�ݺ�ߣshows by User: data-science-club

�ݺ�ߣshows by User: data-science-club / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: data-science-club / Mon, 12 Mar 2018 08:14:27 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: data-science-club How to present campaign results to your boss /slideshow/how-to-present-campaign-results-to-your-boss/90364655 dsclubinstareafinal2-180312081427
Combined graphs with multiple axes are great sources of data, but they are readable only by the author. Have you tried to explain this kind of graph to your grandma? Martin Bago, Data Scientist from Instarea, will come to explain more to you about data presentation and interpretation in the easiest way. He will show you positive and negative examples, as well as available tools/libraries for creation of readable dashboards. These facts can help you simplify each measurable event.]]>
Combined graphs with multiple axes are great sources of data, but they are readable only by the author. Have you tried to explain this kind of graph to your grandma? Martin Bago, Data Scientist from Instarea, will come to explain more to you about data presentation and interpretation in the easiest way. He will show you positive and negative examples, as well as available tools/libraries for creation of readable dashboards. These facts can help you simplify each measurable event.]]> Mon, 12 Mar 2018 08:14:27 GMT /slideshow/how-to-present-campaign-results-to-your-boss/90364655 data-science-club@slideshare.net(data-science-club) How to present campaign results to your boss data-science-club Combined graphs with multiple axes are great sources of data, but they are readable only by the author. Have you tried to explain this kind of graph to your grandma? Martin Bago, Data Scientist from Instarea, will come to explain more to you about data presentation and interpretation in the easiest way. He will show you positive and negative examples, as well as available tools/libraries for creation of readable dashboards. These facts can help you simplify each measurable event. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/dsclubinstareafinal2-180312081427-thumbnail.jpg?width=120&height=120&fit=bounds" /> Combined graphs with multiple axes are great sources of data, but they are readable only by the author. Have you tried to explain this kind of graph to your grandma? Martin Bago, Data Scientist from Instarea, will come to explain more to you about data presentation and interpretation in the easiest way. He will show you positive and negative examples, as well as available tools/libraries for creation of readable dashboards. These facts can help you simplify each measurable event.

How to present campaign results to your boss from Data Science Club

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0 Principles of Big Data Analytics Visualization /slideshow/principles-of-big-data-analytics-visualization/90364487 data-science-mnovotny-180312081238
Big Data was a problem for data visualization, much sooner than Big data became a buzzword. There are a lot of interesting techniques of how to understand big data. You can discover the problems of data visualization and how to solve them with data aggregation, visual abstraction, innovative geometrics and render technique. We will show you the recent and future challenges in visual analytics. And, we will answer the question “if AI replaces human analysts.”]]>
Big Data was a problem for data visualization, much sooner than Big data became a buzzword. There are a lot of interesting techniques of how to understand big data. You can discover the problems of data visualization and how to solve them with data aggregation, visual abstraction, innovative geometrics and render technique. We will show you the recent and future challenges in visual analytics. And, we will answer the question “if AI replaces human analysts.”]]> Mon, 12 Mar 2018 08:12:38 GMT /slideshow/principles-of-big-data-analytics-visualization/90364487 data-science-club@slideshare.net(data-science-club) Principles of Big Data Analytics Visualization data-science-club Big Data was a problem for data visualization, much sooner than Big data became a buzzword. There are a lot of interesting techniques of how to understand big data. You can discover the problems of data visualization and how to solve them with data aggregation, visual abstraction, innovative geometrics and render technique. We will show you the recent and future challenges in visual analytics. And, we will answer the question “if AI replaces human analysts.” <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/data-science-mnovotny-180312081238-thumbnail.jpg?width=120&height=120&fit=bounds" /> Big Data was a problem for data visualization, much sooner than Big data became a buzzword. There are a lot of interesting techniques of how to understand big data. You can discover the problems of data visualization and how to solve them with data aggregation, visual abstraction, innovative geometrics and render technique. We will show you the recent and future challenges in visual analytics. And, we will answer the question “if AI replaces human analysts.”

