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Python libraries for data science

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  Python libraries for data science Python is more prevalent among data scientists due to its embedded characteristics. Although a dozen programming languages are available for data analysis, data mining, and machine learning, Python possesses a special place in the field which ranked as the number 1 programming language in the TIOBE index for data scientists in 2023.  Python is a high-level, interpreted programming language known for its simplicity, readability, and versatility. It was created by Guido van Rossum and released in 1991.  Key Features of Python: 1. Readability: Python's syntax emphasises code readability and uses indentation to define code blocks rather than relying on braces or keywords. 2. Easy to Learn: Python's codes are described as "executable pseudocode" due to their resemblance to natural language.  3. Versatility: Python can be used in many applications, including machine learning, artificial intelligence, web development, scientific computing...

1. Introduction to Data science

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Data Science Data science is a field that combines techniques from mathematics, statistics, computer science, and domain specific knowledge to extract insights and knowledge from the data. The outcomes of data science are effectively utilised in decision making, problem solving, innovation, and optimisation.   Data science lifecycle It is a framework which outlines the key stages involved in a data science project. It provides a structured approach of developing data-driven solutions. The life cycle of data science consists of five key steps: Business understanding, Data gathering, Data preparation and Exploratory data analysis (EDA), Feature Engineering, Model development and Evaluation.    Business understanding : This is the process of identifying the business problem or research question that the project is intended to address. The outcome of this stage ensures that the data science solution aligns with the business goals which can provide values to the stakeholders. ...