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© xxx SAFER, SMARTER, GREENER DATA SCIENCE AND MACHINE LEARNING DIGITAL SOLUTIONS DNV GL AS, [email protected], www.dnvgl.com/digital November 2018 Course code: DI-02 Duration: 3 days Prerequisite: No previous knowledge in statistics is needed. Some Python experience will be if you want to use Jupyter Notebooks. You will need a PC for the hands-on exercises. The first day of this 3-day classroom training gives an 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 data science 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. DESCRIPTION ON COMPLETION OF THE COURSE YOU WILL HAVE Better understanding of what is meant by data science and machine learing, 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 TARGET GROUP The course is most suitable for those who work 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 of management looking for insight into machine learning.

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SAFER, SMARTER, GREENER

DATA SCIENCE AND MACHINE LEARNING

DIGITAL SOLUTIONS

DNV GL AS, [email protected], www.dnvgl.com/digital November 2018

Course code: DI-02Duration: 3 days

Prerequisite: No previous knowledge in statistics is needed. Some Python experience will be if you want to use Jupyter Notebooks. You will need a PC for the hands-on exercises.

The first day of this 3-day classroom training gives an overview, while the next two days are hands-on. The course is suitable for anyone (engineers, programmers) interested in learn-ing more about data science and machine learning and in gaining hands-on data science 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.

DESCRIPTION

ON COMPLETION OF THE COURSE YOU WILL HAVE

� Better understanding of what is meant by data science and machine learing, 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

TARGET GROUP

The course is most suitable for those who work 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 of management looking for insight into machine learning.

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