Data Science Popup Austin: Meet the PyData Community
•
1 like•747 views
Andy Terrel, President of Numfocus and CTO of Fashion Metric, gave a presentation on the PyData community at the Data Science Pop Up in Austin. He discussed who a PyData scientist is, focusing on their skills in data, math/statistics, and understanding of domain-specific problems. Terrel also talked about the importance of open source code and maintenance through organizations like Numfocus that support collaborative data science projects. He provided an example of how IPython was used in a Nature journal article.
Report
Share
Report
Share
Download to read offline
Recommended
Data Science Popup Austin: Applied Machine Learning for IOT
Watch talk ➟ http://bit.ly/1NJLyCN
The Internet of Things is all about the data. By 2018 it is expected that the number of connect things will exceed the combined number of personal computers, smartphones, and tablets. Each ’thing’ can produce a tremendous stream of data from sensors and other sources. A short presentation will be done on some recent applications of Machine Learning, using H2O's Spark solution Sparkling Water, to the domain of data-driven machine prognostics & health management (PHM).
Data Science Popup Austin: For The Internet of Things, The More Things the Me...
Watch talk ➟ http://bit.ly/1NJNJX9
As more and more devices are internet enabled, creating the so called “Internet of Things” (IoT), our ability to understand the nature and state of our environment becomes both more powerful, and more resilient. Much of the early focus on IoT has been on the new capabilities that connected devices can bring. However, much of the power and promise of IoT comes from the combined view that multiple, disparate, connected devices can provide. We will discuss the increased analytical potential of systems that incorporate multiple, overlapping observations of operations or behaviors. We will work through some examples that demonstrate how to benefit from the coming ubiquitous visibility. We will also describe how IoT will help build more resilient systems that degrade gracefully, rather than failing outright. This resilience will be critical as we become more dependent on the successful collaboration and coordination of disparate, connected devices.
Data Science Popup Austin: Back to The Future for Data and Analytics
Big data and analytics—companies everywhere are talking about it, but what are they really delivering? JD Stanley will share his perspective, exploring how emerging tools can reduce inefficiencies and administration in upfront or post algorithm iterative improvement processes. In this session, expect to hear about practical methods of investigative analysis to drive discoveries of attributes and characteristics of the disparate data to achieve quicker business or science efficiencies. JD will highlight machine learning approaches to deriving the propensity of a connected data landscape (or “propensity of connectedness” through co-occurence data scoring) and mixing statistics and modeling into a composition approach to improve the upfront part of analysis and analytics.
Data Science Popup Austin: Ubiquity and Trust Lead to Adoption
Watch talk ➟ http://bit.ly/1NJOrne
In this session, we'll explore the power and importance of user experience and trust within IoT systems. We'll dive into a real-world example to discuss the bigger role IoT can play in shaping our technological expectations and interactions.
Data Science Popup Austin: Data Meet Product
Watch talk ➟ http://bit.ly/1SGjeZD
For many companies, data science has an obvious connection to Marketing and Engineering. The analysis from data science can lead to marketing strategies that, for example, dramatically reduce customer churn or increase lifetime value, and Engineering is instrumental in capturing and storing the historical data for modeling. But what about Product?
In this talk, we’ll discuss the importance of integrating data and data science into your Product team at each stage — to drive a truly “data-driven product,” and in turn derive “product-driven data.” We’ll review examples of what happens when Data and Product work together, how it relates to the Lean Startup-inspired approach, and what happens when companies fail to bridge this gap.
Data science presentation 2nd CI day
A presentation delivered by Mohammed Barakat on the 2nd Jordanian Continuous Improvement Open Day in Amman. The presentation is about Data Science and was delivered on 3rd October 2015.
Python in Data Science Work
My attempt to create an introductory presentation on Python in Data Science Work
Python for Data Science
In this presentation its given an introduction about Data Science, Data Scientist role and features, and how Python ecosystem provides great tools for Data Science process (Obtain, Scrub, Explore, Model, Interpret).
For that, an attached IPython Notebook ( http://bit.ly/python4datascience_nb ) exemplifies the full process of a corporate network analysis, using Pandas, Matplotlib, Scikit-learn, Numpy and Scipy.
Recommended
Data Science Popup Austin: Applied Machine Learning for IOT
Watch talk ➟ http://bit.ly/1NJLyCN
The Internet of Things is all about the data. By 2018 it is expected that the number of connect things will exceed the combined number of personal computers, smartphones, and tablets. Each ’thing’ can produce a tremendous stream of data from sensors and other sources. A short presentation will be done on some recent applications of Machine Learning, using H2O's Spark solution Sparkling Water, to the domain of data-driven machine prognostics & health management (PHM).
Data Science Popup Austin: For The Internet of Things, The More Things the Me...
Watch talk ➟ http://bit.ly/1NJNJX9
As more and more devices are internet enabled, creating the so called “Internet of Things” (IoT), our ability to understand the nature and state of our environment becomes both more powerful, and more resilient. Much of the early focus on IoT has been on the new capabilities that connected devices can bring. However, much of the power and promise of IoT comes from the combined view that multiple, disparate, connected devices can provide. We will discuss the increased analytical potential of systems that incorporate multiple, overlapping observations of operations or behaviors. We will work through some examples that demonstrate how to benefit from the coming ubiquitous visibility. We will also describe how IoT will help build more resilient systems that degrade gracefully, rather than failing outright. This resilience will be critical as we become more dependent on the successful collaboration and coordination of disparate, connected devices.
Data Science Popup Austin: Back to The Future for Data and Analytics
Big data and analytics—companies everywhere are talking about it, but what are they really delivering? JD Stanley will share his perspective, exploring how emerging tools can reduce inefficiencies and administration in upfront or post algorithm iterative improvement processes. In this session, expect to hear about practical methods of investigative analysis to drive discoveries of attributes and characteristics of the disparate data to achieve quicker business or science efficiencies. JD will highlight machine learning approaches to deriving the propensity of a connected data landscape (or “propensity of connectedness” through co-occurence data scoring) and mixing statistics and modeling into a composition approach to improve the upfront part of analysis and analytics.
Data Science Popup Austin: Ubiquity and Trust Lead to Adoption
Watch talk ➟ http://bit.ly/1NJOrne
In this session, we'll explore the power and importance of user experience and trust within IoT systems. We'll dive into a real-world example to discuss the bigger role IoT can play in shaping our technological expectations and interactions.
Data Science Popup Austin: Data Meet Product
Watch talk ➟ http://bit.ly/1SGjeZD
For many companies, data science has an obvious connection to Marketing and Engineering. The analysis from data science can lead to marketing strategies that, for example, dramatically reduce customer churn or increase lifetime value, and Engineering is instrumental in capturing and storing the historical data for modeling. But what about Product?
In this talk, we’ll discuss the importance of integrating data and data science into your Product team at each stage — to drive a truly “data-driven product,” and in turn derive “product-driven data.” We’ll review examples of what happens when Data and Product work together, how it relates to the Lean Startup-inspired approach, and what happens when companies fail to bridge this gap.
Data science presentation 2nd CI day
A presentation delivered by Mohammed Barakat on the 2nd Jordanian Continuous Improvement Open Day in Amman. The presentation is about Data Science and was delivered on 3rd October 2015.
Python in Data Science Work
My attempt to create an introductory presentation on Python in Data Science Work
Python for Data Science
In this presentation its given an introduction about Data Science, Data Scientist role and features, and how Python ecosystem provides great tools for Data Science process (Obtain, Scrub, Explore, Model, Interpret).
For that, an attached IPython Notebook ( http://bit.ly/python4datascience_nb ) exemplifies the full process of a corporate network analysis, using Pandas, Matplotlib, Scikit-learn, Numpy and Scipy.
