This workshop is for people who would like to get an overview of the possibilities and application potentials of Data Science. This includes managers and head of departments, but also employees who are looking for an introduction to Data Science.
Companies today have to deal with an enormous amount of data on a daily basis. The big challenge for companies is not simply to collect this data, but to filter it and use it productively. The goal must always be to gain new insights and to optimize decisions and processes. In this one-day seminar you will learn the most important technical terms around data and learn how to develop added value and data-driven business models from data. An overview of use cases, necessary skills and technologies will be given. All aspects will be explained using practical examples and best practices and the procedure in a real data science use case will be clearly explained using an exemplary data science use case. After completing the training, the participants are familiar with the basics of data science. Our experienced lecturers have several years of professional experience in various industries. This ensures that our training courses are highly practice-oriented.
The workshop takes place in classroom style.
Participants do not require any previous knowledge. No laptop is required.
- Location: meetinn – Munich – Obersendling
- Language: English
- max. participants: 15
- Time: 10:00 a.m. – 05:00 p.m.
Are you interested in this workshop?
- data era
- Why is data the oil of the 21st century?
- What is Big Data?
- What is the potential of Big Data?
- Definition, delimitation and trends
- data science
- data mining
- machine learning
- artifical intelligence
- Practical application of Data Science using Use Cases
- The Different Skills of a Data Scientist, Data Analyst, Business Analyst and Data Engineer
- The Data Science Process: the 4 Steps of the Data Compass
- business processes
- Formulation and understanding of the question/problem
- Application of established creativity techniques and semantic analyses
- data intelligence
- Transformation to a data-driven question
- Identification of relevant data
- Data preparation and data exploration
- predictive analytics
- Analytical modelling of the problem to solve it
- Advanced Analytics Methodologies
- model selection
- feature engineering
- Training & testing the model
- Insights Visualization
- Target group-oriented presentation of the results
- Visualization of the knowledge gained
- business processes
- Real-life examples: Challenges & Best Practices for the Use Case Implementation
- Data Science Toolkit
- Overview of technologies
- Selection criteria for the right tool