Principles of Big Data Analytics Visualization from Data Science Club

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0 Batch (Spark) and Streaming (Kafka) Data-Preprocessing /slideshow/batch-spark-and-streaming-kafka-datapreprocessing/88383925 datascienceclub172f18summer-dataintegration-180220135538
In this talk we will dive deep into data pre-processing or data preparation part of Data Scientist work. Why data pre-processing is such an important topic to pay attention for aspiring Data Scientists / Machine Learning Engineers? How to process TBs of static and moving, aka streaming, schemaless data? How to ensure horizontal scalability for up to PBs when you expect such growth? We'll give you several insights how Apache Spark, a fast and general engine for large-scale data processing, and Apache Kafka helped us to deal with 80% of Data Scientists’ work. Why do you need such high-caliber tools as Spark or Kafka, when is it viable to use them and how to avoid such tools? What are the pitfalls of distributed processing using Spark and Kafka? How can Google Cloud Platform help and save costs up to 90%? We'll share what we've learned along the (hard) way.]]>
In this talk we will dive deep into data pre-processing or data preparation part of Data Scientist work. Why data pre-processing is such an important topic to pay attention for aspiring Data Scientists / Machine Learning Engineers? How to process TBs of static and moving, aka streaming, schemaless data? How to ensure horizontal scalability for up to PBs when you expect such growth? We'll give you several insights how Apache Spark, a fast and general engine for large-scale data processing, and Apache Kafka helped us to deal with 80% of Data Scientists’ work. Why do you need such high-caliber tools as Spark or Kafka, when is it viable to use them and how to avoid such tools? What are the pitfalls of distributed processing using Spark and Kafka? How can Google Cloud Platform help and save costs up to 90%? We'll share what we've learned along the (hard) way.]]> Tue, 20 Feb 2018 13:55:38 GMT /slideshow/batch-spark-and-streaming-kafka-datapreprocessing/88383925 data-science-club@slideshare.net(data-science-club) Batch (Spark) and Streaming (Kafka) Data-Preprocessing data-science-club In this talk we will dive deep into data pre-processing or data preparation part of Data Scientist work. Why data pre-processing is such an important topic to pay attention for aspiring Data Scientists / Machine Learning Engineers? How to process TBs of static and moving, aka streaming, schemaless data? How to ensure horizontal scalability for up to PBs when you expect such growth? We'll give you several insights how Apache Spark, a fast and general engine for large-scale data processing, and Apache Kafka helped us to deal with 80% of Data Scientists’ work. Why do you need such high-caliber tools as Spark or Kafka, when is it viable to use them and how to avoid such tools? What are the pitfalls of distributed processing using Spark and Kafka? How can Google Cloud Platform help and save costs up to 90%? We'll share what we've learned along the (hard) way. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/datascienceclub172f18summer-dataintegration-180220135538-thumbnail.jpg?width=120&height=120&fit=bounds" /> In this talk we will dive deep into data pre-processing or data preparation part of Data Scientist work. Why data pre-processing is such an important topic to pay attention for aspiring Data Scientists / Machine Learning Engineers? How to process TBs of static and moving, aka streaming, schemaless data? How to ensure horizontal scalability for up to PBs when you expect such growth? We'll give you several insights how Apache Spark, a fast and general engine for large-scale data processing, and Apache Kafka helped us to deal with 80% of Data Scientists’ work. Why do you need such high-caliber tools as Spark or Kafka, when is it viable to use them and how to avoid such tools? What are the pitfalls of distributed processing using Spark and Kafka? How can Google Cloud Platform help and save costs up to 90%? We'll share what we've learned along the (hard) way.

