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Welcome to Distributed Ledger Technology in Finance Certificate (DLT)

Start Date: Thursday 4th April 2019

25% Super Early Bird Discount until Friday February 15th 2019

Blockchain and Cryptocurrency have become an essential part of the Fintech field, gaining more and more traction in traditional finance. There are numerous Distributed Ledger Technology (DLT) startups, job openings and internal corporate projects. However, most finance professionals are not proficient in this domain area and there is a lack of consistent educational resources. The Distributed Ledger Technology in Finance Certificate (DLT) will close this gap.

The Distributed Ledger Technology in Finance Certificate (DLT) is a 16 week qualification comprising of 3 modules, 12 lecture weeks, 3 assignments, a practical final project and a final sit down 3 hour examination using our global network of examination centres. This course has been designed to empower individuals who work in or are seeking a career in Blockchain, Cryptocurreny or Distributed Ledger Technology in finance. The DLT is a career-enhancing professional qualification, that can be taken worldwide.

DLT Faculty

Welcome to The Distributed Ledger Technology in Finance Certificate (DLT)

Distributed Ledger Technology in Finance Certificate (DLT) 

Blockchain and Cryptocurrency have become an essential part of the Fintech field, gaining more and more traction in traditional finance. There are numerous Distributed Ledger Technology (DLT) startups, job openings and internal corporate projects. However, most finance professionals are not proficient in this domain area and there is a lack of consistent educational resources. The Distributed Ledger Technology in Finance Certificate (DLT) will close this gap.

The Distributed Ledger Technology in Finance Certificate (DLT) is a 16 week qualification comprising of 3 modules, 12 lecture weeks, 3 assignments, a practical final project and a final sit down 3 hour examination using our global network of examination centres. This course has been designed to empower individuals who work in or are seeking a career in Blockchain, Cryptocurreny or Distributed Ledger Technology in finance. The DLT is a career-enhancing professional qualification, that can be taken worldwide.

 

NEXT COHORT STARTS: Tuesday 4th April 2019

  • SUPER EARLY BIRD DISCOUNT: 25% Discount until 15th February 2019
  • VOLUME DISCOUNT: If 2 or more people from your institution wish to take The DLT Certificate please contact us
  • REGIONAL OFFERS: Get in touch for offers in your geographic region

MODULE ONE: Distributed Ledger Architecture

Week 1:  Introduction to Cryptography                                                   

Intro:                                                                                                     

  • About the course
  • About me
  • Schedule, rules and materials

Introduction to Cryptography                                                     

  • Hash Functions
  • Hash Pointers and data structure
  • Digital Signature

Simple Cryptocurrency                                                   

  • Centralized currency model
  • Decentralized currency model

Bitcoin Consensus Mechanism

Bitcoin Mining                                                                                 

  • Incentives and Proof of work
  • Hash puzzles

Week 2:  Decentralization and Smart Contracts                                   

Previous lecture recap:

Possible attacks                                                                               

  • Double spend
  • 51%

Bitcoin Economy

Bitcoin internal scripting mechanism                                      

  • Turing completeness
  • Ethereum
  • Ethereum Virtual Machine

Smart Contracts

Decentralized applications                                                         

Week 3:  Network Architecture                                                                   

Previous lecture recap

Further development                                                                     

  • Segregated Witness
  • Lightning network
  • Sharding
  • State channels
  • Block size

Consensus mechanisms:                                                          

  • Proof of Work
  • Proof of Stake
  • Hybrid models

Network governance structures                                                                

  • Community decision making
  • Code ownership
  • Development incentives
  • Validation incentives

Week 4:  Guest Lecture     

Home assignment: white paper analysis for every student

Week 5: Home assignment review seminar                                           

MODULE TWO: DLT Domain Projects Review, Research and Classification

Week 6:  DLT Domain Projects Overview and Research                    

Previous module recap

Module intro

Internet of value

Types of tokens:                                                                                               

  • Currency token
  • Utility token
  • Security token

Project classification:                                                                                     

