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 148 essential critical questions to check off in that domain.
The following domains are covered:
Data Science, Cluster analysis, NYU Stern Center for Business and Human Rights, Multilayer perceptron, Graduate school, Academic publishing, Naive Bayes classifier, General Assembly, Local outlier factor, Recurrent neural network, Principal component analysis, Learning to rank, Software engineer, Outline of machine learning, Linear regression, Online machine learning, Turing award, Statistical learning theory, K-nearest neighbors algorithm, Basic research, Regression analysis, C.F. Jeff Wu, CURE data clustering algorithm, The Data Incubator, OPTICS algorithm, Hierarchical clustering, Probably approximately correct learning, Self-organizing map, Data Science, Predictive modelling, Bias-variance dilemma, Feature learning, Bayesian network, Empirical risk minimization, Machine Learning, PubMed Central, Explanatory model, Non-negative matrix factorization, Journal of Machine Learning Research, Bootstrap aggregating, Decision tree learning, Dimensionality reduction, Conditional random field, Academic journal, Semi-supervised learning, Support vector machine, American Statistical Association, Data mining, International Conference on Machine Learning, Linear discriminant analysis, Grammar induction, Conference on Neural Information Processing Systems, Random forest, Independent component analysis, Deep learning, Convolutional neural network, K-nearest neighbors classification, Artificial neural network, Business analyst, Vapnik–Chervonenkis theory, T-distributed stochastic neighbor embedding, Statistical theory:
Data Science Critical Criteria:
Learn from Data Science issues and get answers.
– What is the difference between Data Analytics Data Analysis Data Mining and Data Science?
– Meeting the challenge: are missed Data Science opportunities costing us money?
– Are accountability and ownership for Data Science clearly defined?
– Is the scope of Data Science defined?
Cluster analysis Critical Criteria:
Confer over Cluster analysis projects and spearhead techniques for implementing Cluster analysis.
– Who is the main stakeholder, with ultimate responsibility for driving Data Science forward?
– How do we go about Comparing Data Science approaches/solutions?
– How much does Data Science help?
NYU Stern Center for Business and Human Rights Critical Criteria:
Devise NYU Stern Center for Business and Human Rights results and check on ways to get started with NYU Stern Center for Business and Human Rights.
– Think about the functions involved in your Data Science project. what processes flow from these functions?
– How to deal with Data Science Changes?
Multilayer perceptron Critical Criteria:
Understand Multilayer perceptron adoptions and differentiate in coordinating Multilayer perceptron.
– Think about the people you identified for your Data Science project and the project responsibilities you would assign to them. what kind of training do you think they would need to perform these responsibilities effectively?
– How does the organization define, manage, and improve its Data Science processes?
Graduate school Critical Criteria:
Weigh in on Graduate school projects and ask questions.
– Do those selected for the Data Science team have a good general understanding of what Data Science is all about?
– Who are the people involved in developing and implementing Data Science?
Academic publishing Critical Criteria:
Derive from Academic publishing results and look at the big picture.
– Will Data Science have an impact on current business continuity, disaster recovery processes and/or infrastructure?
– How do we Lead with Data Science in Mind?
– What are our Data Science Processes?
Naive Bayes classifier Critical Criteria:
Conceptualize Naive Bayes classifier strategies and simulate teachings and consultations on quality process improvement of Naive Bayes classifier.
– What new services of functionality will be implemented next with Data Science ?
– Do you monitor the effectiveness of your Data Science activities?
– Is there any existing Data Science governance structure?
General Assembly Critical Criteria:
Deliberate over General Assembly results and report on developing an effective General Assembly strategy.
– What will be the consequences to the business (financial, reputation etc) if Data Science does not go ahead or fails to deliver the objectives?
– Who will be responsible for documenting the Data Science requirements in detail?
– Who will provide the final approval of Data Science deliverables?
Local outlier factor Critical Criteria:
Probe Local outlier factor planning and summarize a clear Local outlier factor focus.
– A compounding model resolution with available relevant data can often provide insight towards a solution methodology; which Data Science models, tools and techniques are necessary?
– Think of your Data Science project. what are the main functions?
Recurrent neural network Critical Criteria:
Align Recurrent neural network results and oversee Recurrent neural network requirements.
– For your Data Science project, identify and describe the business environment. is there more than one layer to the business environment?
