Book Review: Practical Data Science with R by Nina Zumel and Jim Porzak

This is a very very brief collection of points extracted from this book titled "Practical Data Science with R". For those starting in this field of Data Science a recommendable foundational reference.

The main parts: An introduction to Data Science, modelling methods and delivering results.

As always, an important disclaimer when talking about a book review: The reading of this very personal and non-comprehensive list of points, mostly taken verbatim from the book, by no means replaces the reading of the book it refers to; on the contrary, this post is an invite to read the entire work.

Part 1 - Intro to Data Science

I would highlight the method the authors propose to deal with data investigations:

- Define the goal - What problem are you solving?
- Collect and manage data - What info do you need?
- Build the model - Find patterns in data that leads to a solution
- Evaluate and critique the model - Does the model solve my problem?
- Present results and document - Establish that you can solve the data problem and explain how
- Deploy the model - Deploy the model to solve the problem in the real world.

Part 2 - Models

Common classification methods such as e.g. Naive Bayes classifier, Decision trees, Logistic regression, Support vector machine.
To forecast is to assign a probability (the key is how to map data into a model).

Model types: Classification, scoring, probability estimation, ranking and clustering.
For most model evaluations, it is usual to compute one or two summary scores using a few ideal models: a null model, a Bayes rate model and the best single variable model.

Evaluating scoring models:
- Always try single variable models before trying more complicated techniques.
- Single variable modelling techniques give a useful start on variable selection.
- Consider decision trees, nearest neighbour and naive Bayes models as basic data memorization techniques.

- Functional models allow to better explore how changes in inputs affect predictions.
- Linear regression is a good first technique to model quantities.
- Logistics regression is a good first technique to model probabilities.
- Models with simple forms come with very powerful summaries and diagnostics.
- Unsupervised methods find structure (e.g. discovered clusters, discovered rules) in the data, often as a prelude to predictive modelling.

Part 3 - Delivering results 

Nowadays information systems are built off large databases. Most systems are online, mistakes in terms of data interpretation are common and mostly none of these systems are concerned with cause.

Enjoy the data forest

Wannacry related interim timeline

Let me share a timeline I constructed regarding Wannacry during the last days. The interesting point I shared with some colleagues was that the patient zero (o patients) infection vector is not referenced or described as of now yet.

15th February 2017 Microsoft cancels its monthly patching for that month

9th March 2017 Wikileaks press release regarding Vault7, "the largest-ever publication of confidential documents on the agency" according to Wikileaks.

14th March 2017 Microsoft publish security update MS17-010 for SMB Server

14th April 2017 (according to Equation Group (see releases some exploits, EternalBlue among them. EternalBlue took advantage of the vulnerability that Microsoft patch MS17-010 fiexed.

14th April 2017 Microsoft publish their triage analysis on the exploits

15th April 2017 Security companies analyse exploits. One example of the anaylisis of EternalBlue is the following:

15th April 2017 Some news sites start to wonder how come that the patch existed before the release e.g.

12th May 2017 WannaCry appears in the wild

Some sources mention that the infection vector was a phishing email

However, no analysis yet of that mentioned phishing email, its attachment and its modus operandi in general.

Update 1: Response and proposals from Microsoft

Rocky days

Book Review: Bitcoin and other virtual currencies for the 21st Century by J. Anthony Malone

A very handy book to approach Bitcoin.

Let me try to share with you the main learning points I collected from this book. As always, here it goes my personal disclaimer: the reading of this very personal and non-comprehensive summary by no means replaces the reading of the book it refers to; on the contrary, this post is an invite to read the entire work.

The book starts first with the concept of money, how money was an innovation itself, the functions of money as a medium of exchange, a unit of account, a store of value, a deferred payment and a value measure. It also provides some insights on the history of money and how credit is older than cash and, finally, a key concept: the monopolistic role of the government in terms of currency issuance.

There are some hints in the book to consider Bitcoin a starting point to end the monopoly of central banks. It claims that the Bitcoin value scheme is inspired on the old gold standard. It is interesting to read the links that the author sees between the Austrian School of Economics and Bitcoin.

The point that Bitcoin does not have a centralised clearing house is certainly a key point in the book. It also mentions that the blockchain public ledger is the heart of the Bitcoin technology. It also mentions that Bitcoin is inflation-free (there is a fixed number of Bitcoins that can eventually be minted). The supply of Bitcoins does not depend on the monetary policy of a central authority. It also remembers the Keynesian line of thought on deflation and how it encourages individuals and businesses to save money.