  • Currency
  • Platform
  • Marketplace
  • Private Blockchains
  • Scalability/Interoperability
  • Stablecoin

Project examples from every class:                                                          

  • Bitcoin/Litecoin/Monero
  • Ethereum/Zilliqa/EOS
  • 0x/Loopring
  • Hyperledger/Ripple
  • COSMOS/Interledger/Lightning Network
  • Tether/Stasis 

Week 7:  DLT Projects Business Models                                                     

Previous lecture recap:

Public blockchain

Private blockchain

Blockchain as a Service

Marketplaces                                                                                                    

  • Centralized
  • Decentralized
  • Hybrid

Distributed Ledger as an enterprise solution                                       

  • Finance
  • Trade
  • Logistics
  • Property
  • LegalTech
  • GovTech 

Week 8:  Market Dynamics                                                                             

Previous lecture recap

Bitcoin as a starting point

Financial bubbles review & explanation

ICO market dynamics

Other indicators                                                                                                 

  • Bitcoin dominance index
  • Relative asset valuations
  • Capital circulation
  • DCF model in crypto 

Week 9:  Guest Lectures   

Platforms:

  • Corda
  • Hyperledger Fabric
  • Ethereum 

Home assignment due-diligence for a chosen project

Week 10: Home assignment review seminar                                             

MODULE THREE: DLT and Fintech Regulation

Week 11:  Fintech Policy Overview (US, UK, EU, Malta)                    

Previous module recap

Module intro

FinTech definition

Brief history of FinTech

Regulatory challenges faced by Fintech companies

Regulatory infrastructure of different jurisdictions                              

Week 12: Cryptocurrency Regulation (US, UK, EU, Malta)              

Previous lecture recap

Cryptocurrency as money

Cryptocurrency as commodity

Cryptocurrency as property

ICO Regulation                                                                                                    

  • Howey Test
  • DAO example 

Week 13: Cryptocurrency Taxation (US, UK, EU, Malta)                   

Previous lecture recap

General taxation practices

Taxation of Natural Persons

Taxation of Legal Entities

ICO taxation

How to determine what you owe            

Week 14: Guest Lecture on Regulation      

Home assignment: DLT use-case idea research and pitch

Week 15: Home assignment review seminar

Final project: write a white-paper or business plan for a new DLT project or automation of a business process within an existing business.

Week 16: Final project review seminar


Distributed Ledger Technology in Finance Certificate (DLT) Examination

Frankfurt: Machine Learning in Finance Workshop, 27th – 28th February

SPECIAL OFFER When two colleagues attend the 3rd goes free!

25% Super Early Bird Discount until Friday January 11th 2019

Prerequisites:

There are no formal prerequisites for the course, and we’ll endeavour to explain the foundational concepts during the training if required. However, since data science and machine learning rely on the following disciplines, it is good to brush up on:

  • Linear algebra — we are dealing with datasets consisting of many data points and algorithms with many (hyper)parameters; linear algebra is the essential language in this multivariate setting;
  • Probability theory — many of the models are versed in the language of probability: for example, disturbances in linear regression models are random variables; frequentist likelihoods and Bayesian priors and posteriors are probabilities; Somewhat less pertinently:
  • Information theory — this branch of applied mathematics is concerned with quantifying how much information is present in our inputs; in machine learning we are concerned with extracting as much information as possible;
  • Numerical computation — many of the algorithms rely on numerical methods rather than analytical solutions; in practice care is needed to avoid numerical issues such as overflow and underflow, poor conditioning, etc.;
  • Optimisation theory — much of machine learning is concerned with optimising (hyper)parameters and therefore utilises the machinery from optimisation theory, such as gradient-based optimisation.

In order to practise machine learning, one needs

  • A working knowledge of a convenient programming language, such as Python or R;
  • Familiarity with relevant libraries.
  • During our trainings, all code demonstrations will be using Python.

