Learning Resources for Data Science in the Wolfram Language

Question

I'm delivering an upcoming course on Data Science with the programming/analysis done predominantly in the Wolfram Language. I created the following table to leverage students' familiarity with Lists by way of introducing Associations by way of introducing Datasets. Juxtaposing the various invocations one can't help but be struck with the foresight or "naturalness" of the original language design and subsequent integrations - (IMO an extraterrestrial would be hard-pressed to predict the chronology of sequentially added features, and in particular, to note that all three didn't emerge simultaneously).

My sense is that programming expertise in the Wolfram Language starts with an awareness of its existing (or "likely") functionality followed by inculcating and memorising its basic functional forms. While the documentation is essential for understanding all the details and an overall coherency, for quick reference/comparison/contrast, I think cheatsheets could be more commonly deployed (with no doubt improved visual design - the code follows which could potentially be improved/combined with other users' cheatsheets into a larger, more powerful "Demonstration"?)

At any rate, while an introduction involves becoming familiar with the basic syntax and manipulating structured datasets, the next stage involves applying these to some real-life examples. While there are good texts/datasets available (e.g. Luís Torgo's Data Mining with R: Learning with Case Studies) I'm wondering if anyone has found useful, available datasets from a variety of contexts (health, business, economics, ecology, physics, learning analytics) that might be particularly suitable for showcasing some of the benefits of data science in the Wolfram Language (this might be due to their size, unusual analysis, parallelizability, integration with curated data etc).

SetAttributes[{IOCells, DefCells, InsertIOCells, InsertDefCells}, 
  HoldAll];


DefCells[Set[lhs_, rhs_]] := Row[{
    ExpressionCell[Defer@lhs, "Input", ShowStringCharacters -> True, 
     FontSize -> 16],
    ExpressionCell[" = ", ShowStringCharacters -> False, 
     FontSize -> 16],
    ExpressionCell[lhs = rhs, "Input", ShowStringCharacters -> True, 
     FontSize -> 16]}];

(* Need to split in this way since a Defer wrapper seems to produce \
unexpected formatting - compare:
ExpressionCell[Defer[lhs=<|"a"\[Rule]8,"b"\[Rule]9,"c"\[Rule]10|>],\
"Input"]
 ExpressionCell[Defer[lhs=\[LeftAngleBracket]"a"\[Rule]8,"b"\[Rule]9,\
"c"\[Rule]10\[RightAngleBracket]],"Input"]
 *)

DefCells[R : Set[dataset, Dataset[{_}]]] := (R; Row[{
     ExpressionCell[Defer@dataset, "Input", 
      ShowStringCharacters -> True, FontSize -> 16],
     ExpressionCell[" = ", ShowStringCharacters -> False, 
      FontSize -> 16],
     ExpressionCell["Dataset"[HoldForm[{assoc}]], "Input", 
      ShowStringCharacters -> False, FontSize -> 16]}]);

IOCells[expr_] /; ! 
    FreeQ[Hold@expr, 
     Histogram | ListPlot | ListLinePlot | BarChart3D | BarChart | 
      PieChart | SmoothHistogram] := Grid[{
    {ExpressionCell[Defer@expr, "Input", 
      ShowStringCharacters -> True]},
    {ExpressionCell[expr, "Output", Magnification -> 0.41]}
    }, Frame -> True, Alignment -> Left, 
   Background -> {None, {1 -> GrayLevel[.9], 2 -> White}}];


IOCells[expr_] := Grid[{
    {ExpressionCell[Defer@expr, "Input", 
      ShowStringCharacters -> True]},
    {ExpressionCell[expr, "Output"]}
    }, Frame -> True, Alignment -> Left, 
   Background -> {None, {1 -> GrayLevel[.9], 2 -> White}}];

(* For the same reason above need a specific definition when <|  |> \
notation is in input *)

IOCells[R : KeySelect[assoc_, assoc2_]] := With[
   {t = ToExpression@ToBoxes[assoc2]}, Grid[{
     {ExpressionCell[HoldForm@KeySelect[assoc, t], "Input", 
       ShowStringCharacters -> True]},
     {ExpressionCell[ReleaseHold@R, "Output"]}
     }, Frame -> True, Alignment -> Left, 
    Background -> {None, {1 -> GrayLevel[.9], 2 -> White}}]];

