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A prezentációban bemutatunk egy ötletet, amely segítségével feltárható hogy a cégtől elvándorló ügyfelek pontosan hová tűnnek. A prezentáció a Magyar Telekom által szervezett Big data contest-jére készült.

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Big data contest - Személyre szabott behajtáskezelés

A prezentációban bemutatunk egy ötletet, amely adatok felhasználásával teszi személyre szabottá ezáltal hatékonyabbá egy telekommunikációs vállalat behajtáskezelését. A prezentáció a Magyar Telekom által szervezett Big data contest-jére készült, és ott első helyezést ért el.

Balogh gyorgy modern_big_data_megoldasok_sec_world_2014

György Balogh has held a presentation at the SECWorld 2014 conference about the cutting-edge yet also affordable Big Data technologies.

Big Data dilemmák

Vajon a méret a lényeg?

DMC2013 Task 2

This document summarizes the Uni_Budapest_Te_1 solution for the Data Mining Cup 2013 Task 2. It discretized numeric variables with missing values into categories to handle the missing data. It used stochastic gradient descent with Weka to train models on Task 1 data and continued learning during evaluation. Separate models were trained for different parts of sessions to account for transaction differences over the course of a session. The solution achieved good predictive power for early sessions steps and slightly better performance than the benchmark for later steps.

Big Data @ CEMP: Ident projekt

Hargita Nándor és Ihász Ingrid előadása a Kutatás konferencián (Tapolca, 2013. április 9-10.)

Tudásmenedzsment és tudástípusok

„Többet tudunk, mint amennyiről képesek vagyunk beszélni”. (Polányi, 1964)

Data Mining: Mining ,associations, and correlations

Market basket analysis examines customer purchasing patterns to determine which items are commonly bought together. This can help retailers with marketing strategies like product bundling and complementary product placement. Association rule mining is a two-step process that first finds frequent item sets that occur together above a minimum support threshold, and then generates strong association rules from these frequent item sets based on minimum support and confidence. Various techniques can improve the efficiency of the Apriori algorithm for mining association rules, such as hashing, transaction reduction, partitioning, sampling, and dynamic item-set counting. Pruning strategies like item merging, sub-item-set pruning, and item skipping can also enhance efficiency. Constraint-based mining allows users to specify constraints on the type of

Big data contest - Személyre szabott behajtáskezelés

A prezentációban bemutatunk egy ötletet, amely adatok felhasználásával teszi személyre szabottá ezáltal hatékonyabbá egy telekommunikációs vállalat behajtáskezelését. A prezentáció a Magyar Telekom által szervezett Big data contest-jére készült, és ott első helyezést ért el.

Balogh gyorgy modern_big_data_megoldasok_sec_world_2014

György Balogh has held a presentation at the SECWorld 2014 conference about the cutting-edge yet also affordable Big Data technologies.

Big Data dilemmák

Vajon a méret a lényeg?

DMC2013 Task 2

This document summarizes the Uni_Budapest_Te_1 solution for the Data Mining Cup 2013 Task 2. It discretized numeric variables with missing values into categories to handle the missing data. It used stochastic gradient descent with Weka to train models on Task 1 data and continued learning during evaluation. Separate models were trained for different parts of sessions to account for transaction differences over the course of a session. The solution achieved good predictive power for early sessions steps and slightly better performance than the benchmark for later steps.

Big Data @ CEMP: Ident projekt

Hargita Nándor és Ihász Ingrid előadása a Kutatás konferencián (Tapolca, 2013. április 9-10.)

Tudásmenedzsment és tudástípusok

„Többet tudunk, mint amennyiről képesek vagyunk beszélni”. (Polányi, 1964)

Data Mining: Mining ,associations, and correlations

Market basket analysis examines customer purchasing patterns to determine which items are commonly bought together. This can help retailers with marketing strategies like product bundling and complementary product placement. Association rule mining is a two-step process that first finds frequent item sets that occur together above a minimum support threshold, and then generates strong association rules from these frequent item sets based on minimum support and confidence. Various techniques can improve the efficiency of the Apriori algorithm for mining association rules, such as hashing, transaction reduction, partitioning, sampling, and dynamic item-set counting. Pruning strategies like item merging, sub-item-set pruning, and item skipping can also enhance efficiency. Constraint-based mining allows users to specify constraints on the type of

