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CAS Big Data Analytics, Blockchain and Distributed Ledger

Fintech is the buzzword for the technological transformation within the financial sector, driven by the availability of Big Data together with the rapidly advancing digitalisation, in particular data-driven automation (the equivalent of Industry 4.0 in the financial world).

Over the last few years, numerous start-ups were launched with innovative products driven by the merging of analytics and digitalisation. Examples include Peer-to-Peer Lending, cryptocurrencies such as Bitcoin, robo-advisors and countless new Internet applications. This development will drastically reshape the financial sector over the next few years.

The aim of the CAS Big Data Analytics, blockchain and Distributed Ledger is to support the financial sector with this transformation process. As the combination of the technological fields of Big Data analysis and automation through digitalisation is key for the technological transformation of the finance sector, it is important that these two topics are combined together in one training course. This will enable managers and experts with management responsibilities to understand how these technologies work, how to evaluate the technologies, assess their potential for the company, develop solutions that are adapted to the company and plan and manage their implementation within the company.

At a glance

Qualification: Certificate of Advanced Studies in Big Data Analytics, Blockchain and Distributed Ledger (12 ECTS)

Start: 28.02.2020, 18.09.2020, 05.03.2021

Duration: 7 months

Costs: CHF 7'000.00

Location: 

ZHAW School of Engineering - Lagerstrasse 41 - 8004 Zurich

Language of instruction: German, English
With the exception of a few lessons, the language of instruction is German. The documents are in English.

Objectives and content

Target audience

Aimed at managers and experts with management responsibilities across a range of functions, who would like to acquire or broaden their skills in the analysis of Big Data and the IT technologies that are fundamental for digitalisation. Primarily, these are employees within the finance and insurance industry in the areas of IT management, business analytics, business technology, business development, innovation management and project management, who work in a leading function on data analysis projects and/or projects for the development of efficient and innovative business processes, or who would like to innovate their company or business model in light of the technological transformation.

Objectives

The CAS Big Data Analytics, Blockchain and Distributed Ledger enables the participants to perform complex specialist management tasks in the area of business analytics and IT management. In terms of specialist content, the CAS focuses upon the following technologies and techniques:

  • Big Data analysis
  • Programming languages R and Python for rapid prototyping
  • Blockchain/distributed ledger technology

 

The students:

  • learn the basics and characteristics of these technologies
  • understand the implementation concepts
  • learn about example applications for problem-solving
  • can estimate their potential
  • are able to identify innovative areas of activity for the company through the combined use of the technologies covered, draw up technological solutions to tap into these areas of activity and design and manage projects to implement the solution.

Content

Module A: Big Data Analytics

Course contents

Fundamentals of analytics

  • Definition of analytics
  • Use of analytics for problem-solving

Big Data

  • Overview of Big Data (implementation concepts)
  • Use of Big Data technology for analysing structured and unstructured data

Machine learning algorithms versus statistical learning

  • Predictive modelling versus descriptive modelling

Analysis software

  • Introduction to R and Python
  • Possible applications in Big Data analytics, data extraction, data analysis and data visualisation

 

Module B: Blockchain and Distributed Ledger

Course contents

Blockchain

  • Fundamentals of blockchain technology (distributed architectures, security infrastructure, Merkle trees, smart contracts)
  • Cryptocurrencies
  • Characteristics of blockchain such as Ethereum

Distributed ledger

  • Possible applications of blockchain for companies (asset tracking, payment traffic, clearing, settlement, digital identity)

Consensus algorithms, proof of work and alternative concepts

RegTech

Case study

  • Creation of a smart contract in the Ethereum blockchain

Methodology

Lectures, practice-based exercises and example case studies, simulations, group work, self-study (preparation and follow-up) and e-learning

Classes

The instruction will take place alongside the participants' work on 10 Fridays (full days) and 10 Saturdays (half days).

About 3 weeks before the course starts, participants receive documents to prepare for the first day of class. The preparation time is about 16 hours.

After the contact hours the students work on their final thesis for about 3 months.

The students will receive their individual timetable at least one month prior to the start of the course. The holiday periods are based upon the school holiday periods for the city of Zurich.

Enquiries and contact

Provider

Instructors

The team of lecturers is composed of qualified specialists with skills in the academic and practical fields. Here are some of the lecturers:

  • Prof. Dr. Wolfgang Breymann
  • Prof. Dr. Jörg Osterrieder
  • Dr. Flumini Dandolo
  • Prof. Dr. Kurt Stockinger
  • Prof. Dr. Marc Wildi
  • Stephan Meyer
  • Dr. Lukas Lichtensteiger

Qualified experts from the industry will also be involved in some of the teaching units.

Application

Admission

Admission onto a Certificate of Advanced Studies (CAS) programme requires a degree (university of applied sciences, polytechnic, school of economics and business administration, university, ETH). However, participants with comparable professional skills may also be admitted onto the programme if they are able to provide evidence of these skills.

Basic programming knowledge in a programming language and an affinity for quantitative analytics as well as IT-related subjects are very helpful.

Start Application deadline Registration link
28.02.2020 28.01.2020 Application
18.09.2020 18.08.2020 Application
05.03.2021 05.02.2021 Application

Downloads and brochure

Links