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Content and procedure

The one-day seminar introduces users from the specialist field (e.g. business analysts) to the approach and methods of data science. Essential steps of data preparation, data analysis and presentation of results are presented in compact individual sections. This qualifies the course participants

- To formulate analysis problems

- To prepare data

- To identify patterns in data

- To lead the dialog with data scientists

- To interpret results from data labs

On the basis of concrete data and with the help of practical examples, the procedure of the data discovery process is illustrated and made tangible. The manageable number of participants makes it possible to clarify individual questions of the workshop participants.

Sebastian Derwisch-2

Short Facts

  • Trainer: Dr. Sebastian Derwisch
  • Language: English
  • 16th of April 2018
  • 10:00 – 18:15
  • Data Hub, Sapporobogen 6-8, 80637 München

Educational goals

Formulating analytical questions, preparation and visual analysis of data

Analyse analytical questions on the basis of machine learning methods

Strategies to optimize and validate machine learning models

Trainer

Dr. Sebastian Derwisch

is an Analyst and Data Scientist at the Business Application Research Center. He advises companies in the areas of use case identification for data analytics, tool selection for advanced analytics and the organization of data science teams but also conducts proof of data values for advanced analytics projects as well as data science coachings.

Sebastian Derwisch-2

Agenda

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10:00 – 11:15: General introduction & identification of analytical questions

11:15 – 11.30: Coffee break

11:30 – 13:00: Data Preparation & Visual Analytics

13:00 – 14:00: Lunch break

14:00 – 18:15: Advanced Analysis & Result Views (including break)

Requirements

The workshop is aimed at business users who have some previous knowledge of data analysis on the basis of business intelligence systems and would like to build up skills in the field of machine learning. An overview of the procedure in data science projects is helpful  but not required.

Are you interested in this workshop?