The Evolution of Data Science

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A look back at how the practice of data science has evolved over the years, modern trends, and where it might be headed in the future. Starting from before anyone had the title "data scientist" on their resume, to the dawn of the cloud and big data, and the new tools and companies trying to push the state of the art forward. Finally, some wild speculation on where data science might be headed. Presentation given to Seattle Data Science Meetup on Friday July 24th 2015.

Data & Analytics

The Evolution of Data Science
Kenny Daniel
CTO, Algorithmia
July 24, 2015

Kenny Daniel - CTO, Algorithmia
• Graduate research in Artificial Intelligence and Mechanism Design
• Multiple published algorithms and papers in Machine Learning
• Received $1 million from DOT “Engineering Tomorrow’s Transportation Market”
• B.S. Carnegie Mellon University, M.S., Ph.D. (on leave) USC
• Data Scientist and Computer Vision specialist for Delectable, Inc
• Initial and current overall architect of Algorithmia Platform

Make state-of-the-art algorithms
accessible and discoverable by
everyone.

Evolution of Data Science
● History of data science
● Modern data science
● Future speculation

Pre-cloud
● Mainframes
● Universities
● Research Facilities
● Finance
● PhD researchers, highly specialized
More pre-planning, less exploratory

Source and Inspiration: http://www.slideshare.net/AlbertWenger/the-no-stackstartup
1990s Connectivity
$10,000 per month
Servers
$20,000 per box
Storage
$1,000/GB
2000s Connectivity
$1,000 per month
Servers
$1,000 per box
Storage
$10/GB
2010s Connectivity
10 cents/GB
Servers
20 cents/hour
Storage
12 cents/GB
NOW Backend using Parse
Search using Algolia
Synchronization using Firebase
Video calls and SMS using Twilio
Payments using Stripe
Video recording using Ziggeo
Send and track emails using Mailgun
Customer service using Intercom
Ship product using Shyp

“no one got fired for using AWS”
cost, security, convenience

“We used to leak memory.
Now we leak instances.
Soon we will leak entire data centers.”
- Dan Kaminsky

Previously, data analysis was done by domain experts
Now, shift toward data science as its own field
A new field is born

“Data is inherently dumb. It doesn’t actually do anything unless
you know how to use it...
The next digital gold rush will be focused on how you do
something with data.”
- Peter Sondergaard (Gartner Research)

1990s Technology
HPC, Mainframes
2000s
2010s
NOW Generalist Big Data such as Amazon EMR
Large Data Processing such as Databricks
Real Time Processing such as Amazon Kinesis
Data Repositories such as Socrata
Data Collectors such as Kimono
DSaaS for Customer Analytics such as Captricity
DSaaS for Marketing such as Acxiom
DSaaS for Security such as Fortscale
Hosted Machine Learning such as BigML, Dato
Algorithms-as-a-Service such as Algorithmia
Technology
In-house clusters
Technologies
Cloud, Hadoop, Spark
Users
Corporations, tech startups
Users
Individual data scientists
Users
Researchers, hw engineers, committees

Future of Data Science
● How will these trends continue?
● What will future tools look like?
● What is the role of data scientists going forward?

Data is less structured, and less amenable to traditional
data analysis without pre-processing
● Unstructured text
● Images
● Video
Future… new data sources

Future… building blocks
Topic Analysis
Twitter Youtube Satellite Imagery
Computer Vision
Artificial Neural Networks

Future… more autonomous
AutoML
Ensemble learning
Hyperparameter optimization

JOIN:
algorithmia.com/signup?invite=SeattleDS
(will post to meetup group)
REACH OUT:
kenny@algorithmia.com

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怎样办理旧金山城市学院毕业证（CCSF毕业证书）成绩单学校原版复制

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SAC 25 Final National, Regional & Local Angel Group Investing Insights 2024 0...

Elaine Werffeli

Yilin Xia (yilinx2@illinois.edu), Shawn Bowers (bowers@gonzaga.edu), Lan Li (lanl2@illinois.edu), and Bertram Ludäscher (ludaesch@illinois.edu) Presented at IDCC-2024 in Edinburg. ABSTRACT. We propose a new approach for modeling and reconciling conflicting data cleaning actions. Such conflicts arise naturally in collaborative data curation settings where multiple experts work independently and then aim to put their efforts together to improve and accelerate data cleaning. The key idea of our approach is to model conflicting updates as a formal argumentation framework (AF). Such argumentation frameworks can be automatically analyzed and solved by translating them to a logic program PAF whose declarative semantics yield a transparent solution with many desirable properties, e.g., uncontroversial updates are accepted, unjustified ones are rejected, and the remaining ambiguities are exposed and presented to users for further analysis. After motivating the problem, we introduce our approach and illustrate it with a detailed running example introducing both well-founded and stable semantics to help understand the AF solutions. We have begun to develop open source tools and Jupyter notebooks that demonstrate the practicality of our approach. In future work we plan to develop a toolkit for conflict resolution that can be used in conjunction with OpenRefine, a popular interactive data cleaning tool.