– When a Data Science manager recognizes a problem, what options are available?
– Why is Data Science important for you now?
Principal component analysis Critical Criteria:
Use past Principal component analysis results and transcribe Principal component analysis as tomorrows backbone for success.
– To what extent does management recognize Data Science as a tool to increase the results?
– What will drive Data Science change?
Learning to rank Critical Criteria:
Sort Learning to rank decisions and find out.
– What may be the consequences for the performance of an organization if all stakeholders are not consulted regarding Data Science?
– What is the source of the strategies for Data Science strengthening and reform?
Software engineer Critical Criteria:
Reconstruct Software engineer management and get out your magnifying glass.
– DevOps isnt really a product. Its not something you can buy. DevOps is fundamentally about culture and about the quality of your application. And by quality I mean the specific software engineering term of quality, of different quality attributes. What matters to you?
– Can we add value to the current Data Science decision-making process (largely qualitative) by incorporating uncertainty modeling (more quantitative)?
– Can we answer questions like: Was the software process followed and software engineering standards been properly applied?
– Who is responsible for ensuring appropriate resources (time, people and money) are allocated to Data Science?
– Is open source software development faster, better, and cheaper than software engineering?
– What are the Key enablers to make this Data Science move?
– Better, and cheaper than software engineering?
Outline of machine learning Critical Criteria:
Generalize Outline of machine learning leadership and define what our big hairy audacious Outline of machine learning goal is.
– What vendors make products that address the Data Science needs?
– How is the value delivered by Data Science being measured?
Linear regression Critical Criteria:
Study Linear regression tactics and differentiate in coordinating Linear regression.
– Does Data Science systematically track and analyze outcomes for accountability and quality improvement?
– What are the short and long-term Data Science goals?
Online machine learning Critical Criteria:
Design Online machine learning strategies and describe the risks of Online machine learning sustainability.
– What are the key elements of your Data Science performance improvement system, including your evaluation, organizational learning, and innovation processes?
Turing award Critical Criteria:
Dissect Turing award governance and pay attention to the small things.
– Do we aggressively reward and promote the people who have the biggest impact on creating excellent Data Science services/products?
– How do senior leaders actions reflect a commitment to the organizations Data Science values?
Statistical learning theory Critical Criteria:
Brainstorm over Statistical learning theory failures and create a map for yourself.
– How do your measurements capture actionable Data Science information for use in exceeding your customers expectations and securing your customers engagement?
– Are there Data Science problems defined?
K-nearest neighbors algorithm Critical Criteria:
Infer K-nearest neighbors algorithm strategies and clarify ways to gain access to competitive K-nearest neighbors algorithm services.
– Is a Data Science Team Work effort in place?
– Are there recognized Data Science problems?
Basic research Critical Criteria:
Boost Basic research tactics and check on ways to get started with Basic research.
– What are your current levels and trends in key measures or indicators of Data Science product and process performance that are important to and directly serve your customers? how do these results compare with the performance of your competitors and other organizations with similar offerings?
– Why is it important to have senior management support for a Data Science project?
– Does Data Science analysis isolate the fundamental causes of problems?
Regression analysis Critical Criteria:
Reorganize Regression analysis quality and get out your magnifying glass.
– What are specific Data Science Rules to follow?
C.F. Jeff Wu Critical Criteria:
Think about C.F. Jeff Wu quality and know what your objective is.
CURE data clustering algorithm Critical Criteria:
Refer to CURE data clustering algorithm visions and intervene in CURE data clustering algorithm processes and leadership.
– 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 important is Data Science to the user organizations mission?
The Data Incubator Critical Criteria:
Define The Data Incubator management and know what your objective is.
– Does Data Science create potential expectations in other areas that need to be recognized and considered?
– How will you know that the Data Science project has been successful?
OPTICS algorithm Critical Criteria:
Infer OPTICS algorithm strategies and arbitrate OPTICS algorithm techniques that enhance teamwork and productivity.
– Do we monitor the Data Science decisions made and fine tune them as they evolve?
– Are there Data Science Models?
Hierarchical clustering Critical Criteria:
Systematize Hierarchical clustering visions and suggest using storytelling to create more compelling Hierarchical clustering projects.
– How can you negotiate Data Science successfully with a stubborn boss, an irate client, or a deceitful coworker?
