This document discusses how data science involves more than just statistics. It provides examples of how computation can be used to find things to count in text and images, inject context using data from London bike stations and car accident data, change viewpoints such as analyzing data from a supersonic car, and inject new viewpoints like exploring finance portfolio correlations. Computation is a key part of data science that involves techniques beyond just statistics like machine learning, visualization, and other domains.

1. Data Science is More Than Just
Statistics
Please visit us at stand N310 opposite!

2. Data science = Statistics?
2 MoreThanJustStats.nb

3. Data science = Computation with
data
MoreThanJustStats.nb 3

4. Computation ⊃ {Statistics,
modeling, visualization, machine
learning, signal processing,
geometry, image processing,
maths, semantics, networks,
queues, geodesy, random
processes, audio, survival analysi
4 MoreThanJustStats.nb

5. Use computation
– to find things to count
Example: Text
Out[ ]=
Word frequency in Lord of the Flies
In[ ]:= MaximalBy[TextSentences[lotf], Classify["Sentiment", #, {"Probability", "Positive"}] &]
Out[ ]= {We are going to have fun on this island!}
MoreThanJustStats.nb 5

6. Out[ ]=
Example: Images
In[1]:= image =
6 MoreThanJustStats.nb

7. In[2]:= TabView
"Blobs" → i = Image[Rasterize[Graphics[{Disk[], Disk[{0.7, 0}, 1]}]]],
"Distances" → i2 = ImageAdjust@DistanceTransform[ColorNegate@i],
"Maxima" → Dilation[MaxDetect[i2], 2]

Out[2]=
Blobs Distances Maxima
In[4]:= Showimage, GraphicsText[Style["×", FontColor → White], #] & /@
idata = DeleteDuplicates[Last /@ ComponentMeasurements[
MaxDetect[DistanceTransform[DistanceTransform[DeleteSmallComponents[
ColorNegate[DeleteSmallComponents[Binarize[image]]], 100]]]],
"Centroid"], EuclideanDistance[##] < 6 &]
Out[4]=
×
××
× ××
×
×
×
× ×× ×× × ×× × ×× × ×× × ××
× ×
×
MoreThanJustStats.nb 7

8. In[5]:= SmoothHistogram3D[idata, BoxRatios → {3, 2, 1}]
Out[5]=
8 MoreThanJustStats.nb

9. Use computation
– to inject context
Example: London bikes
In[15]:= currentBikeData = Import["http://api.citybik.es/barclays-cycle-hire.json"];
In[16]:= currentBikeData[[1]]
Out[16]= {bikes → 10, name → 000989 - Murray Grove , Hoxton, idx → 0, lat → 51 530 890,
timestamp → 2018-04-25T12:11:42.632000Z, lng → -89 782, id → 0, free → 17, number → 63}
In[13]:= Dataset[Association /@ currentBikeData]
Out[13]=
△
▽
bikes name idx
10 000989 - Murray Grove , Hoxton 0
8 200069 - Knaresborough Place, Earl's Court 1
0 300057 - Westbourne Park Road, Portobello 2
29 000981 - British Museum, Bloomsbury 3
8 001083 - Commercial Street, Shoreditch 4
8 001027 - Warwick Avenue Station, Maida Vale 5
25 000971 - Godliman Street, St. Paul's 6
27 000974 - Guilford Street , Bloomsbury 7
6 001060 - Torrens Street, Angel 8
9 001038 - Harrington Square 1, Camden Town 9
1 001070 - Bricklayers Arms, Borough 10
12 001047 - Falkirk Street, Hoxton 11
0 001041 - Westbourne Grove, Bayswater 12
18 001042 - Woodstock Street, Mayfair 13
14 001049 - Finsbury Leisure Centre, St. Luke's 14
4 001037 - Park Lane , Hyde Park 15
15 001050 - Park Road (Baker Street), The Regent's Park 16
7 000973 - Bethnal Green Road, Shoreditch 17
17 001053 - Clerkenwell Green, Clerkenwell 18
9 001078 - Lambeth Road, Vauxhall 19
showing 1–20 of 784
MoreThanJustStats.nb 9

10. In[14]:= Legended
GeoGraphicsAbsolutePointSize[10], ColorData["DarkRainbow"]
#1[[2]]  0.001 + #1[[2]] + #1[[3]], Point[#[[1]]] & /@
QuietGeoPosition{"lat", "lng"}  1 000 000., "bikes", "free" /.
currentBikeData, PlotLabel → "Availability of bicycles in London",
ImageSize → 600, BarLegend[{"DarkRainbow", {0, 35}}]
Out[14]=
Example: Accidents
Data Local density Local points All density
10 MoreThanJustStats.nb