To use Bitcoins, you just need a Bitcoin wallet and a Bitcoin address. Technically, Bitcoin has currently a transaction limit of 7 per second.

There is a section of the book on legal aspects of Bitcoin. Apparently virtual currencies do not have legal tender status in any jurisdiction. Bitcoin has the properties of a payment system, a currency and a commodity. There is still a bit of regulatory ambiguity in terms of Bitcoin. There are some appendixes in the book related to a very useful glossary of terms, a legal guidance issued by FinCEN in the US, also from US GAO (Accountability Office), from the Inland Revenue Service, some input from revelant regulators and legal documentation on different Bitcoin-related cases.

Happy growing!

Book Review: Bitcoin and Mobile Payments: Constructing a European Union Framework (Palgrave Studies in Financial Services Technology) edited by Gabriella Gimigliano

This book sheds some light on how Bitcoin and mobile payments interact with EU rules and regulations. A key point certainly are the PSD and PSD2 directives on payment services in the internal market.

Let me try to share with you the main learning points I collected from this book. As always, here it goes my personal disclaimer: the reading of this very personal and non-comprehensive summary by no means replaces the reading of the book it refers to; on the contrary, this post is an invite to read the entire work.

The book has been built into 4 parts:

- Institutional strategy and economic background
The institutional strategy can be an enabling factor for a sound growth of new instruments and certainly for the security of payments. The definition of an effective “cyber security strategy” at national and European level is one of the pillars of the creation of the “digital single market”. The financial services and the payment industry are an essential component. Certainly the role of SEPA (Single Euro Payment Area) is considered. Interestingly, Bitcoin is an alternative payment scheme without fiat or banking money. There is an interesting statement, “Bitcoin has a tendency to create an oligopoly in terms of miners”.

- The framework – a European outline and a comparison with other frameworks
There is a lack of specific regulations in terms of virtual currencies. Can they be considered payment instruments? What are they really? What is the role of self-regulation in all this? In Europe we see a technological fragmentation of the payment chain. It is still too early to know which path will be followed. Experts suggest an adaptation of the laws for newcomers such as bitcoin.

- Regulatory challenges (e.g. protection of customers’ funds, data integrity, soundness of payment and financial system, competitiveness of European market)
A basic requirement is to have an adequate security that encourages the usability of the system. What happens when there is no central service provider? The increasingly stronger general rules for data protection in the EU will eventually require equally strong sector-based rules.
Mobile payments’ legal situation regarding Anti Money-Laundering is legally certain. Virtual currencies’ legislation not.
Interesting detail: Bitcoin does not attract too many VAT complications within the EU.
For the time being, there is a lack of a fully implemented and integrated business model in the mobile payments ecosystem in Europe.

- Evolution of payment services
Only two sentences on this topic. Bitcoin is really a conceptual revolution, mobile payments are really an evolution.

Happy constructing!

Quick Book Review: Value Web by Chris Skinner

I thought I would share with my readers a selection of the points mentioned in the book (modest disclaimer: it is a non-comprehensive, and personal, quick summary that does not replace the reading of the book)

The book is titled "Value Web" by Chris Skinner.

-          The author is an independent commentator in the financial industry.
-          Summary in a sentence: There is a network transformation of how we exchange value.
-          This network transformation is linked to our secure digital identities.
-          The author describes the blockchain technology also as an authentication technology.
-          He touches also upon the history of money and how farming created money as an instrument to keep value.
-          A detail: It was China inventing paper-based money.
-          An interesting thought: “Simplification comes from kids and complexity comes from incumbents”
-          Clear statement: Banks don’t trust each other anymore.
-          Interesting story of an attempt to regulate:
-          The author sees banks more than as money stores as value stores. His stance: value stores need regulation.
-          Three different roles played by fintech players in the banking industry: wrappers, replacers and reformers (vis a vis traditional banking).
-          How free apps can make money? By creating additional (not currently existing) value and by being relevant.
-          The potential to re-invent banking (rather than to disrupt banking)
-          However: Let’s be realisitic. In the UK 62% of the population still prefers face to face in a branch as preferred channel to access bank services.
-          Banks already require a digital core, a platform. So that channels are replaced by access. In the digital era, they talk about access (to that digital core) and not channels anymore.

The last part of the book includes interviews to key players in this field. My 2 cents. Follow these three names on twitter: @jonmatonis, @brockpierce and @chrislarsensf

Happy valueing!