PRICES MAY VARY. ★★★★★ --- Alvin Lim, Technical Editor "The best practical data science book I have ever read. Step-by-step guidelines, with detailed explanations, make the reader understand the data science process of using the tools. This book is highly recommended!" ★★★★★ --- Yuhao, Researcher "This book presents a comprehensive explanation of the entire workflow of machine learning in a fascinating manner. If you are someone who has difficulty with programming, this book is your solution. The book is well worth five stars out of five." ------------------------------------- About the Book DATA SCIENCE with KNIME is a book written for individuals, professionals and students who want to learn practical data science without worrying about coding. The book is packed with theories and practical activities that guide students step-by-step to execute actual data science tasks with a visual programming tool, KNIME Analytics Platform. With KNIME software, it is CODELESS, and NO CODING is required. It accelerates the process of turning data into insights. ------------------------------------- List of Chapters Chapter 1 - Data Science Fundamental - Chapter 2 - Visual Programming with KNIME - Chapter 3 - Exploratory Data Analysis - Chapter 4 - Machine Learning Theory - Chapter 5 - Machine Learning Process - Chapter 6 - Regression Model - Chapter 7 - Classification Model - Chapter 8 - Clustering Model - Chapter 9 - Practical Analytics Project ------------------------------------- Chapters Summary Chapter 1 introduces the reader to the fundamental of data science and big data, real applications of data science in industries and the data analytics lifecycle. Chapter 2 teaches readers technical skills to use the KNIME Analytics Platform as a programming tool for data science tasks. It is a primary tool to execute all examples and hands-on in this book. Chapter 3 concentrates on exploratory data analysis, teaching the readers to perform data exploration using univariate and multivariate analysis. This chapter also covers the topic of inferential statistics to conduct hypothesis testing. Chapter 4 through Chapter 8 focus on predictive analytics with machine learning. The chapters cover a range of advanced analytical machine learning algorithms, including Linear Regression, Logistic Regression, Naive Bayes, K-Nearest Neighbors, Support Vector Machine, Decision Tree, Random Forest and K-Means algorithm. Chapter 9 is a chapter dedicated to demonstrating the actual analytics project with the KNIME Analytics Platform. Real-business datasets are used for the practical hands-on in this chapter. ------------------------------------- Who Should Read This Book ✔ Data SCIENTIST and CONSULTANT who want to utilise advanced, free, open-source and user-friendly CODELESS KNIME Analytics Platform to increase productivity and efficiency. ✔ RESEARCHERS who want to fully utilize KNIME Analytics Platform as their main data analysis and analytics work. ✔ EDUCATOR who provide teaching and training on Data Science using KNIME Analytics Platform. ✔ GRADUATE/UNDERGRADUATE student at university who taking data science/analytics courses that utilising KNIME Analytics Platform as main tool. ✔ Any INDIVIDUAL who want to start learning data science, without any experience in programming.