycling can help make cities more sustainable on Tuesday, May 4, at 6:30 p.m. EDT.
Online targeting isolates individual consumers, causing what University of Oxford scholar Silvia Milano calls epistemic fragmentation. Tune in to a virtual discussion with Milano about the civic governance of online advertising on Wednesday, May 5, at 10 a.m. EDT.
Tune in to a workshop hosted by Global Student Success to discover new ways to keep busy during Bostons effervescent summer. Join on Wednesday, May 5, at 1 p.m. EDT.
Take time to congratulate Khoury College of Computer Sciences 2021 graduates in a special event to kick off Commencement. Undergraduate students will be recognized on Thursday, May 6, at 12 p.m. EDT and graduate students will be recognized on Friday, May 7, at 12 p.m. EDT.
In a special research talk hosted by Hazel Sive, dean of the College of Science, discuss how the Extreme Anterior Domain can be a target for microcephaly in developing infants. Join virtually on Friday, May 7, at 12 p.m. EDT.
Head to the Friedman Diamond for the Northeastern Baseball Senior Day celebration on Sunday, May 9, at 1 p.m. to support the first-place Huskies as they look to win their first-ever CAA North Division championship.
Positive messaging and seeing the experiences of peers, trusted faculty, and support staff could be the missing piece that inspires our learners to get vaccinated. Submit a photo or short video of yourself after receiving your COVID-19 vaccine to participate in the I Got My COVID-19 Vaccine campaign. Submit all materials here by Friday, May 14, by 8 p.m. EDT.
In a talk titled Adapt or Die: Transgenerational Inheritance of Pathogen Avoidance, hear from Princeton professor Coleen Murphy as she discusses the intricacies of food poisoning and intergenerational transmission. Tune in on Monday, May 10, at 12 p.m. EDT.
Pondering Python? Tantalized by text analysis? Wondering how Jupyter notebooks work? Join a workshop sponsored by NULab for Texts, Maps, and Networks and Research Data Services to learn basic Python while working in Jupyter notebooks. Get involved on Monday, May 10, at 2 p.m. EDT.
Join a discussion with associate professor of physics Meni Wanunu to discuss their research on biosystems at the nanoscale. Tune in on Tuesday, May 11, at 12 p.m. EDT.
Tune in to Northeastern Nurses Week 2021 to celebrate the spirit of nursing, particularly throughout the COVID-19 pandemic, and the ways in which nurses contribute to the Northeastern community. Join the celebration on Tuesday, May 11, at 5 p.m. EDT.
Recently, U.S. regulation, legislation, and compliance requirements have driven Chinese businesses to go public on non-U.S. stock exchanges. What does this mean for the U.S. stock market? Learn more in a talk with professor David Sherman on Wednesday, May 12, at 6 p.m. EDT.
In a discussion titled The Journey after Huntington Avenue: How Three Trailblazing Black Alumnae Found Their Paths, learn how three former students tapped into Northeasterns resources to develop into the socially responsible professional they are today. Tune in on Monday, May 17, at 12 p.m. EDT.
Join Cornell professor Neil Lewis Jr. in a lecture hosted by Northeasterns Social Impact Lab to discuss Mechanisms of Explanation vs. Mechanisms of Change: Tensions Between Basic Theory Construction and Practical Application. Learn more on Monday, May 17, at 12 p.m. EDT.
Learn about the journey of chef Erin French as she overcame adversity and founded her own restaurant located in the wilds of Maine. Hear excerpts from her new memoir Finding Freedom in a virtual discussion on Tuesday, May 18, at 12 p.m. EDT.
In part two of the Fourth Annual David B. Schulman Distinguished Lecture hosted by the Institute on Race and Justice, learn how monetary sanctions create and exacerbate racial and economic inequality in the U.S. criminal legal system from University of Washington professor Alexes Harris. Tune in on Tuesday, May 18, at 5 p.m. EDT.
