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Welcome to this site which is my tribute to the subject of Data Science, where I share my practical experiences using a range of tools, techniques and technologies.

With a multi-disciplinary career that has spanned mathematical modelling, (procedural and object oriented) software development, business analysis, data analysis and project management, and exposed me to the commercial challenges of many industry sectors, I am naturally drawn to Data Science which represents a convergence of these fields. The ability to interpret data and bring its meaning to life through computational methods and visualisation has been a career-long passion and I have been fascinated by some of the novel software tools that have emerged to support analysis.

Some of the technologies, concepts and numerical models I'll be exploring on these web pages are:

R & RStudio:a platform for handling big data sets with built-in statistical libraries
Python:powerful programming language with statistical and text manipulation libraries
D3.js:(Data Driven Documents) javascript library for visualising data on web pages (see example at the bottom of the page)
Tableau:a platform for interacting with and visualising data
Matlab:a platform for numerical analysis, vector, matrix and tensor manipulation and Principal Component Analysis (PCA)
Hadoop:a framework that supports the distributed storage and processing of big data sets and components that deliver efficient data analysis (such as Map/Reduce and YARN)
Artificial Intelligence, Machine Learning, Deep Learning:an evolution of computer programs from rules-based Expert systems emulating human decision making to algorithms which analyse and learn from data in order to make predictions
Statistical and mathematical principles:matrix manipulation, linear algebra, analysis of data distributions, hypothesis testing

and the stalwart of most analysts' toolbox - Microsoft Excel and VBA. I am a strong proponent of Excel and VBA as they have been of value to me throughout my varied career due to their accessibility, simplicity of use, range of features and ability to be tailored to most business problems. However, as the volume of data to be analysed grows and the data relationships become more complex dedicated platforms for interpreting and visualising data are becoming the norm.

Within this showcase my goal is to share:

  • tutorials on how to access, download and configure the tools;
  • quick reference guides:
  • exemplar projects:
  • and links to resources:

    I hope you will find the resources of value or interest.

    The illustration below is an example of the power of D3.js using code developed by Mike Bostock (creator of the D3.js language) using the Radial Reingold-Tilford Tree algorithm. The data represented is my map of the multifaceted strands of Data Science. I hope you will enjoy sharing my journey...

    Details of my academic, employment and training history can be found on LinkedIn [https://www.linkedin.com/in/neil-gerry-19755b/]