A gondolatolvasás létezik, avagy big data a marketingben @DIMSZ konferencia

Magyar nyelvű prezi - 2014 dec. 3-án a DIMSZ konferencián.
by Mester Tomi @Adatlabor.hu

Big Data Analytics

Slide from general lecturing "Big Data Analytics: Engage with Your Customer" at Muhammadiyah Jakarta University

Intro to Data Science for Enterprise Big Data

If you need a different format (PDF, PPT) instead of Keynote, please email me: pnathan AT concurrentinc DOT com
An overview of Data Science for Enterprise Big Data. In other words, how to combine structured and unstructured data, leveraging the tools of automation and mathematics, for highly scalable businesses. We discuss management strategy for building Data Science teams, basic requirements of the "science" in Data Science, and typical data access patterns for working with Big Data. We review some great algorithms, tools, and truisms for building a Data Science practice, and provide plus some great references to read for further study.
Presented initially at the Enterprise Big Data meetup at Tata Consultancy Services, Santa Clara, 2012-08-20 http://www.meetup.com/Enterprise-Big-Data/events/77635202/

Myths and Mathemagical Superpowers of Data Scientists

1) The document discusses 10 myths about data scientists and provides realities to counter each myth.
2) Some myths include claims that data scientists are mythical beings, elitist academics, or a fading trend. However, the realities note data science requires hands-on work with data and has experienced steady growth.
3) Other myths suggest data scientists are just statisticians or BI specialists, but the realities indicate data scientists come from varied backgrounds and tackle business problems through experimentation and analysis.

Titan: The Rise of Big Graph Data

The document discusses graphs and graph databases. It introduces the concept of property graphs and how they can intuitively model complex relationships between entities. It discusses how graph traversal enables expressive querying and numerous analyses of graph data. The document uses examples involving Greek mythology to illustrate graph concepts and traversal queries.

How to Interview a Data Scientist

How To Interview a Data Scientist
Daniel Tunkelang
Presented at the O'Reilly Strata 2013 Conference
Video: https://www.youtube.com/watch?v=gUTuESHKbXI
Interviewing data scientists is hard. The tech press sporadically publishes “best” interview questions that are cringe-worthy.
At LinkedIn, we put a heavy emphasis on the ability to think through the problems we work on. For example, if someone claims expertise in machine learning, we ask them to apply it to one of our recommendation problems. And, when we test coding and algorithmic problem solving, we do it with real problems that we’ve faced in the course of our day jobs. In general, we try as hard as possible to make the interview process representative of actual work.
In this session, I’ll offer general principles and concrete examples of how to interview data scientists. I’ll also touch on the challenges of sourcing and closing top candidates.

Titan: Big Graph Data with Cassandra

Titan is an open source distributed graph database build on top of Cassandra that can power real-time applications with thousands of concurrent users over graphs with billions of edges. Graphs are a versatile data model for capturing and analyzing rich relational structures. Graphs are an increasingly popular way to represent data in a wide range of domains such as social networking, recommendation engines, advertisement optimization, knowledge representation, health care, education, and security.
This presentation discusses Titan's data model, query language, and novel techniques in edge compression, data layout, and vertex-centric indices which facilitate the representation and processing of Big Graph Data across a Cassandra cluster. We demonstrate Titan's performance on a large scale benchmark evaluation using Twitter data.
Presented at the Cassandra 2012 Summit.

A Statistician's View on Big Data and Data Science (Version 1)

Presentation given by Dr. Diego Kuonen, CStat PStat CSci, on November 20, 2013, at the "IBM Developer Days 2013" in Zurich, Switzerland.
ABSTRACT
There is no question that big data has hit the business, government and scientific sectors. The demand for skills in data science is unprecedented in sectors where value, competitiveness and efficiency are driven by data. However, there is plenty of misleading hype around the terms big data and data science. This presentation gives a professional statistician's view on these terms and illustrates the connection between data science and statistics.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.