In the 1980s, Massachusetts embraced the War on Drugs, enacting harsh mandatory minimum sentences for nonviolent drug offenses. It took decades for institutions to confront the reality that mandatory minimums resulted in the pervasive and disproportionate incarceration of Black individuals. Tune in to a panel discussion to examine this troubling history and the prospects for reforming policies on Wednesday, May 19, at 6 p.m. EDT.
Northeasterns Align Masters in Data Science is a unique interdisciplinary program for students from any background. Hear more about the program and its ties to the world of data science in a webinar on Thursday, May 20, at 7 p.m. EDT.
Join the school of laws annual Women in the Law Conference on Friday, May 21, starting at 8:30 a.m. EDT to examine how the industry can promote the reimagination of inclusive workspaces.
Join a talk with industry experts to discuss the future of nursing from 2020-2030, and the ways in which nursing can be a force in advancing health equity. Tune in on Monday, May 24, at 3:30 p.m. EDT.
For media inquiries, please contact media@northeastern.edu.
View post:
Ring in the 2021 summer semester with these virtual events - News@Northeastern
Global Data Science and Machine Learning Service Market is growing at a High CAGR during the forecast period 2020-2026. The increasing interest of the…
Global Data Science and Machine Learning Service Marketresearch report is the new statistical data source added byA2Z Market Research.
Get the PDF Sample Copy (Including FULL TOC, Graphs and Tables) of this report:
https://www.a2zmarketresearch.com/sample?reportId=394072
Note In order to provide more accurate market forecast, all our reports will be updated before delivery by considering the impact of COVID-19.
Global Data Science and Machine Learning Service Marketresearch is an intelligence report with meticulous efforts undertaken to study the right and valuable information. The data which has been looked upon is done considering both, the existing top players and the upcoming competitors. Business strategies of the key players and the new entering market industries are studied in detail. Well explained SWOT analysis, revenue share and contact information are shared in this report analysis.
Top Key Players Profiled in this report are:
Various factors are responsible for the markets growth trajectory, which are studied at length in the report. In addition, the report lists down the restraints that are posing threat to the global Data Science and Machine Learning Service market. It also gauges the bargaining power of suppliers and buyers, threat from new entrants and product substitute, and the degree of competition prevailing in the market. The influence of the latest government guidelines is also analyzed in detail in the report. It studies the Global Data Science and Machine Learning Service markets trajectory between forecast periods.
Table of Contents
Global Data Science and Machine Learning Service Market Research Report 2020 2026
Chapter 1 Global Data Science and Machine Learning Service Market Overview
Chapter 2 Global Economic Impact on Industry
Chapter 3 Global Market Competition by Manufacturers
Chapter 4 Global Production, Revenue (Value) by Region
Chapter 5 Global Supply (Production), Consumption, Export, Import by Regions
Chapter 6 Global Production, Revenue (Value), Price Trend by Type
Chapter 7 Global Market Analysis by Application
Chapter 8 Manufacturing Cost Analysis
Chapter 9 Industrial Chain, Sourcing Strategy and Downstream Buyers
Chapter 10 Marketing Strategy Analysis, Distributors/Traders
Chapter 11 Market Effect Factors Analysis
Chapter 12 Global Data Science and Machine Learning Service Market Forecast
The key questions answered in this report:
Get up to 20% Discount on this Premium Report:
https://www.a2zmarketresearch.com/discount?reportId=394072
The cost analysis of the Global Data Science and Machine Learning Service Market has been performed while keeping in view manufacturing expenses, labor cost, and raw materials and their market concentration rate, suppliers, and price trend. Other factors such as Supply chain, downstream buyers, and sourcing strategy have been assessed to provide a complete and in-depth view of the market. Buyers of the report will also be exposed to a study on market positioning with factors such as target client, brand strategy, and price strategy taken into consideration.
Regions Covered in the Global Data Science and Machine Learning Service Market Report 2020:The Middle East and Africa(GCC Countries and Egypt)North America(the United States, Mexico, and Canada)South America(Brazil etc.)Europe(Turkey, Germany, Russia UK, Italy, France, etc.)Asia-Pacific(Vietnam, China, Malaysia, Japan, Philippines, Korea, Thailand, India, Indonesia, and Australia)
The report provides insights on the following pointers:
Market Penetration:Comprehensive information on the product portfolios of the top players in the Global Data Science and Machine Learning Service market.
