ºÝºÝߣshows by User: alanderex / http://www.slideshare.net/images/logo.gif ºÝºÝߣshows by User: alanderex / Mon, 05 Nov 2018 22:18:56 GMT ºÝºÝߣShare feed for ºÝºÝߣshows by User: alanderex Deep Learning for Fun and Profit [PyConDE 2018] /slideshow/deep-learning-for-fun-and-profit-pyconde-2018-122008671/122008671 deeplearningforfunandprofitpyconde2018-181105221856
There are all these great blog posts about Deep Learning describing all that awesome stuff. - Is it all that easy? Let's check! This is part 2 of on ongoing series of adventures in Deep Learning for fun, research and business. Alexander' professional career was always about digitalisation: starting from vinyl records in the nineties to to advanced data analytics nowadays. He's program chair of Europe's main Python conference EuroPython, one of the 25 mongoDB masters, organiser of PyConDE and a regular contributor to the tech community. He has spoken at many international conferences in Silicon Valley, New York, London, Florence or Paris. He's a partner at Königsweg (http://koenigsweg.com) consultancy for digitalisation, high-tech and data science where he consults enterprises on data matters and trains individuals in Python and AI. This talk covers style transfer (making a picture look like painting), speech generation (like Siri or Alexa) and text generation (writing a story). In this talk describes the whole journey: A fun ride from the idea to the very end including all the struggles, failures and successes. Steps covered: - The data challenge: get the data ready - Have it run on your Mac with PyTorch and an eGPU - Creating a character-level language models with an Recurrent Neural Network - Creating a text generator - Creating artwork - Data challenges and solutions in the non English NLP space]]>
There are all these great blog posts about Deep Learning describing all that awesome stuff. - Is it all that easy? Let's check! This is part 2 of on ongoing series of adventures in Deep Learning for fun, research and business. Alexander' professional career was always about digitalisation: starting from vinyl records in the nineties to to advanced data analytics nowadays. He's program chair of Europe's main Python conference EuroPython, one of the 25 mongoDB masters, organiser of PyConDE and a regular contributor to the tech community. He has spoken at many international conferences in Silicon Valley, New York, London, Florence or Paris. He's a partner at Königsweg (http://koenigsweg.com) consultancy for digitalisation, high-tech and data science where he consults enterprises on data matters and trains individuals in Python and AI. This talk covers style transfer (making a picture look like painting), speech generation (like Siri or Alexa) and text generation (writing a story). In this talk describes the whole journey: A fun ride from the idea to the very end including all the struggles, failures and successes. Steps covered: - The data challenge: get the data ready - Have it run on your Mac with PyTorch and an eGPU - Creating a character-level language models with an Recurrent Neural Network - Creating a text generator - Creating artwork - Data challenges and solutions in the non English NLP space]]> Mon, 05 Nov 2018 22:18:56 GMT /slideshow/deep-learning-for-fun-and-profit-pyconde-2018-122008671/122008671 alanderex@slideshare.net(alanderex) Deep Learning for Fun and Profit [PyConDE 2018] alanderex There are all these great blog posts about Deep Learning describing all that awesome stuff. - Is it all that easy? Let's check! This is part 2 of on ongoing series of adventures in Deep Learning for fun, research and business. Alexander' professional career was always about digitalisation: starting from vinyl records in the nineties to to advanced data analytics nowadays. He's program chair of Europe's main Python conference EuroPython, one of the 25 mongoDB masters, organiser of PyConDE and a regular contributor to the tech community. He has spoken at many international conferences in Silicon Valley, New York, London, Florence or Paris. He's a partner at Königsweg (http://koenigsweg.com) consultancy for digitalisation, high-tech and data science where he consults enterprises on data matters and trains individuals in Python and AI. This talk covers style transfer (making a picture look like painting), speech generation (like Siri or Alexa) and text generation (writing a story). In this talk describes the whole journey: A fun ride from the idea to the very end including all the struggles, failures and successes. Steps covered: - The data challenge: get the data ready - Have it run on your Mac with PyTorch and an eGPU - Creating a character-level language models with an Recurrent Neural Network - Creating a text generator - Creating artwork - Data challenges and solutions in the non English NLP space <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/deeplearningforfunandprofitpyconde2018-181105221856-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> There are all these great blog posts about Deep Learning describing all that awesome stuff. - Is it all that easy? Let&#39;s check! This is part 2 of on ongoing series of adventures in Deep Learning for fun, research and business. Alexander&#39; professional career was always about digitalisation: starting from vinyl records in the nineties to to advanced data analytics nowadays. He&#39;s program chair of Europe&#39;s main Python conference EuroPython, one of the 25 mongoDB masters, organiser of PyConDE and a regular contributor to the tech community. He has spoken at many international conferences in Silicon Valley, New York, London, Florence or Paris. He&#39;s a partner at Königsweg (http://koenigsweg.com) consultancy for digitalisation, high-tech and data science where he consults enterprises on data matters and trains individuals in Python and AI. This talk covers style transfer (making a picture look like painting), speech generation (like Siri or Alexa) and text generation (writing a story). In this talk describes the whole journey: A fun ride from the idea to the very end including all the struggles, failures and successes. Steps covered: - The data challenge: get the data ready - Have it run on your Mac with PyTorch and an eGPU - Creating a character-level language models with an Recurrent Neural Network - Creating a text generator - Creating artwork - Data challenges and solutions in the non English NLP space
