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Selecting the appropriate chart for strategy presentations

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Calories and body weight

Calories and body weight

I’m currently reading the book, The Signal and the Noise by Nate Silver. This is a very good book for all data science professionals, and is an absolute must for all those aspiring data scientists. (more…)

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Nonlinear dynamical systems are an enormous seam of amazing images. The Ikeda Map is an example of strange attractor which represents the movement of particles under the rules of certain differential equations. (more…)

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Visualising collatz conjecture in R as a fractal

Visualising collatz conjecture in R as a fractal

The Collatz conjecture (or hailstone problem or 3n+1 problem) is a problem that is so simple to state that grade-schoolers can understand it, yet has been approached from an innumerable variety of angles and has resisted mathematicians for decades now. (more…)

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Mandelbrot sonification

Sonification is the act of representing data as sound. Any data will do, but data that has a pattern will make better music, and if you don’t know whether your data has a pattern or not, sonification may help you find out. (more…)

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The need for new customer value creation models

The need for new customer value creation models

All business model frameworks have the customer and the value proposition, or offer, as key elements. The value proposition is described as the products or services offered to different customers segments, and is a function of key resources and key processes (or activities) given to the input from suppliers (or partners). (more…)

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Stop competing on price

Stop competing on price

If you are competing by creating a higher level of value, then you can’t compete against lower levels of value and the accompanying lower price. (more…)

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Customer segmentation with R

Customer segmentation with R

In this post I’m going to talk about an extremely important marketing concept – Customer Segmentation. Central to customer segmentation is the ability to identify different customer types and figure out means to find out more about those individuals, so that you can acquire or retain more customers. In this post, I’ll demonstrate how customer segmentation can be performed using K-Means clustering…

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Getting started with D3JS

Getting started with D3JS

D3.js is a data-driven documents library (thus the three Ds in D3.js) written in JavaScript for creating Data Visualizations that can be rendered in a modern web browser. (more…)

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Should you trust sales research?

Should you trust sales research?

Sales research shows one part of a picture. It’s a view from a certain angle, and from that angle, you get a very clear picture of something, albeit not everything. From a different view, you get a completely different picture, which is as clear and compelling as the other view. (more…)

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Another interesting Mandelbrot fractal

Another interesting Mandelbrot fractal

Today, I generated another high precision Mandelbrot fractal at 22 decimal places using the Fractal workbench software that’s the engine behind the fractals in this blog. (more…)

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Mandelbrot monsters

The Monster is acknowledged to be the largest sporadic finite simple group. The Mandelbrot set is recognized as the most complex object in mathematics, as well as being much admired for its aesthetic appeal. (more…)

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Symmetrical Mandelbrot fractal

Fractals are awesome, if you have a good codebase. I’m using the Java-based software I developed during my Bachelors and it could still produce fascinating fractals. (more…)

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Another Mandelbrot fractal

Today, I was playing around with my Fractals codebase and made some changes to the generation parameters with very interesting results and beautiful fractals. (more…)

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Mandelbrot fractal in Java

Mandelbrot fractal in Java

(more…)

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Similarities and Differences Between Business Intelligence, Predictive Analytics & Big Data

Similarities and Differences Between Business Intelligence, Predictive Analytics & Big Data

Recently, I’ve been reflecting on the degree of overlap between Business Intelligence, Big Data and Predictive Analytics, following an event organised by SAP that I was invited to. (more…)

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Tips for better data science

Tips for better data science

These tips are provided by Dr. Granville, who brings 20 years of varied data-intensive experience working with successful start-ups, small companies across various industries, and eBay, Visa, Microsoft, GE and Wells Fargo. (more…)

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