Product Development/Innovation:Detailed insights on the upcoming technologies, R&D activities, and product launches in the market.
Competitive Assessment: In-depth assessment of the market strategies, geographic and business segments of the leading players in the market.
Market Development:Comprehensive information about emerging markets. This report analyzes the market for various segments across geographies.
Market Diversification:Exhaustive information about new products, untapped geographies, recent developments, and investments in the Global Data Science and Machine Learning Service market.
Buy Exclusive Report @:
https://www.a2zmarketresearch.com/buy?reportId=394072
If you have any special requirements, please let us know and we will offer you the report as you want.
About A2Z Market Research:
The A2Z Market Research library provides syndication reports from market researchers around the world. Ready-to-buy syndication Market research studies will help you find the most relevant business intelligence.
Our Research Analyst Provides business insights and market research reports for large and small businesses.
The company helps clients build business policies and grow in that market area. A2Z Market Research is not only interested in industry reports dealing with telecommunications, healthcare, pharmaceuticals, financial services, energy, technology, real estate, logistics, F & B, media, etc. but also your company data, country profiles, trends, information and analysis on the sector of your interest.
Contact Us:
Roger Smith
1887 WHITNEY MESA DR HENDERSON, NV 89014
+1 775 237 4147
Continued here:
Global Data Science Platform Market To Expand with an ASTONISHING CAGR During The Forecast Period 2020-2026 The Courier – The Courier
The business report released by Zion Market Research onGlobal Data Science Platform Market To Expand with an ASTONISHING CAGR During The Forecast Period 2020-2026is focused to facilitate a deep understanding of the market definition, potential, and scope. The report is curate after deep research and analysis by experts. It consists of an organized and methodical explanation of current market trends to assist the users to entail in-depth market analysis. The report encompasses a comprehensive assessment of different strategies like mergers & acquisitions, product developments, and research & developments adopted by prominent market leaders to stay at the forefront in the global market.
FREE | Request Sample is Available @https://www.zionmarketresearch.com/sample/data-science-platform-market
The major players in the globalData Science Platform MarketareIBM, Microsoft Corporation, RapidMiner Inc., Dataiku, Continuum AnalyticsInc., Domino Data Lab, Wolfram, Sense Inc., DataRobot Inc., and AlteryxInc.
Along with contributing significant value to the users, the report by Zion Market Research has focused on Porters Five Forces analysis to put forward the wide scope of the market in terms of opportunities, threats, and challenges. The information extracted through different business models like SWOT and PESTEL is represented in the form of pie charts, diagrams, and other pictorial representations for a better and faster understanding of facts. The report can be divided into following main parts.
Growth drivers:
The report provides an accurate and professional study of global Data Science Platform Market business scenarios. The complex analysis of opportunities, growth drivers, and the future forecast is presented in simple and easily understandable formats. The report comprehends the Data Science Platform Market by elaborating the technology dynamics, financial position, growth strategy, product portfolio during the forecast period.
Download Free PDF Report Brochure @https://www.zionmarketresearch.com/requestbrochure/data-science-platform-market
Segmentation:
The report is curate on the basis of segmentation and sub-segmentation that are aggregated from primary and secondary research. Segmentation and sub-segmentation is a consolidation of industry segment, type segment, channel segment, and many more. Further, the report is expanded to provide you thorough insights on each segment.
Regional analysis:
The report covers all the regions in the world showing regional developmental status, the market volume, size, and value. It facilitates users valuable regional insights that will provide a complete competitive landscape of the regional market. Further, different regional markets along with their size and value are illustrated thoroughly in the report for precise insights.
Inquire more about this report @https://www.zionmarketresearch.com/inquiry/data-science-platform-market
Competitive analysis:
The report is curate after a SWOT analysis of major market leaders. It contains detailed and strategic inputs from global leaders to help users understand the strength and weaknesses of the key leaders. Expert analysts in the field are following players who are profiled as prominent leaders in the Data Science Platform Market. The report also contains the competitive strategy adopted by these market leaders to the market value. Their research and development process was explained well enough by experts in the global Data Science Platform Market to help users understand their working process.
