“Bigdata Conference” - the largest conference on big data very soon
Colleagues, look around!
“Big data” is much closer to you and there are more of them than it seems. Despite the abundance of events on this topic, few people, “speaking to us girls”, owns the theme. And in order to squeeze the benefits and money from the information - you need to understand very well ... precisely - in the intricacies.
The technology of "spinning big data" is roughly divided into two very different layers - engineering and algorithmic. In the first monolith, the software is still quite raw, booming, from which developers, in simple words, already have a roof: they have to understand tools from the good old Hadoop with HDFS, actively using Hive, Impala, Presto, Vertica and others and so on ... and, in order to keep up with the competitors, to master the secrets of Apache Spark jewelled on the beautiful laconic Scala.
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On the other hand, one needs to be very good at imagining algorithms and methods for extracting “rules and patterns from data” and not flinch from the phrase “linear discriminant analysis”, not sweat when discussing the subtleties of “logistic regression on the annova core”, not to faint when demonstrate “spectral factorization” and not try to wake up while introducing the process of “clustering text in a non-Euclidean space using Locality-Sensitive Hashing” ;-)
The worst thing in this situation is that our children, who do not let go of a personal computer, already understand these technologies much more confidently than us! Guard.
So, in order to subordinate “big data” to business and make them useful, we have 2 ways: long and proper.
The long way is obliging to study the following disciplines:
- probability theory
- linear algebra
- differential calculus
- graph theory
- 100-500 algorithms ...
- processing technology "big data"
It will take about 50 years.
And the right way is to go to our Bigdata Conference on September 11, 2015 in Kiev. And literally in 2-4 days you will be able to organize the processes of collecting and analyzing “big data” in your company, teach your colleagues to use Rapidminer, implement the best machine learning algorithms - and take your business to a new level! And technical specialists will be able to replenish the body of knowledge from the experts of the region - still, we will talk about Spark, and about the clustering of social graphs and, oh yeah, about effective “deep learning”!
We are looking forward to see you! There are only a few days left before the conference.
“Big data” is much closer to you and there are more of them than it seems. Despite the abundance of events on this topic, few people, “speaking to us girls”, owns the theme. And in order to squeeze the benefits and money from the information - you need to understand very well ... precisely - in the intricacies.
The technology of "spinning big data" is roughly divided into two very different layers - engineering and algorithmic. In the first monolith, the software is still quite raw, booming, from which developers, in simple words, already have a roof: they have to understand tools from the good old Hadoop with HDFS, actively using Hive, Impala, Presto, Vertica and others and so on ... and, in order to keep up with the competitors, to master the secrets of Apache Spark jewelled on the beautiful laconic Scala.
')
On the other hand, one needs to be very good at imagining algorithms and methods for extracting “rules and patterns from data” and not flinch from the phrase “linear discriminant analysis”, not sweat when discussing the subtleties of “logistic regression on the annova core”, not to faint when demonstrate “spectral factorization” and not try to wake up while introducing the process of “clustering text in a non-Euclidean space using Locality-Sensitive Hashing” ;-)
The worst thing in this situation is that our children, who do not let go of a personal computer, already understand these technologies much more confidently than us! Guard.
So, in order to subordinate “big data” to business and make them useful, we have 2 ways: long and proper.
The long way is obliging to study the following disciplines:
- probability theory
- linear algebra
- differential calculus
- graph theory
- 100-500 algorithms ...
- processing technology "big data"
It will take about 50 years.
And the right way is to go to our Bigdata Conference on September 11, 2015 in Kiev. And literally in 2-4 days you will be able to organize the processes of collecting and analyzing “big data” in your company, teach your colleagues to use Rapidminer, implement the best machine learning algorithms - and take your business to a new level! And technical specialists will be able to replenish the body of knowledge from the experts of the region - still, we will talk about Spark, and about the clustering of social graphs and, oh yeah, about effective “deep learning”!
We are looking forward to see you! There are only a few days left before the conference.
Source: https://habr.com/ru/post/265887/
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