Government

DI-04 Introduction to data science for industrial application

Data science consulting for industry from DNV GL

This course focuses on the introduction to data science methodology and machine learning algorithms with an emphasis on their applications in the industrial context

Description

The course is set as 2 days of theoretical, demo and hands-on activities + 1 optional day of hackathon/workshop on selected datasets. Alternatively, the course is also offered as 4 x half days of theoretical, demo and hands-on activities + half day of hackathon/workshop on selected datasets.

The course covers a wide range of topics as follows:

  • data science methodology: CRISP-DM,
  • machine learning types and tasks,
  • exploratory data analysis,
  • regression,
  • classification,
  • clustering,
  • principal component analysis,
  • machine learning model deployment,
  • pitfalls to avoid when performing data science, and
  • assurance of data-driven models and algorithms.

Learning objectives

On completing the course, the participant will:

  • have a better understanding of what is meant by Data Science or Machine Learning (i.e. DS/ML an analytic approach and not as a “magical black-box hype”),
  • have understanding what type of problems are suited for machine learning,
  • have some ideas about which types of problems in your own work are candidates for DS/ML approaches,
  • have some kind of awareness on some machine learning pitfalls to avoid,
  • have some experience in using the tools to get started,
  • have insight into what value machine learning and data science can bring, and
  • have motivation for further learning and development.

Target group

This course is suitable for:

  1. Engineers, scientists, programmers, software developers, and other relevant discipline graduates who would like to equip themselves with data science skills (i.e. this course is an excellent entry point), and
  2. Professionals who frequently handle internal and/or external data, as participants will be discovering the untapped potential of the data and the realising more value from their data

It is beneficial if the participant has prior coding/programming experience, however this is not a pre-requisite. We are ready to customize the course and the tools to accommodate participants with no coding experience.

Private training sessions for corporates can be arranged on request.

ONLINE TRAINING:

REGISTER HERE

Prerequisite:

No prior technical knowledge required

Course:

DI-04 Introduction to data science for industrial application

Description

The course is set as 2 days of theoretical, demo and hands-on activities + 1 optional day of hackathon/workshop on selected datasets. Alternatively, the course is also offered as 4 x half days of theoretical, demo and hands-on activities + half day of hackathon/workshop on selected datasets.

The course covers a wide range of topics as follows:

  • data science methodology: CRISP-DM,
  • machine learning types and tasks,
  • exploratory data analysis,
  • regression,
  • classification,
  • clustering,
  • principal component analysis,
  • machine learning model deployment,
  • pitfalls to avoid when performing data science, and
  • assurance of data-driven models and algorithms.

Learning objectives

On completing the course, the participant will:

  • have a better understanding of what is meant by Data Science or Machine Learning (i.e. DS/ML an analytic approach and not as a “magical black-box hype”),
  • have understanding what type of problems are suited for machine learning,
  • have some ideas about which types of problems in your own work are candidates for DS/ML approaches,
  • have some kind of awareness on some machine learning pitfalls to avoid,
  • have some experience in using the tools to get started,
  • have insight into what value machine learning and data science can bring, and
  • have motivation for further learning and development.

Target group

This course is suitable for:

  1. Engineers, scientists, programmers, software developers, and other relevant discipline graduates who would like to equip themselves with data science skills (i.e. this course is an excellent entry point), and
  2. Professionals who frequently handle internal and/or external data, as participants will be discovering the untapped potential of the data and the realising more value from their data

It is beneficial if the participant has prior coding/programming experience, however this is not a pre-requisite. We are ready to customize the course and the tools to accommodate participants with no coding experience.

Private training sessions for corporates can be arranged on request.

DI-04 Introduction to data science for industrial application

REGISTER HERE