Deep Learning for Fun and Profit [PyConDE 2018] from Alexander Hendorf
]]> 188 4 https://cdn.slidesharecdn.com/ss_thumbnails/deeplearningforfunandprofitpyconde2018-181105221856-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Databases for Data Science /slideshow/databases-for-data-science/94600793 pycon-nove-databases-for-data-science-180421160121
Databases have been around for decades and were highly optimised for data aggregations during that time. Not only Big data has changed the landscape of databases massively in the past years - we nowadays can find many Open Source projects among the most popular dbs. After this talk you will be enabled to decide if a database can make your work more efficient and which direction to look to. ]]>
Databases have been around for decades and were highly optimised for data aggregations during that time. Not only Big data has changed the landscape of databases massively in the past years - we nowadays can find many Open Source projects among the most popular dbs. After this talk you will be enabled to decide if a database can make your work more efficient and which direction to look to. ]]> Sat, 21 Apr 2018 16:01:21 GMT /slideshow/databases-for-data-science/94600793 alanderex@slideshare.net(alanderex) Databases for Data Science alanderex Databases have been around for decades and were highly optimised for data aggregations during that time. Not only Big data has changed the landscape of databases massively in the past years - we nowadays can find many Open Source projects among the most popular dbs. After this talk you will be enabled to decide if a database can make your work more efficient and which direction to look to. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/pycon-nove-databases-for-data-science-180421160121-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Databases have been around for decades and were highly optimised for data aggregations during that time. Not only Big data has changed the landscape of databases massively in the past years - we nowadays can find many Open Source projects among the most popular dbs. After this talk you will be enabled to decide if a database can make your work more efficient and which direction to look to.
Databases for Data Science from Alexander Hendorf
]]> 325 4 https://cdn.slidesharecdn.com/ss_thumbnails/pycon-nove-databases-for-data-science-180421160121-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Agile Datenanalsyse - der schnelle Weg zum Mehrwert https://de.slideshare.net/slideshow/agile-datenanalsyse-der-schnelle-weg-zum-mehrwert/77033116 agiledatenanalsyse-derschnellewegzummehrwert-170617164653
Vortrag ZEW, Zentrum für Europäische Wirtschaftsforschung, Mannheim Fast alles und jeder – vom Menschen bis zur Maschine, vom Industriesensor bis zur Armbanduhr – erzeugt immer größere Mengen an Daten, Messergebnissen, Nachrichten und Informationen. Der Vortrag zeigt Wege auf, diese Datenflut zu ordnen und sinnvoll ohne große Erstinvestitionen in Big Data-Lösungen zu nut- zen. Es werden Lösungen präsentiert, wie man von Datentümpeln zu Data-La- kes und damit stabilen Prognosen, z.B. für die Warennachfrage, kommt.]]>
Vortrag ZEW, Zentrum für Europäische Wirtschaftsforschung, Mannheim Fast alles und jeder – vom Menschen bis zur Maschine, vom Industriesensor bis zur Armbanduhr – erzeugt immer größere Mengen an Daten, Messergebnissen, Nachrichten und Informationen. Der Vortrag zeigt Wege auf, diese Datenflut zu ordnen und sinnvoll ohne große Erstinvestitionen in Big Data-Lösungen zu nut- zen. Es werden Lösungen präsentiert, wie man von Datentümpeln zu Data-La- kes und damit stabilen Prognosen, z.B. für die Warennachfrage, kommt.]]> Sat, 17 Jun 2017 16:46:53 GMT https://de.slideshare.net/slideshow/agile-datenanalsyse-der-schnelle-weg-zum-mehrwert/77033116 alanderex@slideshare.net(alanderex) Agile Datenanalsyse - der schnelle Weg zum Mehrwert alanderex Vortrag ZEW, Zentrum für Europäische Wirtschaftsforschung, Mannheim Fast alles und jeder – vom Menschen bis zur Maschine, vom Industriesensor bis zur Armbanduhr – erzeugt immer größere Mengen an Daten, Messergebnissen, Nachrichten und Informationen. Der Vortrag zeigt Wege auf, diese Datenflut zu ordnen und sinnvoll ohne große Erstinvestitionen in Big Data-Lösungen zu nut- zen. Es werden Lösungen präsentiert, wie man von Datentümpeln zu Data-La- kes und damit stabilen Prognosen, z.B. für die Warennachfrage, kommt. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/agiledatenanalsyse-derschnellewegzummehrwert-170617164653-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Vortrag ZEW, Zentrum für Europäische Wirtschaftsforschung, Mannheim Fast alles und jeder – vom Menschen bis zur Maschine, vom Industriesensor bis zur Armbanduhr – erzeugt immer größere Mengen an Daten, Messergebnissen, Nachrichten und Informationen. Der Vortrag zeigt Wege auf, diese Datenflut zu ordnen und sinnvoll ohne große Erstinvestitionen in Big Data-Lösungen zu nut- zen. Es werden Lösungen präsentiert, wie man von Datentümpeln zu Data-La- kes und damit stabilen Prognosen, z.B. für die Warennachfrage, kommt.
