Statistics, Data Mining, and Machine Learning in Astronomy A Practical Python Guide for the Analysis of Survey Data, Updated Edition
Statistics, Data Mining, and Machine Learning in Astronomy is the essential introduction to the statistical methods needed to analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the Large Synoptic Survey Te...
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Princeton, NJ
Princeton University Press
[2019]
|
| Schriftenreihe: | Princeton Series in Modern Observational Astronomy
13 |
| Schlagworte: | |
| Online-Zugang: | FAB01 FAW01 FCO01 FHA01 FHM01 FKE01 FLA01 UER01 UPA01 URL des Erstveröffentlichers |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
MARC
| LEADER | 00000nmm a2200000zcb4500 | ||
|---|---|---|---|
| 001 | BV046646110 | ||
| 003 | DE-604 | ||
| 005 | 20210930 | ||
| 007 | cr|uuu---uuuuu | ||
| 008 | 200331s2019 |||| o||u| ||||||eng d | ||
| 020 | |a 9780691197050 |9 978-0-691-19705-0 | ||
| 024 | 7 | |a 10.1515/9780691197050 |2 doi | |
| 035 | |a (ZDB-23-DGG)9780691197050 | ||
| 035 | |a (OCoLC)1148077127 | ||
| 035 | |a (DE-599)BVBBV046646110 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-1046 |a DE-Aug4 |a DE-859 |a DE-860 |a DE-739 |a DE-29 |a DE-1043 |a DE-M347 |a DE-858 | ||
| 082 | 0 | |a 522/.85 |2 23 | |
| 100 | 1 | |a Ivezić, Željko |d 1965- |e Verfasser |0 (DE-588)1059448750 |4 aut | |
| 245 | 1 | 0 | |a Statistics, Data Mining, and Machine Learning in Astronomy |b A Practical Python Guide for the Analysis of Survey Data, Updated Edition |c Jacob T. VanderPlas, Andrew J. Connolly, Željko Ivezić, Alexander Gray |
| 264 | 1 | |a Princeton, NJ |b Princeton University Press |c [2019] | |
| 264 | 4 | |c © 2020 | |
| 300 | |a 1 online resource (552 pages) |b 12 color + 187 b/w illus. 13 tables | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b c |2 rdamedia | ||
| 338 | |b cr |2 rdacarrier | ||
| 490 | 0 | |a Princeton Series in Modern Observational Astronomy |v 13 | |
| 500 | |a Description based on online resource; title from PDF title page (publisher's Web site, viewed 29. Feb 2020) | ||
| 520 | |a Statistics, Data Mining, and Machine Learning in Astronomy is the essential introduction to the statistical methods needed to analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the Large Synoptic Survey Telescope. Now fully updated, it presents a wealth of practical analysis problems, evaluates the techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. Python code and sample data sets are provided for all applications described in the book. The supporting data sets have been carefully selected from contemporary astronomical surveys and are easy to download and use. The accompanying Python code is publicly available, well documented, and follows uniform coding standards. Together, the data sets and code enable readers to reproduce all the figures and examples, engage with the different methods, and adapt them to their own fields of interest.An accessible textbook for students and an indispensable reference for researchers, this updated edition features new sections on deep learning methods, hierarchical Bayes modeling, and approximate Bayesian computation. The chapters have been revised throughout and the astroML code has been brought completely up to date.Fully revised and expandedDescribes the most useful statistical and data-mining methods for extracting knowledge from huge and complex astronomical data setsFeatures real-world data sets from astronomical surveysUses a freely available Python codebase throughoutIdeal for graduate students, advanced undergraduates, and working astronomers | ||
| 546 | |a In English | ||
| 650 | 7 | |a SCIENCE / Astronomy |2 bisacsh | |
| 650 | 4 | |a Astronomy |x Data processing | |
| 650 | 4 | |a Python (Computer program language) | |
| 650 | 4 | |a Statistical astronomy | |
| 700 | 1 | |a Connolly, Andrew J. |0 (DE-588)1201891744 |4 aut | |
| 700 | 1 | |a VanderPlas, Jake |0 (DE-588)1122834322 |4 aut | |
| 856 | 4 | 0 | |u https://doi.org/10.1515/9780691197050 |x Verlag |z URL des Erstveröffentlichers |3 Volltext |
| 912 | |a ZDB-23-DGG |a ZDB-23-DPC | ||
| 940 | 1 | |q ZDB-23-DPC19 | |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-032057382 | ||
| 966 | e | |u https://doi.org/10.1515/9780691197050?locatt=mode:legacy |l FAB01 |p ZDB-23-DGG |q FAB_PDA_DGG |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1515/9780691197050?locatt=mode:legacy |l FAW01 |p ZDB-23-DGG |q FAW_PDA_DGG |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1515/9780691197050?locatt=mode:legacy |l FCO01 |p ZDB-23-DGG |q FCO_PDA_DGG |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1515/9780691197050?locatt=mode:legacy |l FHA01 |p ZDB-23-DGG |q FHA_PDA_DGG |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1515/9780691197050?locatt=mode:legacy |l FHM01 |p ZDB-23-DPC |q ZDB-23-DPC19 |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1515/9780691197050?locatt=mode:legacy |l FKE01 |p ZDB-23-DGG |q FKE_PDA_DGG_Kauf |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1515/9780691197050?locatt=mode:legacy |l FLA01 |p ZDB-23-DGG |q FLA_PDA_DGG |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1515/9780691197050?locatt=mode:legacy |l UER01 |p ZDB-23-DGG |q UER_PDA_DGG_Kauf |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1515/9780691197050?locatt=mode:legacy |l UPA01 |p ZDB-23-DGG |q UPA_PDA_DGG |x Verlag |3 Volltext | |