Introduction to R for Data Mining

We at Revolution Analytics are often asked “What is the best way to learn R?” While acknowledging that there may be as many effective learning styles as there are people we have identified three factors that greatly facilitate learning R. For a quick start:
- Find a way of orienting yourself in the open source R world
- Have a definite application area in mind
- Set an initial goal of doing something useful and then build on it
In this webinar, we focus on data mining as the application area and show how anyone with just a basic knowledge of elementary data mining techniques can become immediately productive in R. We will:
- Provide an orientation to R’s data mining resources
- Show how to use the "point and click" open source data mining GUI, rattle, to perform the basic data mining functions of exploring and visualizing data, building classification models on training data sets, and using these models to classify new data.
- Show the simple R commands to accomplish these same tasks without the GUI
- Demonstrate how to build on these fundamental skills to gain further competence in R
- Move away from using small test data sets and show with the same level of skill one could analyze some fairly large data sets with RevoScaleR
Data scientists and analysts using other statistical software as well as students who are new to data mining should come away with a plan for getting started with R.

Big Data [sorry] & Data Science: What Does a Data Scientist Do?

What 'kind of things' does a data scientist do? What are the foundations and principles of data science? What is a Data Product? What does the data science process looks like? Learning from data: Data Modeling or Algorithmic Modeling? - talk by Carlos Somohano @ds_ldn at The Cloud and Big Data: HDInsight on Azure London 25/01/13

What is Big Data?

This presentation, by big data guru Bernard Marr, outlines in simple terms what Big Data is and how it is used today. It covers the 5 V's of Big Data as well as a number of high value use cases.

A gondolatolvasás létezik, avagy big data a marketingben @DIMSZ konferencia

Magyar nyelvű prezi - 2014 dec. 3-án a DIMSZ konferencián.
by Mester Tomi @Adatlabor.hu

Big Data Analytics

Slide from general lecturing "Big Data Analytics: Engage with Your Customer" at Muhammadiyah Jakarta University

Intro to Data Science for Enterprise Big Data

If you need a different format (PDF, PPT) instead of Keynote, please email me: pnathan AT concurrentinc DOT com
An overview of Data Science for Enterprise Big Data. In other words, how to combine structured and unstructured data, leveraging the tools of automation and mathematics, for highly scalable businesses. We discuss management strategy for building Data Science teams, basic requirements of the "science" in Data Science, and typical data access patterns for working with Big Data. We review some great algorithms, tools, and truisms for building a Data Science practice, and provide plus some great references to read for further study.
Presented initially at the Enterprise Big Data meetup at Tata Consultancy Services, Santa Clara, 2012-08-20 http://www.meetup.com/Enterprise-Big-Data/events/77635202/

Myths and Mathemagical Superpowers of Data Scientists

1) The document discusses 10 myths about data scientists and provides realities to counter each myth.
2) Some myths include claims that data scientists are mythical beings, elitist academics, or a fading trend. However, the realities note data science requires hands-on work with data and has experienced steady growth.
3) Other myths suggest data scientists are just statisticians or BI specialists, but the realities indicate data scientists come from varied backgrounds and tackle business problems through experimentation and analysis.

Titan: The Rise of Big Graph Data

The document discusses graphs and graph databases. It introduces the concept of property graphs and how they can intuitively model complex relationships between entities. It discusses how graph traversal enables expressive querying and numerous analyses of graph data. The document uses examples involving Greek mythology to illustrate graph concepts and traversal queries.

How to Interview a Data Scientist

How To Interview a Data Scientist
Daniel Tunkelang
Presented at the O'Reilly Strata 2013 Conference
Video: https://www.youtube.com/watch?v=gUTuESHKbXI
Interviewing data scientists is hard. The tech press sporadically publishes “best” interview questions that are cringe-worthy.
At LinkedIn, we put a heavy emphasis on the ability to think through the problems we work on. For example, if someone claims expertise in machine learning, we ask them to apply it to one of our recommendation problems. And, when we test coding and algorithmic problem solving, we do it with real problems that we’ve faced in the course of our day jobs. In general, we try as hard as possible to make the interview process representative of actual work.
In this session, I’ll offer general principles and concrete examples of how to interview data scientists. I’ll also touch on the challenges of sourcing and closing top candidates.