from Alexander Hendorf
]]> 209 8 https://cdn.slidesharecdn.com/ss_thumbnails/agiledatenanalsyse-derschnellewegzummehrwert-170617164653-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Einführung Datenanalyse mit Pandas [data2day] https://de.slideshare.net/slideshow/einfhrung-datenanalyse-mit-pandas-data2day/77033022 einfuhrungdatenanalysemitpandas-170617164037
Die Open-Source-Bibliothek Pandas ist das Schweizer Taschenmesser im Bereich Datenanalyse in Python ohne die Performance Nachteile interpretierter Sprachen. Sie punktet mit: * Hochperformance-Verarbeitungen großer Datenmengen dank Numpy, * Verarbeitung gängiger Datenformate (CSV, Excel, HDF, SQL, JSON, HTML ...), und * direktem Zugriff auf Visualisierung, Aggregationen und Statistikfunktionen. Der Talk gibt eine Einführung in Pandas insbesondere mit Blick auf DataSeries, DataFrames, Zeitreihenanalyse und zeigt anhand von Beispielen, wie man effizient und schnell mit Pandas tiefen Einblick in seine Daten bekommen kann.]]>
Die Open-Source-Bibliothek Pandas ist das Schweizer Taschenmesser im Bereich Datenanalyse in Python ohne die Performance Nachteile interpretierter Sprachen. Sie punktet mit: * Hochperformance-Verarbeitungen großer Datenmengen dank Numpy, * Verarbeitung gängiger Datenformate (CSV, Excel, HDF, SQL, JSON, HTML ...), und * direktem Zugriff auf Visualisierung, Aggregationen und Statistikfunktionen. Der Talk gibt eine Einführung in Pandas insbesondere mit Blick auf DataSeries, DataFrames, Zeitreihenanalyse und zeigt anhand von Beispielen, wie man effizient und schnell mit Pandas tiefen Einblick in seine Daten bekommen kann.]]> Sat, 17 Jun 2017 16:40:37 GMT https://de.slideshare.net/slideshow/einfhrung-datenanalyse-mit-pandas-data2day/77033022 alanderex@slideshare.net(alanderex) Einführung Datenanalyse mit Pandas [data2day] alanderex Die Open-Source-Bibliothek Pandas ist das Schweizer Taschenmesser im Bereich Datenanalyse in Python ohne die Performance Nachteile interpretierter Sprachen. Sie punktet mit: * Hochperformance-Verarbeitungen großer Datenmengen dank Numpy, * Verarbeitung gängiger Datenformate (CSV, Excel, HDF, SQL, JSON, HTML ...), und * direktem Zugriff auf Visualisierung, Aggregationen und Statistikfunktionen. Der Talk gibt eine Einführung in Pandas insbesondere mit Blick auf DataSeries, DataFrames, Zeitreihenanalyse und zeigt anhand von Beispielen, wie man effizient und schnell mit Pandas tiefen Einblick in seine Daten bekommen kann. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/einfuhrungdatenanalysemitpandas-170617164037-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Die Open-Source-Bibliothek Pandas ist das Schweizer Taschenmesser im Bereich Datenanalyse in Python ohne die Performance Nachteile interpretierter Sprachen. Sie punktet mit: * Hochperformance-Verarbeitungen großer Datenmengen dank Numpy, * Verarbeitung gängiger Datenformate (CSV, Excel, HDF, SQL, JSON, HTML ...), und * direktem Zugriff auf Visualisierung, Aggregationen und Statistikfunktionen. Der Talk gibt eine Einführung in Pandas insbesondere mit Blick auf DataSeries, DataFrames, Zeitreihenanalyse und zeigt anhand von Beispielen, wie man effizient und schnell mit Pandas tiefen Einblick in seine Daten bekommen kann.
