Description
Leverage the power of visualization in business intelligence and data science to make quicker and better decisions. Use statistics and data mining to make compelling and interactive dashboards. This book will help those familiar with Tableau software chart their journey to being a visualization expert. Pro Tableau demonstrates the power of visual analytics and teaches you how to: Connect to various data sources such as spreadsheets, text files, relational databases (Microsoft SQL Server, MySQL, etc.), non-relational databases (NoSQL such as MongoDB, Cassandra), R data files, etc. Write your own custom SQL, etc. Perform statistical analysis in Tableau using R Use a multitude of charts (pie, bar, stacked bar, line, scatter plots, dual axis, histograms, heat maps, tree maps, highlight tables, box and whisker, etc.) What you’ll learn Connect to various data sources such as relational databases (Microsoft SQL Server, MySQL), non-relational databases (NoSQL such as MongoDB, Cassandra), write your own custom SQL, join and blend data sources, etc. Leverage table calculations (moving average, year over year growth, LOD (Level of Detail), etc. Integrate Tableau with R Tell a compelling story with data by creating highly interactive dashboards Who this book is for All levels of IT professionals, from executives responsible for determining IT strategies to systems administrators, to data analysts, to decision makers responsible for driving strategic initiatives, etc. The book will help those familiar with Tableau software chart their journey to a visualization expert. Seema Acharya is a Lead Principal with the Education, Training and Assessment department of Infosys Limited. She is an educator by choice and vocation, and has rich experience in both academia and the software industry. She is also the author of the books, “Fundamentals of Business Analytics”, ISBN: 978-81-265-3203-2, publisher – Wiley India and “Big Data and Analytics”, ISBN: 9788126554782, publisher – Wiley India. She has co-authored a paper on “Collaborative Engineering Competency Development” for ASEE (American Society for Engineering Education). She holds the patent on “Method and system for automatically generating questions for a programming language”. Her areas of interest and expertise are centered on Business Intelligence, Big Data and Analytics technologies such as Data Warehousing, Data Mining, Data Analytics, Text Mining and Data Visualisation. Subhashini Chellappan is a Software Engineering Team Lead with the talent division of Accenture. She has rich experience in both academia and the software industry. She has published couple of papers in various Journals and Conferences. Her areas of interest and expertise are centered on Business Intelligence, Big Data and Analytics technologies such as Hadoop, NoSQL Databases, Spark and Machine Learning. Table of content Chapter 1: Introducing Visualization and Tableau Why Visualization? What is Visualization? Positioning of Tableau Tableau product lines File types in Tableau .twb .twbx .tds .tdsx .tde .tbm Chapter 2: Working with Single and Multiple Data Sources Desktop Architecture Data Connection Page Connect to a File Excel Open with legacy connection Text Microsoft Access R data file (.rdata) Connect to a Server Microsoft SQL Server MySQL NoSQL Databases (MongoDB, Cassandra) Metadata Grid Using Data Extracts All about Joins Using Custom SQL Using Data Blending Chapter 3: Simplifying and Sorting your Data Filtering on dimensions and measures Soring on single dimension – Primary sort Sorting on more than one dimension – Secondary Sort Slicing your data by date Discrete dates Continuous dates Organizing your data Groups Hierarchies Sets Static sets Dynamic sets Difference between groups and sets Chapter 4: Measure Values and Measure Names Using measure values and measure names in a view Chapter 5: Using Quick Table Calculations in Tableau Running Total Percent of Total Percentile Rank Moving average Year over Year Growth Level Of Detail (LOD) calculations Chapter 6: Customizing your Data String Calculations Number Calculations Date Calculations Logical Calculations Chapter 7: Statistics Descriptive Statistics Sum Average Min Max Count Count(distinct) Median Standard Deviation Variance Using the Analytics Pane Constant Lines Average Lines Five magical number summary Box and whisker plot Trend Lines Forecast Chapter 8: Chart Forms Bar Chart Pie chart Line graph Scatter Plot Histogram Heat Map Tree Map Highlight Table Chapter 9: Advance Visualization Methods Waterfall Charts Gantt View Bullet Graph Chapter 10: Dashboard and Stories Creating an interactive dashboard Adding Actions to your dashboard Telling stories with data Chapter 11: Integration of R with Tableau Functions such as (SCRIPT_INT(), SCRIPT_REAL(),SCRIPT_BOOL(), SCRIPT_STR()) Data Mining Affinity Analysis K-means Clustering
