Data Science is a process and involves; defining your problem, collecting data, cleaning data, training and deploying machine learning models. The training of machine learning models is what attracts many to take the journey and I guess it is the cool part. However, many Data Scientist in industry will argue that training a model is [...]

# Author: Samantha Van Der Merwe

## R Programming Roadmap

This is a guideline for individuals who regularly work with data or have access to it and are interested in taking their career to the next level. There is no need for this group of individuals to have a software engineering or a computer science background. A good example being Industrial Engineers who need to [...]

## Getting Started with GDELT

A few years ago a friend of friend introduced me to a data set known as the Global Database of Events Language and Tone (GDELT) data. GDELT is an open source event data repository based on news articles and currently contains over a quarter-billion event records starting from the year 1979. The database is updated [...]

## Data Science Getting Started

This is just a list of online resources for getting started with Data Science. I only list resources that I have used, recommended by a lecture or where I have done a brief walk-trough to fill some knowledge gaps. Courses for absolute beginners: Coursera: Data Science Specialization LinkedIn Learning: Data Science foundations Udemy: Complete Python [...]

## Be Lightning

## The Types of Data Scientists You Might Meet

Data Science is currently marketed as a skill/career that anyone can learn. If you can write code you can become a Data Scientist. Ok, that is an argument for another day. However, because of this marketing and hype surrounding AI (Artificial Intelligence ), it is no surprise that I have crossed paths with Data Scientists [...]

## Neural Networks Learning The Basics: The Playground

If you have any interest in the field of machine learning and artificial intelligence (AI), then you must have come across neural networks. I like to think of a neural network as a computer program that learns how to accomplish specific tasks with the help of lots and lots of data. Probably a very common [...]

## Neural Networks Learning The Basics: Gradient Descent and Stochastic Gradient Descent

Overview In the previous post we looked at backpropagation. In the example we calculated the gradient of the loss with respect to each weight parameter. The gradient determined which direction to move the weight parameter and how much to move it by. The new weight parameter was then updated using the formula below: $latex w_{new} [...]

## Neural Networks Learning The Basics: Backpropagation

In this post I will go through a simple backpropagation algorithm that I have implemented on a spreadsheet. It is simple as I will use one training example with two input features to explain the concept. For example: A single training example In the above we have a single observation with two input variables x1 [...]

## Neural Networks Learning The Basics : Layers, Activation

This post continues from neural network basics part 1: Layers Matrix Multiplication. This post therefore assumes that you have basic knowledge on what a neuron is. Why Add an Activation Function? In neural network basics part 1: Layers Matrix Multiplication I covered matrix multiplication and defined a simple neural network as the weighted sum of inputs with the equation: [...]