– What are current Data Science Paradigms?
Probably approximately correct learning Critical Criteria:
Conceptualize Probably approximately correct learning adoptions and sort Probably approximately correct learning activities.
– What sources do you use to gather information for a Data Science study?
– Which Data Science goals are the most important?
Self-organizing map Critical Criteria:
Face Self-organizing map visions and find out what it really means.
– Who will be responsible for making the decisions to include or exclude requested changes once Data Science is underway?
– How do we measure improved Data Science service perception, and satisfaction?
Data Science Critical Criteria:
Powwow over Data Science engagements and interpret which customers can’t participate in Data Science because they lack skills.
– How will we insure seamless interoperability of Data Science moving forward?
– How do we manage Data Science Knowledge Management (KM)?
Predictive modelling Critical Criteria:
Detail Predictive modelling tactics and modify and define the unique characteristics of interactive Predictive modelling projects.
– Is the Data Science organization completing tasks effectively and efficiently?
Bias-variance dilemma Critical Criteria:
Mine Bias-variance dilemma tactics and revise understanding of Bias-variance dilemma architectures.
Feature learning Critical Criteria:
Shape Feature learning governance and remodel and develop an effective Feature learning strategy.
– 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?
– Does our organization need more Data Science education?
Bayesian network Critical Criteria:
Guard Bayesian network planning and raise human resource and employment practices for Bayesian network.
– Where do ideas that reach policy makers and planners as proposals for Data Science strengthening and reform actually originate?
– Are we making progress? and are we making progress as Data Science leaders?
Empirical risk minimization Critical Criteria:
Have a meeting on Empirical risk minimization quality and clarify ways to gain access to competitive Empirical risk minimization services.
– Are there any disadvantages to implementing Data Science? There might be some that are less obvious?
– Is Data Science Required?
Machine Learning Critical Criteria:
Consider Machine Learning issues and frame using storytelling to create more compelling Machine Learning projects.
– What are the long-term implications of other disruptive technologies (e.g., machine learning, robotics, data analytics) converging with blockchain development?
– In a project to restructure Data Science outcomes, which stakeholders would you involve?
– What is the purpose of Data Science in relation to the mission?
PubMed Central Critical Criteria:
Huddle over PubMed Central tasks and describe the risks of PubMed Central sustainability.
– Will new equipment/products be required to facilitate Data Science delivery for example is new software needed?
Explanatory model Critical Criteria:
Revitalize Explanatory model issues and research ways can we become the Explanatory model company that would put us out of business.
– Can Management personnel recognize the monetary benefit of Data Science?
Non-negative matrix factorization Critical Criteria:
Confer over Non-negative matrix factorization visions and observe effective Non-negative matrix factorization.
– What are the record-keeping requirements of Data Science activities?
– How can skill-level changes improve Data Science?
– What about Data Science Analysis of results?
Journal of Machine Learning Research Critical Criteria:
Concentrate on Journal of Machine Learning Research projects and simulate teachings and consultations on quality process improvement of Journal of Machine Learning Research.
– 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?
Bootstrap aggregating Critical Criteria:
Reason over Bootstrap aggregating outcomes and correct better engagement with Bootstrap aggregating results.
– What tools and technologies are needed for a custom Data Science project?
– What are the long-term Data Science goals?
– What is our Data Science Strategy?
Decision tree learning Critical Criteria:
Focus on Decision tree learning risks and oversee implementation of Decision tree learning.
– Among the Data Science product and service cost to be estimated, which is considered hardest to estimate?
– What are all of our Data Science domains and what do they do?
Dimensionality reduction Critical Criteria:
Disseminate Dimensionality reduction tasks and create a map for yourself.
– What potential environmental factors impact the Data Science effort?
Conditional random field Critical Criteria:
Understand Conditional random field tactics and use obstacles to break out of ruts.
– Risk factors: what are the characteristics of Data Science that make it risky?
– How do we keep improving Data Science?
Academic journal Critical Criteria:
Deliberate Academic journal management and reduce Academic journal costs.
– What are the barriers to increased Data Science production?
– How do we Improve Data Science service perception, and satisfaction?
Semi-supervised learning Critical Criteria:
Confer re Semi-supervised learning planning and research ways can we become the Semi-supervised learning company that would put us out of business.
