Predictive analytics and big data tutorial
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This presentation covers data science buzz words, big data introduction, predictive analytics, and model building methods. Structured vs unstructured. Supervised learning vs unsupervised learning.
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3. Documentation provides proof that products are produced according to regulations, procedures, and standards. It is reviewed by internal and external parties and ensures a consistent level of quality is achieved.
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As the worldwide business climate has grown increasingly complex – due to globalization, consolidation, governmental regulation, labour issues, financial pressures, supply chain management and security concerns – the purview of “operations management” has expanded.
Operations management is the business function that plans, organizes coordinates and controls the resources needed to produce a company’s goods and services.
It is mainly concerned with managing the process that converts inputs into outputs.
It closely interacts with the accounting, finance and human resource management function in an organization.
Pharmacy is a highly competitive industry, and companies are experiencing financial pressures more than ever before.
Hence OPERATIONS MANAGEMENT IS THE CENTRAL CORE FUNCTION OF EVERY COMPANY
BUILDING MATERIALS AS A PLASTIC
Plastics are widely used in building construction and materials. They are used for roofing materials, cladding panels, sound and thermal insulation, decorative laminates, adhesives and sealants, and more. Plastics provide advantages over traditional materials like being lightweight, resistant to rot and weather, and requiring little maintenance. Common plastics used in buildings include polycarbonate, PVC, polystyrene, and foams for insulation. While plastics have advantages, they can also soften at high temperatures or become brittle in cold.
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radioligand binding studies
Radioligand binding studies involve incubating radioactive ligands with tissue samples to measure binding to receptors. This provides information on receptor characteristics like binding affinity and number of sites. The radioligand must have high affinity and selectivity for the receptor of interest. Measuring specific binding versus nonspecific binding allows determining properties of the receptor population under study.
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Alternative Approach to Permanent way Alignment Design
The speaker presented a comparison between the Track
alignment design approach based on NR standards and the one based on the European Norms and the Technical Specifications for Interoperability (TSI), highlighting the main area where these approaches are different and touching the subject of the safety design factors embedded in the track alignment design
procedures.
The main topics:
Cant parameters definition, the origin of the 11.82 cant constant. ways of applying cant.
Track geometry recording. Quality Standard deviation. Inherent standard deviation. The advantage of using rolling SDs. Quality bands for low and high speed.
Cant over a reverse transition - the orphan rule of lifting the reversing point to improve the quality of riding.
Designing a sudden change in curvature. Virtual transition - TRK2049. The rules of the European Norm for track geometry EN 13803-1&2
The significance of transition shift.
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Sponsored by Data Transformed, the KNIME Meetup was a big success. Please find the slides for Dan's, Tom's, Anand's and Chhitesh's presentations.
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1:该专业认证可证明留学生真实身份
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4:这个认证书并且可以归档倒地方
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澳洲UNSW毕业证书制作新南威尔士大学假文凭定制Q微168899991做UNSW留信网教留服认证海牙认证改UNSW成绩单GPA做UNSW假学位证假文凭高仿毕业证申请新南威尔士大学University of New South Wales degree offer diploma Transcript
一比一原版(UCSF文凭证书)旧金山分校毕业证如何办理
毕业原版【微信:176555708】【(UCSF毕业证书)旧金山分校毕业证】【微信:176555708】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：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！
Global Situational Awareness of A.I. and where its headed
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
Analysis insight about a Flyball dog competition team's performance
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Predictive analytics and big data tutorial
- 1. Ben Taylor @bentaylordata Predictive Analytics / Data Science
- 2. Presentation Objectives • Enable you to be smarter than your prospect (data history / lingo) • Motivate you to be unstoppable and hyper-confident • Motivate you to begin looking for data driven opportunities • Motivate you to become a data scientist
- 3. "What the hell is cloud computing?" -Larry Ellison, CEO Oracle
- 4. What is cloud computing? ?
- 5. What is big data? Big data includes datasets or problems which exceed the capacity of a single computer and require a distributed data access system. The concept of "big" is relative to the conventional systems and technology and is subject to change in the future with advances in memory and storage solutions. http://www.pcmag.com/article2/0,2817,2453838,00.asp
- 7. What is a data scientist?
- 8. What is a data scientist? Engineering Finance Economics Mathematics Computer Science Physics Data Science 6-10yrs Python Bootcamp $8,000 (3 months) $16,000-$4,000 (3 months) $115K avg
- 9. What is a data scientist?
- 10. What is a data scientist? Master Builder
- 11. What is a data scientist? Reality distortion: Hyper-confidence
- 13. Data Scientist = Peacock
- 15. Smartest pirate 0 20 40 60 80 100 120 140 160 180 200 0 200 400 600 800 1000 1200 1400 Ships captured Treasurechests
- 16. Humans Algorithms VS 0 20 40 60 80 100 120 140 160 180 200 Ships captured NA
- 17. Humans Algorithms VS 0 20 40 60 80 100 120 140 160 180 200 Ships captured German (1795), French (1806) 0 20 40 60 80 100 120 140 160 180 2 0 200 400 600 800 1000 1200 1400 Ships captured Treasurechests
- 18. Humans Algorithms VS 1997, IBM deep blue Kasparov
- 19. Humans Algorithms VS 2011, IBM Watson Ken Jennings & Brad Rutter
- 20. Humans Algorithms VS 2014, HireVue Iris Hiring Panel
- 21. Prediction process Raw data Data munging Training Model
- 22. Data munging Prediction process Raw data Feature selection Training Model Data cleaning Clean data
- 24. Data munging Prediction process Raw data Feature selection Training Model Data cleaning LSR, SVM, RANDOM FOREST, NAÏVE BAYESIAN, NEURAL NET
- 25. Missing values + categorical @bentaylordata
- 27. Data munging Prediction process Raw data Feature selection Training Model Data cleaning LSR, SVM, RANDOM FOREST, NAÏVE BAYESIAN, NEURAL NET Retail > 15, Engineering > 95 > 5.67
- 28. Resume model
- 29. Resume model
- 30. Data munging Prediction process Raw data Feature selection Training Model Data cleaning LSR, SVM, RANDOM FOREST, NAÏVE BAYESIAN, NEURAL NET Retail > 15, Engineering > 95 GPA, Colleges, Hobbies > 5.67
- 31. Text deeper dive
- 34. Sentiment
- 35. Given data, find cat? dog? @bentaylordata
- 36. Talk like a data nerd @bentaylordata
- 39. Data Lingo Supervised vs unsupervised learning Supervised: Training set provided. Unsupervised: No training set, clustering based on similar attributes.
- 40. Data Lingo Analytic Layers Descriptive Analytics: Telling a data story, plotting, or visualization. Predictive Analytics: Predict future outcomes, usually trained on a historical training set Prescriptive Analytics: Using the insight from your predictive model to proactively change something Interview/Interaction Analytics: Any analytics surrounding the interview or interaction.
- 41. Data Lingo Prediction methods Regression: Predicting a continuous output (stock) Classification: Predicting discrete category outputs. i.e. Yes/Maybe/No
- 42. Data Lingo Data Types Structured: Does it play well in Excel? Unstructured: Raw text (Twitter), audio, video, photos, resumes, etc…