IOCells[R : Dataset[expr_][op_]] := Grid[{
    {ExpressionCell["Dataset"[HoldForm@assoc][op], "Input", 
      ShowStringCharacters -> False]},
    {ExpressionCell[ReleaseHold@R, "Output"]}
    }, Frame -> True, Alignment -> Left, 
   Background -> {None, {1 -> GrayLevel[.9], 2 -> White}}];

(* Since Dataset outputs as grid in the input - means copying \
expression from the grid and evaluating won't work *)


InsertDefCells[defs_List] := 
  ReleaseHold@With[{t = Hold@defs}, Map[DefCells, t, {2}]];
InsertIOCells[exprLs_] := 
  Sequence @@ 
   Map[Row, 
    ReleaseHold@With[{t = Hold@exprLs}, Map[IOCells, t, {4}]], {2}];

ColumnHeadStyle[cheads_List] := 
  ExpressionCell[#, Style[#, {Bold, 18}], 
     ShowStringCharacters -> False] & /@ cheads;


Block[{$PlotTheme = "Minimal"}, Grid[{
   {"List", "Association", "Dataset"} // ColumnHeadStyle,
   {ls = {7, 8, 9}, assoc = <| "a" -> 7, b -> 8, "c" -> 9 |>, 
     dataset = Dataset[{assoc}]} // InsertDefCells,
   {
     {{ls[[All]]}, {Values[assoc], Keys[assoc], Normal[assoc], 
       Lookup[assoc, "b", "No-b"]}, {dataset[1, Values], 
       dataset[1, Keys]}},
     {{(Query@1)[ls], ls[[1]], ls[[2]]}, {assoc[["a"]], assoc["a"], 
       assoc[[Key@b]], assoc@Key@b, 
       Query["a"][assoc]}, {Query[1, Key["a"]][dataset], 
       dataset[1, Key["a"]]}},
     {{ls[[3]], ls[[-1]]}, {assoc[[3]], assoc[[-1]], assoc[a], 
       assoc["b"]}, {dataset[1, 3], dataset[1, -1], 
       dataset[1, Key["d"]]}},
     {{ls[[1 ;; 3]], ls[[1 ;; 3 ;; 2]], Take[ls, {1, 3}], 
       Query[Take[{1, 3}]][ls]}, {assoc[[1 ;; 3]], 
       assoc[[1 ;; 3 ;; 2]], Take[assoc, {1, 3}], 
       Take[assoc, {1, 3, 2}], 
       Query[1 ;; 3][assoc]}, {dataset[1, 1 ;; 3], 
       Query[1, 1 ;; 3][dataset]}},
     {{Query[{1, 3}][ls], ls[[{1, 3}]]}, {Query[{1, 3}][assoc], 
       assoc[[{1, 3}]], assoc[[{"a", "c"}]], 
       KeyTake[assoc, {"a", "c"}]}, {dataset[1, {1, 3}], 
       dataset[1, {"a", "c"}]}},
     {{Cases[ls, _?OddQ], Cases[_?OddQ][ls]}, {Cases[assoc, _?OddQ], 
       Cases[_?OddQ][assoc]}, {dataset[1, Cases[_?OddQ]]}},
     {{Query[Select[OddQ]][assoc], Select[ls, OddQ], 
       SelectFirst[ls, OddQ]}, {Select[assoc, OddQ], 
       Select[OddQ][assoc], 
       SelectFirst[assoc, OddQ]}, {dataset[1, Select[OddQ]], 
       dataset[1, SelectFirst[OddQ]]}},
     {{Position[ls, _?OddQ], 
       FirstPosition[ls, _?OddQ]}, {Position[assoc, _?OddQ], 
       Position[_?OddQ][assoc], 
       FirstPosition[assoc, _?OddQ]}, {dataset[1, Position[_?OddQ]], 
       dataset[1, FirstPosition[_?OddQ]]}},
     {{PositionIndex[ls], 
       Query[PositionIndex][ls]}, {PositionIndex[assoc], 
       Query[PositionIndex][assoc]}, {dataset[1, PositionIndex]}},
     {{Select[Keys@assoc, MemberQ[{"a", "c"}, #] &]}, {KeySelect[
        assoc, MemberQ[{"a", "c"}, #] &], 
       KeySelect[assoc, <| "a" -> True, "c" -> True |>]}, {dataset[1, 
        KeySelect[(# == "a" \[Or] # == "c") &]]}},
     {{f[ls[[3]]], Query[3, f][ls]}, {f[assoc[["c"]]], 
       Query["c", f][assoc]}, {dataset[1, "c", f], 