Introduction to Python for Data Science
Webinar: https://www.codementor.io/officehours/1355593426/introduction-to-python-for-data-science
Intro to Data Science Concepts
A 1-hour introduction to data science talk given the to the Data Science for Social Good cohort in June 2015 at UW
Introduction to Big Data Analytics and Data Science
Introduction to Big Data Analytics and Data Science
Komes Chandavimol
How to become a Data Scientist?
HackerEarth is pleased to announce its next session to help you understand what it really takes to become a data scientist.
Agenda of this session will include answers to the following questions:
- Why is it the best time to take up Data Science as a career?
- How can you take the first step in Data Science? (After all, first step is always the hardest!)
- How can you become better and progress fast?
- How is life after becoming a Data Scientist?
Speaker:
Jesse Steinweg-Woods is soon-to-be a Senior Data Scientist at tronc, working on recommender systems for articles and understanding customer behavior. Previously, he worked at Argo Group Insurance on new pricing models that took advantage of machine learning techniques. He received his PhD in Atmospheric Science from Texas A&M University, and his research focused on numerical weather and climate prediction.
Getting Ready For 3rd Generation Platform
Getting Ready For 3rd Generation Platform
Data Science Thailand Meetup#4
Asst. Prof. Dr. Jirapun Daengdej
Vincent Mary School of Science and Technology
Assumption University
jirapun@scitech.au.edu
Data science training institute in hyderabad
Data Science Institutes : kelly technologies is the best Data Science Training Institutes in Hyderabad. Providing Data Science training by real time faculty in Hyderabad.
Data Scientist: The Sexiest Job in the 21st Century
An investigation of the changing roles of Analytic Professionals, resulting from our Data Explosion
Big Data Analytics and Data Science
Big Data Analytics for Khon Kaen University, Thailand
หลักสูตร ปริญญาโท IT (Big Data) ภาควิชา วิทยาการคอมพิวเตอร์
มหาวิทยาลัย ขอนแก่น
Data Science : Make Smarter Business Decisions
Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. A Data Scientist deals with all the phases of data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities.
Data Science: Not Just For Big Data
From the webinar presentation "Data Science: Not Just for Big Data", hosted by Kalido and presented by:
David Smith, Data Scientist at Revolution Analytics, and
Gregory Piatetsky, Editor, KDnuggets
These are the slides for David Smith's portion of the presentation.
Watch the full webinar at:
http://www.kalido.com/data-science.htm
Intro to Python for Data Science
Don't just learn Python, or learn what it means to be a DS, do it all in one workshop
Webinar : Introduction to R Programming and Machine Learning
'Business Analytics with 'R' at Edureka will prepare you to perform analytics and build models for real world data science problems. It is the world’s most powerful programming language for statistical computing and graphics making it a must know language for the aspiring Data Scientists. 'R' wins strongly on Statistical Capability, Graphical capability, Cost and rich set of packages.
The topics covered in the presentation are:
1.What is R
2.Domains and Companies in which R is used
3.Characteristics of R
4.Get an Overview of Machine Learning
5.Understand the difference between supervised and unsupervised learning
6.Learn Clustering and K-means Clustering
7.Implement K-means Clustering in R
8.Google Trends in R
Sentiment Analysis In Retail Domain
Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework.
Spark Social Media
AAI presentation on socialmedia and big data. Includes discussion of Spark architecture.
Data Science using Python
Data Science training in Delhi by ShapeMySkills Pvt.Ltd has proven to be the best by its many enrolled candidates. We provide you the best faculty with industry experience and learning access 24/7, study material, mock tests, and most importantly industry based projects.
For more details visit us : https://shapemyskills.in/courses/data-science/ »
or Contact us : 9873922226
The Internet of Things: filling the Predictive Analytics data void
how the Internet of Things will allow a new era of precision manufacturing
Myths of Data Science
Myths of Data Science
Predictive Analytics and Data Science Conference May 27-28
Jarun Ngamvirojcharoen
Sta. Barbara Heights - The Shophouse District
A New Lifestyle Offering in Iloilo. Sta. Barbara Heights introduces The Shophouse District, set to redefine lifestyle experiences in Iloilo City. Vibrant and festive, the dynamic new district brings a sense of exhilaration to the city to the city with it's diversity of high-end retail and commercial attractions.
More Related Content
What's hot
Introduction to Python for Data Science
Webinar: https://www.codementor.io/officehours/1355593426/introduction-to-python-for-data-science
Intro to Data Science Concepts
A 1-hour introduction to data science talk given the to the Data Science for Social Good cohort in June 2015 at UW
Introduction to Big Data Analytics and Data Science
Introduction to Big Data Analytics and Data Science
Komes Chandavimol
How to become a Data Scientist?
HackerEarth is pleased to announce its next session to help you understand what it really takes to become a data scientist.
Agenda of this session will include answers to the following questions:
- Why is it the best time to take up Data Science as a career?
- How can you take the first step in Data Science? (After all, first step is always the hardest!)
- How can you become better and progress fast?
- How is life after becoming a Data Scientist?
Speaker:
Jesse Steinweg-Woods is soon-to-be a Senior Data Scientist at tronc, working on recommender systems for articles and understanding customer behavior. Previously, he worked at Argo Group Insurance on new pricing models that took advantage of machine learning techniques. He received his PhD in Atmospheric Science from Texas A&M University, and his research focused on numerical weather and climate prediction.
Getting Ready For 3rd Generation Platform
Getting Ready For 3rd Generation Platform
Data Science Thailand Meetup#4
Asst. Prof. Dr. Jirapun Daengdej
Vincent Mary School of Science and Technology
Assumption University
jirapun@scitech.au.edu
Data science training institute in hyderabad
Data Science Institutes : kelly technologies is the best Data Science Training Institutes in Hyderabad. Providing Data Science training by real time faculty in Hyderabad.
Data Scientist: The Sexiest Job in the 21st Century
An investigation of the changing roles of Analytic Professionals, resulting from our Data Explosion
Big Data Analytics and Data Science
Big Data Analytics for Khon Kaen University, Thailand
หลักสูตร ปริญญาโท IT (Big Data) ภาควิชา วิทยาการคอมพิวเตอร์
มหาวิทยาลัย ขอนแก่น
Data Science : Make Smarter Business Decisions
Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. A Data Scientist deals with all the phases of data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities.
Data Science: Not Just For Big Data
From the webinar presentation "Data Science: Not Just for Big Data", hosted by Kalido and presented by:
David Smith, Data Scientist at Revolution Analytics, and
Gregory Piatetsky, Editor, KDnuggets
These are the slides for David Smith's portion of the presentation.
Watch the full webinar at:
http://www.kalido.com/data-science.htm
Intro to Python for Data Science
Don't just learn Python, or learn what it means to be a DS, do it all in one workshop
Webinar : Introduction to R Programming and Machine Learning
'Business Analytics with 'R' at Edureka will prepare you to perform analytics and build models for real world data science problems. It is the world’s most powerful programming language for statistical computing and graphics making it a must know language for the aspiring Data Scientists. 'R' wins strongly on Statistical Capability, Graphical capability, Cost and rich set of packages.
The topics covered in the presentation are:
1.What is R
2.Domains and Companies in which R is used
3.Characteristics of R
4.Get an Overview of Machine Learning
5.Understand the difference between supervised and unsupervised learning
6.Learn Clustering and K-means Clustering
7.Implement K-means Clustering in R
8.Google Trends in R
Sentiment Analysis In Retail Domain
Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework.
Spark Social Media
AAI presentation on socialmedia and big data. Includes discussion of Spark architecture.
Data Science using Python
Data Science training in Delhi by ShapeMySkills Pvt.Ltd has proven to be the best by its many enrolled candidates. We provide you the best faculty with industry experience and learning access 24/7, study material, mock tests, and most importantly industry based projects.
