Lifelong Learning

Traditional approaches to time series analysis and forecasting, like Linear Regression, Holt-Winters Exponential Smoothing, ARMA/ARIMA/SARIMA and ARCH/GARCH, have been well-established for decades and find applications in fields as varied as business and finance (e.g. predict stock prices and analyse trends in financial markets), the energy sector (e.g. forecast electricity consumption) and academia (e.g. measure socio-political phenomena). In more recent times, the popularisation and wider availability of open source frameworks like Keras, TensorFlow and scikit-learn helped machine learning approaches like Random Forest, Extreme Gradient Boosting, Time Delay Neural Network and Recurrent Neural Network to gain momentum in time series applications. ...

This year has been rather rewarding for me! After completing some of the excellent Business Science University courses, I have worked on a number of Customer Analytics & Business Intelligence projects and summarised them into technical articles that I published on Medium’s Towards Data Science. This opened up an entirely new world to me and generated many new connections within the analytics and data science community the world over! ...

Clustering is one of my favourite analytic methods: it resonates well with clients as I’ve found from my consulting experience, and is a relatively straightforward concept to explain non technical audiences. Earlier this year I’ve used the popular K-Means clustering algorithm to segment customers based on their response to a series of marketing campaigns. For that post I’ve deliberately used a basic dataset to show that it is not only a relatively easy analysis to carry out but can also help unearthing interesting patterns of behaviour in your customer base even when using few customer attributes. ...

This is the coding necessary to assemble the various data feeds and sort out the likes of variables naming & new features creation plus some general housekeeping tasks. In order to simulate normal working conditions I would face if the data was stored on a database, I’ve uploaded the excel files onto a local PostgreSQL database that I’ve created on my machine. I am going to go through the steps I followed to set up a connection between RStudio and said database and extract the information I needed. ...

This is the minimal coding necessary to assemble various data feeds and sort out the likes of variables naming & new features creation plus some general housekeeping tasks. It includes general housekeeping tasks like shortening variables names to ease visualisations, creating essential new features and sorting out variables order The Dataset library(tidyverse) library(lubridate) library(readr) The dataset I’m using here accompanies a Redbooks publication called Building 360-Degree Information Applications which is available as a free PDF download. ...

Recently I have completed the online course Business Analysis With R focused on applied data and business science with R, which introduced me to a couple of new modelling concepts and approaches. One that especially captured my attention is parsnip and its attempt to implement a unified modelling and analysis interface (similar to python’s scikit-learn) to seamlessly access several modelling platforms in R. parsnip is the brainchild of RStudio’s Max Khun (of caret fame) and Davis Vaughan and forms part of tidymodels, a growing ensemble of tools to explore and iterate modelling tasks that shares a common philosophy (and a few libraries) with the tidyverse. ...