Introduction Data Science (NL)

273586

What is Introduction Data Science

“The sexiest job of the 21st century”, claims the Harvard Business Review in 2012. Data Science is an extension of classic data analysis as most know it. With Data Science, you try to make predictions and discover patterns from large amounts of data, which can provide valuable information needed to make decisions.
In Introduction Data Science you learn to spot opportunities for a potential Data Science project and you take the first steps towards the real Data Science work.
You will be introduced to terms such as
Data Science Cycle

• Supervised learning
• Unsupervised learning
• Time Series Forecasting
• Natural Language Processing
• Computer Vision

We will discuss different types of Data Science methodologies and models. We also discuss how the correct formulation of a problem statement and good preparation of the data plays a major role in the validity of your analysis.
During the training, the question "I have problem X, how do I solve this with the help of data?" central. During the training, participants are challenged to apply the theory to practical cases. The training is given by people who carry out Data Science projects in practice and who know what it is about. They want to pass on that practical knowledge.

Who should attend Introduction Data Science

The training Introduction Data Science is suitable for:

• Starters at the start of their career
• People making a career switch to Data Driven Working
• People working in IT, especially in departments such as Data Analytics
• Data Analysts
• Data Scientists

Prerequisites

Basic skills and general knowledge of data analysis. The 'Introduction to Data Analysis' course addresses these topics and is ideally suited to follow as a training course prior to this.
During this training you need a laptop with access to internet.

Objectives

After this training, the participant is able to answer the questions:

• What steps do you take in a typical Data Science project?
• What is the difference between Supervised and Unsupervised learning and when do you use one or the other?
• What are common pitfalls and how do you avoid them?

273586

What is Introduction Data Science

“The sexiest job of the 21st century”, claims the Harvard Business Review in 2012. Data Science is an extension of classic data analysis as most know it. With Data Science, you try to make predictions and discover patterns from large amounts of data, which can provide valuable information needed to make decisions.
In Introduction Data Science you learn to spot opportunities for a potential Data Science project and you take the first steps towards the real Data Science work.
You will be introduced to terms such as
Data Science Cycle

• Supervised learning
• Unsupervised learning
• Time Series Forecasting
• Natural Language Processing
• Computer Vision

We will discuss different types of Data Science methodologies and models. We also discuss how the correct formulation of a problem statement and good preparation of the data plays a major role in the validity of your analysis.
During the training, the question "I have problem X, how do I solve this with the help of data?" central. During the training, participants are challenged to apply the theory to practical cases. The training is given by people who carry out Data Science projects in practice and who know what it is about. They want to pass on that practical knowledge.

Who should attend Introduction Data Science

The training Introduction Data Science is suitable for:

• Starters at the start of their career
• People making a career switch to Data Driven Working
• People working in IT, especially in departments such as Data Analytics
• Data Analysts
• Data Scientists

Prerequisites

Basic skills and general knowledge of data analysis. The 'Introduction to Data Analysis' course addresses these topics and is ideally suited to follow as a training course prior to this.
During this training you need a laptop with access to internet.

Objectives

After this training, the participant is able to answer the questions:

• What steps do you take in a typical Data Science project?
• What is the difference between Supervised and Unsupervised learning and when do you use one or the other?
• What are common pitfalls and how do you avoid them?