Key Details of the Existing Report Study:
Frequently Asked Questions
Thanks for reading this article;you can also get individual chapter wise section or region wise report version like North America, Europe or Asia.
About Us:
Zion Market Research is an obligated company. We create futuristic, cutting-edge, informative reports ranging from industry reports, the company reports to country reports. We provide our clients not only with market statistics unveiled by avowed private publishers and public organizations but also with vogue and newest industry reports along with pre-eminent and niche company profiles. Our database of market research reports comprises a wide variety of reports from cardinal industries. Our database is been updated constantly in order to fulfill our clients with prompt and direct online access to our database. Keeping in mind the clients needs, we have included expert insights on global industries, products, and market trends in this database. Last but not the least, we make it our duty to ensure the success of clients connected to usafter allif you do well, a little of the light shines on us.
Contact Us:
Zion Market Research244 Fifth Avenue, Suite N202New York, 10001, United StatesTel: +49-322 210 92714USA/Canada Toll-Free No.1-855-465-4651Email:sales@zionmarketresearch.comWebsite:https://www.zionmarketresearch.com
Read the rest here:
What is Data Science? | The Data Science Career Path
Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals. Today, successful data professionals understand that they must advance past the traditional skills of analyzing large amounts of data, data mining, and programming skills. In order to uncover useful intelligence for their organizations, data scientists must master the full spectrum of the data science life cycle and possess a level of flexibility and understanding to maximize returns at each phase of the process.
The term data scientist was coined as recently as 2008 when companies realized the need for data professionals who are skilled in organizing and analyzing massive amounts of data.1 In a 2009 McKinsey&Company article, Hal Varian, Googles chief economist and UC Berkeley professor of information sciences, business, and economics, predicted the importance of adapting to technologys influence and reconfiguration of different industries.2
The ability to take data to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it thats going to be a hugely important skill in the next decades.
Hal Varian, chief economist at Google and UC Berkeley professor of information sciences, business, and economics3
Effective data scientists are able to identify relevant questions, collect data from a multitude of different data sources, organize the information, translate results into solutions, and communicate their findings in a way that positively affects business decisions. These skills are required in almost all industries, causing skilled data scientists to be increasingly valuable to companies.
Advance Your Career with an Online Short Course
Take theData Science Essentialsonline short course and earn a certificatefrom the UC Berkeley School of Information.
In the past decade, data scientists have become necessary assets and are present in almost all organizations. These professionals are well-rounded, data-driven individuals with high-level technical skills who are capable of building complex quantitative algorithms to organize and synthesize large amounts of information used to answer questions and drive strategy in their organization. This is coupled with the experience in communication and leadership needed to deliver tangible results to various stakeholders across an organization or business.
Data scientists need to be curious and result-oriented, with exceptional industry-specific knowledge and communication skills that allow them to explain highly technical results to their non-technical counterparts. They possess a strong quantitative background in statistics and linear algebra as well as programming knowledge with focuses in data warehousing, mining, and modeling to build and analyze algorithms.
They must also be able to utilize key technical tools and skills, including:
R
Python
Apache Hadoop
MapReduce
Apache Spark
NoSQL databases
Cloud computing
D3
Apache Pig
Tableau
iPython notebooks
GitHub
Glassdoor ranked data scientist as the #1 Best Job in America in 2018 for the third year in a row.4 As increasing amounts of data become more accessible, large tech companies are no longer the only ones in need of data scientists. The growing demand for data science professionals across industries, big and small, is being challenged by a shortage of qualified candidates available to fill the open positions.
The need for data scientists shows no sign of slowing down in the coming years. LinkedIn listed data scientist as one of the most promising jobs in 2017 and 2018, along with multiple data-science-related skills as the most in-demand by companies.5
The statistics listed below represent the significant and growing demand for data scientists.
