This document contains summaries of "folk knowledge" or common sayings related to data science. Some key points included are:
- Machine learning requires both data and some prior knowledge or assumptions in order to generalize beyond the training data.
- Overfitting can take many forms like high bias, high variance, or sampling bias.
- Intuition fails in high dimensions according to Bellman's curse of dimensionality.
- Feature engineering is key, and more data often beats a cleverer algorithm.
R, Data Wrangling & Kaggle Data Science CompetitionsKrishna Sankar
Presentation for my tutorial at Big Data Tech Con http://goo.gl/ZRoFHi
This is the R version of my pycon tutorial + a few updates
It is work in progress. I will update with daily snapshot until done.
Data Science with Spark - Training at SparkSummit (East)Krishna Sankar
Slideset of the training we gave at the Spark Summit East.
Blog : https://doubleclix.wordpress.com/2015/03/25/data-science-with-spark-on-the-databricks-cloud-training-at-sparksummit-east/
Video is posted at Youtube https://www.youtube.com/watch?v=oTOgaMZkBKQ
Companies are finding that data can be a powerful differentiator and are investing heavily in infrastructure, tools and personnel to ingest and curate raw data to be "analyzable". This process of data curation is called "Data Wrangling"
This task can be very cumbersome and requires trained personnel. However with the advances in open source and commercial tooling, this process has gotten a lot easier and the technical expertise required to do this effectively has dropped several notches.
In this tutorial, we will get a feel for what data wranglers do and use R, RStudio, Trifacta Wrangler, Open Refine tools with some hands-on exercises available at http://akuntamukkala.blogspot.com/2016/05/data-wrangling-examples.html
R, Data Wrangling & Kaggle Data Science CompetitionsKrishna Sankar
Presentation for my tutorial at Big Data Tech Con http://goo.gl/ZRoFHi
This is the R version of my pycon tutorial + a few updates
It is work in progress. I will update with daily snapshot until done.
Data Science with Spark - Training at SparkSummit (East)Krishna Sankar
Slideset of the training we gave at the Spark Summit East.
Blog : https://doubleclix.wordpress.com/2015/03/25/data-science-with-spark-on-the-databricks-cloud-training-at-sparksummit-east/
Video is posted at Youtube https://www.youtube.com/watch?v=oTOgaMZkBKQ
Companies are finding that data can be a powerful differentiator and are investing heavily in infrastructure, tools and personnel to ingest and curate raw data to be "analyzable". This process of data curation is called "Data Wrangling"
This task can be very cumbersome and requires trained personnel. However with the advances in open source and commercial tooling, this process has gotten a lot easier and the technical expertise required to do this effectively has dropped several notches.
In this tutorial, we will get a feel for what data wranglers do and use R, RStudio, Trifacta Wrangler, Open Refine tools with some hands-on exercises available at http://akuntamukkala.blogspot.com/2016/05/data-wrangling-examples.html
Data Science Provenance: From Drug Discovery to Fake FansJameel Syed
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
Combining Data Mining and Machine Learning for Effective User ProfilingCodePolitan
Slide presentasi ini dibawakan oleh Anne Regina pada Seminar & Workshop Pengenalan & Potensi Big Data & Machine Learning yang diselenggarakan oleh KUDIO pada tanggal 14 Mei 2016.
GalvanizeU Seattle: Eleven Almost-Truisms About DataPaco Nathan
http://www.meetup.com/Seattle-Data-Science/events/223445403/
Almost a dozen almost-truisms about Data that almost everyone should consider carefully as they embark on a journey into Data Science. There are a number of preconceptions about working with data at scale where the realities beg to differ. This talk estimates that number to be at least eleven, through probably much larger. At least that number has a great line from a movie. Let's consider some of the less-intuitive directions in which this field is heading, along with likely consequences and corollaries -- especially for those who are just now beginning to study about the technologies, the processes, and the people involved.
Slide presentasi ini dibawakan oleh Jony Sugianto dalam Seminar & Workshop Pengenalan & Potensi Big Data & Machine Learning yang diselenggarakan oleh KUDO pada tanggal 14 Mei 2016.
PyData 2015 Keynote: "A Systems View of Machine Learning" Joshua Bloom
Despite the growing abundance of powerful tools, building and deploying machine-learning frameworks into production continues to be major challenge, in both science and industry. I'll present some particular pain points and cautions for practitioners as well as recent work addressing some of the nagging issues. I advocate for a systems view, which, when expanded beyond the algorithms and codes to the organizational ecosystem, places some interesting constraints on the teams tasked with development and stewardship of ML products.
