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Curriculum Bachelorstudiengang Data Science (EN)

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Curriculum BSc Data Science Free choice of modules in the module group You must achieve the minimum number of credits in each module group; in addition, you are free to choose which module group(s) you want to deepen.

Mathematical Competency

Min. Credits Total Credits

of

Data Analysis

12 18

of

Computer Science and Programming

15 22

Level

Data Processing & Infrastructure

Machine Learning

12 26

7 17

10 18

of

of

of

Applied Data Science

of

Data Visualisation

11 21

Humanities & Economics

Communication

4 7

of

of

9 15

of

World of Work

14 33

Social Embedding of a Data Science Challenge

of

Basic

Intermediate

Advanced

Challenges & Projects

10 20

42 75

maximum

3

Digital Entrepreneurship & Innovation

Bachelor-Thesis

4 High Performance Computing

Introduction to Business Administration 4

Stochastic Processes and Time Series Bayesian Data Analysis

Optimization for Machine Learning 3

Cloud Infrastructure & Computing 3

2

Statistical Learning

Advanced Calculus

3

Efficient Machine Learning Algorithms Webtechnology

4 3 Applications of Linear Algebra

Foundation in Mathematics

Object-Oriented & Functional Progr.

Linear and Logistic Regression 3

3 Foundation in Linear Algebra

Probability Modelling 3

3 Foundation in Calcalus 3

3

2

3

3 3

Foundation in Image & Signal Porcessing

Software Construction

Multiple Linear Regression

Programming Algorithms

Clicking on a module name will take you to the module description.

Web Data Collection

3

2

Reinforcement Learning

2

4 3

Deep Learning

Data Wrangling

Social Network Analysis

Programming in R Foundation in Databases

Foundation in Information Technology 2

Digital Communication

2

Effective Argumentation

2

Presenting

4 Recommender Systems

Foundation in Machine Learning

Foundation in DS 6

Visual Analytics

Interactive Visualisation 4

3

3

2

2

2

Foundation in Writing 3

2

Foundation in Data Visualisation 3

Politics, History, Culture

2

Ethical Implementation Ethical Reflection

2

2

2

Social Impact of AI Design 3 Design Thinking for Data Scientists 2

3 3

4

Data Storytelling

Applied Machine Learning

Advanced Deep Learning

Database Design & Implementation

3

4

3

2

2

Data Law

Advanced Writing

Natural Language Processing

Explainable AI

2

Foundation in Programming 4

Data Collection IoT

3

3 2

3

Exploratory Data Analysis

Deep Learning on Image & Signal

3

Knowledge Management

Tutorial Communication English 0 Tutorial Communication German 0

Data-Driven Business Models Scientific Methods

Information Technology Law Information Literacy

Project 5 Data Science Various Challenges

Actively Shaping your Career Path Leadership

2

2

2

Personal Knowledge Management 2 Intercultural Competency

12

6

Challenge X6

Formulate and solve your own Challenge

Selfmanagement

Learning Workshop

Project 3 interdisciplinary

Challenge X3 Formulate and solve your own Challenge

6

6

2

2

Project 4 interdisciplinary

Challenge X4 Formulate and solve your own Challenge

Various Challenges E. g. Data Analysis or Data Communication

2

6

6

3 Participative Events

Project 3 Data Science

Challenge X5

E. g. Technology & Society, Webengineering or Machine Learning

6

Digital Portfolio

Project 4 Data Science

Formulate and solve your own Challenge

2

3

6

Various Challenges

Collaboration in Teams

2

2

2

E. g. Natural Language Processing, Computer Vision, Deep Learning, Graph Machine Learning

6

On-The-Job Project 2 Projects from your daily work

Project 2 Data Science

Various Challenges On-The-Job Project 1

E. g. Probability Modelling or Data Engineering

Projects from your daily work

4

6

Project 1 Data Science 6

Subject to change. Update: 2024


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Curriculum Bachelorstudiengang Data Science (EN) by Fachhochschule Nordwestschweiz FHNW - Issuu