{"id":169,"date":"2020-07-01T13:01:36","date_gmt":"2020-07-01T13:01:36","guid":{"rendered":"https:\/\/aaidm.buas.nl\/?page_id=169"},"modified":"2020-07-07T15:14:29","modified_gmt":"2020-07-07T15:14:29","slug":"preparation-for-week-1","status":"publish","type":"page","link":"https:\/\/aaidm.buas.nl\/?page_id=169","title":{"rendered":"DL1: Preparation for Week 1"},"content":{"rendered":"\n
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Author:<\/td>Hans Bouwknegt, Abhishek Biswas, Frank Peters<\/td><\/tr>
Learning Line:<\/td>DataLab<\/td><\/tr>
Course:<\/td>DataLab 1<\/td><\/tr>
Week:<\/td>1<\/td><\/tr>
Competencies:
<\/td>		2, 4, 5, 6, 7, 8, 9, 10, 11, 12
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BoKS:












<\/td>		1K1, Project Management: Knows how to plan, control, budget and assess the risk of an AI & Data Science project.
1K4, Knows how to report and present AI & Data Science projects.
1S1, (Agile) project management: Is able to plan, control, budget and assess the risk of an AI & Data Science project.
1S2, Is able to communicate AI & Data Science projects, in written (document), oral (presentation) and hybrid form (poster presentation).
1S4, Is able to integrate various AI & Data Science expertise areas in a project.
4K1, Knows user-centered design principles and processes.
A2, Learning as a basic attitude, self-development.
A4, Resilient in challenges.
A5, Collaborative.
A8, Self-reflective.
A9, Pro-active.
A11, Investigative: be genuinely interested in learning and find critical aspects for their further improvement.

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Learning Goals:










<\/td>		Within the requirements of a basic project, in agiven environment and under guidance, the student will:

Content<\/strong> (Focus on Data Science):
– The student understands how to approach challenges data-analytically, using the data-mining process to gather good data in the most appropriate way
– The student understands the general concepts for extracting knowledge from data and knows how to apply them on basic data sets
– The student had a basic understanding of how data science can help organizations solving problems  
 
Process:<\/strong>
– The student is able to distinguish the different phases of a data science project and know how to apply parts of those phases
– The student knows how to integrate one expertise area -data science- in an AI or data science project
The student is able to set up a solid argument, which is supported by data insights.<\/td><\/tr><\/tbody><\/table><\/figure>\n<\/div><\/div>\n\n\n\n

Content<\/h2>\n\n\n\n

Data science projects have a common approach which is displayed below in figure 1. This scheme serves the standard approach to all the projects you are going to do in the next four years. You\u2019ll be taught to use knowledge on Data Science, Artificial Intelligence, digital transformation processes and programming to solve various practical problems, using the standard approach displayed below in figure 1.<\/p>\n\n\n\n

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Figure 1: Data science Project approach<\/figcaption><\/figure><\/div>\n\n\n\n

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Figure 2: Project focus DataLab 1<\/figcaption><\/figure><\/div>\n\n\n\n

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In order to clarify this approach, the video below is placed here. Watch it, not focussing on the details (we don’t educate astrologists), but on the approach. You can fast forward the video where it becomes too detailled.<\/p>\n\n\n\n