– What are the business goals Data Science is aiming to achieve?
Support vector machine Critical Criteria:
Confer over Support vector machine decisions and attract Support vector machine skills.
American Statistical Association Critical Criteria:
Facilitate American Statistical Association risks and look at it backwards.
– What are your most important goals for the strategic Data Science objectives?
Data mining Critical Criteria:
Reconstruct Data mining governance and frame using storytelling to create more compelling Data mining projects.
– 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?
– 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?
– What is the difference between business intelligence business analytics and data mining?
– Is business intelligence set to play a key role in the future of Human Resources?
– What programs do we have to teach data mining?
International Conference on Machine Learning Critical Criteria:
Graph International Conference on Machine Learning quality and don’t overlook the obvious.
– Is there a Data Science Communication plan covering who needs to get what information when?
– What are our needs in relation to Data Science skills, labor, equipment, and markets?
– Can we do Data Science without complex (expensive) analysis?
Linear discriminant analysis Critical Criteria:
Extrapolate Linear discriminant analysis adoptions and look for lots of ideas.
– Have the types of risks that may impact Data Science been identified and analyzed?
Grammar induction Critical Criteria:
Paraphrase Grammar induction planning and question.
– Are assumptions made in Data Science stated explicitly?
Conference on Neural Information Processing Systems Critical Criteria:
Rank Conference on Neural Information Processing Systems engagements and remodel and develop an effective Conference on Neural Information Processing Systems strategy.
Random forest Critical Criteria:
Deliberate over Random forest planning and oversee implementation of Random forest.
– What is our formula for success in Data Science ?
– How can the value of Data Science be defined?
Independent component analysis Critical Criteria:
Pilot Independent component analysis visions and acquire concise Independent component analysis education.
– 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?
Deep learning Critical Criteria:
X-ray Deep learning outcomes and figure out ways to motivate other Deep learning users.
– Will Data Science deliverables need to be tested and, if so, by whom?
Convolutional neural network Critical Criteria:
Administer Convolutional neural network tactics and report on developing an effective Convolutional neural network strategy.
– What are your key performance measures or indicators and in-process measures for the control and improvement of your Data Science processes?
K-nearest neighbors classification Critical Criteria:
Substantiate K-nearest neighbors classification quality and adjust implementation of K-nearest neighbors classification.
Artificial neural network Critical Criteria:
Mine Artificial neural network issues and look in other fields.
Business analyst Critical Criteria:
Win new insights about Business analyst issues and point out Business analyst tensions in leadership.
– 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?
Vapnik–Chervonenkis theory Critical Criteria:
Consolidate Vapnik–Chervonenkis theory failures and use obstacles to break out of ruts.
– What are the usability implications of Data Science actions?
T-distributed stochastic neighbor embedding Critical Criteria:
Have a round table over T-distributed stochastic neighbor embedding projects and research ways can we become the T-distributed stochastic neighbor embedding company that would put us out of business.
– How do we make it meaningful in connecting Data Science with what users do day-to-day?
Statistical theory Critical Criteria:
Concentrate on Statistical theory issues and clarify ways to gain access to competitive Statistical theory services.
