TU Delft
Year
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NEDERLANDSENGLISH
Organization
2016/2017 Electrical Engineering, Mathematics and Computer Science Master Computer Science
IN4400
Programming and data science for the 99%
ECTS: 5
Responsible Instructor
Name E-mail
Dr.ir. F.F.J. Hermans    F.F.J.Hermans@tudelft.nl
Contact Hours / Week x/x/x/x
0/2/0/0
Education Period
2
Start Education
2
Exam Period
2
3
Course Language
English
Course Contents
Programming and data analysis are more important today than ever before and as a student as TU Delft, you are bound to deal with it at one point: in a course, for a project, for your graduation project or when you start your career.

But how do you handle your data? With Excel, with Python, with MatLab? How to quickly build a program that exactly solves your problem? The one library you thought could help does not compile and the code form this paper does not cover your edge case. Help!

This course will teach you about data analysis, based on the TU Delft MOOC ex101x. We will follow the course work of the MOOC, combined with extra exercises and group work.

For the end project, you can pick your own problem and/or data set, and build any tool or analysis on it you want. The only requirement is that you demonstrate what you learned in the previous lectures. We close the course with presentations on your projects, as well as an individual assignment in which you have to put the lessons learned into context.

About the professor: Felienne Hermans is assistant professor in the Software Engineering Research Group. Having written a dissertation on spreadsheet problems, she knows about the problems that non-programmers run in to when working with data.
Study Goals
Learning the basics of programming and data analysis:

After this course, a student will be able to:
1. Choose and explain what analysis tool to use in a given data problem
2. Identify and explain strong and weak points of the following popular analysis tools: spreadsheets, databases, programming languages
3. Apply the following spreadsheet concepts in a correct way: pivot tables, filtering, lookup formulas, conditional formulas and named ranges
4. Choose the right language concepts to build a query in Cypher for a given problem
5. Predict and explain the execution of a given piece of Python 2.7 code
6. Choose the right language concepts to build a Python 2.7 program for a given problem
Education Method
Lectures and hands-on exercises
Assessment
Two assignments:

Individual assignment (30%)
End project (70%)

In addition you will need to score 60% of the MOOC Data analysis to the MAX()
Elective
Yes