Datensatz im Suchindex
| _version_ | 1804181344058605568 |
|---|---|
| adam_txt | |
| any_adam_object | |
| any_adam_object_boolean | |
| author | Ivezić, Željko 1965- Connolly, Andrew J. VanderPlas, Jake |
| author_GND | (DE-588)1059448750 (DE-588)1201891744 (DE-588)1122834322 |
| author_facet | Ivezić, Željko 1965- Connolly, Andrew J. VanderPlas, Jake |
| author_role | aut aut aut |
| author_sort | Ivezić, Željko 1965- |
| author_variant | ž i ži a j c aj ajc j v jv |
| building | Verbundindex |
| bvnumber | BV046646110 |
| collection | ZDB-23-DGG ZDB-23-DPC |
| ctrlnum | (ZDB-23-DGG)9780691197050 (OCoLC)1148077127 (DE-599)BVBBV046646110 |
| dewey-full | 522/.85 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 522 - Techniques, equipment & materials |
| dewey-raw | 522/.85 |
| dewey-search | 522/.85 |
| dewey-sort | 3522 285 |
| dewey-tens | 520 - Astronomy and allied sciences |
| discipline | Physik |
| discipline_str_mv | Physik |
| doi_str_mv | 10.1515/9780691197050 |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>04666nmm a2200565zcb4500</leader><controlfield tag="001">BV046646110</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20210930 </controlfield><controlfield tag="007">cr|uuu---uuuuu</controlfield><controlfield tag="008">200331s2019 |||| o||u| ||||||eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9780691197050</subfield><subfield code="9">978-0-691-19705-0</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1515/9780691197050</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-23-DGG)9780691197050</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1148077127</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV046646110</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-1046</subfield><subfield code="a">DE-Aug4</subfield><subfield code="a">DE-859</subfield><subfield code="a">DE-860</subfield><subfield code="a">DE-739</subfield><subfield code="a">DE-29</subfield><subfield code="a">DE-1043</subfield><subfield code="a">DE-M347</subfield><subfield code="a">DE-858</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">522/.85</subfield><subfield code="2">23</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Ivezić, Željko</subfield><subfield code="d">1965-</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)1059448750</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Statistics, Data Mining, and Machine Learning in Astronomy</subfield><subfield code="b">A Practical Python Guide for the Analysis of Survey Data, Updated Edition</subfield><subfield code="c">Jacob T. VanderPlas, Andrew J. Connolly, Željko Ivezić, Alexander Gray</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Princeton, NJ</subfield><subfield code="b">Princeton University Press</subfield><subfield code="c">[2019]</subfield></datafield><datafield tag="264" ind1=" " ind2="4"><subfield code="c">© 2020</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 online resource (552 pages)</subfield><subfield code="b">12 color + 187 b/w illus. 13 tables</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">Princeton Series in Modern Observational Astronomy</subfield><subfield code="v">13</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Description based on online resource; title from PDF title page (publisher's Web site, viewed 29. Feb 2020)</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Statistics, Data Mining, and Machine Learning in Astronomy is the essential introduction to the statistical methods needed to analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the Large Synoptic Survey Telescope. Now fully updated, it presents a wealth of practical analysis problems, evaluates the techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. Python code and sample data sets are provided for all applications described in the book. The supporting data sets have been carefully selected from contemporary astronomical surveys and are easy to download and use. The accompanying Python code is publicly available, well documented, and follows uniform coding standards. Together, the data sets and code enable readers to reproduce all the figures and examples, engage with the different methods, and adapt them to their own fields of interest.An accessible textbook for students and an indispensable reference for