Big Data concept relates to a set of technologies designed to solve three key tasks: processing huge (i.e. larger than standard) data amounts, handling high-speed data flows, and parallel sorting of both structured and poorly-structured data.

Big Data analysis is a quite complex and ever-complicating process. Today, many businesses are faced with the need to design efficient computing systems and algorithms to analyze rapidly growing information flows.


Duration: 3 days


  • Understand Big Data concept
  • Learn about potential data sources that can be used for solving real-life business problems
  • Get a brief overview of data mining process and tools

This is a fundamental course with practical exercises designed to provide you with hands-on experience in using two of the most popular Big Data processing technologies: Hadoop and MongoDB.


Duration: 4 days

The key idea of this course is to provide attendees with strong practical knowledge in using Big Data, Machine Learning and Data Analysis in real-world tasks and challenges.

The last day of the course is fully dedicated to solving practical analytical tasks using new knowledge and skills. The workshop will be based on real-life data. All models and analytical procedures will be made available to attendees after training and can be used in real work right away.

At the end of the course, every attendee will receive a virtual machine file with installed software, which can be used later, along with presentations and certificates.

Software and tools for workshop: RapidMiner Studio and R (language and environment).


Day 1. Introduction to Big Data and Analytics

  • What is Big Data and what it is not (from the perspective of business, tech people, and your customers)

  • Most valuable Big Data cases

  • Exploring top analytical software tools, considering their pros and cons

  • Hardware issues

Day 2. Important technical details any Big Data manager should know

  • What is "predictive model" about: opening a black box

  • Understanding uncertainty: precision, recall, accuracy and how to deal with fuzzy information

  • Tidy data importance: how to clean and validate your data and why master data really matters

  • Sizing hardware and software for your project

Day 3. Big Data project aspects: how to align technical and business requirements, and why it’s not so easy

  • What is a decision threshold and how to choose it correctly while considering cost, risks and potential revenues

  • Big Data can make your customers annoyed. How to not overdo when you know your customer so well

  • Using external data properly: from publicly available to third-party and commercial

  • We strongly discourage you from buying any personal and illegal data – but in case you do, you should know how to play this card in the right way…

  • Organizing a Big Data project: phases, lifecycle, and common pitfalls

Day 4. Big Data workshop

Practice day. During the workshop, attendees and trainers will formally define Big Data project purposes and recognize potential drivers and problems. After the workshop, the attendees will have enough information to execute a Big Data project in a coherent, clear, and less risky manner

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