       dataset[1, 3, f]}},
     {{f /@ ls, Map[f][ls], Query[Map[f]][ls]}, {f /@ assoc, 
       Map[f][assoc]}, {dataset[1, Map@f], dataset[1, All, f]}},
     {{MapIndexed[f, ls], MapIndexed[f][ls]}, {MapIndexed[f, assoc], 
       MapIndexed[f][assoc]}, {dataset[1, MapIndexed[f]]}},
     {{MapAt[f, ls, 2], MapAt[f, 2][ls]}, {MapAt[f, assoc, 2], 
       MapAt[f, 2][assoc], 
       MapAt[f, Key[b]][assoc]}, {dataset[1, MapAt[f, Key@b]]}},
     {{MapAt[f, Normal@assoc, {All, 1}]}, {KeyMap[f, assoc], 
       KeyMap[f][assoc]}, {dataset[1, KeyMap[f]]}},
     {{Reverse /@ Normal@assoc, 
       Association[f /@ Normal@assoc]}, {AssociationMap[Reverse, 
        assoc], AssociationMap[f, assoc]}, {dataset[1, 
        AssociationMap@Reverse], dataset[1, AssociationMap[f]]}},
     {{Sort@ls, Sort[ls, Greater], 
       Query[Sort[#1, Greater] &][ls]}, {Sort@assoc, 
       Sort[assoc, Greater], 
       Query[Sort[#1, Greater] &][assoc]}, {dataset[1, Sort], 
       dataset[1, Sort[#, Greater] &]}},
     {{Total[ls]}, {Total[assoc], Query[Total][assoc], 
       Query[Values /* Total][assoc]}, {dataset[1, Total], 
       dataset[1, All /* Total], dataset[1, Total@*Values]}},
     {{Through[{Min, Max, Total, Variance, Median}@
         ls]}, {Query[{Min, Max, Total, Variance, Median}][
        assoc]}, {dataset[1, {Min, Max, Total, Variance, Median}]}},
     {{Through[{Histogram, BarChart, SmoothHistogram, PieChart, 
          ListLinePlot[#, Filling -> Axis] &}@ls]},
      {Query[{Histogram, BarChart, SmoothHistogram, PieChart, 
          ListLinePlot[#1, Filling -> Axis] &}][
        assoc]}, {dataset[
         1, {Histogram, BarChart, SmoothHistogram, PieChart, 
          ListLinePlot[#1, Filling -> Axis] &}] // Normal}},
     {{Thread[Keys[assoc] -> ls]}, {AssociationThread[
        Keys[assoc] -> ls], 
       AssociationThread[Keys[assoc], 
        ls]}, {dataset[
         1, {AssociationThread[Keys@#, 
            Values@#] &, (Keys@# -> Values@#) & /* 
           AssociationThread}] // Normal}},
     {{Thread[Keys[assoc] -> f /@ Keys[assoc]]}, {AssociationMap[f, 
        Keys[assoc]], 
       Query[AssociationMap[f]@*Keys][assoc]}, {dataset[1, 
        Keys /* AssociationMap[f]]}},
     {{(f[#1, #2, #3] &)[Sequence @@ ls]}, {(f[#a, #1[Key[b]], #c] &)[
        assoc], (f[#1["a"], #1[Key[b]], #1["c"]] &)[assoc]}, {dataset[
        1, All /* (f[#a, #1[Key[b]], #c] &)], 
       dataset[1, (Apply@f)@*Catenate]}},
     {{(f[##1] &)[Sequence @@ ls], (f[##2] &)[
        Sequence @@ ls]}, {((f[##] &)[Sequence @@ Values@#] &)[
        assoc]}, {dataset[1, 
        Values /* (Sequence @@ # &) /* (f[##] &)], 
       dataset[1, (f[##] &)@*(Sequence @@ # &)@*Values]}}
     } // InsertIOCells
   }, Frame -> True, 
  Dividers -> {Thick, {Thick, {None}, Thick}, {3 -> Thick, 8 -> Thick,
      13 -> Thick, 19 -> Thick, 23 -> Thick, 25 -> Thick}}, 
  Alignment -> {Center, Center},
  Background -> {Automatic, {ColorData[17, 4], 
     LightBrown, {LightBlue, LightGreen}}}, 
  ItemStyle -> {Automatic, {19}}, Spacings -> {1, {1, 1, 1, {0.5}}}]]