For more details visit us : https://shapemyskills.in/courses/data-science/ »
or Contact us : 9873922226
The Internet of Things: filling the Predictive Analytics data void
how the Internet of Things will allow a new era of precision manufacturing
Myths of Data Science
Myths of Data Science
Predictive Analytics and Data Science Conference May 27-28
Jarun Ngamvirojcharoen
What's hot (20)
Introduction to Big Data Analytics and Data Science
Introduction to Big Data Analytics and Data Science
Data Scientist: The Sexiest Job in the 21st Century
Data Scientist: The Sexiest Job in the 21st Century
Webinar : Introduction to R Programming and Machine Learning
Webinar : Introduction to R Programming and Machine Learning
The Internet of Things: filling the Predictive Analytics data void
The Internet of Things: filling the Predictive Analytics data void
Viewers also liked
Sta. Barbara Heights - The Shophouse District
A New Lifestyle Offering in Iloilo. Sta. Barbara Heights introduces The Shophouse District, set to redefine lifestyle experiences in Iloilo City. Vibrant and festive, the dynamic new district brings a sense of exhilaration to the city to the city with it's diversity of high-end retail and commercial attractions.
Swift - Under the Hood
Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/1GHc3rO.
Alex Blewitt introduces the history behind Swift, why it was created, how it differs from Objective-C and how Swift is compiled and executed under the covers. Alex goes into details about how LLVM is used, the way that memory is managed, how objects are laid out, and a prediction of the way Swift and Objective-C will evolve over time. Filmed at qconlondon.com.
Dr Alex Blewitt has over 20 years of experience in Objective-C and has been using Apple frameworks since NeXTSTEP 3.0. He upgraded his NeXTstation for a TiBook when Apple released Mac OS X in 2001 and has been developing on it ever since. He is author of the recently published Swift Essentials.
Building Custom
Machine Learning Algorithms
with Apache SystemML
An overview, history, and demo of Apache SystemML
When recommendation go bad
Presentation given by Evan Estola (Meetup, New-York) at the Big Data & Society conference held at RTBF on 13 Dec 2016. Main topic was the design of recommendation algorithms and how to make them ethical
Data driven prioritization & roadmap planning
Data Driven Prioritization and Roadmap Planning for Product Managers
As armas "artesanais" da guerra na Síria
Veja como são feitas e usadas as armas "artesanais" na guerra da Síria.
Latihan 3 tutorialmailmerge jasmine_x
Presentase ini menjelaskan bagaimana langkah mudah membuat mail marge bagi imac.
Realtime Learning: Using Triggers to Know What the ?$# is Going On
Realtime Learning: Using Triggers to Know What the ?$# is Going On. By Jane Kelly, Director of Data Products, Buzzfeed
Capturing the Mirage: Machine Learning in Media and Entertainment Industries
These slides were presented by Zecca Lehn, Data Scientist, Earth Advocacy Project for his talk at Data Science Pop Up LA in September 14, 2016.
Tech Competency Institute for College Student Educators
Originally presented at the 2016 Convention of ACPA--College Student Educators International in Montreal, Quebec, Canada. This institute provided an overview of the ACPA/NASPA Technology Competency as well as information about the themes present within it.
Past present and future of Recommender Systems: an Industry Perspective
Talk presented at the ACM Recommender Systems Conference in Boston, 2016
44CON London 2015 - Old Dog, New Tricks: Forensics With PowerShell
Old Dog, New Tricks: Forensics With PowerShell by Jared Atkinson
Past, Present & Future of Recommender Systems: An Industry Perspective
Slides from our talk at the RecSys 2016 conference in Boston, MA 2016-09-18 on our perspective for what are important areas for future work in recommender systems.
Factorization Meets the Item Embedding: Regularizing Matrix Factorization wit...
Presented at RecSys 2016
Swarm Intelligence - An Introduction
An introduction to Swarm Intelligence, the most popular algorithms used and the applications of swarm intelligence.
This presentation talks about the Ant Colony Optimization and the Particle Swarm Optimization, while mentioning the other algorithms used.
Mobile TV and IPTV: Enhancing the User Experience, IP&TV World Forum Middle E...
Pay-TV operators of all types need to unify their reach with IP and OTT delivery models
Adaptive streaming standards like HLS successfully enable OTT services to mobile TV platforms
A unified approach to security enables a extended value proposition to consumers
Multi-screen service offerings create an enhanced user experience
Digital Social Justice
Originally presented in January 2015 with Vernon Wall. This presentation discusses the intersections of concepts of social justice with what happens digitally on social media.
Viewers also liked (20)
Building Custom
Machine Learning Algorithms
with Apache SystemML
Building Custom
Machine Learning Algorithms
with Apache SystemML
Realtime Learning: Using Triggers to Know What the ?$# is Going On
Realtime Learning: Using Triggers to Know What the ?$# is Going On
Capturing the Mirage: Machine Learning in Media and Entertainment Industries
Capturing the Mirage: Machine Learning in Media and Entertainment Industries
Tech Competency Institute for College Student Educators
Tech Competency Institute for College Student Educators
Past present and future of Recommender Systems: an Industry Perspective
Past present and future of Recommender Systems: an Industry Perspective
44CON London 2015 - Old Dog, New Tricks: Forensics With PowerShell
44CON London 2015 - Old Dog, New Tricks: Forensics With PowerShell
Past, Present & Future of Recommender Systems: An Industry Perspective
Past, Present & Future of Recommender Systems: An Industry Perspective
Factorization Meets the Item Embedding: Regularizing Matrix Factorization wit...
Factorization Meets the Item Embedding: Regularizing Matrix Factorization wit...
Mobile TV and IPTV: Enhancing the User Experience, IP&TV World Forum Middle E...
Mobile TV and IPTV: Enhancing the User Experience, IP&TV World Forum Middle E...
Similar to Data Science Popup Austin: Meet the PyData Community
Open Data in a Big Data World: easy to say, but hard to do?
Presentation at 3rd LEARN workshop on Research Data Management, “Make research data management policies work”
Helsinki, 28 June 2016, by Sarah Callaghan, STFC Rutherford Appleton Laboratory
PDT: Personal Data from Things, and its provenance
What is the role and relevance of data provenance in the IoT landscape?
NORFest2023 Keynote address: Chelle Gentemann (NASA)
Keynote address 'Opening Science' at NORFest 2023 on November 2, 2023 at the Royal Irish Academy in Dublin Ireland. Keynote speaker: Chelle Gentemann, science lead for NASA’s Transform to Open Science Mission and co-chair of the U.S. White House Office for Science and Technology and Policy (OSTP) Sub-working group on the Year of Open Science
Citizen Science and the Environment
This is my presentation from the ISDE7 / WALIS / NRM Conference held in Perth. It focuses on my personal experiences with citizen science and the environment over the last couple of years.
OSFair2017 | Barriers to Open Science for junior researchers
Jon Tennant talks about barriers to open science for junior researchers
Plenary: Open for all? Diversity & disparity in Open Science.
Presentation Abstract:
What are the barriers to Open Science, and how do they impact upon different demographics? Open Science is supposed to be about inclusivity, equality, and rigour. But is the way it is implemented meeting these ideals, or simply creating a new set of barriers to scholarship? Younger researchers are basically trying to survive within a hyper-competitive academic system. They are beset on all sides by systemic control and inertia, power dynamics, and fear. What they want to do for science is not always what is best for their career. This creates a system of social barriers that cannot be overcome by mandates and policies that do little to address these structural biases.