11. MoreThanJustStats.nb 11

12. 0 5 10 15 20
0.75
1.00
1.25
1.50
1.75
Relative Danger Near Fitch Learning
0:00 4:49 9:38 14:27 19:16 0
0
200
400
600
800
1000
Accidents By Time Of Day
12 MoreThanJustStats.nb

13. Use computation
– to change the viewpoint
Example: Supersonic car
2000 4000 6000 8000
time
-500
500
1000
1500
2000
MoreThanJustStats.nb 13

14. Apply some calculus
2000 4000 6000 8000
-0.5
0.5
Apply some signal processing
2000 4000 6000 8000 10000
12
11
10
9
8
7
6
5
4
3
2
1
Load on front left wheel
14 MoreThanJustStats.nb

15. Use computation
– to inject a new viewpoint
Example: Finance
Correlation Threshold 0.1 0.3 0.45 0.5 0.55
Portfolio Correlation
ATHX PFBX NKTR PRGX THLD REGN IDXX WBMD TSRA CSII
ATHX 1. -0.155746 0.192554 0.0346706 0.199476 0.215293 0.055311 0.0598668 0.0667703 0.256818
PFBX -0.155746 1. 0.0551318 -0.0238231 0.0864465 -0.0371775 0.0372411 0.0297157 0.0501491 -0.0263306
NKTR 0.192554 0.0551318 1. 0.173956 0.408224 0.430739 0.252799 0.244477 0.240393 0.340634
PRGX 0.0346706 -0.0238231 0.173956 1. 0.107611 0.152259 0.187019 0.147743 0.224447 0.233454
THLD 0.199476 0.0864465 0.408224 0.107611 1. 0.340108 0.194287 0.231985 0.209756 0.280469
REGN 0.215293 -0.0371775 0.430739 0.152259 0.340108 1. 0.328254 0.21232 0.195491 0.282689
IDXX 0.055311 0.0372411 0.252799 0.187019 0.194287 0.328254 1. 0.21433 0.186771 0.287849
WBMD 0.0598668 0.0297157 0.244477 0.147743 0.231985 0.21232 0.21433 1. 0.203065 0.202096
TSRA 0.0667703 0.0501491 0.240393 0.224447 0.209756 0.195491 0.186771 0.203065 1. 0.252917
CSII 0.256818 -0.0263306 0.340634 0.233454 0.280469 0.282689 0.287849 0.202096 0.252917 1.
CFBK -0.0202622 0.047074 0.045182 -0.0116863 -0.00255542 -0.000900992 -0.0169211 -0.00205736 0.0692481 -0.0932367
SHOO 0.0666881 0.0877096 0.137083 0.246094 0.0914212 0.17177 0.172684 0.146134 0.156682 0.224934
NCMI -0.00881864 0.0229376 0.0943068 0.26504 0.117862 -0.0183203 0.120724 0.118175 0.205415 0.199415
BMRN 0.210411 0.11726 0.427514 0.222912 0.377895 0.555568 0.42669 0.2383 0.222794 0.395739
MGLN 0.0861668 0.0244271 0.269405 0.228394 0.217646 0.253427 0.435732 0.241649 0.322608 0.290727
CRDC -0.0246006 0.0999991 0.0603159 0.09501 0.00876257 -0.0257783 0.00368514 0.0795676 0.00799325 -0.0202939
SURG 0.0793505 -0.0290028 0.0249819 -0.0184576 0.0949218 0.120998 0.0330482 0.165451 0.0105265 0.128889
HFFC 0.0159068 0.0453208 0.056407 -0.133485 0.0857123 0.0728086 0.0179226 -0.0582327 0.0575192 0.0567152
LTRX 0.160061 0.0175897 0.11774 0.0961482 0.22694 0.0688794 -0.0311349 0.197049 0.211728 0.134419
