Data Science - A Birds' Eye View

Data science or machine learning is the process of giving the machines the ability to learn from a dataset without being told or programmed. For instance, it is extremely hard to write a program that can take a hand-written digit as an input image and outputs a value from 0-9 according to the image that's written. The same applies to the task of classifying incoming emails as spam or non-spam. For solving such tasks, data scientists use learning methods and tools from the field of data science or machine learning to teach the computer how to automatically recognize digits, by giving it some explanatory features that can distinguish one digit from another. The same for the spam/non-spam problem, instead of using regular expressions and writing hundred of rules to classify the incoming email, we can teach the computer through specific learning algorithms how to distinguish between spam and non-spam emails.

You are probably using applications of data science on a daily basis, often without knowing it. For example, your country might be using a system to detect the ZIP code of your posted letter in order to automatically forward it to the correct area. If you are using Amazon, they often recommend things for you to buy and they do this by learning what sort of things you often search for or buy.

Building a learned/trained machine learning algorithm will require a base of historical data samples from which it's going to learn how to distinguish between different examples and to come up with some knowledge and trends from that data. After that, the learned/trained algorithm could be used for making predictions on unseen data. The learning algorithm will be using raw historical data and will try to come up with some knowledge and trends from that data.

In this chapter, we are going to have a bird's-eye view of data science, how it works as a black box, and the challenges that data scientists face on a daily basis. We are going to cover the following topics:

• Understanding data science by an example
• Design procedure of data science algorithms
• Getting to learn
• Implementing the fish recognition/detection model
• Different learning types
• Data size and industry needs