About: Dr. Joshua Bloom is an astronomy professor at the University of California, Berkeley where he teaches high-energy astrophysics and Python for data scientists. He has published over 250 refereed articles largely on time-domain transients events and telescope/insight automation. His book on gamma-ray bursts, a technical introduction for physical scientists, was published recently by Princeton University Press. He is also co-founder and CTO of wise.io, a startup based in Berkeley. Josh has been awarded the Pierce Prize from the American Astronomical Society; he is also a former Sloan Fellow, Junior Fellow at the Harvard Society, and Hertz Foundation Fellow. He holds a PhD from Caltech and degrees from Harvard and Cambridge University.
Module 1 introduction to machine learningSara Hooker
We believe in building technical capacity all over the world.
We are building and teaching an accessible introduction to machine learning for students passionate about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our work, visit www.deltanalytics.org
Data Science Provenance: From Drug Discovery to Fake FansJameel Syed
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
Combining Data Mining and Machine Learning for Effective User ProfilingCodePolitan
Slide presentasi ini dibawakan oleh Anne Regina pada Seminar & Workshop Pengenalan & Potensi Big Data & Machine Learning yang diselenggarakan oleh KUDIO pada tanggal 14 Mei 2016.
GalvanizeU Seattle: Eleven Almost-Truisms About DataPaco Nathan
http://www.meetup.com/Seattle-Data-Science/events/223445403/
Almost a dozen almost-truisms about Data that almost everyone should consider carefully as they embark on a journey into Data Science. There are a number of preconceptions about working with data at scale where the realities beg to differ. This talk estimates that number to be at least eleven, through probably much larger. At least that number has a great line from a movie. Let's consider some of the less-intuitive directions in which this field is heading, along with likely consequences and corollaries -- especially for those who are just now beginning to study about the technologies, the processes, and the people involved.
Slide presentasi ini dibawakan oleh Jony Sugianto dalam Seminar & Workshop Pengenalan & Potensi Big Data & Machine Learning yang diselenggarakan oleh KUDO pada tanggal 14 Mei 2016.
PyData 2015 Keynote: "A Systems View of Machine Learning" Joshua Bloom
Despite the growing abundance of powerful tools, building and deploying machine-learning frameworks into production continues to be major challenge, in both science and industry. I'll present some particular pain points and cautions for practitioners as well as recent work addressing some of the nagging issues. I advocate for a systems view, which, when expanded beyond the algorithms and codes to the organizational ecosystem, places some interesting constraints on the teams tasked with development and stewardship of ML products.
About: Dr. Joshua Bloom is an astronomy professor at the University of California, Berkeley where he teaches high-energy astrophysics and Python for data scientists. He has published over 250 refereed articles largely on time-domain transients events and telescope/insight automation. His book on gamma-ray bursts, a technical introduction for physical scientists, was published recently by Princeton University Press. He is also co-founder and CTO of wise.io, a startup based in Berkeley. Josh has been awarded the Pierce Prize from the American Astronomical Society; he is also a former Sloan Fellow, Junior Fellow at the Harvard Society, and Hertz Foundation Fellow. He holds a PhD from Caltech and degrees from Harvard and Cambridge University.
Module 1 introduction to machine learningSara Hooker
We believe in building technical capacity all over the world.
We are building and teaching an accessible introduction to machine learning for students passionate about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our work, visit www.deltanalytics.org
Data Science & AI Road Map by Python & Computer science tutor in MalaysiaAhmed Elmalla
The slides were used in a trial session for a student aiming to learn python to do Data science projects .
The session video can be watched from the link below
https://youtu.be/CwCe1pKOVI8
I have over 20 years of experience in both teaching & in completing computer science projects with certificates from Stanford, Alberta, Pennsylvania, California Irvine universities.
