Training

Training Courses

Big Data processing with Apache Spark
2 day training course

Course intended for

Course main audience are software engineers and data analysts, who want to learn the basis of Big Data processing, which surpasses the capabilities of traditional processing, using tools of Apache Spark family. The course is both for people interested in starting to work with Big Data, as well as, people with previous experience in other Big Data systems, such as Apache Hadoop, who want to learn new technology.

Course objective

The attendees will learn about new problems that arise during Big Data analysis from various sources using Apache Spark family tools. During the course a general set of typical Big Data problems and their solutions using Apache Spark will be presented. Moreover, the attendees will have a general overview of the pros and cons of using Apache Spark for their business problem solving. In addition, the course allows the attendees to familiarize themselves with fast-moving Big Data processing field and the novel approach to problem solving that Apache Spark presents.

Foundations of Machine Learning in Python
3 day training course

Course intended for

This course is designated for data analysts who wish to enter the world of machine learning. The course is also addressed to programmers who wish to begin their adventure with machine learning. The course facilitates familiarisation with a quickly developing domain of predictive data analysis which is becoming a standard due to data flooding. In addition participants may better understand the use of machine learning tools and techniques in the environment of Python, a mature production language.

Course objective

The participants will get familiar with the basics of machine learning and the related tools in Python. The course utilises a combination of various tools, which in turn enables execution of more complex analyses and predictions using machine learning algorithms. Furthermore, algorithms and models of machine learning in Python may very often be easily transferred to the production environment in a relatively short time.

Foundations of Text Mining and Natural Language Processing
3 day training course

Course intended for

Text Mining constitute at least 70% of all data generated in IT systems. Such data is rarely used for analytical purposes or knowledge discovery. This course covers the problems related to the processing and analysis of Text Mining. The course is addressed to:

  • programmers who wish to use the knowledge discovery methods using Text Mining in their systems,

  • analysts who wish to develop their analytical workshop by a Text Mining analysis tool,

  • those interested in using statistical tools and machine learning methods when working with Text Mining.

Basic programming knowledge in any language is required (for example Python, R, matlab etc.).

Course objective

Participants will learn a number of tools designated for working with Text Mining. A number of examples of their use will be presented which cover the majority of topics from that domain.The basic languages in working with texts will be presented: R, Python and Java.

Deep Learning
5 day training course

Course intended for:

Training course is aimed at developers and data analysts who want to learn the concepts of deep neural networks.

Course objective:

The course objective is to equip participants with knowledge about deep neural networks. The participants will be able to program and debug deep neural network, including convolutional and the recurrent neural networks. Neural network architecture discussed during the training will be presented by means of basic concepts of computer vision (classification) and processing of natural language (sentiment analysis, machine translation). Additionally, newest research along with the most popular uses of deep learning will be presented, such as automated generation of description for images and transfer of style between images.

Designing Big Data solutions using Apache Hadoop
5 day training course

Course intended for:

The training is aimed at developers, architects and application administrators who want to create or maintain systems based on scalable architectures such as Big Data, in particular specialists for whom performance and volume of processed data is the highest priority. The course is aimed at people currently involved in creation of relational databases who want to gain knowledge of alternative technologies, which gradually displace relational databases from different application areas. The training is also dedicated for people who want to supplement their knowledge of concepts of Big Data, MapReduce, NoSQL and their implementation using Apache Hadoop & Family software.

Course objective:

Course participants will gain knowledge of cross-cutting concepts such as MapReduce algorithm, Big Data, BigTable, DFS distributed file systems, NoSQL databases. After the training, course participants will be able to choose the right techniques for their projects. In addition to general introduction to Big Data, this training is focusing on the entire Apache Hadoop stack.

Stream Data Processing
2 day training course

Course intended for

Training course is intended mainly for programmers/developers and data analysts who want to learn the basics of Big Data processing. Tools from Apache Storm and Spark family can be used to process very big sets of data in (almost) real time. The training offers a good background knowledge both for those who want to start working with Big Data stream processing and for those who are already experienced in such systems, e.g. Apache Hadoop, and want to learn this new technology.

Course objective

Participants will learn about the problem of analysis of very big sets of data (Big Data) in (almost) real time from various sources. During the course a basic set of problems with stream processing of Big Data will be presented along with the solutions using the tools from Apache Storm and Spark family. What is more, participants will be made aware of the advantages and disadvantages of these technologies when approaching real-world, business-related problems.

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