from Alexander Hendorf
]]> 1186 12 https://cdn.slidesharecdn.com/ss_thumbnails/einfuhrungdatenanalysemitpandas-170617164037-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Introduction to Pandas and Time Series Analysis [Budapest BI Forum] /alanderex/introduction-to-pandas-and-time-series-analysis-budapest-bi-forum introductiontopandasandtimeseriesanalysis-170617163829
As senior consultant of German management consultancy Königsweg, Alexander is guiding enterprises and institutions through change processes of digitalisation and automation. Alexander always loved data almost as much as music and so no wonder he’s organiser of local meet ups and one of the 25 mongoDB Community Masters. He loves to share this expertise and engages in the global community as organiser and program chair of the EuroPython conference, speaker and trainer at multiple international conferences as mongoDB World, EuroPython, Cebit or PyData.]]>
As senior consultant of German management consultancy Königsweg, Alexander is guiding enterprises and institutions through change processes of digitalisation and automation. Alexander always loved data almost as much as music and so no wonder he’s organiser of local meet ups and one of the 25 mongoDB Community Masters. He loves to share this expertise and engages in the global community as organiser and program chair of the EuroPython conference, speaker and trainer at multiple international conferences as mongoDB World, EuroPython, Cebit or PyData.]]> Sat, 17 Jun 2017 16:38:29 GMT /alanderex/introduction-to-pandas-and-time-series-analysis-budapest-bi-forum alanderex@slideshare.net(alanderex) Introduction to Pandas and Time Series Analysis [Budapest BI Forum] alanderex As senior consultant of German management consultancy Königsweg, Alexander is guiding enterprises and institutions through change processes of digitalisation and automation. Alexander always loved data almost as much as music and so no wonder he’s organiser of local meet ups and one of the 25 mongoDB Community Masters. He loves to share this expertise and engages in the global community as organiser and program chair of the EuroPython conference, speaker and trainer at multiple international conferences as mongoDB World, EuroPython, Cebit or PyData. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/introductiontopandasandtimeseriesanalysis-170617163829-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> As senior consultant of German management consultancy Königsweg, Alexander is guiding enterprises and institutions through change processes of digitalisation and automation. Alexander always loved data almost as much as music and so no wonder he’s organiser of local meet ups and one of the 25 mongoDB Community Masters. He loves to share this expertise and engages in the global community as organiser and program chair of the EuroPython conference, speaker and trainer at multiple international conferences as mongoDB World, EuroPython, Cebit or PyData.
Introduction to Pandas and Time Series Analysis [Budapest BI Forum] from Alexander Hendorf
]]> 291 3 https://cdn.slidesharecdn.com/ss_thumbnails/introductiontopandasandtimeseriesanalysis-170617163829-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Introduction to Pandas and Time Series Analysis [PyCon DE] /slideshow/introduction-to-pandas-and-time-series-analysis-pycon-de/77032981 introductiontopandasandtimeseriesanalysispyconde-170617163724
Most data is allocated to a period or to some point in time. We can gain a lot of insight by analyzing what happened when. The better the quality and accuracy of our data, the better our predictions can become. Unfortunately the data we have to deal with is often aggregated for example on a monthly basis, but not all months are the same, they may have 28 days, 31 days, have four or five weekends,…. It’s made fit to our calendar that was made fit to deal with the earth surrounding the sun, not to please Data Scientists. Dealing with periodical data can be a challenge. This talk will show to how you can deal with it with Pandas.]]>
Most data is allocated to a period or to some point in time. We can gain a lot of insight by analyzing what happened when. The better the quality and accuracy of our data, the better our predictions can become. Unfortunately the data we have to deal with is often aggregated for example on a monthly basis, but not all months are the same, they may have 28 days, 31 days, have four or five weekends,…. It’s made fit to our calendar that was made fit to deal with the earth surrounding the sun, not to please Data Scientists. Dealing with periodical data can be a challenge. This talk will show to how you can deal with it with Pandas.]]> Sat, 17 Jun 2017 16:37:24 GMT /slideshow/introduction-to-pandas-and-time-series-analysis-pycon-de/77032981 alanderex@slideshare.net(alanderex) Introduction to Pandas and Time Series Analysis [PyCon DE] alanderex Most data is allocated to a period or to some point in time. We can gain a lot of insight by analyzing what happened when. The better the quality and accuracy of our data, the better our predictions can become. Unfortunately the data we have to deal with is often aggregated for example on a monthly basis, but not all months are the same, they may have 28 days, 31 days, have four or five weekends,…. It’s made fit to our calendar that was made fit to deal with the earth surrounding the sun, not to please Data Scientists. Dealing with periodical data can be a challenge. This talk will show to how you can deal with it with Pandas. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/introductiontopandasandtimeseriesanalysispyconde-170617163724-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Most data is allocated to a period or to some point in time. We can gain a lot of insight by analyzing what happened when. The better the quality and accuracy of our data, the better our predictions can become. Unfortunately the data we have to deal with is often aggregated for example on a monthly basis, but not all months are the same, they may have 28 days, 31 days, have four or five weekends,…. It’s made fit to our calendar that was made fit to deal with the earth surrounding the sun, not to please Data Scientists. Dealing with periodical data can be a challenge. This talk will show to how you can deal with it with Pandas.