Batch (Spark) and Streaming (Kafka) Data-Preprocessing from Data Science Club

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0 A Big (Query) Frog in a Small Pond, Jakub Motyl, BuffPanel /slideshow/a-big-query-frog-in-a-small-pond-jakub-motyl-buffpanel/88383615 talkabigqueryfroginasmallpond-datascienceclub1172f18-180220135201
How does one process 200GB of streaming raw data, daily? Where dedicated servers and home-made solutions fail, BigQuery comes out the victor. We will talk about the big data architecture with over 110 million players total on record, how we managed to implement it, and how is it possible that we keep daily operational costs under $50. In the beginning we will explain what kinds of data sources a top-selling game has to integrate and analyze and how to pre-process the data to avoid ramping up costs in disaster scenarios. Part of the talk is also dedicated to all the components that are involved in the many transformations the data undergoes and we will show you how the output from the entire pipeline looks.]]>
How does one process 200GB of streaming raw data, daily? Where dedicated servers and home-made solutions fail, BigQuery comes out the victor. We will talk about the big data architecture with over 110 million players total on record, how we managed to implement it, and how is it possible that we keep daily operational costs under $50. In the beginning we will explain what kinds of data sources a top-selling game has to integrate and analyze and how to pre-process the data to avoid ramping up costs in disaster scenarios. Part of the talk is also dedicated to all the components that are involved in the many transformations the data undergoes and we will show you how the output from the entire pipeline looks.]]> Tue, 20 Feb 2018 13:52:01 GMT /slideshow/a-big-query-frog-in-a-small-pond-jakub-motyl-buffpanel/88383615 data-science-club@slideshare.net(data-science-club) A Big (Query) Frog in a Small Pond, Jakub Motyl, BuffPanel data-science-club How does one process 200GB of streaming raw data, daily? Where dedicated servers and home-made solutions fail, BigQuery comes out the victor. We will talk about the big data architecture with over 110 million players total on record, how we managed to implement it, and how is it possible that we keep daily operational costs under $50. In the beginning we will explain what kinds of data sources a top-selling game has to integrate and analyze and how to pre-process the data to avoid ramping up costs in disaster scenarios. Part of the talk is also dedicated to all the components that are involved in the many transformations the data undergoes and we will show you how the output from the entire pipeline looks. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/talkabigqueryfroginasmallpond-datascienceclub1172f18-180220135201-thumbnail.jpg?width=120&height=120&fit=bounds" /> How does one process 200GB of streaming raw data, daily? Where dedicated servers and home-made solutions fail, BigQuery comes out the victor. We will talk about the big data architecture with over 110 million players total on record, how we managed to implement it, and how is it possible that we keep daily operational costs under $50. In the beginning we will explain what kinds of data sources a top-selling game has to integrate and analyze and how to pre-process the data to avoid ramping up costs in disaster scenarios. Part of the talk is also dedicated to all the components that are involved in the many transformations the data undergoes and we will show you how the output from the entire pipeline looks.

A Big (Query) Frog in a Small Pond, Jakub Motyl, BuffPanel from Data Science Club

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0 Why Successful Games Need Analytics /data-science-club/why-successful-games-need-analytics datascienceclub3-171122095630
Many people do not realize how current games rely on data and analytics, so significantly, to become successful. We'll show you how the world's best-selling PC/Console and mobile game developers utilize data and game analytics to improve the player experience and marketing of their games.]]>
Many people do not realize how current games rely on data and analytics, so significantly, to become successful. We'll show you how the world's best-selling PC/Console and mobile game developers utilize data and game analytics to improve the player experience and marketing of their games.]]> Wed, 22 Nov 2017 09:56:30 GMT /data-science-club/why-successful-games-need-analytics data-science-club@slideshare.net(data-science-club) Why Successful Games Need Analytics data-science-club Many people do not realize how current games rely on data and analytics, so significantly, to become successful. We'll show you how the world's best-selling PC/Console and mobile game developers utilize data and game analytics to improve the player experience and marketing of their games. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/datascienceclub3-171122095630-thumbnail.jpg?width=120&height=120&fit=bounds" /> Many people do not realize how current games rely on data and analytics, so significantly, to become successful. We'll show you how the world's best-selling PC/Console and mobile game developers utilize data and game analytics to improve the player experience and marketing of their games.

Why Successful Games Need Analytics from Data Science Club

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0 Introduction to data science club /slideshow/introduction-to-data-science-club/81943430 introductiontodatascienceclub-171112201138
Being able to make data driven decisions is a crucial skill for any company. The requirements are growing tougher - the volume of collected data keeps increasing in orders of magnitude and the insights must be smarter and faster. Come learn more about why data science is important and what challenges the data teams need to face.]]>
Being able to make data driven decisions is a crucial skill for any company. The requirements are growing tougher - the volume of collected data keeps increasing in orders of magnitude and the insights must be smarter and faster. Come learn more about why data science is important and what challenges the data teams need to face.]]> Sun, 12 Nov 2017 20:11:38 GMT /slideshow/introduction-to-data-science-club/81943430 data-science-club@slideshare.net(data-science-club) Introduction to data science club data-science-club Being able to make data driven decisions is a crucial skill for any company. The requirements are growing tougher - the volume of collected data keeps increasing in orders of magnitude and the insights must be smarter and faster. Come learn more about why data science is important and what challenges the data teams need to face. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/introductiontodatascienceclub-171112201138-thumbnail.jpg?width=120&height=120&fit=bounds" /> Being able to make data driven decisions is a crucial skill for any company. The requirements are growing tougher - the volume of collected data keeps increasing in orders of magnitude and the insights must be smarter and faster. Come learn more about why data science is important and what challenges the data teams need to face.

