Python Programming: Fundamentals for Data Analytics and Real Process Management

This book is written for programmers, software developers, managers, data analysts, researchers, teachers, students, professionals, and general consumers on how to solve real-life process problems and challenges through cutting-edge technologies, done by development and mastery of programming and data manipulation skills in Python. These days, "Computer Intelligence," which refers to the ability of a computer to learn a specific task from data or experimental observation, has become a field and technique that can be used to critically analyze data (quantitative and qualitative information) derived from the various processes of different organizations. This can be used to facilitate decision-making by applying ground-breaking and cutting-edge technological tools such as mathematical reasoning, programming techniques, and statistical methods. This book covers the design of programming logic for the understanding of information flows, while building databases to solve various process and organizational problems through the combination of Computer programming and Process modelling techniques. It describes the specific design processes, flowchart, and algorithms that model real-life situations and translate them into technological applications that improve and innovate everyday processes. The main topics covered include the analysis of information flows, designs processes, and algorithms that model real-world process and situations, import and export of data from one platform to another, and the analysis and manipulation of data to support decision-making and digital transformation, in general. For all intents and purposes, this book serves as both textbook and guideline for Python users particularly for solving everyday process or problems. Resourcefully, encompassing different examples of everyday process and case studies, the envisioned subcompetencies and mastery level skills aimed to be developed by the readers and audience of this book using Python include:

Computational resolution of simulated real processes using different examples and case studies

Flow chart modeling and IPO (input, process, output) diagrams

Code optimization and proper program execution

Information flow in simulated process management

Search and analysis tools, e.g., descriptive statistics

Programming-based real-life solutions and decision making

Modelling of real situations and professional activities through algorithmic reasoning, data manipulations, and visualizations that can be applied to any typical real-life process management.