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:
UW Data Science Master’s Program – Seattle
Earn your Data Science Degree Online
The Harvard Data Science Initiative
Cluster analysis External links:
How to do a cluster analysis of data in Excel – Quora
Cluster Analysis Procedures – SAS Support
[PDF]Comparing Scoring Systems From Cluster Analysis …
NYU Stern Center for Business and Human Rights External links:
About – NYU Stern Center for Business and Human Rights
Team – NYU Stern Center for Business and Human Rights
Multilayer perceptron External links:
Multilayer Perceptron — DeepLearning 0.1 documentation
Graduate school External links:
UW Graduate School
Samuel Curtis Johnson Graduate School of Management …
Stanford Graduate School of Education – Official Site
Academic publishing External links:
What is Lambert Academic Publishing? – Quora
Academic publishing – New World Encyclopedia
Æ Academic Publishing (INTERIM SITE)
General Assembly External links:
Find Your Legislator – PA General Assembly
Home – Delaware General Assembly
General Assembly – Official Site
Local outlier factor External links:
Anomaly detection with Local Outlier Factor (LOF) — …
Where can I get C code for Local Outlier Factor? – quora.com
Recurrent neural network External links:
MariFlow – Self-Driving Mario Kart w/Recurrent Neural Network
Principal component analysis External links:
Principal Component Analysis in MATLAB – Stack Overflow
11.1 – Principal Component Analysis (PCA) Procedure | …
pca – Principal Component Analysis in R – Stack Overflow
Learning to rank External links:
[PDF]Learning to Rank by Optimizing NDCG Measure
Learning to rank基本算法小结 – zhuanlan.zhihu.com
[PDF]Learning to Rank (part 2) – Filip Radlinski
Software engineer External links:
Title Software Engineer Jobs, Employment | Indeed.com
Software Engineer Title Ladder – ChangeLog.ca
Linear regression External links:
Testing the assumptions of linear regression – Duke …
Introduction to Linear Regression – Free Statistics Book
Chapter 6 Linear Regression Using Excel 2010
Online machine learning External links:
What is online machine learning? | E-learning
[PDF]Online Machine Learning Algorithms For Currency …
Turing award External links:
Edgar F. Codd – A.M. Turing Award Winner
“Machine learning” expert wins Turing award – CBS News
A.M. Turing Award Winners by Year
Statistical learning theory External links:
Syllabus for Statistical Learning Theory
SVM Support Vector Machine Statistical Learning Theory
[PDF]Statistical Learning Theory: A Tutorial – Princeton …
K-nearest neighbors algorithm External links:
Using the k-Nearest Neighbors Algorithm in R « Web Age …
Basic research External links:
[PDF]IBR New York State Institute for Basic Research
Basic Research Requirements – Office of Research Oversight
UAB – School of Medicine – Nephrology – Basic Research
Regression analysis External links:
Regression Analysis | SAS Annotated Output – IDRE Stats
How to Read Regression Analysis Summary in Excel: 4 …
Automated Regression Analysis for Real Estate …
C.F. Jeff Wu External links:
C.F. Jeff Wu | Department of Statistics
C.F. Jeff Wu | Department of Mathematics
CURE data clustering algorithm External links:
How To Pronounce CURE data clustering algorithm
http://www.pronouncekiwi.com/CURE data clustering algorithm
CURE data clustering algorithm – pediaview.com
CURE data clustering algorithm – Revolvy
https://update.revolvy.com/topic/CURE data clustering algorithm
The Data Incubator External links:
In-depth report on The Data Incubator. Browse reviews, job outcomes, blogs, courses, and join our discussions.
The Data Incubator Team | The Data Incubator
The Data Incubator is an intensive 8-week data science fellowship free to students. Learn more with course info, alumni reviews and interviews on Course Report!
OPTICS algorithm External links:
OPTICS Algorithm — Shane Grigsby’s website
Hierarchical clustering External links:
Hierarchical clustering of Exchange-Traded Funds – …
Hierarchical Clustering – Saed Sayad
10.1 – Hierarchical Clustering | STAT 555
Probably approximately correct learning External links:
Probably approximately correct learning – …
[PDF]Probably Approximately Correct Learning – III
CiteSeerX — Probably Approximately Correct Learning
Self-organizing map External links:
[PDF]Performance evaluation of the self-organizing map …
MATLAB: help needed with Self-Organizing Map (SOM) clustering
R code of Self-Organizing Map (SOM) – Gumroad
Data Science External links:
Data Science & Analytics | University of Missouri
University of Wisconsin Data Science Degree Online
UW Data Science Master’s Program – Seattle
Bias-variance dilemma External links:
[PDF]A Bias-Variance Dilemma in Joint Diagonalization …
Bias-Variance Dilemma – YouTube
Difference between bias-variance dilemma and overfitting
Feature learning External links:
[PDF]Unsupervised Feature Learning in Computer Vision
Bay Area Vision Meeting: Unsupervised Feature Learning …
Bayesian network External links:
CiteSeerX — Bayesian Network Classifiers
Bayes Server – Bayesian network software
Empirical risk minimization External links:
10: Empirical Risk Minimization – Cornell University
[PDF]Private Empirical Risk Minimization, Revisited
[PDF]Empirical Risk Minimization and Optimization 1 …
Machine Learning External links:
Machine Learning Guide – sas.com
http://Ad · www.sas.com/MachineLearning/WhitePaper
ZestFinance.com: Machine Learning & Big Data …
Machine Learning | Microsoft Azure
PubMed Central External links:
PubMed Tutorial – Getting the Articles – PubMed Central
TMC Library | PubMed Central
PubMed Central | NIH Library
Explanatory model External links:
medanth – Explanatory Model
Non-negative matrix factorization External links:
[PDF]When Does Non-Negative Matrix Factorization Give a …
[1701.00016] Non-Negative Matrix Factorization Test …
When Does Non-Negative Matrix Factorization Give …
Journal of Machine Learning Research External links:
Journal of machine learning research | ROAD
The Journal of Machine Learning Research
Journal of Machine Learning Research
Bootstrap aggregating External links:
Bootstrap aggregating – YouTube
Ensemble Learning, Bootstrap Aggregating (Bagging) …
Bootstrap aggregating bagging – YouTube
Decision tree learning External links:
[PDF]Decision Tree Learning on Very Large Data Sets
Decision Tree Learning | Statistics | Applied Mathematics
Dimensionality reduction External links:
Dimensionality Reduction Algorithms: Strengths and Weaknesses
Influence over the Dimensionality Reduction and …
[PDF]Lecture 6: Dimensionality reduction (LDA)
Conditional random field External links:
CRF – Conditional Random Fields | AcronymAttic
[PDF]Tutorial on Conditional Random Fields for Sequence …
[PDF]Conditional Random Fields
Academic journal External links:
LEO « The official academic journal of St. Mark’s School
Semi-supervised learning External links:
[PDF]Semi-Supervised Learning for Natural Language
Semi-supervised learning (Book, 2006) [WorldCat.org]
Semi-Supervised Learning: From Gaussian Fields to …
Support vector machine External links:
One-Class Support Vector Machine – msdn.microsoft.com
Support Vector Machine – Python Tutorial
Introduction to Support Vector Machines¶ – OpenCV
American Statistical Association External links:
[PDF]American Statistical Association Style Guide
The American Statistical Association – Home | Facebook
American Statistical Association – GuideStar Profile
Data mining External links:
Data Mining (Book, 2016) [WorldCat.org]
[PDF]Data Mining Report – Federation of American Scientists
Title Data Mining Jobs, Employment | Indeed.com
International Conference on Machine Learning External links:
International Conference on Machine Learning – 10times
International Conference on Machine Learning and …
Linear discriminant analysis External links:
9.2.2 – Linear Discriminant Analysis | STAT 897D
10.3 – Linear Discriminant Analysis | STAT 505
[PDF]Eﬁective Linear Discriminant Analysis for High …
Grammar induction External links:
[PDF]Unsupervised Grammar Induction of Clinical Report …
Grammar induction – Infogalactic: the planetary knowledge …
CiteSeerX — Phylogenetic Grammar Induction
Conference on Neural Information Processing Systems External links:
Conference on Neural Information Processing Systems …
Conference on Neural Information Processing Systems …
Random forest External links:
GCD.5 – Random Forest | STAT 897D
How to implement random forest in WEKA – Quora
Independent component analysis External links:
[PDF]Independent Component Analysis: Algorithms and …
[PDF]An Independent Component Analysis Mixture Model …
[1404.2986] A Tutorial on Independent Component Analysis
Deep learning External links:
Amazon Deep Learning AMIs
MIT 6.S094: Deep Learning for Self-Driving Cars
Deep Learning – Google+
Convolutional neural network External links:
Convolutional neural network-based encoding and …
Deep Learning[Convolutional Neural Network in depth] …
Convolutional Neural Networks – Stanford University
Artificial neural network External links:
[PDF]J3.4 USE OF AN ARTIFICIAL NEURAL NETWORK TO …
Best Artificial Neural Network Software in 2018 | G2 Crowd
Artificial neural network – ScienceDaily
Business analyst External links:
Business Analyst Job Description – Job Descriptions
Project Management and Business Analyst Conferences
Business Analyst Training – Business Analyst Training
T-distributed stochastic neighbor embedding External links:
t-Distributed Stochastic Neighbor Embedding – MATLAB tsne
Statistical theory External links:
Statistical Theory 1 (Exam 1) Flashcards | Quizlet
Statistical Theory for the RCT-YES Software: Design …
[PDF]Statistical Theory for the RCT-YES Software: Design …