Introduction to Pandas and Time Series Analysis [PyCon DE] from Alexander Hendorf
]]> 1241 6 https://cdn.slidesharecdn.com/ss_thumbnails/introductiontopandasandtimeseriesanalysispyconde-170617163724-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Data Mangling with mongoDB the Right Way [PyData London] 2016] /slideshow/data-mangling-with-mongodb-the-right-way-pydata-london-2016/77032952 datamanglingwithmongodbtherightwaypydatalondon2016-170617163600
Walk though the mongoDB aggregation framework.]]>
Walk though the mongoDB aggregation framework.]]> Sat, 17 Jun 2017 16:36:00 GMT /slideshow/data-mangling-with-mongodb-the-right-way-pydata-london-2016/77032952 alanderex@slideshare.net(alanderex) Data Mangling with mongoDB the Right Way [PyData London] 2016] alanderex Walk though the mongoDB aggregation framework. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/datamanglingwithmongodbtherightwaypydatalondon2016-170617163600-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Walk though the mongoDB aggregation framework.
Data Mangling with mongoDB the Right Way [PyData London] 2016] from Alexander Hendorf
]]> 201 4 https://cdn.slidesharecdn.com/ss_thumbnails/datamanglingwithmongodbtherightwaypydatalondon2016-170617163600-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Introduction to Data Analtics with Pandas [PyCon Cz] /slideshow/introduction-to-data-analtics-with-pandas-pycon-cz/77032928 introductiontodataanalticswithpandaspyconcz-170617163446
Pandas is the Swiss-Multipurpose Knife for Data Analysis in Python. With Pandas dealing with data-analysis is easy and simple but there are some things you need to get your head around first as Data-Frames and Data-Series. The talk with provide an introduction to Pandas for beginners and cover reading and writing data across multiple formats (CSV, Excel, JSON, SQL, HTML,…) statistical data analysis and aggregation. work with built-in data visualisation inner-mechanics of Pandas: Data-Frames, Data-Series & Numpy. how to work effectively with Pandas.]]>
Pandas is the Swiss-Multipurpose Knife for Data Analysis in Python. With Pandas dealing with data-analysis is easy and simple but there are some things you need to get your head around first as Data-Frames and Data-Series. The talk with provide an introduction to Pandas for beginners and cover reading and writing data across multiple formats (CSV, Excel, JSON, SQL, HTML,…) statistical data analysis and aggregation. work with built-in data visualisation inner-mechanics of Pandas: Data-Frames, Data-Series & Numpy. how to work effectively with Pandas.]]> Sat, 17 Jun 2017 16:34:46 GMT /slideshow/introduction-to-data-analtics-with-pandas-pycon-cz/77032928 alanderex@slideshare.net(alanderex) Introduction to Data Analtics with Pandas [PyCon Cz] alanderex Pandas is the Swiss-Multipurpose Knife for Data Analysis in Python. With Pandas dealing with data-analysis is easy and simple but there are some things you need to get your head around first as Data-Frames and Data-Series. The talk with provide an introduction to Pandas for beginners and cover reading and writing data across multiple formats (CSV, Excel, JSON, SQL, HTML,…) statistical data analysis and aggregation. work with built-in data visualisation inner-mechanics of Pandas: Data-Frames, Data-Series & Numpy. how to work effectively with Pandas. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/introductiontodataanalticswithpandaspyconcz-170617163446-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Pandas is the Swiss-Multipurpose Knife for Data Analysis in Python. With Pandas dealing with data-analysis is easy and simple but there are some things you need to get your head around first as Data-Frames and Data-Series. The talk with provide an introduction to Pandas for beginners and cover reading and writing data across multiple formats (CSV, Excel, JSON, SQL, HTML,…) statistical data analysis and aggregation. work with built-in data visualisation inner-mechanics of Pandas: Data-Frames, Data-Series &amp; Numpy. how to work effectively with Pandas.
