Rarefied talent in big data technology and analytics

Data Science Knowledge Repo

A central knowledge resource for data scientists / analytics experts

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Data Science Repo

Data Technology Repo

A prevailing characteristic of data scientists is deep intellectual curiosity – a trait that drives them to be passionate learners, always picking up new skills on their own volition. Many of these fascinating but difficult techniques of data science are grounded in hard math and machine learning — e.g. Bayesian inference, nonparametric regression, neural net classifiers, hidden markov models, evolutionary algorithms, content/collaborative filters, NLP, etc. Data science is so broad and deep that even the most seasoned experts always have something new to learn; there is simply too much collective knowledge out there.

The purpose of the "Data Science Knowledge Repo" is to provide a central resource that data scientists can revisit frequently to refresh knowledge or learn new skills. If you have any recommended additions – guides, technical papers, and other resources – email frank@datajobs.com.

A

Auto-Regressive Models

B

Bayesian Inference

C

Collaborative Filtering

Clustering Methods

D

Decision Tree Learning

Dominance Analysis

E

Ensemble Methods

Expectation-Maximization Algorithm

F

Factor Analysis

Fixed Effects Models

G

Genetic Algorithms

Gradient Descent

H

Hidden Markov Models

Hierarchical Bayes Models

I

Independent Component Analysis (ICA)

J

K

K-Means Clustering

L

Linear Algebra

Linear Discriminant Analysis (LDA)

M

Machine Learning

Markov Chain Monte Carlo (MCMC)

N

Naive Bayes

Natural Language Processing (NLP)

Neural Nets

O

Ordinary Least-Squares

P

Principal Component Analysis (PCA)

Probability Theory

Q

R

R (Statistical Computing Software)

Recommender Systems

Regression Analysis

S

SAS (Statistical Computing Software)

Singular Value Decomposition (SVD)

Supervised Learning

Support Vector Machines (SVM)

T

Time-Series Analysis

U

Unsupervised Learning

V

W

X

Y

Z


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