Data Analytics

 
Course code:
E_EOR3_DA
Period:
Period 2
Credits:
6.0
Language of tuition:
English
Faculty:
School of Business and Economics
Coordinator:
dr. R. Heijungs
Examinator:
dr. R. Heijungs
Teaching method(s):
Lecture, Instruction course
Level:
300

Course objective

This course teaches the students the importance of data analysis as the
process of transforming data into useful information in order to support
decision making. It equips the students with the tools, techniques and
common practices used in the field of data analytics, including how to
obtain, manipulate, explore, model, and present data.

Course content

Data analytics is a booming term that is used for the use of large
amounts of data to gain knowledge, to optimize operations, and to
explore markets. An example is the use of real-time traffic data to
analyze vehicle movements, to predict congestions, to find the fastest
route, and to schedule maintenance operations. Underlying data analytics
is a series of methods and tools that include querying databases, using
multivariate statistics, and visualizing high-dimensional data. This
course will address theoretical and practical aspects in a number of
selected topics relating to data analytics.

The following approaches to data analysis will be covered:
• Exploring data
• Preprocessing
• Statistics
• Regression
• Beyond regression
• Classification
• Clustering
• Importing data
• Missing data and outliers
• Validation

We will use flipped classroom approach, in which most of the time will
be devoted to in-class working on assignments, helping your fellow
students, and discussing suitable approaches.

Form of tuition

Lectures, computer assignments, student presentations

Type of assessment

Written exam – individual assessment
Individual assignments – individual assessment
Team assignments –team assessment
Participation and attendance – individual assessment

Course reading

D.T.Larose, Dicovering Knowledge in Data: An Introduction to Data
Mining, 2nd Edition, Wiley
Extra documents (articles, data sets, weblinks, etc.) will be provided
through Canvas

Entry requirements

Basic course in statistics

Recommended background knowledge

Elementary computer skills, handling spreadsheets or programming

Remarks

For doing the in-class work of this course, you are strongly recommended
to bring a laptop with internet connection. This may be a Windows, Mac
OS or Linux computer, at your choice. It is convenient when you have
some of the programs that you can operate (e.g., Excel, SPSS, Matlab, R,
etc.) available on this laptop.

© Copyright VU University Amsterdam
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