Reconciling Conflicting Data Curation Actions: Transparency Through Argument...

Bertram Ludäscher

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怎样办理纽约州立大学宾汉姆顿分校毕业证（SUNY-Bin毕业证书）成绩单学校原版复制

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Ranking and Scoring Exercises for Research

Rajesh Mondal

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怎样办理伦敦大学毕业证（UoL毕业证书）成绩单学校原版复制

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Switzerland Constitution 2002.pdf.........

EfruzAsilolu

原版定制【微信:176555708】【圣路易斯大学毕业证（SLU毕业证书）】【微信:176555708】（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【微信176555708】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176555708】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：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部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！

怎样办理圣路易斯大学毕业证（SLU毕业证书）成绩单学校原版复制

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Digital Transformation Playbook by Graham Ware

Graham Ware

Harnessing the Power of GenAI for BI and Reporting.pptx

Paras Gupta

Discover Why Less is More in B2B Research

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The Evolution of Data Science

1. The Evolution of Data Science Kenny Daniel CTO, Algorithmia July 24, 2015

2. Kenny Daniel - CTO, Algorithmia • Graduate research in Artificial Intelligence and Mechanism Design • Multiple published algorithms and papers in Machine Learning • Received $1 million from DOT “Engineering Tomorrow’s Transportation Market” • B.S. Carnegie Mellon University, M.S., Ph.D. (on leave) USC • Data Scientist and Computer Vision specialist for Delectable, Inc • Initial and current overall architect of Algorithmia Platform

3. Make state-of-the-art algorithms accessible and discoverable by everyone.

4. Evolution of Data Science ● History of data science ● Modern data science ● Future speculation

5. Pre-cloud ● Mainframes ● Universities ● Research Facilities ● Finance ● PhD researchers, highly specialized More pre-planning, less exploratory

6. Source and Inspiration: http://www.slideshare.net/AlbertWenger/the-no-stackstartup 1990s Connectivity $10,000 per month Servers $20,000 per box Storage $1,000/GB 2000s Connectivity $1,000 per month Servers $1,000 per box Storage $10/GB 2010s Connectivity 10 cents/GB Servers 20 cents/hour Storage 12 cents/GB NOW Backend using Parse Search using Algolia Synchronization using Firebase Video calls and SMS using Twilio Payments using Stripe Video recording using Ziggeo Send and track emails using Mailgun Customer service using Intercom Ship product using Shyp

7. “no one got fired for using AWS” cost, security, convenience

8. “We used to leak memory. Now we leak instances. Soon we will leak entire data centers.” - Dan Kaminsky

9. Previously, data analysis was done by domain experts Now, shift toward data science as its own field A new field is born

10.

11. “Hi, I’m a Data Scientist”

12. Lots of Data Little Intelligence

13. “Data is inherently dumb. It doesn’t actually do anything unless you know how to use it... The next digital gold rush will be focused on how you do something with data.” - Peter Sondergaard (Gartner Research)

14. 1990s Technology HPC, Mainframes 2000s 2010s NOW Generalist Big Data such as Amazon EMR Large Data Processing such as Databricks Real Time Processing such as Amazon Kinesis Data Repositories such as Socrata Data Collectors such as Kimono DSaaS for Customer Analytics such as Captricity DSaaS for Marketing such as Acxiom DSaaS for Security such as Fortscale Hosted Machine Learning such as BigML, Dato Algorithms-as-a-Service such as Algorithmia Technology In-house clusters Technologies Cloud, Hadoop, Spark Users Corporations, tech startups Users Individual data scientists Users Researchers, hw engineers, committees

15. Behold... Data Science in a Spreadsheet

16. Future of Data Science ● How will these trends continue? ● What will future tools look like? ● What is the role of data scientists going forward?

17. Data is less structured, and less amenable to traditional data analysis without pre-processing ● Unstructured text ● Images ● Video Future… new data sources

18. Future… building blocks Topic Analysis Twitter Youtube Satellite Imagery Computer Vision Artificial Neural Networks

19. Future… more autonomous AutoML Ensemble learning Hyperparameter optimization

20. JOIN: algorithmia.com/signup?invite=SeattleDS (will post to meetup group) REACH OUT: kenny@algorithmia.com

The Evolution of Data Science

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