Paywalls inflict a huge level of disparity on younger researchers. They may feel they cannot afford the exorbitant fees charged by some publishers for Open Access, even if their research funders provide support for it. The way OA is currently implemented has switched the barrier from the reader side to the author side, due in part to political broadsiding from commercial publishers. Unfunded or self-funded grad students, as well as those from emerging economies, are basically doomed when it comes to such high author-facing charges.
More: http://opensciencefair.eu/speakers/jon-tennant
DAY 2 - PLENARY
Tracking Social Practices with Big(ish) data
Paper presented at 'Methodology' session of PRACTICES, THE BUILT ENVIRONMENT AND SUSTAINABILITY EARLY CAREER RESEARCHER NETWORK Workshop,
26-27 June 2014, Cambridge
Better Data for a Better World
How open data contribute to improving the world. The life science use case. The technical, social, ethical issues.
This was a talk given within the iGEM 2020 programme by the London Imperial College students group (https://2020.igem.org/Team:Imperial_College), in a webinar organised by the SOAPLab group on the topic of Ethics of Automation. Excellent Dr Brandon Sepulvado was the other speaker of the day.
Podstemic
PODSTEMIC: Changing the face of STEM curriculum with mobile devices
Did you know that all jobs of the future will require a basic understanding of math and science? The most recent ten year employment projections by the U.S. Labor Department show that of the 20 fastest growing occupations projected for 2014, 15 of them require significant mathematics or science preparation to successfully compete for a job. As educators, what can we do to ensure our students are ready for the future? ENTER PODSTEMIC.
In this session, attendees will learn 10 different innovative ways to integrate Apple’s iPod touch and iPad into STEM curriculum. Attendees will discover valuable resources on new ways to infuse mobile devices into tough STEM classes. This session will provide something for everyone, even if STEM content areas are not your focus. At Podstemic, you will find how to manage these devices, how to use third party equipment in conjunction with these devices, and much more! Come and ENTER PODSTEMIC!
Data Science For Social Good: Tackling the Challenge of Homelessness
A talk presented at the Champions Leadership Conference Series - leveraging data provided by New York City’s Department of Homeless Services, software vendor Tibco partnered with SumAll.Org to help tackle the societal challenge of homelessness in New York City.
The African Open Science Platform: Policy, Infrastructure, Skills and Incenti...
The African Open Science Platform: Policy, Infrastructure, Skills and Incenti...African Open Science Platform
Presented during the PV2018 conference. Data Science Provenance: From Drug Discovery to Fake Fans
Knowledge work adds value to raw data; how this activity is performed is critical for how reliably results can be reproduced and scrutinized. With a brief diversion into epistemology, the presentation will outline the challenges for practitioners and consumers of Big Data analysis, and demonstrate how these were tackled at Inforsense (life sciences workflow analytics platform) and Musicmetric (social media analytics for music).
The talk covers the following issues with concrete examples:
- Representations of provenance
- Considerations to allow analysis computation to be recreated
- Reliable collection of noisy data from the internet
- Archiving of data and accommodating retrospective changes
- Using linked data to direct Big Data analytics
Data science presentation - Management career institute
1.Basic Definition: data, science, data science
1.Data: Facts and statistics collected together for reference or analysis
2.Science: The intellectual and practical activity encompassing the systematic study of the structure and behavior of the physical and natural world through observation and experiment.
3.Data Science: Data science is a multidisciplinary blend of data inference, algorithm development, and technology in order to solve analytically complex problems
2. Who is a Data Scientist?
A Data Scientist is the master of all trades! He should be proficient in maths, he should be acing the Business field and should have great Computer Science skills as well
.
3.Data Science Skills
•SQL
•Hadoop
•Python
•Java
•R
4.Data science scope
There is a big scope for data Science in India. The profession has been named the sexiest job of the 21st century.
5.Data science job trends:
Data science actually makes sense, not only because it is very useful, but also you have a great career in it in the near future.
6.Data Scientist Salary Trends:
The report goes on to say that the median salary for a Data Scientist is an impressive $116,000 and there are over 1,736 job openings posted on the site.
7.Data Scientist Job Roles:
•Data Scientist
•Data Engineer
•Data Architect
•Data Administrator
•Data Analyst
•Business Analyst
•Data/Analytics Manager
•Business Intelligence Manager
#1 FAIR: Into to FAIR and F for Findable
#1 FINDABLE covers: -- an overview of the FAIR principles: their origins, Australian FAIR initiatives, what FAIR is (and what it is not) -- the 4 FINDABLE principles which underpin the discoverability of data -- resources to support institutional awareness and uptake of Findable principles to make your institutional data globally discoverable
Speakers
1) Keith Russell, ANDS, will introduce FAIR
2) Nick Thieberger, Director of Paradisec, will present how Paradisec has made their data findable via rich metadata, identifiers through Research Data Australia and disciplinary discovery portals.
YouTube : https://youtu.be/vn2pr2dGzCs
Transcript: https://www.slideshare.net/AustralianNationalDataService/transcript-1-fair-intro-into-fair-and-f-for-findable
Top data science projects
This presentation educates you about top data science project ideas for Beginner, Intermediate and Advanced. the ideas such as Fake News Detection Using Python, Data Science Project on, Detecting Forest Fire, Detection of Road Lane Lines, Project on Sentimental Analysis, Speech Recognition, Developing Chatbots, Detection of Credit Card Fraud and Customer Segmentations etc:
For more topics stay tuned with Learnbay.
Zeng marcia ifla-subjectaccesssmartdatadh
This presentation will discuss how the structured data, together with the semantically indexed/mined entities in semi-structured and unstructured data, are contributing to researches beyond libraries, especially in digital humanities. It aims to explore the opportunities and strategies to use, reuse, share, and effectively elaborate the smart data -- generated or to be generated -- in libraries.
A coordinated framework for open data open science in Botswana/Simon Hodson
A coordinated framework for open data open science in Botswana/Simon HodsonAfrican Open Science Platform
Presentation during the Botswana National Forum to discuss a national Open Science Open Data policy, 30-31 October 2017.Presentación Ciro Cattuto, ISI Foundation en VI Summit País Digital 2018
Exposición “ Data Science for private and public good” de Ciro Cattuto, Scientific Director, ISI Foundation, en el marco del VI Summit País Digital 2018, realizado el 4 y 5 de septiembre en Santiago, Chile.
Similar to Data Science Popup Austin: Meet the PyData Community (20)
Open Data in a Big Data World: easy to say, but hard to do?
Open Data in a Big Data World: easy to say, but hard to do?
PDT: Personal Data from Things, and its provenance
PDT: Personal Data from Things, and its provenance
NORFest2023 Keynote address: Chelle Gentemann (NASA)
NORFest2023 Keynote address: Chelle Gentemann (NASA)
OSFair2017 | Barriers to Open Science for junior researchers
OSFair2017 | Barriers to Open Science for junior researchers
Data Science For Social Good: Tackling the Challenge of Homelessness
Data Science For Social Good: Tackling the Challenge of Homelessness
The African Open Science Platform: Policy, Infrastructure, Skills and Incenti...
The African Open Science Platform: Policy, Infrastructure, Skills and Incenti...
Data Science Provenance: From Drug Discovery to Fake Fans
Data Science Provenance: From Drug Discovery to Fake Fans
Data science presentation - Management career institute
Data science presentation - Management career institute
A coordinated framework for open data open science in Botswana/Simon Hodson
A coordinated framework for open data open science in Botswana/Simon Hodson
Presentación Ciro Cattuto, ISI Foundation en VI Summit País Digital 2018
Presentación Ciro Cattuto, ISI Foundation en VI Summit País Digital 2018
More from Domino Data Lab
What's in your workflow? Bringing data science workflows to business analysis...
While business analysis rapidly grows more data-driven, the analyst community is slow to adapt the best practices of data science workflows. Many parallels exists between data science “top topics” (e.g. reproducibility) and business pain points, but these common needs are obscured by the different “languages” of these two communities. The opportunity cost is greatest in heavily regulated industries such as finance and insurance where documentation and compliance are paramount.
