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A Statistical Viewpoint on Data Science, Data Mining and Big Data Alec Stephenson DATA ANALYTICS, DIGITAL PRODUCTIVITY AND SERVICES.

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Presentation on theme: "A Statistical Viewpoint on Data Science, Data Mining and Big Data Alec Stephenson DATA ANALYTICS, DIGITAL PRODUCTIVITY AND SERVICES."— Presentation transcript:

1 A Statistical Viewpoint on Data Science, Data Mining and Big Data Alec Stephenson DATA ANALYTICS, DIGITAL PRODUCTIVITY AND SERVICES

2  Statistics Vs Data Science  Statistician Vs Data Scientist  Data Science in Predictive Analytics  Data Science in Consulting  Big Data: Are Statisticians Relevant? Introduction

3 Data Science Venn Diagram (Drew Conway)

4 Statistician Vs Data Scientist

5  On Linkedin  On my email signature  To market myself to internal and external clients I am a Data Scientist I am a Statistician  At academic conferences  Providing expertise for journal articles  Any role as a technical expert

6  Experfy  Melbourne Data Science Meet-Up BUT:  Kaggle Connect No longer exists (March-December 2013) Is There A Greater Demand For Data Scientists?

7 Essential:  Statistical Modelling: e.g. R, Matlab, Python  Data Munging: e.g. Perl, Python, Ruby Additional:  Fast Computation: C, C++, Java  Data Storage: SQL, noSQL  Big Data: MapReduce, Mahout, Hive, Pig Data Science Skills

8 Good For Building Essential Skills In Predictive Analytics Only Three Steps To Winning:  Data Munging  Machine Learning / Statistical Modelling  Ensembling Data Mining Competitions

9 General Advice:  Just because you have data, does not mean that you have to use it  There is no such thing a single best model  Different models can capture different features  Visualize the data Data Mining Competitions

10 General Advice:  If something takes more than one minute to run, do you really need to run it?  Spend more time on trying different data transformations and models, and less on parameter specification  Just have a go.  How much time can you afford? Data Mining Competitions

11 Usually Good Methods:  Gradient boosting machine (gbm / mboost)  Random forest (randomForest)  Elastic net (glmnet)  Support Vector Machine (kernlab / e1071)  Neural networks (nnet) Data Mining Competitions

12 Usually Not So-Good Methods:  Recursive Partitioning (rpart / tree)  Nearest neighbour (class)  Multivariate Adaptive Regression Splines (earth)  Naive Bayes (e1071) Data Mining Competitions

13 library(randomForest) library(gbm) library(glmnet) data <- as.matrix(iris[,-5]) set.seed(100) ind <- sample(150, 15) train <- data[-ind,] test <- data[ind,] Data Mining Example I

14 set.seed(100) m1 <- randomForest(train[,2:4], train[,1], ntree = 1000, mtry = 2) pm1 <- predict(m1, test[,-1]) mean((pm1 - test[,1])^2) set.seed(100) m2 <-[,2:4], train[,1], distribution = "gaussian", n.trees = 10000, shrinkage = 0.001, interaction.depth = 2) pm2 <- predict(m2, test[,-1], n.trees = 10000) mean((pm2 - test[,1])^2) set.seed(100) m3 <- glmnet(train[,2:4], train[,1], family = "gaussian", alpha = 0.5) pm3 <- predict(m3, test[,-1]) pm3 <- pm3[,ncol(pm3)] mean((pm3 - test[,1])^2) Data Mining Example II

15 mean(((pm1 + pm2)/2 - test[,1])^2) mean(((pm1 + pm3)/2 - test[,1])^2) mean(((pm2 + pm3)/2 - test[,1])^2) mean(((pm1 + pm2 + pm3)/3 - test[,1])^2) Data Mining Example III

16  Predictive analytics is a black box  Simplicity vs Predictive Accuracy  Communication with client  Reporting: methods or conclusions  Variable Importance  Client Implementation Prediction: Competitions Vs Clients

17  Means different things to different people  SKA: 10 petabytes per hour by 2025  Statisticians typically consider a few gigabytes to be a huge dataset  Do statisticians have a role to play? Big Data

18  Volume: MB, GB, TB, PB,...  Velocity: Real-Time, Hourly, Weekly, Batch,  Variety: Structured, Unstructured  Veracity: How accurate?  Value: How valuable? Big Data 3V’s: Volume Velocity Variety

19 Gartner Hype Cycle 2013

20  Will say that they are heavily involved in big data  Will use big data for marketing purposes  Will never have programmed a MapReduce job  Will have never used datasets of 0.5TB+  Will not know about big data technologies  Why is this? Big Data: A typical statistician…

21  Deciding what data is relevant to the question  Subsetting and sampling big data  Modelling these subsets Statisticians may have a role in Statistician may not have a role  If you need to touch all of the data (0.5TB+)  Restriction to linear (or linearithmic) algorithms  Sums / Averages / Graph Search / Sorting

22 Robust Statistics and Extremes 8 – 11 September, 2014 Australian National University Statistics today is faced with many challenges, especially relating to such topical issues as the analysis of "big data" through to understanding the complexities of climate change - and many others. Floods, fires, variations in temperature on local through to global scales, etc., have provided impetus for recent vigorous redevelopments of extreme value analysis. Extremely large data sets and high dimensional data now becoming available in genetics, finance, physics, astronomy, and many other areas, have spurred exponential advances in statistical theory and practice with special emphasis on robustness issues, in recent years. The need to analyse large, linked, data sets in health, crime, agriculture, surveys, and industry, just to name a few, has revolutionised our profession. It's an exciting time to be a statistician. The aim of the Robust Statistics and Extremes (RS&E) conference is to provide an opportunity for researchers to present up-to-date accounts of the present state of the art in the topics of Robust Statistics and Extremes. A number of distinguished speakers, both international and Australian, will give keynote addresses in their areas of interest. Special provision will be made for student participation. Big Data???

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