MFNC -0.102578 0.00566642 0.070634 0.0481224 0.0240564 0.0164561 -0.0230522 -0.0500249 0.0625739 0.0602153
SHLO 0.137258 0.00834566 0.259109 0.255858 0.22768 0.170004 0.184693 0.262615 0.283098 0.19992
LABC 0.0582621 -0.0770103 0.0336639 -0.0219564 -0.0555295 0.0168567 -0.035154 -0.000979898 -0.0312983 -0.0374193
SNFCA 0.106298 -0.0796818 0.0353886 -0.0379301 0.102008 0.0133965 -0.0160458 0.064443 0.00624401 0.0744798
ASTI 0.0296936 -0.10231 0.0579198 0.130839 0.170478 0.159669 0.0242666 0.060238 0.103079 0.0802336
STRM 0.103553 -0.027044 0.015168 0.0813282 0.0710839 0.0916784 0.11691 0.0622404 0.10743 0.03938
RLOC 0.0673226 0.12551 0.174601 0.0897312 0.204838 0.117319 0.115966 0.214765 0.200785 0.178144
HSKA 0.0696611 0.0310147 0.0391807 0.0775681 0.140841 0.185131 -0.0176538 0.0500578 0.0610371 0.10533
NWFL -0.145537 0.12346 -0.0149798 -0.0737182 -0.0392499 -0.0887732 0.0110006 0.00857444 -0.0703103 -0.0690263
IFON 0.173712 0.0506925 0.112228 0.118179 0.168008 0.183346 0.146735 0.103064 0.0700051 0.1546
GENC -0.00454872 0.111557 0.0929517 0.118871 0.0893299 0.0951799 0.0683046 0.162016 -0.0174889 0.0333547
VSCI 0.0934438 -0.122724 -0.0337841 -0.11932 -0.0100928 -0.0106619 0.0262221 0.0221221 -0.0462289 0.00243774
HNSN 0.0728787 -0.0202281 0.0217612 0.0479466 0.039112 0.0492618 0.0121075 -0.00590073 0.0782787 0.177993
AMGN 0.144263 0.0524144 0.434697 0.164497 0.363856 0.567489 0.345919 0.270955 0.339446 0.383243
TOPS 0.0165096 0.0629182 0.0856118 -0.0616743 0.0295842 -0.006062 -0.0335838 0.143903 0.0738433 -0.0262043
UTSI 0.0574989 0.0159024 0.0864641 0.0859226 0.117787 0.0371759 -0.0538848 0.137577 0.0161543 0.0243469
INFN 0.136798 -0.172368 0.304939 0.257152 0.275845 0.267789 0.20102 0.283399 0.254023 0.283986
SAAS 0.100494 0.0334147 0.284865 0.292893 0.343925 0.250811 0.263846 0.298446 0.235286 0.290863
KRNY 0.00778643 0.0275327 0.261778 0.327411 0.227934 0.16828 0.237698 0.192625 0.336821 0.250728
ACUR 0.0599088 0.0654299 -0.0287304 0.0594885 0.0324952 0.0809222 0.0524753 0.065247 -0.0453231 0.107133
BERK -0.0263423 0.0857371 -0.0267503 -0.0363498 0.0123773 0.0174475 0.0648471 -0.0242965 0.079019 -0.045243
QCCO -0.036335 -0.0247951 0.125918 0.0854495 0.0777864 0.0406812 0.113156 0.0767831 0.0439639 0.0665833
LINC 0.109821 0.0471629 0.0797274 0.0278627 0.0350469 0.0664748 0.0578671 0.00775897 0.11751 0.0977395
KOPN 0.185468 0.00644997 0.398103 0.277598 0.350097 0.290742 0.260754 0.312968 0.341296 0.394899
ICCC 0.152975 -0.14512 0.00441828 0.0278111 0.0303676 -0.0192398 -0.0681873 0.0840796 0.0132102 0.0659621
Data Visualized Graph Communities
MoreThanJustStats.nb 15