Introduction to Data Analtics with Pandas [PyCon Cz] from Alexander Hendorf
]]> 262 4 https://cdn.slidesharecdn.com/ss_thumbnails/introductiontodataanalticswithpandaspyconcz-170617163446-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 NoSQL oder: Freiheit ist nicht schmerzfrei - IT Tage /slideshow/nosql-oder-freiheit-ist-nicht-schmerzfrei-it-tage/77032902 nosqloderfreiheitistnichtschmerzfreiittageffm4-3-170617163259
Der Vortrag zeigt, dass bei NoSQL auch nicht alles ganz einfach ist und genauso harte Entscheidungen getroffen werden müssen wie bei RDBMS. Anhand eines echten Use Cases werden wir die Unterschiede, Vor- und Nachteile von NoSQL am Beispiel von MongoDB beleuchten.]]>
Der Vortrag zeigt, dass bei NoSQL auch nicht alles ganz einfach ist und genauso harte Entscheidungen getroffen werden müssen wie bei RDBMS. Anhand eines echten Use Cases werden wir die Unterschiede, Vor- und Nachteile von NoSQL am Beispiel von MongoDB beleuchten.]]> Sat, 17 Jun 2017 16:32:59 GMT /slideshow/nosql-oder-freiheit-ist-nicht-schmerzfrei-it-tage/77032902 alanderex@slideshare.net(alanderex) NoSQL oder: Freiheit ist nicht schmerzfrei - IT Tage alanderex Der Vortrag zeigt, dass bei NoSQL auch nicht alles ganz einfach ist und genauso harte Entscheidungen getroffen werden müssen wie bei RDBMS. Anhand eines echten Use Cases werden wir die Unterschiede, Vor- und Nachteile von NoSQL am Beispiel von MongoDB beleuchten. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/nosqloderfreiheitistnichtschmerzfreiittageffm4-3-170617163259-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Der Vortrag zeigt, dass bei NoSQL auch nicht alles ganz einfach ist und genauso harte Entscheidungen getroffen werden müssen wie bei RDBMS. Anhand eines echten Use Cases werden wir die Unterschiede, Vor- und Nachteile von NoSQL am Beispiel von MongoDB beleuchten.
NoSQL oder: Freiheit ist nicht schmerzfrei - IT Tage from Alexander Hendorf
]]> 132 4 https://cdn.slidesharecdn.com/ss_thumbnails/nosqloderfreiheitistnichtschmerzfreiittageffm4-3-170617163259-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Neat Analytics with Pandas 4 3 [PyParis] /slideshow/neat-analytics-with-pandas-4-3-pyparis/77032861 neatanalyticswithpandas43pyparis-170617163008
Pandas is the Swiss-Multipurpose Knife for Data Analysis in Python. In this talk we will look deeper into how to gain productivity utilising Pandas powerful indexing and make advanced analytics a piece of cake. ]]>
Pandas is the Swiss-Multipurpose Knife for Data Analysis in Python. In this talk we will look deeper into how to gain productivity utilising Pandas powerful indexing and make advanced analytics a piece of cake. ]]> Sat, 17 Jun 2017 16:30:08 GMT /slideshow/neat-analytics-with-pandas-4-3-pyparis/77032861 alanderex@slideshare.net(alanderex) Neat Analytics with Pandas 4 3 [PyParis] alanderex Pandas is the Swiss-Multipurpose Knife for Data Analysis in Python. In this talk we will look deeper into how to gain productivity utilising Pandas powerful indexing and make advanced analytics a piece of cake. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/neatanalyticswithpandas43pyparis-170617163008-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Pandas is the Swiss-Multipurpose Knife for Data Analysis in Python. In this talk we will look deeper into how to gain productivity utilising Pandas powerful indexing and make advanced analytics a piece of cake.
