Master in Applied Data Science: Quantitative Fundamentals
Master in Applied Data Science / 12. November 2018
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Master in Applied Data Science Class of 2020
Lenka Ting is a Master in Applied Data Science, class of 2020 student.


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My name is Wen-Hui, TING <LENKA>, educated and raised in Taiwan before coming to Germany. I have just received my double bachelor’s degree in Economics and in German Language and Cultures and after completing an internship, I have developed an eagerness to deepen my interest in data science, leading me to Frankfurt, the heart of financial industry in Europe.

Why Master in Applied Data Science?

The thought of being able to provide high quality analysis reports, which would ease the process of making critical business decisions, fundamentally intrigues me. My interest in data science has therefore been aroused and driven me to Frankfurt School, Master in Applied Data Science program.

Why Frankfurt School?

We are currently experiencing a digital revolution led by Artificial Intelligence, Big Data and Internet of Things. Most companies are undergoing digital transformation. As a person seeking an international career in Germany, it is essential for me to also be “digitalized.” Since Frankfurt School stands in a leading position of business school in Germany and from the perspective of how Frankfurt School quickly reacted to the demand of the business industry by offering a whole new applied data science program. I firmly believe that Frankfurt School will be able to help me build professional skills and equip me with the data science knowledge necessary to survive and triumph in the digital era as well as deepen my personal interest.

A short overview of topics covered in Quantitative Fundamentals

  • Linear Algebra
  • Probability

Linear Algebra covers several topics, for example, matrices, eigenvalue and norms. The topic of Probability does not only include basic concepts, but also includes different kinds of distributions, such as statistics knowledge and maximum likelihood estimation.

Other than these theoretical concepts, this course also combines practical demonstration on how mathematical concepts fundamentally work in machine learning and data science with the use of Python.

Most interesting part of the module

Being able to see the practical applications of theories to data science and real cases has amazed me. Taking this course has definitely changed my view to see things more from a data scientist perspective. I gradually understand the connection and the importance between abstract mathematical calculation and concrete computer science examples, which motivated me to think several layers deeper when facing technical problems afterwards. The module covered the most fundamental parts of machine learning and has significant importance to build foundation knowledge for our future courses.

Advice to future students

Although this module has no pre-course requirement, it would be a good idea to prepare prior to the commencement, since the course would be taught intensively. You should take some time to teach yourselves some statistics, or learn from someone who has the knowledge, and begin a revision before the course starts.

Paying good attention in class as well as practicing note taking skills should assist you in developing some basic sense of computational language. Be well prepared, you will find out how machine learning works in our normal daily life and you could even try to experiment depends on your own preference.

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