Not clear what is the question. It's about public datasets? Maybe you can see kaggle.com — Murta, Commented Jul 28, 2014 at 3:05
In your chart I miss some nice examples on how to change values and append new columns and lines in current List/Associations/DataSet. Unfortunately L-value assignment are not working yet. What make such operations very difficult to Datasets, and make Datasets itself not very interesting to Data Science at the present stage. Another serious limitation is the memory issue, as you can see here. — Murta, Commented Jul 28, 2014 at 3:20
Yes, datasets for which analysis is made possible or easier using WL capabilities. — Ronald Monson, Commented Jul 28, 2014 at 5:16
@Murta, I agree, some basic assignments should probably be in the chart - it was already getting a little large although better design would free up some space. Actually another follow-up cheatsheet with assignments/manipulations involving deeper datasets, use of (presumably upcoming) Replace, ReplaceAt etc usage, other aggregation operators and especially GroupBy would be useful. I’m assuming these assignments, and efficient memory management issues will be resolved in upcoming months but IMO of interest will be the ability to refine/test a wider range of algorithms on an “industrial scale”. — Ronald Monson, Commented Jul 28, 2014 at 5:36
related regarding adding a column to a Dataset mathematica.stackexchange.com/q/51472/66 — faysou, Commented Nov 17, 2014 at 9:06

Vitaliy Kaurov · Accepted Answer · 2018-09-18 22:33:56Z

5

I think now this question can be answered with the launch of Wolfram Data Repository. This release blog walks you through the usage. Important fact is you can publish your own data and benefit community and your own goals; the process is free. For example, this very nice dataset and examples ( Atlantic Hurricane Data 1851-2017 ) are published by renowned Seth Chandler with the authorship publication and update dates clearly stated. This is quite neat as it can make a nice addition to your data-scientist resume ;-) Don't forget about very relevant to data science and analogical in nature Wolfram Neural Net Repository (see also release blog). Neural Nets are modern data-operators and are pertinent to the data per se.

Besides free availability on the web with downloadable datasets and usage examples at Wolfram Data Repository site:

the datasets are available from within Wolfram Language as ResourceObject and have access framework:

Search

ResourceSearch["Shakespeare"]

Datasets

meteorites = ResourceData["Meteorite Landings"]

GeoGraphics[{Red,PointSize[0.009],Opacity[0.2],
Point@DeleteMissing[meteorites[All,"Coordinates"]]},
GeoRange->Entity["Country","Australia"],ImageSize->600]

Currently available types are (expanding):

Currently available categories are (expanding):

edited Sep 18, 2018 at 22:33

answered Sep 18, 2018 at 22:14

Vitaliy Kaurov

73.4k9 gold badges206 silver badges365 bronze badges

$\begingroup$ This is interesting as is the way questions get answered years later as part of some inexorable roll-out. Now the challenge seems to be how to make best use of all these datasets for a given task and/or what combination of tutorials/examples give coverage of all the main methods/idioms. $\endgroup$
– Ronald Monson
Commented Sep 20, 2018 at 6:11
$\begingroup$ @RonaldMonson did you develop your course on data science? I'd love to take a look if there are some notebooks. $\endgroup$
– Vitaliy Kaurov
Commented Dec 6, 2020 at 14:47
$\begingroup$ Still a work in progress due to a couple of realisations. The first is that my sense is that most treatments of Data-Science/Statistics don't typically connect with Information Theory or even convey non-superficial understandings of randomness which seem fundamental but are nonetheless tricky to intuitively integrate (and which if I'm honest I myself didn't actually have a good handle on, so it has been a bit of a learning curve there). The second issue is the proverbial elephant in the room when it comes to the WL. Is any course based on the WL designed for .... $\endgroup$
– Ronald Monson
Commented Dec 8, 2020 at 3:57
$\begingroup$ ... 1) As a way of exploring ideas/concepts/intuitions at a high level or 2) as a viable language for deployment in the real world. Ideally given WL's current capabilities one would like to say both but I think 2) remains a challenge especially when it comes to material in Data Science/Statistics where I think one needs to demonstrate work-place applicability and advantages over Python. I firmly believe this is possible and perhaps even inevitable although I also think some extra tools are required - hence the need to create some applications to go with those educational notebooks ... soon! $\endgroup$
– Ronald Monson
Commented Dec 8, 2020 at 4:10

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