Neat Analytics with Pandas 4 3 [PyParis] from Alexander Hendorf
]]> 106 2 https://cdn.slidesharecdn.com/ss_thumbnails/neatanalyticswithpandas43pyparis-170617163008-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Data analysis and visualization with mongo db [mongodb world 2016] /slideshow/data-analysis-and-visualization-with-mongo-db-mongodb-world-2016/63995387 dataanalysisandvisualizationwithmongodbmongodbworld2016-160713161849
MongoDB World, New York, June 29th This talk will feature why MongoDB was the right choice and how one can visualize data via the MongoDB Connector for BI (e.g. with Tableau or open source libraries like bokeh) straight from MongoDB. We'll be building an application that offers real-time insights for the music industry.]]>
MongoDB World, New York, June 29th This talk will feature why MongoDB was the right choice and how one can visualize data via the MongoDB Connector for BI (e.g. with Tableau or open source libraries like bokeh) straight from MongoDB. We'll be building an application that offers real-time insights for the music industry.]]> Wed, 13 Jul 2016 16:18:49 GMT /slideshow/data-analysis-and-visualization-with-mongo-db-mongodb-world-2016/63995387 alanderex@slideshare.net(alanderex) Data analysis and visualization with mongo db [mongodb world 2016] alanderex MongoDB World, New York, June 29th This talk will feature why MongoDB was the right choice and how one can visualize data via the MongoDB Connector for BI (e.g. with Tableau or open source libraries like bokeh) straight from MongoDB. We'll be building an application that offers real-time insights for the music industry. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/dataanalysisandvisualizationwithmongodbmongodbworld2016-160713161849-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> MongoDB World, New York, June 29th This talk will feature why MongoDB was the right choice and how one can visualize data via the MongoDB Connector for BI (e.g. with Tableau or open source libraries like bokeh) straight from MongoDB. We&#39;ll be building an application that offers real-time insights for the music industry.
Data analysis and visualization with mongo db [mongodb world 2016] from Alexander Hendorf
]]> 531 3 https://cdn.slidesharecdn.com/ss_thumbnails/dataanalysisandvisualizationwithmongodbmongodbworld2016-160713161849-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Time travel and time series analysis with pandas + statsmodels /slideshow/time-travel-and-time-series-analysis-with-pandas-statsmodels/61156000 timetravelandtimeseriesanalysiswithpandasstatsmodels-160420171341
Most data is allocated to a period or to some point in time. We can gain a lot of insight by analysing what happened when. The better the quality and accuracy of our data, the better our predictions can become. Unfortunately the data we have to deal with is often aggregated for example on a monthly basis, but not all months are the same, they may have 28 days, 31 days, have four or five weekends,… It’s made fit to our calendar that was made fit to deal with the earth surrounding the sun, not to please Data Scientists. Dealing with periodical data can be a challenge. Pandas is a powerful framework for working with time series data and can make your life a lot easier. This talks will feature: how to analyse periodical data with pandas read and write data in various formats how to mangle, reshape and pivot gain insights with statsmodels (e.g. seasonality) caveats when working with timed data visualize your data on the fly]]>
Most data is allocated to a period or to some point in time. We can gain a lot of insight by analysing what happened when. The better the quality and accuracy of our data, the better our predictions can become. Unfortunately the data we have to deal with is often aggregated for example on a monthly basis, but not all months are the same, they may have 28 days, 31 days, have four or five weekends,… It’s made fit to our calendar that was made fit to deal with the earth surrounding the sun, not to please Data Scientists. Dealing with periodical data can be a challenge. Pandas is a powerful framework for working with time series data and can make your life a lot easier. This talks will feature: how to analyse periodical data with pandas read and write data in various formats how to mangle, reshape and pivot gain insights with statsmodels (e.g. seasonality) caveats when working with timed data visualize your data on the fly]]> Wed, 20 Apr 2016 17:13:40 GMT /slideshow/time-travel-and-time-series-analysis-with-pandas-statsmodels/61156000 alanderex@slideshare.net(alanderex) Time travel and time series analysis with pandas + statsmodels alanderex Most data is allocated to a period or to some point in time. We can gain a lot of insight by analysing what happened when. The better the quality and accuracy of our data, the better our predictions can become. Unfortunately the data we have to deal with is often aggregated for example on a monthly basis, but not all months are the same, they may have 28 days, 31 days, have four or five weekends,… It’s made fit to our calendar that was made fit to deal with the earth surrounding the sun, not to please Data Scientists. Dealing with periodical data can be a challenge. Pandas is a powerful framework for working with time series data and can make your life a lot easier. This talks will feature: how to analyse periodical data with pandas read and write data in various formats how to mangle, reshape and pivot gain insights with statsmodels (e.g. seasonality) caveats when working with timed data visualize your data on the fly <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/timetravelandtimeseriesanalysiswithpandasstatsmodels-160420171341-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Most data is allocated to a period or to some point in time. We can gain a lot of insight by analysing what happened when. The better the quality and accuracy of our data, the better our predictions can become. Unfortunately the data we have to deal with is often aggregated for example on a monthly basis, but not all months are the same, they may have 28 days, 31 days, have four or five weekends,… It’s made fit to our calendar that was made fit to deal with the earth surrounding the sun, not to please Data Scientists. Dealing with periodical data can be a challenge. Pandas is a powerful framework for working with time series data and can make your life a lot easier. This talks will feature: how to analyse periodical data with pandas read and write data in various formats how to mangle, reshape and pivot gain insights with statsmodels (e.g. seasonality) caveats when working with timed data visualize your data on the fly
