The Data Analytics Blog

Our news and views relating to Data Analytics, Big Data, Machine Learning, and the world of Credit.

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Our 2017 Top Blogs: Data Science

December 15, 2017 at 3:01 PM

Data science continues to be a hot topic in many large firms globally.  2017 saw data science subjects such as R vs. Python, deep learning, natural language, gamification, AI and machine learning being arguably the most topical.

We continue to be passionate about this rapidly evolving world and as it’s the end of the year we’ve decided to look back at our top data science blogs of the year.  We are running a series of these retrospective assemblies of blogs having previously looked back at our best Machine Learning blogs and thereafter our Credit Risk blogs.

If you missed them during the year, I hope you enjoy them now.

The Four Types of Data Analytics

Simplistically, analytics can be divided into four key categories. In this blog post, we focus on the four types of data analytics we encounter in data science: Descriptive, Diagnostic, Predictive and Prescriptive.

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The Seven Logical Fallacies to Avoid in Data Analytics

“Lies, damned lies and statistics” is the frequently quoted adage attributed to former British Prime Minister Benjamin Disraeli. The manipulation of data to fit a narrative is a very common occurrence from politics, economics to business and beyond.

In this blog post, we'll touch on the more common logical fallacies that can be encountered and should be avoided in data analysis.

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What is Mathematical Optimisation and How Does It Benefit Business?

The word optimisation is used quite loosely and can relate to many different areas.  For example, there is search engine optimisation (getting your website pages to the top of online search results), process optimisation (making existing processes more efficient), code optimisation (making your code run more efficiently) and then there is mathematical optimisation.

In this blog post, we'll be focusing on mathematical optimisation: what it is, how it can be applied in making more optimal business decisions at a customer level, and specifically how it's applied in credit risk.

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Things You Wanted to Know About Mathematical Optimisation, but were Afraid to Ask

Today we explore some of the frequently asked questions around mathematical optimisation.  For the most part the questions are answered in the context of credit risk.  However mathematical optimisation and operations research in general have many applications.

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Thomas Maydon
Thomas Maydon
Thomas Maydon is the Head of Credit Solutions at Principa. With over 17 years of experience in the Southern African, West African and Middle Eastern retail credit markets, Tom has primarily been involved in consulting, analytics, credit bureau and predictive modelling services. He has experience in all aspects of the credit life cycle (in multiple industries) including intelligent prospecting, originations, strategy simulation, affordability analysis, behavioural modelling, pricing analysis, collections processes, and provisions (including Basel II) and profitability calculations.

Latest Posts

My learnings on the effective use of automated self-service bots.

Organisations and individuals, need to adapt and change to the new ways of working to ensure that we survive this pandemic, and protect our sustainability for the future.

The time is NOW for model validation and adjustment.

One of the major premises used in credit scoring is that “the future is like the past”. It’s usually a rational assumption and gives us a reasonable platform on which to build scorecards whether they be application scorecards, behavioural scores, collection scores or financial models.  That is reasonable until something unprecedented comes along.  You can read about this black swan event in our previous two blogs here and here

10 ways the COVID-19 crisis will affect your credit models (PART 2)

This is the second of a 2-part blog. You can read the first blog here.