I teach the following subjects:
1) IGCSE A-level 9618 / AS-Level
2) AP Computer Science exam A
3) Python (basics, automating staff, Data Analysis, AI & Flask)
4) Java (using Duke University syllabus)
5) Descriptive statistics using SQL
6) PHP, SQL, MYSQL & Codeigniter framework (using University of Michigan syllabus)
7) Android Apps development using Java
8) C / C++ (using University of Colorado syllabus)
Check Trial Classes:
1) A-Level Trial Class : https://youtu.be/v3k7A0nNb9Q
2) AS level trial Class : https://youtu.be/wj14KpfbaPo
3) 0478 IGCSE class : https://youtu.be/sG7PrqagAes
4) AI & Data Science class: https://youtu.be/CwCe1pKOVI8
https://elmalla.info/blog/68-tutor-profile-slide-share
You can get your trial Class now by booking : https://calendly.com/ahmed-elmalla/30min
And you can contact me on
https://wa.me/0060167074241
by Python & Computer science tutor in Malaysia
Learning Objective: Increase professional effectiveness, data management, and analytical skills
With evolving technology, many people are overloaded and overwhelmed with information and data. Businesses now have access to large amounts of feedback from internal and external sources. How do we make sense of the all of the information? Is the data reliable? How can we manage and utilize the data in order to impact business goals, visions, mission? This seminar with help you turn your information overload into powerful and reliable data that you can use to meet organizational goals.
At the end of this seminar, participants will be able to:
a. Assess and categorize data and information.
b. Identify tools and techniques to organize and interpret data.
c. Explore productivity tools and techniques.
d. Examine common data management challenges and solutions.
Data Communities - reusable data in and outside your organization.Paul Groth
Description
Data is a critical both to facilitate an organization and as a product. How can you make that data more usable for both internal and external stakeholders? There are a myriad of recommendations, advice, and strictures about what data providers should do to facilitate data (re)use. It can be overwhelming. Based on recent empirical work (analyzing data reuse proxies at scale, understanding data sensemaking and looking at how researchers search for data), I talk about what practices are a good place to start for helping others to reuse your data. I put this in the context of the notion data communities that organizations can use to help foster the use of data both within your organization and externally.
Presented at OECD Workshop on Systematic Reviews in the Scope of the Endocrine Disrupter Testing and Assessment (EDTA) Conceptual Framework Level 1 in Paris, France
Five Things I Learned While Building Anomaly Detection Tools - Toufic Boubez ...tboubez
This is my presentation from LISA 2014 in Seattle on November 14, 2014.
Most IT Ops teams only keep an eye on a small fraction of the metrics they collect because analyzing this haystack of data and extracting signal from the noise is not easy and generates too many false positives.
In this talk I will show some of the types of anomalies commonly found in dynamic data center environments and discuss the top 5 things I learned while building algorithms to find them. You will see how various Gaussian based techniques work (and why they don’t!), and we will go into some non-parametric methods that you can use to great advantage.
Fortune Teller API - Doing Data Science with Apache SparkBas Geerdink
This presentation of the Endpoint 2015 conference gives an overview of a short data science project: predicting the future happiness of a person, as if he or she walks into a circus tent! First, the domain problem is analyzed. Then, the data is gathered and analyzed. Finally a linear regression model is created and the app is published in the form of a REST API. The technology that is demoed is using Apache Spark and Zeppelin, and can be found on Github: https://github.com/geerdink/FortuneTellerApi
Notes about Amazon VPC, a canonical architecture and finally how to implement MongoDB replica sets. My blog http://goo.gl/0guF2 has the color pictures. And the file is at http://doubleclix.files.wordpress.com/2012/10/vpc-distilled-04.pdf. For some reason, slideshare trims the colors.
My talk on NOSQL at OGF29.[Update with OSCON'10 presentation!] But updates do not work reliably in slideshare. So I also have latest version with my blog.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™UiPathCommunity
In questo evento online gratuito, organizzato dalla Community Italiana di UiPath, potrai esplorare le nuove funzionalità di Autopilot, il tool che integra l'Intelligenza Artificiale nei processi di sviluppo e utilizzo delle Automazioni.
