I have put together a series of video clips over the past few years to assist (mainly TU Dublin students) to do project work and continuous assessment. These have served me well in that I spent less time repeating myself and students received prompter resolution to queries. When an original/novel question was asked I recorded some new video footage and posted it on youtube. Also, Mr Youtube was a patient and indefatigable servant. Always there waiting to be called upon to deliver consolation and stretch a helping hand. Even at 2am in the morning - sometimes with just hours remaining before a critically important deadline. Somehow without cost or too much burden, I magicked away a lot of pain. The adage "no pain no gain" became quite old school. Over time my Youtube page became a bit chaotic with videos here and there and no real order. This web portal (thank you google) is an attempt to partially provide some structure to bits of that content.
This web page was also motivated by my involvement with the http://www.difme.eu/ project and some Erasmus + research which convinced me that there was a relatively broad spectrum of interest in data analytic/business intelligence and financial tools. One tread of this research pointed me towards developing some learning materials that could be of interest to startups who may be part of the finance industry or might not be. In recent years, I have attended with increasing frequency a number of technical talks at C++ meetups in Dublin's silicon docks. (Apparently, they are less well attended in Paris maybe because they don't do free beer and pizza there/ maybe there is less a tech scene). I found in silicon docks a fairly active community of computer engineers who knew a lot about coding but not so much about finance even though many are heavily involved in fintech, blockchain and online payments. Many of the participants interestingly function in the gig economy and operate like sole traders. Others are employees with formal contracts of employment but tend to move between tech companies sometimes with an ambition to ultimately start their own business. After processing a bit the results of some DIFME surveys that I opportunistically foisted upon some gallant cyber warriors attending a particularly technical C++ presentation, I discerned some broad interest in finance and data skills. In personal interactions I also found a number of the attendees expressed interest in the area of Employee Stock Options which is a popular means to remunerate and retain staff given that exiting a company leads to forfeiture. Given my perennial interest in valuing financial instruments - I thought I could bring my modeling skills to bear some fruit.
I got to thinking that I could re-tool and re-purpose my small archive of video tutorials to meet some needs within that the small but bustling community of entrepreneurs. I also decided to add some new content tailored for start-ups on tight budgets using R for more business related purposes, report writing, visualization and data transformation. Given the ever increasing influence of AI in all our lives, I included some machine learning content that Hal Varian (chief economist of google) has published using R. Within the data science arena there are many software tools but these often come with a hefty price tag and little by way of autonomous control over content. This portal is a blend of old and new. I have created here new content using R tidyverse and machine learning that maybe of interest generally to businesses and start-ups. R requires no financial outlay, runs on many platforms (including the cloud: https://rstudio.cloud/ and IBM Watson Cloud ) and can be learned quickly by implementing scripts (analogous to cookbooks). R is an incredibly powerful and widely used programming language for forecasting, statistical analysis and modelling. This could be of interest to participants in the fintech sector or more generally. For instance, I was asked to develop some workshops for accountants operating in the SME sector wanting to re-invigorate their grasp of Statistics. Much of what I addressed was a replay of a traditional format using R but we also touched on Machine Learning because the tools were available seamlessly in R and it was easy to download Hal's code. (Hal's exposition of the Titanic Kaggle Dataset was also as a sort of handy "proof of concept" for those new to Machine Learning). It is not an exaggeration to state that there is something of a Data Klondyke underway and that is perhaps more salient in Dublin that elsewhere given the proximity of the Facebook-Google-Microsoft ecosystem. Python is also extremely useful, but I will give some focus here to R because the focus initially will be a bit on start-ups and business intelligence. Broadly, computer engineers tend to favour Python and R seems more of a default for everybody else. Both permit control over their data science workflow through implementing scripts. See global rankings. In the linked pages I additionally will provide examples that use some:
Excel VBA C++ Python Javascript
R has some advantages in that it can be simple to implement as scores of relevant libraries (see: https://www.kaggle.com/rtatman/kernels & http://www.daveondata.com/ ) and web resources are widely available and fully primed to go. See links to some interesting downloads:
https://cran.r-project.org/bin/windows/base/
https://rstudio.com/products/rstudio/download/
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