researchers, this updated edition features new sections on deep learning methods, hierarchical Bayes modeling, and approximate Bayesian computation. The chapters have been revised throughout and the astroML code has been brought completely up to date.Fully revised and expandedDescribes the most useful statistical and data-mining methods for extracting knowledge from huge and complex astronomical data setsFeatures real-world data sets from astronomical surveysUses a freely available Python codebase throughoutIdeal for graduate students, advanced undergraduates, and working astronomers</subfield></datafield><datafield tag="546" ind1=" " ind2=" "><subfield code="a">In English</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">SCIENCE / Astronomy</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Astronomy</subfield><subfield code="x">Data processing</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Python (Computer program language)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Statistical astronomy</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Connolly, Andrew J.</subfield><subfield code="0">(DE-588)1201891744</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">VanderPlas, Jake</subfield><subfield code="0">(DE-588)1122834322</subfield><subfield code="4">aut</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1515/9780691197050</subfield><subfield code="x">Verlag</subfield><subfield code="z">URL des Erstveröffentlichers</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-23-DGG</subfield><subfield code="a">ZDB-23-DPC</subfield></datafield><datafield tag="940" ind1="1" ind2=" "><subfield code="q">ZDB-23-DPC19</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-032057382</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1515/9780691197050?locatt=mode:legacy</subfield><subfield code="l">FAB01</subfield><subfield code="p">ZDB-23-DGG</subfield><subfield code="q">FAB_PDA_DGG</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1515/9780691197050?locatt=mode:legacy</subfield><subfield code="l">FAW01</subfield><subfield code="p">ZDB-23-DGG</subfield><subfield code="q">FAW_PDA_DGG</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1515/9780691197050?locatt=mode:legacy</subfield><subfield code="l">FCO01</subfield><subfield code="p">ZDB-23-DGG</subfield><subfield code="q">FCO_PDA_DGG</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1515/9780691197050?locatt=mode:legacy</subfield><subfield code="l">FHA01</subfield><subfield code="p">ZDB-23-DGG</subfield><subfield code="q">FHA_PDA_DGG</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1515/9780691197050?locatt=mode:legacy</subfield><subfield code="l">FHM01</subfield><subfield code="p">ZDB-23-DPC</subfield><subfield code="q">ZDB-23-DPC19</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1515/9780691197050?locatt=mode:legacy</subfield><subfield code="l">FKE01</subfield><subfield code="p">ZDB-23-DGG</subfield><subfield code="q">FKE_PDA_DGG_Kauf</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1515/9780691197050?locatt=mode:legacy</subfield><subfield code="l">FLA01</subfield><subfield code="p">ZDB-23-DGG</subfield><subfield code="q">FLA_PDA_DGG</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1515/9780691197050?locatt=mode:legacy</subfield><subfield code="l">UER01</subfield><subfield code="p">ZDB-23-DGG</subfield><subfield code="q">UER_PDA_DGG_Kauf</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1515/9780691197050?locatt=mode:legacy</subfield><subfield code="l">UPA01</subfield><subfield code="p">ZDB-23-DGG</subfield><subfield code="q">UPA_PDA_DGG</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield></record></collection> |
| id | DE-604.BV046646110 |
| illustrated | Illustrated |
| index_date | 2024-07-03T14:15:29Z |
| indexdate | 2024-07-10T08:50:09Z |
| institution | BVB |
| isbn | 9780691197050 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032057382 |
| oclc_num | 1148077127 |
| open_access_boolean | |
| owner | DE-1046 DE-Aug4 DE-859 DE-860 DE-739 DE-29 DE-1043 DE-M347 DE-858 |
| owner_facet | DE-1046 DE-Aug4 DE-859 DE-860 DE-739 DE-29 DE-1043 DE-M347 DE-858 |
| physical | 1 online resource (552 pages) 12 color + 187 b/w illus. 13 tables |
| psigel | ZDB-23-DGG ZDB-23-DPC ZDB-23-DPC19 ZDB-23-DGG FAB_PDA_DGG ZDB-23-DGG FAW_PDA_DGG ZDB-23-DGG FCO_PDA_DGG ZDB-23-DGG FHA_PDA_DGG ZDB-23-DPC ZDB-23-DPC19 ZDB-23-DGG FKE_PDA_DGG_Kauf ZDB-23-DGG FLA_PDA_DGG ZDB-23-DGG UER_PDA_DGG_Kauf ZDB-23-DGG UPA_PDA_DGG |
| publishDate | 2019 |
| publishDateSearch | 2019 |
| publishDateSort | 2019 |
| publisher | Princeton University Press |
| record_format | marc |
| series2 | Princeton Series in Modern Observational Astronomy |