Introduction to data science club from Data Science Club

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0 Live predictions with schemaless data at scale. MLMU Kosice, Exponea /data-science-club/live-predictions-with-schemaless-data-at-scale-mlmu-kosice-exponea mlmukosice-expone-171108112139
Imagine you have huge amounts of data about your customers. All this data is schemaless and represents everything a customer is doing in your e-shop. From page visits and banner showings to purchases or registrations. Having all this data is a data scientists wet dream but also a nightmare at the same time. The data is schemaless and every project you track can send you different attributes and event types. Now, here comes the hard work. Create some universal data preprocessing engine which can turn all of this data into something that is reasonable and useful for machine learning algorithms for any project you have. We will show you, how this is done at Exponea and much more. How to connect this data to Spark ML library and then translate the model into a sequence of mathematical functions and aggregation methods for our in memory database to evaluate it on all customers in real time.Ondrej Brichta – currently working at Exponea as AI Engineer. Studying Logic and computability at Vienna University of Technology, alumni of Nexteria Leadership Academy and Matfyz in Bratislava]]>
Imagine you have huge amounts of data about your customers. All this data is schemaless and represents everything a customer is doing in your e-shop. From page visits and banner showings to purchases or registrations. Having all this data is a data scientists wet dream but also a nightmare at the same time. The data is schemaless and every project you track can send you different attributes and event types. Now, here comes the hard work. Create some universal data preprocessing engine which can turn all of this data into something that is reasonable and useful for machine learning algorithms for any project you have. We will show you, how this is done at Exponea and much more. How to connect this data to Spark ML library and then translate the model into a sequence of mathematical functions and aggregation methods for our in memory database to evaluate it on all customers in real time.Ondrej Brichta – currently working at Exponea as AI Engineer. Studying Logic and computability at Vienna University of Technology, alumni of Nexteria Leadership Academy and Matfyz in Bratislava]]> Wed, 08 Nov 2017 11:21:39 GMT /data-science-club/live-predictions-with-schemaless-data-at-scale-mlmu-kosice-exponea data-science-club@slideshare.net(data-science-club) Live predictions with schemaless data at scale. MLMU Kosice, Exponea data-science-club Imagine you have huge amounts of data about your customers. All this data is schemaless and represents everything a customer is doing in your e-shop. From page visits and banner showings to purchases or registrations. Having all this data is a data scientists wet dream but also a nightmare at the same time. The data is schemaless and every project you track can send you different attributes and event types. Now, here comes the hard work. Create some universal data preprocessing engine which can turn all of this data into something that is reasonable and useful for machine learning algorithms for any project you have. We will show you, how this is done at Exponea and much more. How to connect this data to Spark ML library and then translate the model into a sequence of mathematical functions and aggregation methods for our in memory database to evaluate it on all customers in real time.Ondrej Brichta – currently working at Exponea as AI Engineer. Studying Logic and computability at Vienna University of Technology, alumni of Nexteria Leadership Academy and Matfyz in Bratislava <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/mlmukosice-expone-171108112139-thumbnail.jpg?width=120&height=120&fit=bounds" /> Imagine you have huge amounts of data about your customers. All this data is schemaless and represents everything a customer is doing in your e-shop. From page visits and banner showings to purchases or registrations. Having all this data is a data scientists wet dream but also a nightmare at the same time. The data is schemaless and every project you track can send you different attributes and event types. Now, here comes the hard work. Create some universal data preprocessing engine which can turn all of this data into something that is reasonable and useful for machine learning algorithms for any project you have. We will show you, how this is done at Exponea and much more. How to connect this data to Spark ML library and then translate the model into a sequence of mathematical functions and aggregation methods for our in memory database to evaluate it on all customers in real time.Ondrej Brichta – currently working at Exponea as AI Engineer. Studying Logic and computability at Vienna University of Technology, alumni of Nexteria Leadership Academy and Matfyz in Bratislava

Live predictions with schemaless data at scale. MLMU Kosice, Exponea from Data Science Club

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0 https://cdn.slidesharecdn.com/profile-photo-data-science-club-48x48.jpg?cb=1539766122 https://cdn.slidesharecdn.com/ss_thumbnails/dsclubinstareafinal2-180312081427-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/how-to-present-campaign-results-to-your-boss/90364655 How to present campaig... https://cdn.slidesharecdn.com/ss_thumbnails/data-science-mnovotny-180312081238-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/principles-of-big-data-analytics-visualization/90364487 Principles of Big Data... https://cdn.slidesharecdn.com/ss_thumbnails/datascienceclub172f18summer-dataintegration-180220135538-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/batch-spark-and-streaming-kafka-datapreprocessing/88383925 Batch (Spark) and Stre...