Time travel and time series analysis with pandas + statsmodels from Alexander Hendorf
]]> 728 5 https://cdn.slidesharecdn.com/ss_thumbnails/timetravelandtimeseriesanalysiswithpandasstatsmodels-160420171341-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Data mangling with mongo db the right way [pyconit 2016] /alanderex/data-mangling-with-mongo-db-the-right-way-pyconit-2016 datamanglingwithmongodbtherightwaypyconit2016-160420171121
MongoDB provides MapReduce and an own aggregation framework. Many new users tend to jump straight to MapReduce. While MapReduce is powerful, it is often more difficult than necessary for most common aggregation tasks and using it is expensive. This talk showcases how to use MapReduce and the built-in data-aggregation-pipelines for averages, summation, grouping, reshaping. One will learn how to work with documents, sub-documents, grouping by year, month, day and the new relational features recently added to MongoDB. See how to boost the performance of the aggregations by factor 8+ while working on a single machine. The talk includes many live examples with the iPython/Yupiter notebook and is designed to be an entertaining and engaging. After this talks you’ll know when to use MapReduce or the aggregation framework and how to get the most out of your data, efficently.]]>
MongoDB provides MapReduce and an own aggregation framework. Many new users tend to jump straight to MapReduce. While MapReduce is powerful, it is often more difficult than necessary for most common aggregation tasks and using it is expensive. This talk showcases how to use MapReduce and the built-in data-aggregation-pipelines for averages, summation, grouping, reshaping. One will learn how to work with documents, sub-documents, grouping by year, month, day and the new relational features recently added to MongoDB. See how to boost the performance of the aggregations by factor 8+ while working on a single machine. The talk includes many live examples with the iPython/Yupiter notebook and is designed to be an entertaining and engaging. After this talks you’ll know when to use MapReduce or the aggregation framework and how to get the most out of your data, efficently.]]> Wed, 20 Apr 2016 17:11:21 GMT /alanderex/data-mangling-with-mongo-db-the-right-way-pyconit-2016 alanderex@slideshare.net(alanderex) Data mangling with mongo db the right way [pyconit 2016] alanderex MongoDB provides MapReduce and an own aggregation framework. Many new users tend to jump straight to MapReduce. While MapReduce is powerful, it is often more difficult than necessary for most common aggregation tasks and using it is expensive. This talk showcases how to use MapReduce and the built-in data-aggregation-pipelines for averages, summation, grouping, reshaping. One will learn how to work with documents, sub-documents, grouping by year, month, day and the new relational features recently added to MongoDB. See how to boost the performance of the aggregations by factor 8+ while working on a single machine. The talk includes many live examples with the iPython/Yupiter notebook and is designed to be an entertaining and engaging. After this talks you’ll know when to use MapReduce or the aggregation framework and how to get the most out of your data, efficently. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/datamanglingwithmongodbtherightwaypyconit2016-160420171121-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> MongoDB provides MapReduce and an own aggregation framework. Many new users tend to jump straight to MapReduce. While MapReduce is powerful, it is often more difficult than necessary for most common aggregation tasks and using it is expensive. This talk showcases how to use MapReduce and the built-in data-aggregation-pipelines for averages, summation, grouping, reshaping. One will learn how to work with documents, sub-documents, grouping by year, month, day and the new relational features recently added to MongoDB. See how to boost the performance of the aggregations by factor 8+ while working on a single machine. The talk includes many live examples with the iPython/Yupiter notebook and is designed to be an entertaining and engaging. After this talks you’ll know when to use MapReduce or the aggregation framework and how to get the most out of your data, efficently.
Data mangling with mongo db the right way [pyconit 2016] from Alexander Hendorf
]]> 244 4 https://cdn.slidesharecdn.com/ss_thumbnails/datamanglingwithmongodbtherightwaypyconit2016-160420171121-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-alanderex-48x48.jpg?cb=1732610358 koenigsweg.com https://cdn.slidesharecdn.com/ss_thumbnails/deeplearningforfunandprofitpyconde2018-181105221856-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/deep-learning-for-fun-and-profit-pyconde-2018-122008671/122008671 Deep Learning for Fun ... https://cdn.slidesharecdn.com/ss_thumbnails/pycon-nove-databases-for-data-science-180421160121-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/databases-for-data-science/94600793 Databases for Data Sci... https://cdn.slidesharecdn.com/ss_thumbnails/agiledatenanalsyse-derschnellewegzummehrwert-170617164653-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/agile-datenanalsyse-der-schnelle-weg-zum-mehrwert/77033116 Agile Datenanalsyse - ...