Data Analytics with MATLAB, in a “Big Data” World

Location: Hotel Radi un draugi, Str. Marstalu 3, Riga

Time: 13:30

Price: FREE

As the amount of data held by all businesses grows, companies are increasingly turning to agile data analytics techniques to make better decisions, find new opportunities and reduce risks. Whether it’s experimental data, data from simulation packages, or even sales and marketing data, come to this free seminar to experience how to quickly and easily create advanced analytic tools for your own business.

Big data represents an opportunity for analysts and data scientists to gain greater insight and to make more informed decisions, but it also presents a number of challenges. Big data sets may not fit into available memory, may take too long to process, or may stream too quickly to store. Standard algorithms are usually not designed to process big data sets in reasonable amounts of time or memory and there is no single approach to big data.

Who Should Attend:

Data analysts, scientists, engineers, managers, quantitative analysts, analytical researchers, risk managers, financial engineers, actuaries and traders, anyone wanting to improve how data is used with their business and/or who might be interested in learning more about statistics, machine learning, data analytics and “big data”.

This is a free event but seats are limited. Please register in order to reserve your seat.


13:30 Registration

14:00 Understand Your Data

  • Connect to data sources, including spreadsheets, SQL and NoSQL databases
  • Explore your data with engaging 2D and 3D graphs; make sense of complex data with MATLAB analysis functions and simple apps

14:40 Put Your Data to Work

  • Run and automate predictive analytics with “what-if” scenarios, statistics and machine learning techniques
  • Examples will be selected from energy demand forecasting, risk management, seismic data processing and health monitoring

15:15 Coffee break

15:35 Scale Up: Process Big Data using:

  • Datastore objects
  • MapReduce and Hadoop
  • Parallel and distributed applications