| spelling | Ivezić, Željko 1965- Verfasser (DE-588)1059448750 aut Statistics, Data Mining, and Machine Learning in Astronomy A Practical Python Guide for the Analysis of Survey Data, Updated Edition Jacob T. VanderPlas, Andrew J. Connolly, Željko Ivezić, Alexander Gray Princeton, NJ Princeton University Press [2019] © 2020 1 online resource (552 pages) 12 color + 187 b/w illus. 13 tables txt rdacontent c rdamedia cr rdacarrier Princeton Series in Modern Observational Astronomy 13 Description based on online resource; title from PDF title page (publisher's Web site, viewed 29. Feb 2020) Statistics, Data Mining, and Machine Learning in Astronomy is the essential introduction to the statistical methods needed to analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the Large Synoptic Survey Telescope. Now fully updated, it presents a wealth of practical analysis problems, evaluates the techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. Python code and sample data sets are provided for all applications described in the book. The supporting data sets have been carefully selected from contemporary astronomical surveys and are easy to download and use. The accompanying Python code is publicly available, well documented, and follows uniform coding standards. Together, the data sets and code enable readers to reproduce all the figures and examples, engage with the different methods, and adapt them to their own fields of interest.An accessible textbook for students and an indispensable reference for researchers, this updated edition features new sections on deep learning methods, hierarchical Bayes modeling, and approximate Bayesian computation. The chapters have been revised throughout and the astroML code has been brought completely up to date.Fully revised and expandedDescribes the most useful statistical and data-mining methods for extracting knowledge from huge and complex astronomical data setsFeatures real-world data sets from astronomical surveysUses a freely available Python codebase throughoutIdeal for graduate students, advanced undergraduates, and working astronomers In English SCIENCE / Astronomy bisacsh Astronomy Data processing Python (Computer program language) Statistical astronomy Connolly, Andrew J. (DE-588)1201891744 aut VanderPlas, Jake (DE-588)1122834322 aut https://doi.org/10.1515/9780691197050 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Ivezić, Željko 1965- Connolly, Andrew J. VanderPlas, Jake Statistics, Data Mining, and Machine Learning in Astronomy A Practical Python Guide for the Analysis of Survey Data, Updated Edition SCIENCE / Astronomy bisacsh Astronomy Data processing Python (Computer program language) Statistical astronomy |
| title | Statistics, Data Mining, and Machine Learning in Astronomy A Practical Python Guide for the Analysis of Survey Data, Updated Edition |
| title_auth | Statistics, Data Mining, and Machine Learning in Astronomy A Practical Python Guide for the Analysis of Survey Data, Updated Edition |
| title_exact_search | Statistics, Data Mining, and Machine Learning in Astronomy A Practical Python Guide for the Analysis of Survey Data, Updated Edition |
| title_exact_search_txtP | Statistics, Data Mining, and Machine Learning in Astronomy A Practical Python Guide for the Analysis of Survey Data, Updated Edition |
| title_full | Statistics, Data Mining, and Machine Learning in Astronomy A Practical Python Guide for the Analysis of Survey Data, Updated Edition Jacob T. VanderPlas, Andrew J. Connolly, Željko Ivezić, Alexander Gray |
| title_fullStr | Statistics, Data Mining, and Machine Learning in Astronomy A Practical Python Guide for the Analysis of Survey Data, Updated Edition Jacob T. VanderPlas, Andrew J. Connolly, Željko Ivezić, Alexander Gray |
| title_full_unstemmed | Statistics, Data Mining, and Machine Learning in Astronomy A Practical Python Guide for the Analysis of Survey Data, Updated Edition Jacob T. VanderPlas, Andrew J. Connolly, Željko Ivezić, Alexander Gray |
| title_short | Statistics, Data Mining, and Machine Learning in Astronomy |
| title_sort | statistics data mining and machine learning in astronomy a practical python guide for the analysis of survey data updated edition |
| title_sub | A Practical Python Guide for the Analysis of Survey Data, Updated Edition |
| topic | SCIENCE / Astronomy bisacsh Astronomy Data processing Python (Computer program language) Statistical astronomy |
| topic_facet | SCIENCE / Astronomy Astronomy Data processing Python (Computer program language) Statistical astronomy |
| url | https://doi.org/10.1515/9780691197050 |
| work_keys_str_mv | AT iveziczeljko statisticsdataminingandmachinelearninginastronomyapracticalpythonguidefortheanalysisofsurveydataupdatededition AT connollyandrewj statisticsdataminingandmachinelearninginastronomyapracticalpythonguidefortheanalysisofsurveydataupdatededition AT vanderplasjake statisticsdataminingandmachinelearninginastronomyapracticalpythonguidefortheanalysisofsurveydataupdatededition |