The first day of this 3-day classroom training gives and overview, while the next two days are hands-on. The course is suitable for anyone (engineers, programmers) interested in learning more about data science and machine learning and in gaining hands-on experience.
Day 1 of the course is lecture-based - no programming experience is required. Topics covered are: business understanding (how to set up and start data science projects), a workflow for data science projects, data preparation, regression, classification, model evaluation, clustering and big data.
Days 2 and 3 go in-depth into the same topics, plus provide hands-on experience with common data science and machine learning tools: Orange ML, Jupyter Notebooks and scikit-learn. You can choose which tool to focus on, depending on Python skills.
On completion of the course you will have:
- Better understanding of what is meant by data science and machine learning, and their value
- Ideas about which types of problems in your own work are candidates for data science and machine learning
- Experience in using the tools to get started
- A basis for communicating in a meaningful way with others in the field
- An understanding of machine learning as an analytic approach and not as a 'magical black-box hype'
- Tips on some machine learning pitfalls to avoid
- Introduction to analytics tools available and familiarization with some of them
The course is most suitable for those who are somehow working with data on a regular basis and would benefit from getting insights and motivation to what that data potentially could be used for.
The first day of the course is useful also for non-technical staff or management who wants to get insight into what machine learning is.
Please note that we currently only offer online training in these countries due to local VAT restrictions.