📕 Vedremo insieme alcuni esempi dell'utilizzo di Autopilot in diversi tool della Suite UiPath:
Autopilot per Studio Web
Autopilot per Studio
Autopilot per Apps
Clipboard AI
GenAI applicata alla Document Understanding
👨🏫👨💻 Speakers:
Stefano Negro, UiPath MVPx3, RPA Tech Lead @ BSP Consultant
Flavio Martinelli, UiPath MVP 2023, Technical Account Manager @UiPath
Andrei Tasca, RPA Solutions Team Lead @NTT Data
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
2. Data Science “folk knowledge” (1 of A)
o "If you torture the data long enough, it will confess to anything." – Hal Varian, Computer
Mediated Transactions
o Learning = Representation + Evaluation + Optimization
o It’s Generalization that counts
• The fundamental goal of machine learning is to generalize beyond the
examples in the training set
o Data alone is not enough
• Induction not deduction - Every learner should embody some knowledge
or assumptions beyond the data it is given in order to generalize beyond
it
o Machine Learning is not magic – one cannot get something from nothing
• In order to infer, one needs the knobs & the dials
• One also needs a rich expressive dataset
A few useful things to know about machine learning - by Pedro Domingos
http://dl.acm.org/citation.cfm?id=2347755
3. Data Science “folk knowledge” (2 of A)
o Over fitting has many faces
• Bias – Model not strong enough. So the learner has the tendency to learn the
same wrong things
• Variance – Learning too much from one dataset; model will fall apart (ie much
less accurate) on a different dataset
• Sampling Bias
o Intuition Fails in high Dimensions –Bellman
• Blessing of non-conformity & lower effective dimension; many applications
have examples not uniformly spread but concentrated near a lower dimensional
manifold eg. Space of digits is much smaller then the space of images
o Theoretical Guarantees are not What they seem
• One of the major developments o f recent decades has been the realization that
we can have guarantees on the results of induction, particularly if we are
willing to settle for probabilistic guarantees.
o Feature engineering is the Key
A few useful things to know about machine learning - by Pedro Domingos
http://dl.acm.org/citation.cfm?id=2347755
4. Data Science “folk knowledge” (3 of A)
o More Data Beats a Cleverer Algorithm
• Or conversely select algorithms that improve with data
• Don’t optimize prematurely without getting more data
o Learn many models, not Just One
• Ensembles ! – Change the hypothesis space
• Netflix prize
• E.g. Bagging, Boosting, Stacking
o Simplicity Does not necessarily imply Accuracy
o Representable Does not imply Learnable
• Just because a function can be represented does not mean
it can be learned
o Correlation Does not imply Causation
o http://doubleclix.wordpress.com/2014/03/07/a-glimpse-of-google-nasa-peter-norvig/
o A few useful things to know about machine learning - by Pedro Domingos
§ http://dl.acm.org/citation.cfm?id=2347755
5. Data Science “folk knowledge” (4 of A)
o The simplest hypothesis that fits the data is also the most
plausible
• Occam’s Razor
• Don’t go for a 4 layer Neural Network unless
you have that complex data
• But that doesn’t also mean that one should
choose the simplest hypothesis
• Match the impedance of the domain, data & the
algorithms
o Think of over fitting as memorizing as opposed to learning.
o Data leakage has many forms
o Sometimes the Absence of Something is Everything
o [Corollary] Absence of Evidence is not the Evidence of
Absence
§ Simple
Model
§ High
Error
line
that
cannot
be
compensated
with
more
data
§ Gets
to
a
lower
error
rate
with
less
data
points
§ Complex
Model
§ Lower
Error
Line
§ But
needs
more
data
points
to
reach
decent
error
New to Machine Learning? Avoid these three mistakes, James Faghmous
https://medium.com/about-data/73258b3848a4Ref: Andrew Ng/Stanford, Yaser S./CalTech
6. Check your assumptions
o The decisions a model makes, is directly related to the it’s assumptions about the
statistical distribution of the underlying data
o For example, for regression one should check that:
① Variables are normally distributed
• Test for normality via visual inspection, skew & kurtosis, outlier inspections via
plots, z-scores et al
② There is a linear relationship between the dependent & independent
variables
• Inspect residual plots, try quadratic relationships, try log plots et al
③ Variables are measured without error
④ Assumption of Homoscedasticity
§ Homoscedasticity assumes constant or near constant error variance
§ Check the standard residual plots and look for heteroscedasticity
§ For example in the figure, left box has the errors scattered randomly around zero; while the
right two diagrams have the errors unevenly distributed
Jason W. Osborne and Elaine Waters, Four assumptions of multiple regression that researchers should always test,
http://pareonline.net/getvn.asp?v=8&n=2
7. Data Science “folk knowledge” (5 of A)
Donald Rumsfeld is an armchair Data Scientist !
http://smartorg.com/2013/07/valuepoint19/
The
World
Knowns
Unknowns
You
UnKnown
Known
o Others
know,
you
don’t
o What
we
do
o Facts,
outcomes
or
scenarios
we
have
not
encountered,
nor
considered
o “Black
swans”,
outliers,
long
tails
of
probability
distribuHons
o Lack
of
experience,
imaginaHon
o PotenHal
facts,
outcomes
we
are
aware,
but
not
with
certainty
o StochasHc
processes,
ProbabiliHes
o Known Knowns
o There are things we know that we know
o Known Unknowns
o That is to say, there are things that we
now know we don't know
o But there are also Unknown Unknowns
o There are things we do not know we
don't know
8. Data Science “folk knowledge” (6 of A) - Pipeline
o Scalable
Model
Deployment
o Big
Data
automation
&
purpose
built
appliances
(soft/
hard)
o Manage
SLAs
&
response
times
o Volume
o Velocity
o Streaming
Data
o Canonical
form
o Data
catalog
o Data
Fabric
across
the
organization
o Access
to
multiple
sources
of
data
o Think
Hybrid
–
Big
Data
Apps,
Appliances
&
Infrastructure
Collect Store Transform
o Metadata
o Monitor
counters
&
Metrics
o Structured
vs.
Multi-‐
structured
o Flexible
&
Selectable
§ Data
Subsets
§ Attribute
sets
o Refine
model
with
§ Extended
Data
subsets
§ Engineered
Attribute
sets
o Validation
run
across
a
larger
data
set
Reason Model Deploy
Data Management
Data Science
o Dynamic
Data
Sets
o 2
way
key-‐value
tagging
of
datasets
o Extended
attribute
sets
o Advanced
Analytics
ExploreVisualize Recommend Predict
o Performance
o Scalability
o Refresh
Latency
o In-‐memory
Analytics
o Advanced
Visualization
o Interactive
Dashboards
o Map
Overlay
o Infographics
¤ Bytes to Business
a.k.a. Build the full
stack
¤ Find Relevant Data
For Business
¤ Connect the Dots
9. Volume
Velocity
Variety
Data Science “folk knowledge” (7 of A)
Context
Connect
edness
Intelligence
Interface
Inference
“Data of unusual size”
that can't be brute forced
o Three Amigos
o Interface = Cognition
o Intelligence = Compute(CPU) & Computational(GPU)
o Infer Significance & Causality
10. Data Science “folk knowledge” (8 of A)
Jeremy’s Axioms
o Iteratively explore data
o Tools
• Excel Format, Perl, Perl Book
o Get your head around data
• Pivot Table
o Don’t over-complicate
o If people give you data, don’t assume that you
need to use all of it
o Look at pictures !
o History of your submissions – keep a tab
o Don’t be afraid to submit simple solutions
• We will do this during this workshop
Ref: http://blog.kaggle.com/2011/03/23/getting-in-shape-for-the-sport-of-data-sciencetalk-by-jeremy-howard/
11. Data Science “folk knowledge” (9 of A)
① Common Sense (some features make more sense then others)
② Carefully read these forums to get a peak at other peoples’ mindset
③ Visualizations
④ Train a classifier (e.g. logistic regression) and look at the feature weights
⑤ Train a decision tree and visualize it
⑥ Cluster the data and look at what clusters you get out
⑦ Just look at the raw data
⑧ Train a simple classifier, see what mistakes it makes
⑨ Write a classifier using handwritten rules
⑩ Pick a fancy method that you want to apply (Deep Learning/Nnet)
-- Maarten Bosma
-- http://www.kaggle.com/c/stumbleupon/forums/t/5761/methods-for-getting-a-first-overview-over-the-data
12. Data Science “folk knowledge” (A of A)
Lessons from Kaggle Winners
① Don’t over-fit
② All predictors are not needed
• All data rows are not needed, either
③ Tuning the algorithms will give different results
④ Reduce the dataset (Average, select transition data,…)
⑤ Test set & training set can differ
⑥ Iteratively explore & get your head around data
⑦ Don’t be afraid to submit simple solutions
⑧ Keep a tab & history your submissions
13. The curious case of the Data Scientist
o Data Scientist is multi-faceted & Contextual
o Data Scientist should be building Data Products
o Data Scientist should tell a story
Data Scientist (noun): Person who is better at
statistics than any software engineer & better
at software engineering than any statistician
– Josh Wills (Cloudera)
Data Scientist (noun): Person who is worse at
statistics than any statistician & worse at
software engineering than any software
engineer – Will Cukierski (Kaggle)
http://doubleclix.wordpress.com/2014/01/25/the-curious-case-of-the-data-scientist-profession/
Large is hard; Infinite is much easier !
– Titus Brown
14. Essential Reading List
o A few useful things to know about machine learning - by Pedro Domingos
• http://dl.acm.org/citation.cfm?id=2347755
o The Lack of A Priori Distinctions Between Learning Algorithms by David H. Wolpert
• http://mpdc.mae.cornell.edu/Courses/MAE714/Papers/
lack_of_a_priori_distinctions_wolpert.pdf
o http://www.no-free-lunch.org/
o Controlling the false discovery rate: a practical and powerful approach to multiple testing Benjamini, Y. and Hochberg,
Y. C
• http://www.stat.purdue.edu/~‾doerge/BIOINFORM.D/FALL06/Benjamini%20and%20Y
%20FDR.pdf
o A Glimpse of Googl, NASA,Peter Norvig + The Restaurant at the End of the Universe
• http://doubleclix.wordpress.com/2014/03/07/a-glimpse-of-google-nasa-peter-norvig/
o Avoid these three mistakes, James Faghmo
• https://medium.com/about-data/73258b3848a4
o Leakage in Data Mining: Formulation, Detection, and Avoidance
• http://www.cs.umb.edu/~‾ding/history/470_670_fall_2011/papers/
cs670_Tran_PreferredPaper_LeakingInDataMining.pdf
15. For your reading & viewing pleasure … An ordered List
① An Introduction to Statistical Learning
• http://www-bcf.usc.edu/~‾gareth/ISL/
② ISL Class Stanford/Hastie/Tibsharani at their best - Statistical Learning
• http://online.stanford.edu/course/statistical-learning-winter-2014
③ Prof. Pedro Domingo
• https://class.coursera.org/machlearning-001/lecture/preview
④ Prof. Andrew Ng
• https://class.coursera.org/ml-003/lecture/preview
⑤ Prof. Abu Mostafa, CaltechX: CS1156x: Learning From Data
• https://www.edx.org/course/caltechx/caltechx-cs1156x-learning-data-1120
⑥ Mathematicalmonk @ YouTube
• https://www.youtube.com/playlist?list=PLD0F06AA0D2E8FFBA
⑦ The Elements Of Statistical Learning
• http://statweb.stanford.edu/~‾tibs/ElemStatLearn/
http://www.quora.com/Machine-Learning/Whats-the-easiest-way-to-learn-machine-learning/
17. What does it mean ? Let us ponder ….
o We have a training data set representing a domain
• We reason over the dataset & develop a model to predict outcomes
o How good is our prediction when it comes to real life scenarios ?
o The assumption is that the dataset is taken at random
• Or Is it ? Is there a Sampling Bias ?
• i.i.d ? Independent ? Identically Distributed ?
• What about homoscedasticity ? Do they have the same finite variance ?
o Can we assure that another dataset (from the same domain) will give us the same
result ?
o Will our model & it’s parameters remain the same if we get another data set ?
o How can we evaluate our model ?
o How can we select the right parameters for a selected model ?
18. Bias/Variance (1 of 2)
o Model Complexity
• Complex Model increases the
training data fit
• But then it overfits & doesn't
perform as well with real data
o Bias vs. Variance
o Classical diagram
o From ELSII, By Hastie, Tibshirani & Friedman
o Bias – Model learns wrong things; not
complex enough; error gap small; more
data by itself won’t help
o Variance – Different dataset will give
different error rate; over fitted model;
larger error gap; more data could help
Prediction Error
Training
Error
Ref: Andrew Ng/Stanford, Yaser S./CalTech
Learning Curve
19. Bias/Variance (2 of 2)
o High Bias
• Due to Underfitting
• Add more features
• More sophisticated model
• Quadratic Terms, complex equations,…
• Decrease regularization
o High Variance
• Due to Overfitting
• Use fewer features
• Use more training sample
• Increase Regularization
Prediction Error
Training
Error
Ref: Strata 2013 Tutorial by Olivier Grisel
Learning Curve
Need
more
features
or
more
complex
model
to
improve
Need
more
data
to
improve
20. Partition Data !
• Training (60%)
• Validation(20%) &
• “Vault” Test (20%) Data sets
k-fold Cross-Validation
• Split data into k equal parts
• Fit model to k-1 parts &
calculate prediction error on kth
part
• Non-overlapping dataset
Data Partition &
Cross-Validation
— Goal
◦ Model Complexity (-)
◦ Variance (-)
◦ Prediction Accuracy (+)
Train
Validate
Test
#2
#3
#4
#5
#1
#2
#3
#5
#4
#1
#2
#4
#5
#3
#1
#3
#4
#5
#2
#1
#3
#4
#5
#1
#2
K-‐fold
CV
(k=5)
Train
Validate
21. Bootstrap
• Draw datasets (with replacement) and fit model for each dataset
• Remember : Data Partitioning (#1) & Cross Validation (#2) are without
replacement
Bootstrap & Bagging
— Goal
◦ Model Complexity (-)
◦ Variance (-)
◦ Prediction Accuracy (+)
Bagging (Bootstrap aggregation)
◦ Average prediction over a collection of
bootstrap-ed samples, thus reducing
variance
22. ◦ “Output
of
weak
classifiers
into
a
powerful
commiSee”
◦ Final
PredicHon
=
weighted
majority
vote
◦ Later
classifiers
get
misclassified
points
– With
higher
weight,
– So
they
are
forced
– To
concentrate
on
them
◦ AdaBoost
(AdapHveBoosting)
◦ BoosHng
vs
Bagging
– Bagging
–
independent
trees
– BoosHng
–
successively
weighted
Boosting
— Goal
◦ Model Complexity (-)
◦ Variance (-)
◦ Prediction Accuracy (+)
23. ◦ Builds
large
collecHon
of
de-‐correlated
trees
&
averages
them
◦ Improves
Bagging
by
selecHng
i.i.d*
random
variables
for
spli_ng
◦ Simpler
to
train
&
tune
◦ “Do
remarkably
well,
with
very
li6le
tuning
required”
–
ESLII
◦ Less
suscepHble
to
over
fi_ng
(than
boosHng)
◦ Many
RF
implementaHons
– Original
version
-‐
Fortran-‐77
!
By
Breiman/Cutler
– Python,
R,
Mahout,
Weka,
Milk
(ML
toolkit
for
py),
matlab
* i.i.d – independent identically distributed
+ http://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm
Random Forests+
— Goal
◦ Model Complexity (-)
◦ Variance (-)
◦ Prediction Accuracy (+)
24. Random Forests
o While Boosting splits based on best among all variables, RF splits based on best among
randomly chosen variables
o Simpler because it requires two variables – no. of Predictors (typically √k) & no. of trees
(500 for large dataset, 150 for smaller)
o Error prediction
• For each iteration, predict for dataset that is not in the sample (OOB data)
• Aggregate OOB predictions
• Calculate Prediction Error for the aggregate, which is basically the OOB
estimate of error rate
• Can use this to search for optimal # of predictors
• We will see how close this is to the actual error in the Heritage Health Prize
o Assumes equal cost for mis-prediction. Can add a cost function
o Proximity matrix & applications like adding missing data, dropping outliers
Ref: R News Vol 2/3, Dec 2002
Statistical Learning from a Regression Perspective : Berk
A Brief Overview of RF by Dan Steinberg
25. ◦ Two
Step
– Develop
a
set
of
learners
– Combine
the
results
to
develop
a
composite
predictor
◦ Ensemble
methods
can
take
the
form
of:
– Using
different
algorithms,
– Using
the
same
algorithm
with
different
se_ngs
– Assigning
different
parts
of
the
dataset
to
different
classifiers
◦ Bagging
&
Random
Forests
are
examples
of
ensemble
method
Ref: Machine Learning In Action
Ensemble Methods
— Goal
◦ Model Complexity (-)
◦ Variance (-)
◦ Prediction Accuracy (+)
26. Algorithms for the
Amateur Data Scientist
“A towel is about the most massively useful thing an interstellar hitchhiker can have … any
man who can hitch the length and breadth of the Galaxy, rough it … win through, and still
know where his towel is, is clearly a man to be reckoned with.”
- From The Hitchhiker's Guide to the Galaxy, by Douglas Adams.
Algorithms ! The Most Massively useful thing an Amateur Data Scientist can have …
2:30
27. Ref: Anthony’s Kaggle Presentation
Data Scientists apply different techniques
• Support Vector Machine
• adaBoost
• Bayesian Networks
• Decision Trees
• Ensemble Methods
• Random Forest
• Logistic Regression
• Genetic Algorithms
• Monte Carlo Methods
• Principal Component Analysis
• Kalman Filter
• Evolutionary Fuzzy Modelling
• Neural Networks
Quora
• http://www.quora.com/What-are-the-top-10-data-mining-or-machine-learning-algorithms
28. Algorithm spectrum
o Regression
o Logit
o CART
o Ensemble :
Random
Forest
o Clustering
o KNN
o Genetic Alg
o Simulated
Annealing
o Collab
Filtering
o SVM
o Kernels
o SVD
o NNet
o Boltzman
Machine
o Feature
Learning
Machine
Learning
Cute
Math
Ar?ficial
Intelligence
29. Classifying Classifiers
Statistical
Structural
Regression
Naïve
Bayes
Bayesian
Networks
Rule-‐based
Distance-‐based
Neural
Networks
Production
Rules
Decision
Trees
Multi-‐layer
Perception
Functional
Nearest
Neighbor
Linear
Spectral
Wavelet
kNN
Learning
vector
Quantization
Ensemble
Random
Forests
Logistic
Regression1
SVM
Boosting
1Max
Entropy
Classifier
Ref: Algorithms of the Intelligent Web, Marmanis & Babenko
32. Cross Validation
o Reference:
• https://www.kaggle.com/wiki/
GettingStartedWithPythonForDataScience
• Chris Clark ‘s blog :
http://blog.kaggle.com/2012/07/02/up-and-running-with-python-
my-first-kaggle-entry/
• Predicive Modelling in py with scikit-learning, Olivier Grisel Strata
2013
• titanic from pycon2014/parallelmaster/An introduction to Predictive
Modeling in Python
Refer
to
iPython
notebook
<2-‐Model-‐EvaluaHon>
at
hSps://github.com/xsankar/freezing-‐bear
33. Model Evaluation - Accuracy
o Accuracy =
o For cases where tn is large compared tp, a degenerate return(false) will be
very accurate !
o Hence the F-measure is a better reflection of the model strength
Predicted=1
Predicted=0
Actual
=1
True+
(tp)
False-‐
(fn)
Actual=0
False+
(fp)
True-‐
(tn)
tp
+
tn
tp+fp+fn+tn
34. Model Evaluation – Precision & Recall
o Precision = How many items we identified are relevant
o Recall = How many relevant items did we identify
o Inverse relationship – Tradeoff depends on situations
• Legal – Coverage is important than correctness
• Search – Accuracy is more important
• Fraud
• Support cost (high fp) vs. wrath of credit card co.(high fn)
Predicted=1
Predicted=0
Actual=1
True
+ve
-‐
tp
False
-‐ve
-‐
fn
Actual=0
False
+ve
-‐
fp
True
–ve
-‐
tn
tp
tp+fp
• Precision
• Accuracy
• Relevancy
tp
tp+fn
• Recall
• True
+ve
Rate
• Coverage
• Sensitivity
• Hit
Rate
http://nltk.googlecode.com/svn/trunk/doc/book/ch06.html
fp
fp+tn
• Type
1
Error
Rate
• False
+ve
Rate
• False
Alarm
Rate
• Specificity
=
1
–
fp
rate
• Type
1
Error
=
fp
• Type
2
Error
=
fn
36. Model Evaluation : F-Measure
Precision = tp / (tp+fp) : Recall = tp / (tp+fn)
F-Measure
Balanced, Combined, Weighted Harmonic Mean, measures effectiveness
Predicted=1
Predicted=0
Actual=1
True+
(tp)
False-‐
(fn)
Actual=0
False+
(fp)
True-‐
(tn)
=
β2
P
+
R
Common Form (Balanced F1) : β=1 (α = ½ ) ; F1 = 2PR / P+R
+
(1
–
α)
α
1
P
1
R
1
(β2
+
1)PR
37. Hands-on Walkthru - Model Evaluation
Train
Test
712 (80%) 179
891
Refer
to
iPython
notebook
<2-‐Model-‐EvaluaHon>
at
hSps://github.com/xsankar/freezing-‐bear
38. ROC Analysis
o “How good is my model?”
o Good Reference : http://people.inf.elte.hu/kiss/13dwhdm/roc.pdf
o “A receiver operating characteristics (ROC) graph is a technique for visualizing,
organizing and selecting classifiers based on their performance”
o Much better than evaluating a model based on simple classification accuracy
o Plots tp rate vs. fp rate
o After understanding the ROC Graph, we will draw a few for our models in
iPython notebook <2-Model-Evaluation> at https://github.com/xsankar/
freezing-bear
39. ROC Graph - Discussion
o E = Conservative, Everything
NO
o H = Liberal, Everything YES
o Am not making any
political statement !
o F = Ideal
o G = Worst
o The diagonal is the chance
o North West Corner is good
o South-East is bad
• For example E
• Believe it or Not - I have
actually seen a graph
with the curve in this
region !
E
F
G
H
40. ROC Graph – Clinical Example
Ifcc
:
Measures
of
diagnostic
accuracy:
basic
definitions
41. ROC Graph Walk thru
o iPython notebook <2-Model-Evaluation> at https://github.com/xsankar/
freezing-bear