In this talk, we will review our experience transitioning Capital One business analysts from legacy systems to open-source workflows by developing user-friendly tools. We incentivized business analysts to adopt the data science mindset by curating open-source tools and developing code packages which simplify workflows and eliminate pain points.
Our internal R package, tidycf, reimagines cumbersome Excel cashflow statements as dataframes and uses RMarkdown templates and the RStudio IDE for an intuitive, user-friendly experience without the overhead of maintaining a custom GUI. We tackle challenges in documentation and communication while immersing new users in the R language.
We will share best practices and lessons learned from our experience designing tools for non-technical end-users, standardizing workflows based on the RStudio IDE’s infrastructure, and evangelizing data science methods.
The Proliferation of New Database Technologies and Implications for Data Scie...
In this talk, we’ll describe NoSQL (“not-only SQL”) and document-oriented databases and the value they provide for data science companies like Uptake. We will walk through the unique challenges such datastores pose for data science workflows. To make these challenges and lessons learned concrete, we’ll explore data science workflows through a discussion of the development efforts that led to “uptasticsearch”, an R package released by the Uptake Data Science team to reduce friction in interacting with a document store called Elasticsearch. The talk will conclude with a discussion of recent developments in NoSQL technologies and implications for data scientists.
Racial Bias in Policing: an analysis of Illinois traffic stops data
Since 2004, Illinois has collected demographic information about traffic stops conducted by police in an effort to identify racial bias. This data has been used by groups such as the ACLU and the Stanford Open Policing Project to identify key markers that infer racial bias in policing. We have applied exploratory data analysis to investigate whether systemic racial bias may appear and to what extent. This talk will walk the audience through the insights gleaned from the exploration of this data along with the challenges posed and ongoing questions raised.
Data Quality Analytics: Understanding what is in your data, before using it
Analytics and data science are ever growing fields, as business decision makers continue to use data to drive decisions. The pinnacle of these fields are the models and their accuracy/fit,; what about the data? Is your data clean, and how do you know that? Our discussion will focus on best practices for data preprocessing for analytic uses. Beginning with essential distributional checks of a dataset to a propose method for automated data validation process during ETL for transactional data.
Supporting innovation in insurance with randomized experimentation
Recent technological advances, a dynamic competitive landscape, and an evolving regulatory environment have led to a period of rapid innovation for many insurance providers. Here, we’ll explore how data scientists may use randomized experiments to rigorously assess the causal impact of innovations on business outcomes. Particular emphasis will be placed on experimentation in “offline” channels, with some of the challenges and mitigation strategies highlighted.
Leveraging Data Science in the Automotive Industry
Cars.com Inc. is a decision engine for car buyers and a growth engine for our partners. Data Science is the bread and butter of any decision engine and Cars is no different. In this talk, I will discuss how we quantify various parameters of a car and plan to make use of all the data in hand to put predictive models at various stages of a users’ automobile lifecycle. This talk will also cater to students looking to gain knowledge on how data science is utilized at scale while still following certain processes and leading the way for business and product partners.
Summertime Analytics: Predicting E. coli and West Nile Virus
Lake Michigan and outdoor recreation are enjoyable aspects of summers in Chicago, but it can come with risk of potential E. coli in Lake Michigan or West Nile Virus from mosquitos. This summer, the City of Chicago launched two new predictive analytics projects to forecasts the risks and to proactively limit these risks. Members of the research team, Gene Leynes and Nick Lucius discuss the projects and how they’re being used as part of city operations.
Reproducible Dashboards and other great things to do with Jupyter
Mac Rogers of Domino Data Lab discusses Jupyter
GeoViz: A Canvas for Data Science
Today, more than ever before, maps are being used to bring data to life. In this presentation I will demonstrate how geoviz can make data science more tangible by providing an interactive canvas for spatial data. Gregory Brunner will shows several examples of how maps are being used enhance how we communicate data and how this applies across all scales, including spatial, temporal, and size of data.
Managing Data Science | Lessons from the Field
What you till learn:
GOALS - What is the bar for data science teams
PITFALLS - What are common data science struggles
DIAGNOSES - Why so many of our efforts fail to deliver value
RECOMMENDATIONS - How to address these struggles with best practices
Presented by Mac Steele
Director of Product at Domino Data Lab
Doing your first Kaggle (Python for Big Data sets)
You love python. You love Data Science. But the size of your data set keeps crashing your code. Is it time to bring in big data tools or simply code smarter? Lee is going to show you efficiency hacks, drawn from top Kaggle competitors, to get python to work on large data sets. Skip the hassle of creating a Big Data infrastructure. Let’s find out how far we can push our home laptop first.
Leveraged Analytics at Scale
Most of analytics modeling work today focuses on the production of single-purpose "artisanal" models for predictions. This approach to analytics is fragile with respect to model consistency, reorganization, and resource availability. This talk will argue that instead the focus of analytics modeling should be toward the production of analytics interchangeable parts, which can be combined in creative ways to produce a wide variety of analytics results. This "nuts and bolts" approach allows analytics groups to produce results in an agile way where the time between ask and answer is determined by the right combination of analytics, rather than the modeling.
How I Learned to Stop Worrying and Love Linked Data
In this presentation, Jon Loyens will share:
-Best practices for sharing context and knowledge about your data projects
-How linked data can augment your existing data science workflow and toolchain to accelerate your work
-How a social network can unlock power of Linked Data and data collaboration
-How Linked Data can help you easily combine private and Open Data for fun and profit
Software Engineering for Data Scientists
Although both disciplines are unique in their own ways, Software Engineering and Data Science make heavy use of programing languages to do their respective jobs. Data Science is a relatively new discipline and many of its practitioners have not previously been professional software engineers. There are a few techniques that Data Scientists can leverage from Software Engineering in order to make their tooling and environments, faster to design, more easily debugged and most importantly, clearer to read. This talk will be going over some practical tips that anyone can use to help better understand their code; give clarity around cloud environments, their uses and drawbacks and finally briefly touching on the Software Development Lifecycle.
Making Big Data Smart
Within marketing research, big data is often described as being “census” data for the population that it represents. The devil is in the details and when we take a closer look we can see that this isn’t the case. There are many situations that are not captured within the population that big data purports to be a census of. Big data isn’t even a census of itself since it’s not uncommon for records to be excluded either by accident during the collection process or by design in the cleaning processor. Unfortunately, our industry is so enamored with the size of big data that some users of data are willing to trade off precision for tonnage. Fortunately, if the shortcomings of big data are understood and corrected it can accurately represent the population that it measures in the correct proportion to the universe. We will discuss a method that Nielsen has developed called “Common Homes” that is designed to identify and correct the shortcomings of big data sets that represent media consumption.
Moving Data Science from an Event to A Program: Considerations in Creating Su...
The exponential growth of Big Data and Analytics has outpaced the ability of organizations to govern their data appropriately. The ability to reuse the work done by data scientists work is becoming an economic necessity. The mix of data sources is changing from tradition transactional and ERP systems to include a mix of structured, semi-structured and unstructured data. Data Governance needs to adapt to these changes. This session discusses these data changes and proposed how to adapt current data governance processes. These include, how the concept of a stakeholder has changed and the need for expansion of communications and content management. We look at need to consolidate data from disparate systems and how it governed. Lastly we will investigate how context is emerging as an important factor in governance and how it can be leveraged to provide for accurate, reliable data reuse.