Delegate Requirements:

Laptops required for all or in groups (we encourage collaboration during the tutorials.) Solutions to problems, including programming assignments, will be provided during the training, so you can follow them. Python is a (de facto) lingua franca of data science and machine learning, so we’ll use it as our primary programming language. We advise that you install the Anaconda Python distribution (64-bit, version 5.0.0, the Python 3.6 variant at the time of writing) by Continuum Analytics. This distribution includes the following libraries, some of which we may use during the training:

  • NumPy — the fundamental package for scientific computing with Python; it contains, among other things, a powerful n-dimensional array object;
  • SciPy (pronounced “Sigh Pie”)—Python-based ecosystem of open-source software for mathematics, science, and engineering;
  • SciKits — SciPy Toolkits;
  • Pandas — Python Data Analysis Library;
  • StatsModels— Statistics in Python;
  • Keras — the Python deep learning library, a high-level neural networks API running on top of TensorFlow, CNTK, or Theano.

Presenter:

Paul Bilokon: Founder, CEO, Thalesians & Senior Quantitative Consultant, BNP Paribas

Dr. Paul Bilokon is CEO and Founder of Thalesians Ltd and an expert in electronic and algorithmic trading across multiple asset classes, having helped build such businesses at Deutsche Bank and Citigroup. Before focussing on electronic trading, Paul worked on derivatives and has served in quantitative roles at Nomura, Lehman Brothers, and Morgan Stanley. Paul has been educated at Christ Church College, Oxford, and Imperial College. Apart from mathematical and computational finance, his academic interests include machine learning and mathematical logic.


The Thalesians

This event is in conjunction with The Thalesians.

The Thalesians are a think tank of dedicated professionals with an interest in quantitative finance, economics, mathematics, physics and computer science, not necessarily in that order.

The Thalesians Homepage

DAY ONE

09:00 – 10:00 – Lecture 1: Probability and Statistics

  • Interpretation of probability – classical, frequentist, Bayesian, axiomatic
  • Statistical inference and estimation theory

10:00 – 10:30 – Tutorial 1: Statistical inference and estimation theory

10:30 – 11:00 – Coffee break

11:00 – 12:00 – Lecture 2: Linear regression

  • A geometric perspective
  • Interpreting the linear regression, multicollinearity

12:00 – 12:30 – Tutorial 2: Linear regression

12:30 – 13:30 – Lunch

13:30 – 14:30 – Lecture 3: PCA and dimensionality reduction

  • The geometry of eigenvectors and eigenvalues, covariance and correlation matrixes
  • PCA and dimensionality reduction

14:30 – 15:00 – Tutorial 3: Demo of PCA

15:00 – 15:30 – Coffee break

15:30 – 16:30 – Lecture 4: Unsupervised machine learning

  • Anomaly detection
  • Clustering

16:30 – 17:00 – Tutorial 4: Demo of clustering analysis

DAY TWO

09:00 – 10:00 – Lecture 5: From statistics to supervised machine learning

  • Bias-variance tradeoff
  • Under and over fitting

10:00 – 10:30 – Tutorial 5: Demo of bias-variance tradeoff

10:30 – 11:00 – Coffee break

11:00 – 12:00 – Lecture 6: Model and features selection

  • Cross-validation
  • Bootstrap
  • Regularization: shrinkage methods

12:00 – 12:30 – Tutorial 6: Demo of model selection for market impact assessment

12:30 – 13:30 – Lunch

13:30 – 14:30 – Lecture 7: Classification methods

  • Logistic regression
  • Decision Trees and Random Forests

14:30 – 15:00 – Tutorial 7: Solving classification problem and features selection by random forests

15:00 – 15:30 – Coffee break

15:30 – 16:30 – Lecture 8: Deep-learning

  • Optimization, gradient descend
  • Inference with Neural Networks: the theory
  • Feed-forward neural networks and backpropagation

16:30 – 17:00 – Tutorial 8: Construction of NN and backpropagation algorithm

Organisation

There are limited sponsorship opportunities for this event so please contact neil@wbstraining.com