16. 16 MoreThanJustStats.nb

17. Use computation
– to separate signal from noise
MoreThanJustStats.nb 17

18. 4.91 s | 8192
Sound Frequency shifts Model Result
18 MoreThanJustStats.nb

19. So why is most data science just
counting?
MoreThanJustStats.nb 19

20. So why is most data science just
counting?
Computation can be difficult
You have to know what’s possible
20 MoreThanJustStats.nb

21. The role of automation
– making computation easier
Example: Titanic
In[ ]:= Short[tData = ExampleData[{"MachineLearning", "Titanic"}, "Data"]]
Out[ ]//Short= {{1st, 29., female} → survived,
{1st, 0.9167, male} → survived, 1306, {3rd, 29., male} → died}
In[ ]:= titanicSurvival = Classify[tData]
Out[ ]= ClassifierFunction
Input type: {Nominal, Numerical, Nominal}
Classes: died, survived

In[ ]:= titanicSurvival[{"1st", 46, "male"}]
Out[ ]= died
In[ ]:= Plot[titanicSurvival[{"1st", age, "male"}, {"Probability", "died"}], {age, 0, 60}]
Out[ ]=
10 20 30 40 50 60
0.4
0.5
0.6
0.7
MoreThanJustStats.nb 21

22. Example: Day & night
In[ ]:= daynight = Classify
 → "Night", → "Day", → "Night", → "Night", → "Day",
→ "Night", → "Day", → "Day", → "Night", → "Night",
→ "Day", → "Night", → "Night", → "Day", → "Night",
→ "Night", → "Day", → "Day", → "Day", → "Day",
→ "Night", → "Night", → "Day", → "Night", → "Night",
→ "Day", → "Day", → "Day", → "Night", → "Day"
Out[ ]= ClassifierFunction
Input type: Image
Classes: Day, Night

In[ ]:= daynight , , ,
, , 
Out[ ]= {Day, Night, Day, Night, Night, Night}
22 MoreThanJustStats.nb

23. The role of automation
– automating insights
Example: Image identification
Example: Supervising the computer
Data = 0
Reset
Capture: Rock Paper Scissors Watch Stop
Train
MoreThanJustStats.nb 23

24. Example: No supervision - “Hands off the wheel”
Dogs
In[ ]:= Dataset[dogs]
Out[ ]=
24 MoreThanJustStats.nb

25. In[ ]:= FeatureSpacePlot[Take[dogs, 60], LabelingSize → 70]
Out[ ]=
In[ ]:= nearestDog = FeatureNearest[dogs]
Out[ ]= NearestFunction
Input type: Image
Output property: Element
Unable to store data in notebook.

In[ ]:= Grid[{testDogs, First /@ nearestDog[testDogs]}]
Out[ ]=
MoreThanJustStats.nb 25

26. Financial Assets
AMEX:MSN
MSADX
MSAIX
MSBNX
MSBYX
MSCCUX
MSCDX
MSCFX
MSDIX
MSDVX
MSEDX
MSEEX
MSEGX
MSELX
MSENX
MSEPX
MSFAX
MSFIX
MSFYX
MSGFX
MSGIX
MSGOX
MSGVX
MSHEX
MSHYX
MSIAX
MSIDX
MSIIX
MSIJX
MSIRX
MSIZX
MSJLX
MSMBX
MSMUX
MSNEOX
MSOCX
MSPDX
MSPMX
MSPRX
MSRBX
MSSAX
MSSFX
MSSIX
MSTCX
MSTIX
MSTLX
MSTZX
MSUAX
MSUGPX
MSULX
MSVCX
MSVNX
NASDAQ:MSEX
NASDAQ:MSON
NYSE:MSB
NYSE:MSPENYSE:MSPF
NYSE:MSPG
NYSE:MSPI
NYSE:MS-PK
26 MoreThanJustStats.nb

27. MoreThanJustStats.nb 27

28. The role of automation
– after the computation
Deployment
Firewall
App deployment
bikeApp = DynamicModule
{url = "http://api.citybik.es/barclays-cycle-hire.json", city = "London"},
ColumnActionMenu"Choose city", SortBy"city" ⧴ city = "city";
url = "url" /.
Import["http://api.citybik.es/networks.json", "JSON"], First,
DynamicDataset[Association /@ Import[url, "JSON"]]
Legended[GeoGraphics[#, ImageSize → 600,
PlotLabel → "Availability of bicycles in " <> city],
BarLegend[{"DarkRainbow", {0, 100}}]] &,
AbsolutePointSize[10], ColorData["DarkRainbow"]#bikes   #bikes + #free,
PointGeoPosition{#lat, #lng}  1 000 000. &,
SynchronousUpdating → False, TrackedSymbols ⧴ {url, city}
CloudDeploy[bikeApp, Permissions → "Public"]
28 MoreThanJustStats.nb

29. API deployment
In[ ]:= CloudDeploy[
APIFunction[{"class" → "String", "age" → "Number", "sex" → "String"},
Function[titanicSurvival[{#class, #age, #sex}]],
AllowedCloudExtraParameters → All],
"TitanicPredictor",
Permissions → "Public"
]
Out[ ]= CloudObjecthttps://www.wolframcloud.com/objects/jonm/TitanicPredictor
In[ ]:= EmbedCode[%, "Java"]
Out[ ]=
Embeddable Code
Use the code below to call the Wolfram Cloud function from Java:
Code
Copy to Clipboard
if (_conn.getResponseCode() != 200) {
throw new IOException(_conn.getResponseMessage());
}
BufferedReader _rdr = new BufferedReader(new
InputStreamReader(_conn.getInputStream()));
StringBuilder _sb = new StringBuilder();
String _line;
while ((_line = _rdr.readLine()) != null) {
_sb.append(_line);
}
_rdr.close();
_conn.disconnect();
return _sb.toString();
}
}
MoreThanJustStats.nb 29

30. Breaking the Boundaries of
Traditional Data Science
The toolset is HUGE - use more of it
Automation makes the toolset accessible
The human’s role is to ask deeper questions of the data
30 MoreThanJustStats.nb