Building Data Analytics pipelines in the cloud using serverless technology
Big Data analytics is well known to uncover hidden insights that gives an organization an edge over the competition. But data does not need to be big in order to be useful. Smaller companies and startups may lack the volume of data that qualifies as big data, yet the variety of data can still yield a trove of insights that helps in driving the business strategies of a company. Startups may also lack the resources to fund an additional, seemingly expensive development project. The key is in simplicity, start small, simple and architect for scalability and performance. But how do you start? In this presentation, we share our experience in building a cost effective, AWS serverless data analytics platform that became an invaluable tool for sales, marketing and operational efficiencies.Serverless architectures simplify development work where servers and software are managed by a third party cloud provider. Developers can focus on just building the data wrangling and data analysis logic where critical aspects like scalability and high availability are guaranteed by the cloud provider. Besides, serverless services offer the pay as you go model, where you pay only based on the amount of resources you use. This turns out to be another attractive aspect where costs can be managed based on the usage. In this presentation we will focus on techniques and best practices to build a big data analytics platform using AWS serverless services like Lambda, DynamoDB, S3, Kinesis, Athena, QuickSight and Amazon ML. We will highlight the strengths of each of these services and what role each plays in the data analytics pipeline. We compare and contrast these services with some of the other popularly used big data technologies like Hadoop, Spark and Kafka. We also demonstrate the usage of these services to build intelligent components that detect anomalies, yield recommendations, simulate chat bots and generate predictive analytics.
Leveraging Open Source Automated Data Science Tools
The data science process seeks to transform and empower organizations by finding and exploiting market inefficiencies and potentially hidden opportunities, but this is often an expensive, tedious process. However, many steps can be automated to provide a streamlined experience for data scientists. Eduardo Arino de la Rubia explores the tools being created by the open source community to free data scientists from tedium, enabling them to work on the high-value aspects of insight creation and impact validation.
The promise of the automated statistician is almost as old as statistics itself. From the creations of vast tables, which saved the labor of calculation, to modern tools which automatically mine datasets for correlations, there has been a considerable amount of advancement in this field. Eduardo compares and contrasts a number of open source tools, including TPOT and auto-sklearn for automated model generation and scikit-feature for feature generation and other aspects of the data science workflow, evaluates their results, and discusses their place in the modern data science workflow.
Along the way, Eduardo outlines the pitfalls of automated data science and applications of the “no free lunch” theorem and dives into alternate approaches, such as end-to-end deep learning, which seek to leverage massive-scale computing and architectures to handle automatic generation of features and advanced models.
Domino and AWS: collaborative analytics and model governance at financial ser...
by Glen Leppert
CTO, DBRS
The Role and Importance of Curiosity in Data Science
by Alfred Lee
Lead Data Scientist, White Ops
Is curiosity useful for more than serendipitous discovery? Can curiosity be taught? How do I foster curiosity in my team? Can someone be too curious? Questions!
More from Domino Data Lab (20)
What's in your workflow? Bringing data science workflows to business analysis...
What's in your workflow? Bringing data science workflows to business analysis...
The Proliferation of New Database Technologies and Implications for Data Scie...
The Proliferation of New Database Technologies and Implications for Data Scie...
Racial Bias in Policing: an analysis of Illinois traffic stops data
Racial Bias in Policing: an analysis of Illinois traffic stops data
Data Quality Analytics: Understanding what is in your data, before using it
Data Quality Analytics: Understanding what is in your data, before using it
Supporting innovation in insurance with randomized experimentation
Supporting innovation in insurance with randomized experimentation
Leveraging Data Science in the Automotive Industry
Leveraging Data Science in the Automotive Industry
Summertime Analytics: Predicting E. coli and West Nile Virus
Summertime Analytics: Predicting E. coli and West Nile Virus
Reproducible Dashboards and other great things to do with Jupyter
Reproducible Dashboards and other great things to do with Jupyter