28%Demand Increase by 2020
Number of Job Openings
Average Base Salary
Best Job in America 2016, 2017, 2018
Sources:GlassdoorandForbes
Data is everywhere and expansive. A variety of terms related to mining, cleaning, analyzing, and interpreting data are often used interchangeably, but they can actually involve different skill sets and complexity of data.
Data scientists examine which questions need answering and where to find the related data. They have business acumen and analytical skills as well as the ability to mine, clean, and present data. Businesses use data scientists to source, manage, and analyze large amounts of unstructured data. Results are then synthesized and communicated to key stakeholders to drive strategic decision-making in the organization.
Skills needed:Programming skills (SAS, R, Python), statistical and mathematical skills, storytelling and data visualization, Hadoop, SQL, machine learning
Data analysts bridge the gap between data scientists and business analysts. They are provided with the questions that need answering from an organization and then organize and analyze data to find results that align with high-level business strategy. Data analysts are responsible for translating technical analysis to qualitative action items and effectively communicating their findings to diverse stakeholders.
Skills needed:Programming skills (SAS, R, Python), statistical and mathematical skills, data wrangling, data visualization
Data engineers manage exponential amounts of rapidly changing data. They focus on the development, deployment, management, and optimization of data pipelines and infrastructure to transform and transfer data to data scientists for querying.
Skills needed:Programming languages (Java, Scala), NoSQL databases (MongoDB, Cassandra DB), frameworks (Apache Hadoop)
Data science professionals are rewarded for their highly technical skill set with competitive salaries and great job opportunities at big and small companies in most industries. With over 4,500 open positions listed on Glassdoor, data science professionals with the appropriate experience and education have the opportunity to make their mark in some of the most forward-thinking companies in the world.6
Below are the average base salaries for the following positions:7
Data analyst:$65,470
Data scientist:$120,931
Senior data scientist:$141,257
Data engineer:$137,776
Gaining specialized skills within the data science field can distinguish data scientists even further. For example, machine learning experts utilize high-level programming skills to create algorithms that continuously gather data and automatically adjust their function to be more effective.
1hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century. Accessed April 2018.arrow_upwardReturn to footnote reference2 http://www.mckinsey.com/industries/high-tech/our-insights/hal-varian-on-how-the-web-challenges-managers. Accessed July 2018.arrow_upwardReturn to footnote reference3www.mckinsey.com/industries/high-tech/our-insights/hal-varian-on-how-the-web-challenges-managers. Accessed July 2018.arrow_upwardReturn to footnote reference4 http://www.glassdoor.com/List/Best-Jobs-in-America-LST_KQ0,20.htm. Accessed April 2018.arrow_upwardReturn to footnote reference5 blog.linkedin.com/2018/january/11/linkedin-data-reveals-the-most-promising-jobs-and-in-demand-skills-2018. Accessed April 2018.arrow_upwardReturn to footnote reference6 http://www.glassdoor.com/List/Best-Jobs-in-America-LST_KQ0,20.htm. Accessed April 2018.arrow_upwardReturn to footnote reference7 http://www.glassdoor.com/Salaries/index.htm. Accessed April 2018.arrow_upwardReturn to footnote reference
Visit link:
Data science – Wikipedia
Interdisciplinary field of study focused on deriving knowledge and insights from data
Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data,[1][2] and apply knowledge and actionable insights from data across a broad range of application domains. Data science is related to data mining, machine learning and big data. Data science is the studythat deals with large volumes of data using modern tools and techniques.
