No-Code Data Science

No-Code Data ScienceNo Code Data Science is a revolutionary book that democratizes the application of predictive analytics for organizations of all sizes. This first of its kind textbook book is designed to empower readers with the ability to leverage advanced analytics, machine learning, and AI without using a programming language, such as Python or R. Its a comprehensive guide to no code data science (NCDS) that applies free, no code, and open source software with

No Code Data Science is a revolutionary book that democratizes the application of predictive analytics for organizations of all sizes. This first-of-its-kind textbook book is designed to empower readers with the ability to leverage advanced analytics, machine learning, and AI without using a programming language, such as Python or R.
ItÕs a comprehensive guide to no-code data science (NCDS) that applies free, no-code, and open-source software with Orange visual programming software, JASP, and BlueSky Statistics. A no-shortcuts approach to ML and AI is applied to maximize the accuracy and application potential of predictive models.
The NCDS approach is akin to constructing predictive models with pre-made LEGO bricks (visual programming) versus tediously molding shapes from clay (manual coding). A practical how-to approach to predictive modeling is offered while insisting on the rigor of our disciplined NCDS process. Hands-on data exercises are included in the first eleven chapters. QR code links to educational videos are included in most chapters.
Data science background is first explored, discussing basic definitions and data scientist skill sets. This is followed by chapters on data preparation, wrangling, and data visualization. Predictive analytics is covered in chapters on machine learning models and model evaluation. Both supervised and unsupervised learning are included in the discourse. Time series forecasting, survival analysis, and geolocation are covered in separate chapters. Artificial intelligence is featured in chapters on image analysis and text mining. Lastly, the potential impact of machine learning and artificial intelligence on Industry 4.0 is covered in the last chapter. A pathway for statisticians, Lean Six Sigma practitioners, and other professionals is offered to learn predictive modeling techniques to enable organizations to successfully pursue Industry 4.0 goals.