Doing your first Kaggle (Python for Big Data sets)
Doing your first Kaggle (Python for Big Data sets)
How I Learned to Stop Worrying and Love Linked Data
How I Learned to Stop Worrying and Love Linked Data
Moving Data Science from an Event to A Program: Considerations in Creating Su...
Moving Data Science from an Event to A Program: Considerations in Creating Su...
Building Data Analytics pipelines in the cloud using serverless technology
Building Data Analytics pipelines in the cloud using serverless technology
Leveraging Open Source Automated Data Science Tools
Leveraging Open Source Automated Data Science Tools
Domino and AWS: collaborative analytics and model governance at financial ser...
Domino and AWS: collaborative analytics and model governance at financial ser...
The Role and Importance of Curiosity in Data Science
The Role and Importance of Curiosity in Data Science
Recently uploaded
Opendatabay - Open Data Marketplace.pptx
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
一比一原版(Coventry毕业证书)考文垂大学毕业证如何办理
毕业原版【微信:41543339】【(Coventry毕业证书)考文垂大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Predicting Product Ad Campaign Performance: A Data Analysis Project Presentation
Predicting Product Ad Campaign Performance: A Data Analysis Project PresentationBoston Institute of Analytics
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/一比一原版(Deakin毕业证书)迪肯大学毕业证如何办理
原版定制【微信:41543339】【(Deakin毕业证书)迪肯大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版(BU毕业证)波士顿大学毕业证成绩单
BU毕业证【微信95270640】购买（波士顿大学毕业证成绩单硕士学历）Q微信95270640代办BU学历认证留信网伪造波士顿大学学位证书精仿波士顿大学本科/硕士文凭证书补办波士顿大学 diplomaoffer,Transcript购买波士顿大学毕业证成绩单购买BU假毕业证学位证书购买伪造波士顿大学文凭证书学位证书,专业办理雅思、托福成绩单，学生ID卡，在读证明，海外各大学offer录取通知书，毕业证书，成绩单，文凭等材料:1:1完美还原毕业证、offer录取通知书、学生卡等各种在读或毕业材料的防伪工艺（包括 烫金、烫银、钢印、底纹、凹凸版、水印、防伪光标、热敏防伪、文字图案浮雕，激光镭射，紫外荧光，温感光标）学校原版上有的工艺我们一样不会少，不论是老版本还是最新版本，都能保证最高程度还原，力争完美以求让所有同学都能享受到完美的品质服务。
专业为留学生办理波士顿大学波士顿大学毕业证offer【100%存档可查】留学全套申请材料办理。本公司承诺所有毕业证成绩单成品全部按照学校原版工艺对照一比一制作和学校一样的羊皮纸张保证您证书的质量！
如果你回国在学历认证方面有以下难题请联系我们我们将竭诚为你解决认证瓶颈
1所有材料真实但资料不全无法提供完全齐整的原件。【如：成绩单丶毕业证丶回国证明等材料中有遗失的。】
2获得真实的国外最终学历学位但国外本科学历就读经历存在问题或缺陷。【如：国外本科是教育部不承认的或者是联合办学项目教育部没有备案的或者外本科没有正常毕业的。】
3学分转移联合办学等情况复杂不知道怎么整理材料的。时间紧迫自己不清楚递交流程的。
如果你是以上情况之一请联系我们我们将在第一时间内给你免费咨询相关信息。我们将帮助你整理认证所需的各种材料.帮你解决国外学历认证难题。
国外波士顿大学波士顿大学毕业证offer办理方法：
1客户提供办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询波士顿大学波士顿大学毕业证offer）；
2开始安排制作波士顿大学毕业证成绩单电子图；
3波士顿大学毕业证成绩单电子版做好以后发送给您确认；
4波士顿大学毕业证成绩单电子版您确认信息无误之后安排制作成品；
5波士顿大学成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。二条巴掌般大的裤衩衩走出泳池山娃感觉透身粘粘乎乎散发着药水味有点痒山娃顿时留恋起家乡的小河潺潺活水清凉无比日子就这样孤寂而快乐地过着寂寞之余山娃最神往最开心就是晚上无论多晚多累父亲总要携山娃出去兜风逛夜市流光溢彩人潮涌动的都市夜生活总让山娃目不暇接惊叹不已父亲老问山娃想买什么想吃什么山娃知道父亲赚钱很辛苦除了书籍和文具山娃啥也不要能牵着父亲的手满城闲逛他已心满意足了父亲连挑了三套童装叫山娃试穿山伸
一比一原版(UIUC毕业证)伊利诺伊大学|厄巴纳-香槟分校毕业证如何办理
UIUC毕业证offer【微信95270640】☀《伊利诺伊大学|厄巴纳-香槟分校毕业证购买》GoogleQ微信95270640《UIUC毕业证模板办理》加拿大文凭、本科、硕士、研究生学历都可以做,二、业务范围：
★、全套服务：毕业证、成绩单、化学专业毕业证书伪造《伊利诺伊大学|厄巴纳-香槟分校大学毕业证》Q微信95270640《UIUC学位证书购买》
(诚招代理)办理国外高校毕业证成绩单文凭学位证,真实使馆公证（留学回国人员证明）真实留信网认证国外学历学位认证雅思代考国外学校代申请名校保录开请假条改GPA改成绩ID卡
1.高仿业务:【本科硕士】毕业证,成绩单（GPA修改）,学历认证（教育部认证）,大学Offer,,ID,留信认证,使馆认证,雅思,语言证书等高仿类证书；
2.认证服务: 学历认证（教育部认证）,大使馆认证（回国人员证明）,留信认证（可查有编号证书）,大学保录取,雅思保分成绩单。
3.技术服务：钢印水印烫金激光防伪凹凸版设计印刷激凸温感光标底纹镭射速度快。
办理伊利诺伊大学|厄巴纳-香槟分校伊利诺伊大学|厄巴纳-香槟分校毕业证offer流程：
1客户提供办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）
-办理真实使馆公证（即留学回国人员证明）
-办理各国各大学文凭（世界名校一对一专业服务,可全程监控跟踪进度）
-全套服务：毕业证成绩单真实使馆公证真实教育部认证。让您回国发展信心十足！
（详情请加一下 文凭顾问+微信:95270640）欢迎咨询！的鬼地方父亲的家在高楼最底屋最下面很矮很黑是很不显眼的地下室父亲的家安在别人脚底下须绕过高楼旁边的垃圾堆下八个台阶才到父亲的家很狭小除了一张单人床和一张小方桌几乎没有多余的空间山娃一下子就联想起学校的男小便处山娃很想笑却怎么也笑不出来山娃很迷惑父亲的家除了一扇小铁门连窗户也没有墓穴一般阴森森有些骇人父亲的城也便成了山娃的城父亲的家也便成了山娃的家父亲让山娃呆在屋里做作业看电视最多只能在门口透透气间
一比一原版(爱大毕业证书)爱丁堡大学毕业证如何办理
毕业原版【微信:41543339】【(爱大毕业证书)爱丁堡大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版(UniSA毕业证书)南澳大学毕业证如何办理
原版定制【微信:41543339】【(UniSA毕业证书)南澳大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版(UPenn毕业证)宾夕法尼亚大学毕业证成绩单
UPenn毕业证【微信95270640】办理宾夕法尼亚大学毕业证原版一模一样、UPenn毕业证制作【Q微信95270640】《宾夕法尼亚大学毕业证购买流程》《UPenn成绩单制作》宾夕法尼亚大学毕业证书UPenn毕业证文凭宾夕法尼亚大学
本科毕业证书,学历学位认证如何办理【留学国外学位学历认证、毕业证、成绩单、大学Offer、雅思托福代考、语言证书、学生卡、高仿教育部认证等一切高仿或者真实可查认证服务】代办国外（海外）英国、加拿大、美国、新西兰、澳大利亚、新西兰等国外各大学毕业证、文凭学历证书、成绩单、学历学位认证真实可查。
办国外宾夕法尼亚大学宾夕法尼亚大学硕士学位证成绩单教育部学历学位认证留信认证大使馆认证留学回国人员证明修改成绩单信封申请学校offer录取通知书在读证明offer letter。
快速办理高仿国外毕业证成绩单：
1宾夕法尼亚大学毕业证+成绩单+留学回国人员证明+教育部学历认证（全套留学回国必备证明材料给父母及亲朋好友一份完美交代）;
2雅思成绩单托福成绩单OFFER在读证明等留学相关材料（申请学校转学甚至是申请工签都可以用到）。
3.毕业证 #成绩单等全套材料从防伪到印刷从水印到钢印烫金高精仿度跟学校原版100%相同。
专业服务请勿犹豫联系我！联系人微信号：95270640诚招代理：本公司诚聘当地代理人员如果你有业余时间有兴趣就请联系我们。
国外宾夕法尼亚大学宾夕法尼亚大学硕士学位证成绩单办理过程：
1客户提供办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。我们在哪里父母对我们的爱和思念为我们的生命增加了光彩给予我们自由追求的力量生活的力量我们也不忘感恩正因为这股感恩的线牵着我们使我们在一年的结束时刻义无反顾的踏上了回家的旅途人们常说父母恩最难回报愿我能以当年爸爸妈妈对待小时候的我们那样耐心温柔地对待我将渐渐老去的父母体谅他们以反哺之心奉敬父母以感恩之心孝顺父母哪怕只为父母换洗衣服为父母喂饭送汤按摩酸痛的腰背握着父母的手扶着他们一步一步地慢慢散步.娃
一比一原版(ArtEZ毕业证)ArtEZ艺术学院毕业证成绩单
ArtEZ毕业证【微信95270640】☀《ArtEZ艺术学院毕业证购买》Q微信95270640《ArtEZ毕业证模板办理》文凭、本科、硕士、研究生学历都可以做,《文凭ArtEZ毕业证书原版制作ArtEZ成绩单》《仿制ArtEZ毕业证成绩单ArtEZ艺术学院学位证书pdf电子图》毕业证
[留学文凭学历认证(留信认证使馆认证)ArtEZ艺术学院毕业证成绩单毕业证证书大学Offer请假条成绩单语言证书国际回国人员证明高仿教育部认证申请学校等一切高仿或者真实可查认证服务。
