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Machine learning is the subfield of computer science that, according to Arthur Samuel in 1959, gives "computers the ability to learn without being explicitly programmed." Evolved from the study of pattern recognition and computational learning theory in artificial intelligence, machine learning explores the study and construction of algorithms that can learn from and make predictions on data – such algorithms overcome following strictly static program instructions by making data driven predictions or decisions, through building a model from sample inputs.

Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or unfeasible; example applications include email filtering, detection of network intruders or malicious insiders working towards a data breach, optical character recognition (OCR), learning to rank and computer vision.

Python is a widely used high-level programming language for general-purpose programming, created by Guido van Rossum and first released in 1991.

An interpreted language, Python has a design philosophy which emphasizes code readability (notably using whitespace indentation to delimit code blocks rather than curly braces or keywords), and a syntax which allows programmers to express concepts in fewer lines of code than possible in languages such as C++ or Java.

The language provides constructs intended to enable writing clear programs on both a small and large scale.

Data science, also known as data-driven science, is an interdisciplinary field about scientific methods, processes and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to Knowledge Discovery in Databases (KDD).

Data visualization or data visualisation is viewed by many disciplines as a modern equivalent of visual communication.

It involves the creation and study of the visual representation of data, meaning "information that has been abstracted in some schematic form, including attributes or variables for the units of information".

Entrepreneurship has traditionally been defined as the process of designing, launching and running a new business, which typically begins as a small business, such as a startup company, offering a product, process or service for sale or hire, and the people who do so are called 'entrepreneurs'.

It has been defined as the "...capacity and willingness to develop, organize, and manage a business venture along with any of its risks in order to make a profit." While definitions of entrepreneurship typically focus on the launching and running of businesses, due to the high risks involved in launching a start-up, a significant proportion of businesses have to close, due to a "...lack of funding, bad business decisions, an economic crisis -- or a combination of all of these" or due to lack of market demand.

In the 2000s, the definition of "entrepreneurship" has been expanded to explain how and why some individuals (or teams) identify opportunities, evaluate them as viable, and then decide to exploit them, whereas others do not, and, in turn, how entrepreneurs use these opportunities to develop new products or services, launch new firms or even new industries and create wealth.

Recent advances stress the fundamentally uncertain nature of the entrepreneurial process, because although opportunities exist their existence cannot be discovered or identified prior to their actualization into profits.

What appears as a real opportunity ex ante might actually be a non-opportunity or one that cannot be actualized by entrepreneurs lacking the necessary business skills, financial or social capital.