Titan: Big Graph Data with Cassandra

Titan is an open source distributed graph database build on top of Cassandra that can power real-time applications with thousands of concurrent users over graphs with billions of edges. Graphs are a versatile data model for capturing and analyzing rich relational structures. Graphs are an increasingly popular way to represent data in a wide range of domains such as social networking, recommendation engines, advertisement optimization, knowledge representation, health care, education, and security.
This presentation discusses Titan's data model, query language, and novel techniques in edge compression, data layout, and vertex-centric indices which facilitate the representation and processing of Big Graph Data across a Cassandra cluster. We demonstrate Titan's performance on a large scale benchmark evaluation using Twitter data.
Presented at the Cassandra 2012 Summit.

A Statistician's View on Big Data and Data Science (Version 1)

Presentation given by Dr. Diego Kuonen, CStat PStat CSci, on November 20, 2013, at the "IBM Developer Days 2013" in Zurich, Switzerland.
ABSTRACT
There is no question that big data has hit the business, government and scientific sectors. The demand for skills in data science is unprecedented in sectors where value, competitiveness and efficiency are driven by data. However, there is plenty of misleading hype around the terms big data and data science. This presentation gives a professional statistician's view on these terms and illustrates the connection between data science and statistics.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.

Introduction to R for Data Mining

We at Revolution Analytics are often asked “What is the best way to learn R?” While acknowledging that there may be as many effective learning styles as there are people we have identified three factors that greatly facilitate learning R. For a quick start:
- Find a way of orienting yourself in the open source R world
- Have a definite application area in mind
- Set an initial goal of doing something useful and then build on it
In this webinar, we focus on data mining as the application area and show how anyone with just a basic knowledge of elementary data mining techniques can become immediately productive in R. We will:
- Provide an orientation to R’s data mining resources
- Show how to use the "point and click" open source data mining GUI, rattle, to perform the basic data mining functions of exploring and visualizing data, building classification models on training data sets, and using these models to classify new data.
- Show the simple R commands to accomplish these same tasks without the GUI
- Demonstrate how to build on these fundamental skills to gain further competence in R
- Move away from using small test data sets and show with the same level of skill one could analyze some fairly large data sets with RevoScaleR
Data scientists and analysts using other statistical software as well as students who are new to data mining should come away with a plan for getting started with R.

Big Data [sorry] & Data Science: What Does a Data Scientist Do?

What 'kind of things' does a data scientist do? What are the foundations and principles of data science? What is a Data Product? What does the data science process looks like? Learning from data: Data Modeling or Algorithmic Modeling? - talk by Carlos Somohano @ds_ldn at The Cloud and Big Data: HDInsight on Azure London 25/01/13

What is Big Data?

This presentation, by big data guru Bernard Marr, outlines in simple terms what Big Data is and how it is used today. It covers the 5 V's of Big Data as well as a number of high value use cases.

A gondolatolvasás létezik, avagy big data a marketingben @DIMSZ konferencia

A gondolatolvasás létezik, avagy big data a marketingben @DIMSZ konferencia

Big Data Analytics

Big Data Analytics

Intro to Data Science for Enterprise Big Data

Intro to Data Science for Enterprise Big Data

Myths and Mathemagical Superpowers of Data Scientists

Myths and Mathemagical Superpowers of Data Scientists

Titan: The Rise of Big Graph Data

Titan: The Rise of Big Graph Data

How to Interview a Data Scientist

How to Interview a Data Scientist

Titan: Big Graph Data with Cassandra

Titan: Big Graph Data with Cassandra

A Statistician's View on Big Data and Data Science (Version 1)

A Statistician's View on Big Data and Data Science (Version 1)

Introduction to R for Data Mining

Introduction to R for Data Mining

Big Data [sorry] & Data Science: What Does a Data Scientist Do?

Big Data [sorry] & Data Science: What Does a Data Scientist Do?

What is Big Data?

What is Big Data?

- 1. Big Data Contest 2016
- 2. B A Egyik konkurenshez átigazoló ügyfelek Másik konkurenshez átigazoló ügyfelek Új ügyfélnek álcázva besétáló ügyfelek Hazai mobil távközlési piacról elillanó ügyfelek
- 4. B A új 2015 Q1 2015 Q2 Lehet, hogy számot vált, debarátokat nem! Data science & big data technologiesData science & big data technologies Referenciák
- 5. Big Data Contest 2016 Elvándorló ügyfelek szegmentálása