多年留学服务公司,拥有海外样板无数能完美1:1还原海外各国大学degreeDiplomaTranscripts等毕业材料。海外大学毕业材料都有哪些工艺呢？工艺难度主要由：烫金.钢印.底纹.水印.防伪光标.热敏防伪等等组成。而且我们每天都在更新海外文凭的样板以求所有同学都能享受到完美的品质服务。
国外毕业证学位证成绩单办理方法：
1客户提供办理ArtEZ艺术学院ArtEZ艺术学院毕业证假文凭信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）
— — — — 我们是挂科和未毕业同学们的福音我们是实体公司精益求精的工艺！ — — — -
一真实留信认证的作用(私企外企荣誉的见证):
1：该专业认证可证明留学生真实留学身份同时对留学生所学专业等级给予评定。
2：国家专业人才认证中心颁发入库证书这个入网证书并且可以归档到地方。
3：凡是获得留信网入网的信息将会逐步更新到个人身份内将在公安部网内查询个人身份证信息后同步读取人才网入库信息。
4：个人职称评审加20分个人信誉贷款加10分。
5：在国家人才网主办的全国网络招聘大会中纳入资料供国家500强等高端企业选择人才。却怎么也笑不出来山娃很迷惑父亲的家除了一扇小铁门连窗户也没有墓穴一般阴森森有些骇人父亲的城也便成了山娃的城父亲的家也便成了山娃的家父亲让山娃呆在屋里做作业看电视最多只能在门口透透气不能跟陌生人搭腔更不能乱跑一怕迷路二怕拐子拐人山娃很惊惧去年村里的田鸡就因为跟父亲进城一不小心被人拐跑了至今不见踪影害得田鸡娘天天哭得死去活来疯了一般那情那景无不令人摧肝裂肺山娃很听话天天呆在小屋里除了看书写作业就是睡带
一比一原版(CBU毕业证)不列颠海角大学毕业证成绩单
CBU毕业证【微信95270640】《如何办理不列颠海角大学毕业证认证》【办证Q微信95270640】《不列颠海角大学文凭毕业证制作》《CBU学历学位证书哪里买》办理不列颠海角大学学位证书扫描件、办理不列颠海角大学雅思证书！
国际留学归国服务中心《如何办不列颠海角大学毕业证认证》《CBU学位证书扫描件哪里买》实体公司，注册经营，行业标杆，精益求精！
1:1完美还原海外各大学毕业材料上的工艺：水印阴影底纹钢印LOGO烫金烫银LOGO烫金烫银复合重叠。文字图案浮雕激光镭射紫外荧光温感复印防伪。
可办理以下真实不列颠海角大学存档留学生信息存档认证：
1不列颠海角大学真实留信网认证（网上可查永久存档无风险百分百成功入库）；
2真实教育部认证（留服）等一切高仿或者真实可查认证服务（暂时不可办理）；
3购买英美真实学籍（不用正常就读直接出学历）；
4英美一年硕士保毕业证项目（保录取学校挂名不用正常就读保毕业）
留学本科/硕士毕业证书成绩单制作流程：
1客户提供办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询不列颠海角大学不列颠海角大学本科学位证成绩单）；
2开始安排制作不列颠海角大学毕业证成绩单电子图；
3不列颠海角大学毕业证成绩单电子版做好以后发送给您确认；
4不列颠海角大学毕业证成绩单电子版您确认信息无误之后安排制作成品；
5不列颠海角大学成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）
— — — — — — — — — — — 《文凭顾问Q/微：95270640》这么大这么美的地方赚大钱高楼大厦鳞次栉比大街小巷人潮涌动山娃一路张望一路惊叹他发现城里的桥居然层层叠叠扭来扭去桥下没水却有着水一般的车水马龙山娃惊诧于城里的公交车那么大那么美不用买票乖乖地掷下二枚硬币空调享受还能坐着看电视呢屡经辗转山娃终于跟着父亲到家了山娃没想到父亲城里的家会如此寒碜更没料到父亲的城里竟有如此简陋的鬼地方父亲的家在高楼最底屋最下面很矮很黑是很不显眼的地下室父亲的家安在别人脚底下孰
一比一原版(Adelaide毕业证书)阿德莱德大学毕业证如何办理
原版定制【微信:41543339】【(Adelaide毕业证书)阿德莱德大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Quantitative Data AnalysisReliability Analysis (Cronbach Alpha) Common Method...
Quantitative data Analysis
Overview
Reliability Analysis (Cronbach Alpha)
Common Method Bias (Harman Single Factor Test)
Frequency Analysis (Demographic)
Descriptive Analysis
一比一原版(Bradford毕业证书)布拉德福德大学毕业证如何办理
原版定制【微信:41543339】【(Bradford毕业证书)布拉德福德大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
My burning issue is homelessness K.C.M.O.
My burning issue is homelessness in Kansas City, MO
To: Tom Tresser
From: Roger Warren
一比一原版(TWU毕业证)西三一大学毕业证成绩单
TWU毕业证【微信95270640】西三一大学没毕业>办理西三一大学毕业证成绩单【微信TWU】TWU毕业证成绩单TWU学历证书TWU文凭《TWU毕业套号文凭网认证西三一大学毕业证成绩单》《哪里买西三一大学毕业证文凭TWU成绩学校快递邮寄信封》《开版西三一大学文凭》TWU留信认证本科硕士学历认证
[留学文凭学历认证(留信认证使馆认证)西三一大学毕业证成绩单毕业证证书大学Offer请假条成绩单语言证书国际回国人员证明高仿教育部认证申请学校等一切高仿或者真实可查认证服务。
多年留学服务公司,拥有海外样板无数能完美1:1还原海外各国大学degreeDiplomaTranscripts等毕业材料。海外大学毕业材料都有哪些工艺呢？工艺难度主要由：烫金.钢印.底纹.水印.防伪光标.热敏防伪等等组成。而且我们每天都在更新海外文凭的样板以求所有同学都能享受到完美的品质服务。
国外毕业证学位证成绩单办理方法：
1客户提供办理西三一大学西三一大学本科学位证成绩单信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）
— — — — 我们是挂科和未毕业同学们的福音我们是实体公司精益求精的工艺！ — — — -
一真实留信认证的作用(私企外企荣誉的见证):
1：该专业认证可证明留学生真实留学身份同时对留学生所学专业等级给予评定。
2：国家专业人才认证中心颁发入库证书这个入网证书并且可以归档到地方。
3：凡是获得留信网入网的信息将会逐步更新到个人身份内将在公安部网内查询个人身份证信息后同步读取人才网入库信息。
4：个人职称评审加20分个人信誉贷款加10分。
5：在国家人才网主办的全国网络招聘大会中纳入资料供国家500强等高端企业选择人才。广州火车东站的那一刻山娃感受到了一种从未有过的激动和震撼太美了可爱的广州父亲的城山娃惊喜得几乎叫出声来山娃觉得父亲太伟大了居然能单匹马地跑到这么远这么大这么美的地方赚大钱高楼大厦鳞次栉比大街小巷人潮涌动山娃一路张望一路惊叹他发现城里的桥居然层层叠叠扭来扭去桥下没水却有着水一般的车水马龙山娃惊诧于城里的公交车那么大那么美不用买票乖乖地掷下二枚硬币空调享受还能坐着看电视呢屡经辗转山娃终于跟着父亲到拉
哪里卖(usq毕业证书)南昆士兰大学毕业证研究生文凭证书托福证书原版一模一样
原版定制【Q微信:741003700】《(usq毕业证书)南昆士兰大学毕业证研究生文凭证书》【Q微信:741003700】成绩单 、雅思、外壳、留信学历认证永久存档查询，采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【Q微信741003700】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信741003700】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
Adjusting primitives for graph : SHORT REPORT / NOTES
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Recently uploaded (20)
Predicting Product Ad Campaign Performance: A Data Analysis Project Presentation
Predicting Product Ad Campaign Performance: A Data Analysis Project Presentation
Quantitative Data AnalysisReliability Analysis (Cronbach Alpha) Common Method...
Quantitative Data AnalysisReliability Analysis (Cronbach Alpha) Common Method...
Data_and_Analytics_Essentials_Architect_an_Analytics_Platform.pptx
Data_and_Analytics_Essentials_Architect_an_Analytics_Platform.pptx
Adjusting primitives for graph : SHORT REPORT / NOTES
Adjusting primitives for graph : SHORT REPORT / NOTES
Data Science Popup Austin: Meet the PyData Community
- 1. DATA SCIENCE POP UP AUSTIN Andy Terrel President, Numfocus partiallyd Bonus! Meet the PyData Community
- 2. DATA SCIENCE POP UP AUSTIN #datapopupaustin April 13, 2016 Galvanize, Austin Campus
- 4. Meet the Community Andy R. Terrel, PhD President NumFOCUS Foundation Chief Technology Officer Fashion Metric
- 5. Who is the PyData Scientist Data Math / Stats Machines Domain
- 6. Who is the PyData Scientist Data Math / Stats Machines Domain http://www.ndbc.noaa.gov/
- 7. Who is the PyData Scientist Data Math / Stats Machines Domain
- 8. Open Science through Open Code
- 12. What happens to code that is not maintained?
- 17. Case Study
- 20. Thank you for your attention! andy@numfocus.org