Data science is a "concept to unify statistics, data analysis, informatics, and their related methods" in order to "understand and analyze actual phenomena" with data.[3] It uses techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, information science, and domain knowledge. Turing Award winner Jim Gray imagined data science as a "fourth paradigm" of science (empirical, theoretical, computational, and now data-driven) and asserted that "everything about science is changing because of the impact of information technology" and the data deluge.[4][5]
Data science is an interdisciplinary field focused on extracting knowledge from data sets, which are typically large (see big data), and applying the knowledge and actionable insights from data to solve problems in a wide range of application domains.[6] The field encompasses preparing data for analysis, formulating data science problems, analyzing data, developing data-driven solutions, and presenting findings to inform high-level decisions in a broad range of application domains. As such, it incorporates skills from computer science, statistics, information science, mathematics, information visualization, data integration, graphic design, complex systems, communication and business.[7][8] Statistician Nathan Yau, drawing on Ben Fry, also links data science to human-computer interaction: users should be able to intuitively control and explore data.[9][10] In 2015, the American Statistical Association identified database management, statistics and machine learning, and distributed and parallel systems as the three emerging foundational professional communities.[11]
Many statisticians, including Nate Silver, have argued that data science is not a new field, but rather another name for statistics.[12] Others argue that data science is distinct from statistics because it focuses on problems and techniques unique to digital data.[13] Vasant Dhar writes that statistics emphasizes quantitative data and description. In contrast, data science deals with quantitative and qualitative data (e.g. images) and emphasizes prediction and action.[14] Andrew Gelman of Columbia University and data scientist Vincent Granville have described statistics as a nonessential part of data science.[15][16]Stanford professor David Donoho writes that data science is not distinguished from statistics by the size of datasets or use of computing, and that many graduate programs misleadingly advertise their analytics and statistics training as the essence of a data science program. He describes data science as an applied field growing out of traditional statistics.[17] In summary, data science can be therefore described as an applied branch of statistics.
In 1962, John Tukey described a field he called data analysis, which resembles modern data science.[17] In 1985, in a lecture given to the Chinese Academy of Sciences in Beijing, C.F. Jeff Wu used the term Data Science for the first time as an alternative name for statistics.[18] Later, attendees at a 1992 statistics symposium at the University of Montpellier II acknowledged the emergence of a new discipline focused on data of various origins and forms, combining established concepts and principles of statistics and data analysis with computing.[19][20]
The term data science has been traced back to 1974, when Peter Naur proposed it as an alternative name for computer science.[21] In 1996, the International Federation of Classification Societies became the first conference to specifically feature data science as a topic.[21] However, the definition was still in flux. After the 1985 lecture in the Chinese Academy of Sciences in Beijing, in 1997 C.F. Jeff Wu again suggested that statistics should be renamed data science. He reasoned that a new name would help statistics shed inaccurate stereotypes, such as being synonymous with accounting, or limited to describing data.[22] In 1998, Hayashi Chikio argued for data science as a new, interdisciplinary concept, with three aspects: data design, collection, and analysis.[20]
During the 1990s, popular terms for the process of finding patterns in datasets (which were increasingly large) included knowledge discovery and data mining.[23][21]
The modern conception of data science as an independent discipline is sometimes attributed to William S. Cleveland.[24] In a 2001 paper, he advocated an expansion of statistics beyond theory into technical areas; because this would significantly change the field, it warranted a new name.[23] "Data science" became more widely used in the next few years: in 2002, the Committee on Data for Science and Technology launched Data Science Journal. In 2003, Columbia University launched The Journal of Data Science.[23] In 2014, the American Statistical Association's Section on Statistical Learning and Data Mining changed its name to the Section on Statistical Learning and Data Science, reflecting the ascendant popularity of data science.[25]
The professional title of data scientist has been attributed to DJ Patil and Jeff Hammerbacher in 2008.[26] Though it was used by the National Science Board in their 2005 report, "Long-Lived Digital Data Collections: Enabling Research and Education in the 21st Century," it referred broadly to any key role in managing a digital data collection.[27]
There is still no consensus on the definition of data science and it is considered by some to be a buzzword.[28]
Big data is very quickly becoming a vital tool for businesses and companies of all sizes.[29] The availability and interpretation of big data has altered the business models of old industries and enabled the creation of new ones.[29] Data-driven businesses are worth $1.2 trillion collectively in 2020, an increase from $333 billion in the year 2015.[30] Data scientists are responsible for breaking down big data into usable information and creating software and algorithms that help companies and organizations determine optimal operations.[30] As big data continues to have a major impact on the world, data science does as well due to the close relationship between the two.[30]
There are a variety of different technologies and techniques that are used for data science which depend on the application. More recently, full-featured, end-to-end platforms have been developed and heavily used for data science and machine learning.
More: