What is involved in Data Science
Find out what the related areas are that Data Science connects with, associates with, correlates with or affects, and which require thought, deliberation, analysis, review and discussion. This unique checklist stands out in a sense that it is not per-se designed to give answers, but to engage the reader and lay out a Data Science thinking-frame.
How far is your company on its Data Science journey?
Take this short survey to gauge your organization’s progress toward Data Science leadership. Learn your strongest and weakest areas, and what you can do now to create a strategy that delivers results.
To address the criteria in this checklist for your organization, extensive selected resources are provided for sources of further research and information.
Start the Checklist
Below you will find a quick checklist designed to help you think about which Data Science related domains to cover and 206 essential critical questions to check off in that domain.
The following domains are covered:
Data Science, Big data, Prasanta Chandra Mahalanobis, Grammar induction, Linear regression, Data Science, Bias-variance dilemma, Semi-supervised learning, Ensemble learning, Factor analysis, Canonical correlation analysis, Statistical theory, Business analytics, Expectation–maximization algorithm, Regression analysis, American Statistical Association, Turing award, Bayesian network, NYU Stern Center for Business and Human Rights, Graphical model, Local outlier factor, Statistical classification, Feature learning, Empirical research, DJ Patil, Indian Statistical Institute, Computer science, Information science, Cluster analysis, Hierarchical clustering, Predictive modelling, Artificial neural network, Self-organizing map, National Institutes of Health, Social science, Probably approximately correct learning, Data mining, T-distributed stochastic neighbor embedding, Structured prediction, Random forest, Recurrent neural network, Data set, K-nearest neighbors algorithm, Principal component analysis, Dimensionality reduction, Temporal difference learning, Non-negative matrix factorization, Pattern recognition, OPTICS algorithm, Unsupervised learning, Statistical learning theory, Relevance vector machine, Online machine learning, Logistic regression, CURE data clustering algorithm, Basic research, Applied science, Deep learning, K-means clustering, Conference on Neural Information Processing Systems, Academic journal, Occam learning, Computational learning theory, Outline of machine learning, PubMed Central, Business analyst:
Data Science Critical Criteria:
Discuss Data Science tactics and find answers.
– Consider your own Data Science project. what types of organizational problems do you think might be causing or affecting your problem, based on the work done so far?
– How can you negotiate Data Science successfully with a stubborn boss, an irate client, or a deceitful coworker?
– What is the difference between Data Analytics Data Analysis Data Mining and Data Science?
– Do you monitor the effectiveness of your Data Science activities?
Big data Critical Criteria:
Participate in Big data leadership and give examples utilizing a core of simple Big data skills.
– While a move from Oracles MySQL may be necessary because of its inability to handle key big data use cases, why should that move involve a switch to Apache Cassandra and DataStax Enterprise?
– What is the quantifiable ROI for this solution (cost / time savings / data error minimization / etc)?
– To what extent does your organization have experience with big data and data-driven innovation (DDI)?
– How should we organize to capture the benefit of Big Data and move swiftly to higher maturity stages?
– Does big data threaten the traditional data warehouse business intelligence model stack?
– Wheres the evidence that using big data intelligently will improve business performance?
– Quality vs. Quantity: What data are required to satisfy the given value proposition?
– What new definitions are needed to describe elements of new Big Data solutions?
– What is the Quality of the Result if the Quality of the Data/Metadata is poor?
– Which other Oracle Business Intelligence products are used in your solution?
– What if the needle in the haystack happens to be a complex data structure?
– Quantity: What data are required to satisfy the given value proposition?
– Can good algorithms, models, heuristics overcome Data Quality problems?
– How do you handle Big Data in Analytic Applications?
– How fast can we adapt to changes in the data stream?
– Are our Big Data investment programs results driven?
– Which Oracle applications are used in your project?
– Are we Assessing Data Science and Risk?
– How to deal with ambiguity?
– What are we missing?
Prasanta Chandra Mahalanobis Critical Criteria:
Scan Prasanta Chandra Mahalanobis quality and remodel and develop an effective Prasanta Chandra Mahalanobis strategy.
– What are your key performance measures or indicators and in-process measures for the control and improvement of your Data Science processes?
– Do we have past Data Science Successes?
Grammar induction Critical Criteria:
Confer re Grammar induction quality and give examples utilizing a core of simple Grammar induction skills.
– How do we ensure that implementations of Data Science products are done in a way that ensures safety?
– Are there Data Science problems defined?
– What is our Data Science Strategy?
Linear regression Critical Criteria:
Refer to Linear regression goals and tour deciding if Linear regression progress is made.
– For your Data Science project, identify and describe the business environment. is there more than one layer to the business environment?
– What may be the consequences for the performance of an organization if all stakeholders are not consulted regarding Data Science?
– What are the usability implications of Data Science actions?
Data Science Critical Criteria:
Coach on Data Science management and pioneer acquisition of Data Science systems.
– Does Data Science analysis show the relationships among important Data Science factors?
– Why should we adopt a Data Science framework?
– How can we improve Data Science?
Bias-variance dilemma Critical Criteria:
Infer Bias-variance dilemma quality and gather practices for scaling Bias-variance dilemma.
– What is the purpose of Data Science in relation to the mission?
– How will you measure your Data Science effectiveness?
– Who sets the Data Science standards?
Semi-supervised learning Critical Criteria:
Do a round table on Semi-supervised learning decisions and figure out ways to motivate other Semi-supervised learning users.
– How do your measurements capture actionable Data Science information for use in exceeding your customers expectations and securing your customers engagement?
Ensemble learning Critical Criteria:
Frame Ensemble learning adoptions and mentor Ensemble learning customer orientation.
– How do we measure improved Data Science service perception, and satisfaction?
– How do we manage Data Science Knowledge Management (KM)?
– Do we all define Data Science in the same way?
Factor analysis Critical Criteria:
Refer to Factor analysis strategies and point out Factor analysis tensions in leadership.
– Where do ideas that reach policy makers and planners as proposals for Data Science strengthening and reform actually originate?
– Is Supporting Data Science documentation required?
– What threat is Data Science addressing?
Canonical correlation analysis Critical Criteria:
Audit Canonical correlation analysis management and ask questions.
– Are there Data Science Models?
Statistical theory Critical Criteria:
Detail Statistical theory engagements and gather Statistical theory models .
– Is the Data Science organization completing tasks effectively and efficiently?
– What are the short and long-term Data Science goals?
– Is a Data Science Team Work effort in place?
Business analytics Critical Criteria:
Deliberate Business analytics engagements and correct Business analytics management by competencies.
– what is the most effective tool for Statistical Analysis Business Analytics and Business Intelligence?
– What is the difference between business intelligence business analytics and data mining?
– How do we make it meaningful in connecting Data Science with what users do day-to-day?
– Is there a mechanism to leverage information for business analytics and optimization?
– What new services of functionality will be implemented next with Data Science ?
– What is the difference between business intelligence and business analytics?
– what is the difference between Data analytics and Business Analytics If Any?
– Is Data Science dependent on the successful delivery of a current project?
– How do you pick an appropriate ETL tool or business analytics tool?
– What are the trends shaping the future of business analytics?
Expectation–maximization algorithm Critical Criteria:
Face Expectation–maximization algorithm decisions and display thorough understanding of the Expectation–maximization algorithm process.
– Does Data Science create potential expectations in other areas that need to be recognized and considered?
– In what ways are Data Science vendors and us interacting to ensure safe and effective use?
– What are your most important goals for the strategic Data Science objectives?
Regression analysis Critical Criteria:
Debate over Regression analysis visions and perfect Regression analysis conflict management.
– Record-keeping requirements flow from the records needed as inputs, outputs, controls and for transformation of a Data Science process. ask yourself: are the records needed as inputs to the Data Science process available?
– How do we know that any Data Science analysis is complete and comprehensive?
– Are there recognized Data Science problems?
American Statistical Association Critical Criteria:
Have a round table over American Statistical Association failures and look in other fields.
– What role does communication play in the success or failure of a Data Science project?
Turing award Critical Criteria:
Probe Turing award planning and correct better engagement with Turing award results.
– Are there any easy-to-implement alternatives to Data Science? Sometimes other solutions are available that do not require the cost implications of a full-blown project?
– Why is it important to have senior management support for a Data Science project?
– Are assumptions made in Data Science stated explicitly?
Bayesian network Critical Criteria:
Closely inspect Bayesian network engagements and pioneer acquisition of Bayesian network systems.
– Among the Data Science product and service cost to be estimated, which is considered hardest to estimate?
– Are we making progress? and are we making progress as Data Science leaders?
– How can skill-level changes improve Data Science?
NYU Stern Center for Business and Human Rights Critical Criteria:
Do a round table on NYU Stern Center for Business and Human Rights engagements and correct better engagement with NYU Stern Center for Business and Human Rights results.
– Who will be responsible for deciding whether Data Science goes ahead or not after the initial investigations?
– What are current Data Science Paradigms?
– How much does Data Science help?
Graphical model Critical Criteria:
Review Graphical model tasks and assess what counts with Graphical model that we are not counting.
– Who is responsible for ensuring appropriate resources (time, people and money) are allocated to Data Science?
– Will Data Science deliverables need to be tested and, if so, by whom?
– Why is Data Science important for you now?
Local outlier factor Critical Criteria:
Familiarize yourself with Local outlier factor failures and oversee Local outlier factor requirements.
– What about Data Science Analysis of results?
Statistical classification Critical Criteria:
Group Statistical classification failures and adjust implementation of Statistical classification.
– How will we insure seamless interoperability of Data Science moving forward?
– How is the value delivered by Data Science being measured?
– Is Data Science Required?
Feature learning Critical Criteria:
Accumulate Feature learning issues and tour deciding if Feature learning progress is made.
– How to deal with Data Science Changes?
Empirical research Critical Criteria:
Guard Empirical research leadership and arbitrate Empirical research techniques that enhance teamwork and productivity.
– Which customers cant participate in our Data Science domain because they lack skills, wealth, or convenient access to existing solutions?
– Think about the functions involved in your Data Science project. what processes flow from these functions?
DJ Patil Critical Criteria:
Boost DJ Patil planning and get the big picture.
– Do those selected for the Data Science team have a good general understanding of what Data Science is all about?
– Can we do Data Science without complex (expensive) analysis?
Indian Statistical Institute Critical Criteria:
Prioritize Indian Statistical Institute visions and don’t overlook the obvious.
– Risk factors: what are the characteristics of Data Science that make it risky?
– Have all basic functions of Data Science been defined?
Computer science Critical Criteria:
Prioritize Computer science decisions and probe using an integrated framework to make sure Computer science is getting what it needs.
– Who will be responsible for documenting the Data Science requirements in detail?
– Are accountability and ownership for Data Science clearly defined?
Information science Critical Criteria:
Survey Information science risks and arbitrate Information science techniques that enhance teamwork and productivity.
– What knowledge, skills and characteristics mark a good Data Science project manager?
– What is the source of the strategies for Data Science strengthening and reform?
– How do we Identify specific Data Science investment and emerging trends?
Cluster analysis Critical Criteria:
Bootstrap Cluster analysis engagements and mentor Cluster analysis customer orientation.
– What are the key elements of your Data Science performance improvement system, including your evaluation, organizational learning, and innovation processes?
Hierarchical clustering Critical Criteria:
Confer re Hierarchical clustering management and oversee Hierarchical clustering management by competencies.
– Does Data Science include applications and information with regulatory compliance significance (or other contractual conditions that must be formally complied with) in a new or unique manner for which no approved security requirements, templates or design models exist?
– How important is Data Science to the user organizations mission?
Predictive modelling Critical Criteria:
Audit Predictive modelling visions and differentiate in coordinating Predictive modelling.
Artificial neural network Critical Criteria:
Scrutinze Artificial neural network governance and don’t overlook the obvious.
– Marketing budgets are tighter, consumers are more skeptical, and social media has changed forever the way we talk about Data Science. How do we gain traction?
– What tools do you use once you have decided on a Data Science strategy and more importantly how do you choose?
Self-organizing map Critical Criteria:
Scrutinze Self-organizing map leadership and prioritize challenges of Self-organizing map.
– What other organizational variables, such as reward systems or communication systems, affect the performance of this Data Science process?
National Institutes of Health Critical Criteria:
Devise National Institutes of Health projects and diversify disclosure of information – dealing with confidential National Institutes of Health information.
– Do Data Science rules make a reasonable demand on a users capabilities?
– What is our formula for success in Data Science ?
Social science Critical Criteria:
Focus on Social science leadership and oversee Social science management by competencies.
Probably approximately correct learning Critical Criteria:
Examine Probably approximately correct learning risks and summarize a clear Probably approximately correct learning focus.
– How do you incorporate cycle time, productivity, cost control, and other efficiency and effectiveness factors into these Data Science processes?
Data mining Critical Criteria:
Consult on Data mining adoptions and track iterative Data mining results.
– Do you see the need to clarify copyright aspects of the data-driven innovation (e.g. with respect to technologies such as text and data mining)?
– What types of transactional activities and data mining are being used and where do we see the greatest potential benefits?
– Is business intelligence set to play a key role in the future of Human Resources?
– Who is the main stakeholder, with ultimate responsibility for driving Data Science forward?
– What programs do we have to teach data mining?
T-distributed stochastic neighbor embedding Critical Criteria:
Chat re T-distributed stochastic neighbor embedding decisions and describe the risks of T-distributed stochastic neighbor embedding sustainability.
– What prevents me from making the changes I know will make me a more effective Data Science leader?
– Does Data Science appropriately measure and monitor risk?
Structured prediction Critical Criteria:
Consider Structured prediction engagements and create a map for yourself.
– What will be the consequences to the business (financial, reputation etc) if Data Science does not go ahead or fails to deliver the objectives?
Random forest Critical Criteria:
Confer re Random forest results and differentiate in coordinating Random forest.
– Which individuals, teams or departments will be involved in Data Science?
– What is Effective Data Science?
Recurrent neural network Critical Criteria:
Ventilate your thoughts about Recurrent neural network governance and look at the big picture.
– Who will be responsible for making the decisions to include or exclude requested changes once Data Science is underway?
– How do mission and objectives affect the Data Science processes of our organization?
– Is Data Science Realistic, or are you setting yourself up for failure?
Data set Critical Criteria:
Mine Data set planning and visualize why should people listen to you regarding Data set.
– What are your results for key measures or indicators of the accomplishment of your Data Science strategy and action plans, including building and strengthening core competencies?
– What are the success criteria that will indicate that Data Science objectives have been met and the benefits delivered?
– For hosted solutions, are we permitted to download the entire data set in order to maintain local backups?
– How was it created; what algorithms, algorithm versions, ancillary and calibration data sets were used?
– Is data that is transcribed or copied checked for errors against the original data set?
– What needs to be in the plan related to the data capture for the various data sets?
– Is someone responsible for migrating data sets that are in old/outdated formats?
– You get a data set. what do you do with it?
K-nearest neighbors algorithm Critical Criteria:
Have a session on K-nearest neighbors algorithm results and point out improvements in K-nearest neighbors algorithm.
– How do we Lead with Data Science in Mind?
Principal component analysis Critical Criteria:
Meet over Principal component analysis failures and do something to it.
– What are the disruptive Data Science technologies that enable our organization to radically change our business processes?
– Which Data Science goals are the most important?
– How would one define Data Science leadership?
Dimensionality reduction Critical Criteria:
Use past Dimensionality reduction risks and define Dimensionality reduction competency-based leadership.
Temporal difference learning Critical Criteria:
Incorporate Temporal difference learning tasks and separate what are the business goals Temporal difference learning is aiming to achieve.
– Does the Data Science task fit the clients priorities?
Non-negative matrix factorization Critical Criteria:
Infer Non-negative matrix factorization issues and slay a dragon.
– What sources do you use to gather information for a Data Science study?
Pattern recognition Critical Criteria:
Closely inspect Pattern recognition tasks and arbitrate Pattern recognition techniques that enhance teamwork and productivity.
OPTICS algorithm Critical Criteria:
Experiment with OPTICS algorithm adoptions and visualize why should people listen to you regarding OPTICS algorithm.
Unsupervised learning Critical Criteria:
Substantiate Unsupervised learning tactics and attract Unsupervised learning skills.
– Do we monitor the Data Science decisions made and fine tune them as they evolve?
– How do we go about Comparing Data Science approaches/solutions?
Statistical learning theory Critical Criteria:
Steer Statistical learning theory adoptions and report on setting up Statistical learning theory without losing ground.
– What business benefits will Data Science goals deliver if achieved?
– How do we keep improving Data Science?
Relevance vector machine Critical Criteria:
Air ideas re Relevance vector machine tactics and frame using storytelling to create more compelling Relevance vector machine projects.
– How do we Improve Data Science service perception, and satisfaction?
– How can you measure Data Science in a systematic way?
Online machine learning Critical Criteria:
Learn from Online machine learning quality and describe the risks of Online machine learning sustainability.
– How can the value of Data Science be defined?
– What are our Data Science Processes?
Logistic regression Critical Criteria:
Match Logistic regression failures and separate what are the business goals Logistic regression is aiming to achieve.
– What tools and technologies are needed for a custom Data Science project?
CURE data clustering algorithm Critical Criteria:
Cut a stake in CURE data clustering algorithm tactics and look at the big picture.
– What is the total cost related to deploying Data Science, including any consulting or professional services?
Basic research Critical Criteria:
Consolidate Basic research governance and correct Basic research management by competencies.
Applied science Critical Criteria:
Canvass Applied science issues and diversify disclosure of information – dealing with confidential Applied science information.
– In the case of a Data Science project, the criteria for the audit derive from implementation objectives. an audit of a Data Science project involves assessing whether the recommendations outlined for implementation have been met. in other words, can we track that any Data Science project is implemented as planned, and is it working?
– what is the best design framework for Data Science organization now that, in a post industrial-age if the top-down, command and control model is no longer relevant?
– How will you know that the Data Science project has been successful?
Deep learning Critical Criteria:
Set goals for Deep learning risks and diversify disclosure of information – dealing with confidential Deep learning information.
K-means clustering Critical Criteria:
Check K-means clustering engagements and acquire concise K-means clustering education.
– Will new equipment/products be required to facilitate Data Science delivery for example is new software needed?
– Can Management personnel recognize the monetary benefit of Data Science?
Conference on Neural Information Processing Systems Critical Criteria:
Have a meeting on Conference on Neural Information Processing Systems management and find the ideas you already have.
Academic journal Critical Criteria:
Have a session on Academic journal management and probe using an integrated framework to make sure Academic journal is getting what it needs.
– What are the top 3 things at the forefront of our Data Science agendas for the next 3 years?
Occam learning Critical Criteria:
Systematize Occam learning outcomes and track iterative Occam learning results.
Computational learning theory Critical Criteria:
Exchange ideas about Computational learning theory management and define what our big hairy audacious Computational learning theory goal is.
– What are the Key enablers to make this Data Science move?
Outline of machine learning Critical Criteria:
Understand Outline of machine learning issues and report on the economics of relationships managing Outline of machine learning and constraints.
PubMed Central Critical Criteria:
Demonstrate PubMed Central tactics and budget for PubMed Central challenges.
– How can we incorporate support to ensure safe and effective use of Data Science into the services that we provide?
Business analyst Critical Criteria:
Match Business analyst results and adjust implementation of Business analyst.
– What are our best practices for minimizing Data Science project risk, while demonstrating incremental value and quick wins throughout the Data Science project lifecycle?
– Does Data Science systematically track and analyze outcomes for accountability and quality improvement?
– What are typical responsibilities of someone in the role of Business Analyst?
– What is the difference between a Business Architect and a Business Analyst?
– Do business analysts know the cost of feature addition or modification?
This quick readiness checklist is a selected resource to help you move forward. Learn more about how to achieve comprehensive insights with the Data Science Self Assessment:
Author: Gerard Blokdijk
CEO at The Art of Service | http://theartofservice.com
Gerard is the CEO at The Art of Service. He has been providing information technology insights, talks, tools and products to organizations in a wide range of industries for over 25 years. Gerard is a widely recognized and respected information expert. Gerard founded The Art of Service consulting business in 2000. Gerard has authored numerous published books to date.
To address the criteria in this checklist, these selected resources are provided for sources of further research and information:
Data Science External links:
Data science (Book, 2017) [WorldCat.org]
DataScience.com | Enterprise Data Science Platform …
What is Data Science?
Prasanta Chandra Mahalanobis External links:
Prasanta Chandra Mahalanobis Mahavidyalaya Library
Grammar induction External links:
Title: Complexity of Grammar Induction for Quantum Types
Automatic grammar induction and parsing free text
Bayesian Grammar Induction for Language Modeling
Linear regression External links:
Simple Linear Regression Tool – DAU Home
Least-Squares Regression – Linear Regression | …
TI-Nspire CX Mini-Tutorial: Linear Regression – YouTube
Data Science External links:
DataScience.com | Enterprise Data Science Platform …
Data science (Book, 2017) [WorldCat.org]
What is Data Science?
Bias-variance dilemma External links:
Difference between bias-variance dilemma and overfitting
Bias-Variance Dilemma – YouTube
[PDF]A Bias-Variance Dilemma in Joint Diagonalization …
Semi-supervised learning External links:
[PDF]Semi-Supervised Learning for Natural Language
Title: Semi-Supervised Learning with Deep Generative Models
Semi-Supervised Learning Software
Ensemble learning External links:
Ensemble learning – Scholarpedia
Must-Know: What is the idea behind ensemble learning?
Scalable data analytics for ensemble learning
Factor analysis External links:
Factor Analysis | SPSS Annotated Output – IDRE Stats
Factor Analysis – SPSS (part 1) – YouTube
Factor Analysis: A Short Introduction, Part 1
Canonical correlation analysis External links:
Canonical Correlation Analysis | Stata Data Analysis …
The Redundancy Index in Canonical Correlation Analysis.
[PDF]Chapter 8: Canonical Correlation Analysis and …
Statistical theory External links:
Statistical Theory for the RCT-YES Software: Design …
STAT 420: Statistical Theory I | Cal State Monterey Bay
Regression on Linear Composites: Statistical Theory …
Regression analysis External links:
Regression Analysis Made Easy with Excel – WorldatWork
Explanation of Regression Analysis Results – YouTube
Introduction to Regression Analysis – YouTube
American Statistical Association External links:
The American Statistical Association – Home | Facebook
American Statistical Association – Official Site
[PDF]American Statistical Association Style Guide
Turing award External links:
Apr 04, 2017 · Inventor Of The Web Wins 50th Annual Turing Award, Including $1M From Google. Janet Burns, Women@Forbes. Sir …
http://mhaloin.com – CCIRA/CCBA 2017-18
Bayesian network External links:
[PPT]Bayesian networks – University of California, Berkeley
[1511.08488] Bayesian Network Models for Adaptive Testing
Bayesian Network Meta-Analysis for Unordered …
Graphical model External links:
Network Topology Graphical Model – US Academic Writers
Local outlier factor External links:
Anomaly detection with Local Outlier Factor (LOF) — …
Where can I get C code for Local Outlier Factor? – Quora
Statistical classification External links:
[PDF]History of the statistical classification of diseases …
What Is Statistical Classification? (with pictures) – wiseGEEK
Feature learning External links:
Unsupervised Feature Learning and Deep Learning Tutorial
Automatic feature learning for glaucoma detection …
Prototype Abstraction and Distinctive Feature Learning…
Empirical research External links:
Empirical Research Examples – LibGuides at CSU, Chico
DJ Patil External links:
Session with DJ Patil – Quora
DJ Patil news, features and videos – WOW.com
Computer science External links:
TEALS – Computer Science in Every High School
Erik Jonsson School of Engineering & Computer Science …
NDSU Computer Science (NDSU)
Information science External links:
Computer & Information Science & Engineering …
Association for Library and Information Science Educators
News | Department of Information Science
Cluster analysis External links:
Chapter 9: Cluster analysis Flashcards | Quizlet
Lesson 14: Cluster Analysis | STAT 505
Cluster Analysis in Data Mining | Coursera
Hierarchical clustering External links:
14.4 – Agglomerative Hierarchical Clustering | STAT 505
ERIC – U-Statistic Hierarchical Clustering, …
Predictive modelling External links:
STA 521: Modern Regression and Predictive Modelling …
Artificial neural network External links:
Training an Artificial Neural Network – Intro | solver
Best Artificial Neural Network Software in 2017 | G2 Crowd
Artificial neural network – ScienceDaily
Self-organizing map External links:
How is a self-organizing map implemented? – Quora
Self-organizing Map SOM in Python | robin meier
Self-organizing map – an overview | ScienceDirect Topics
National Institutes of Health External links:
[PDF]NATIONAL INSTITUTES OF HEALTH
National Institutes of Health – SourceWatch
[PDF]National Institutes of Health
Social science External links:
Harvard Institute for Quantitative Social Science
Irrational Game | A fun Social Science game by Dan Ariely
What Can I do With a Degree in Social Science?
Probably approximately correct learning External links:
CiteSeerX — Probably Approximately Correct Learning
[PDF]Probably Approximately Correct Learning – III
Data mining External links:
data aggregation in data mining ppt
UT Data Mining
[PDF]Project Title: Data Mining to Improve Water Management
Structured prediction External links:
[PDF]End-to-End Learning for Structured Prediction …
Random forest External links:
R – Random Forest
Unsupervised Learning With Random Forest Predictors
Random Forest in R – Tutorial – YouTube
Recurrent neural network External links:
Particle Learning and Gated Recurrent Neural Network …
How to build a Recurrent Neural Network in TensorFlow (1/7)
Data set External links:
OpenFEMA Dataset: OpenFEMA Data Sets – V1 | FEMA.gov
Limited Data Set | HHS.gov
K-nearest neighbors algorithm External links:
Using the k-Nearest Neighbors Algorithm in R « Web Age …
Principal component analysis External links:
Principal Component Analysis | Quantdare
[PDF]PRINCIPAL COMPONENT ANALYSIS – SAS Support
11.1 – Principal Component Analysis (PCA) Procedure | …
Temporal difference learning External links:
Temporal difference learning and TD-gammon (1995) – …
Non-negative matrix factorization External links:
[PDF]When Does Non-Negative Matrix Factorization Give a …
[1701.00016] Non-Negative Matrix Factorization Test …
CiteSeerX — Algorithms for Non-negative Matrix Factorization
Pattern recognition External links:
Pattern Recognition. (eBook, 2008) [WorldCat.org]
Title: Pattern Recognition
Pattern Recognition – Official Site
Unsupervised learning External links:
Unsupervised Learning With Random Forest Predictors
Supervised and Unsupervised Learning of …
Statistical learning theory External links:
SVM Support Vector Machine Statistical Learning Theory
Syllabus for Statistical Learning Theory
Relevance vector machine External links:
Relevance Vector Machine Regression Applied to …
Online machine learning External links:
Online Machine Learning Specialization Courses | Turi
Logistic regression External links:
[PDF]Analyzing Rare Events with Logistic Regression
What is Logistic Regression? – Statistics Solutions
Logistic Regression – San Francisco State University
CURE data clustering algorithm External links:
CURE data clustering algorithm – WOW.com
CURE data clustering algorithm – Revolvy
https://topics.revolvy.com/topic/CURE data clustering algorithm
Basic research External links:
Civil War Records: Basic Research Sources | National Archives
Basic Research | Charles River
Applied science External links:
Applied Science – AbeBooks
Applied science (eBook, 2012) [WorldCat.org]
School of Engineering and Applied Science
Deep learning External links:
Webmicroscope – Deep Learning AI Image Analysis – …
Lambda Labs – Deep Learning Machines
Deep Learning for Computer Vision with TensorFlow
K-means clustering External links:
K-Means Clustering With SAS – DZone Big Data
[PDF]K-Means Clustering – Home Page | Jonathan Templin’s …
Conference on Neural Information Processing Systems External links:
Conference on Neural Information Processing Systems …
Conference on Neural Information Processing Systems …
Academic journal External links:
LEO « The official academic journal of St. Mark’s School
Submit an Article : Academic Journal – pvamu.edu
Occam learning External links:
[PDF]OCCAM Learning Management System Student FAQs
Occam Learning Solutions, LLC
Computational learning theory External links:
ERIC – Topics in Computational Learning Theory and …
Computational Learning Theory: PAC Learning
PubMed Central External links:
Need Images? Try PubMed Central | HSLS Update
MEDLINE, PubMed, and PMC (PubMed Central): How are …
PubMed Central | Rutgers University Libraries
Business analyst External links:
Here are 13 Jobs that Can Lead to a Business Analyst Job
Title Business Analyst Jobs, Employment | Indeed.com