Tableau 2019.1 for Data Scientists

Video description

You’ve just completed an incredible data science or data analytics project. You still need to present your findings to your manager, client or even a large audience at the conference. In these kinds of situations, powerful visualization can make or break your project. What should you do? With Tableau 2019.1 for Data Scientists, you’ll be able to answer key data decision questions, learn how to deal with disorganized data, and even visualize your results with maps and dashboards.

What makes this training module different? This step-by-step guide is designed to give you practical and essential skills that anyone doing data visualization and analytics needs to have. You’ll be able to boost your visualizations by learning techniques such as adding filters and quick filters, and using color schemas in dashboards.

By the end of this course, you’ll have the skills to make your Tableau data visualization projects a success by creating fascinating stories and offering invaluable guidance when strategic business decisions are being made.

What You Will Learn

  • Connect Tableau to various datasets and gather data from sources such as Excel and CSV files
  • Work with full-suite visuals and create bar charts, area charts, maps and scatterplots, and treemaps and pie charts
  • Explore storytelling and how to choose the best colors for your dashboards
  • Discover the types of joins and how they work
  • Work with data blending in Tableau
  • Export results from Tableau into PowerPoint, Word, and other software
  • Understand aggregation, granularity, and level of detail
  • Study advanced data preparation in Tableau and profit analysis


This training module is designed for aspiring data scientists and data analysts who want to develop their skills in Tableau 2019.1. The course is also ideal if you’re simply looking to stay up-to-date with Tableau’s new features and hone your skills further. If you’re already a competent Tableau user, this course will still be able to provide additional value.

About The Author

Manja Bogicevic: Manja Bogicevic has a mission to help decision-makers gain more profit with machine learning insights. She is one of the first self-made women data science entrepreneurs in the world. Currently, she is pursuing her Micromasters in MIT (Data Science & Statistics). She completed her MBA (Leader Project) at Ivey Business School in London, Canada and her BA at the Faculty of Economics in Belgrade, Serbia.

She is also Co-Founder at Kagera and a data science mentor and consultant at Impact Hub, Belgrade, and works as a data scientist on projects in marketing, fintech, digital health, and the sports industry.

Her strong economics and business background, in combination with her technical skills, mean she delivers innovative and actionable data science solutions in business.

For more information about her, please see her Linkedin profile:

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Table of contents

  1. Chapter 1 : Tableau Installation
    1. Course Overview
    2. What Is Tableau 2019.1?
    3. Tableau 20 19.1 Installation
    4. Navigating Tableau 2019.1
  2. Chapter 2 : Tableau Basics
    1. Case Study: Who Is the Best Sales Manager?
    2. Connecting Tableau with the Dataset
    3. Creating Calculated Fields, Adding Colors and Labels – Part 1
    4. Creating Calculated Fields, Adding Colors and Labels – Part 2
    5. Creating Calculated Fields, Adding Colors and Labels – Part 3
    6. Exporting Your Worksheet and Final Visualization
  3. Chapter 3 : Your First Dashboard
    1. Case Study: Unemployment Rate
    2. Connecting Tableau with the Dataset
    3. Overview of Aggregation and Granularity
    4. Creating Our Area Chart
    5. Adding Filters
    6. My First Dashboard
  4. Chapter 4 : Color Schemes
    1. Introduction to color Schemes
    2. Monochromatic Color and Analogous Color with Industry Data Examples
    3. Complementary Colors with Data Examples
    4. Triadic and Tetradic Colors with Data Examples
  5. Chapter 5 : Color Project
    1. Case Study: Brief Introduction
    2. Connecting Tableau with the Dataset
    3. Testing Our Color Palette and Making Visualizations
    4. Color Split Scheme Implementation
    5. Triad Color Scheme Implementation
    6. Analogue Color Scheme Implementation
    7. Your Dashboard
  6. Chapter 6 : Profit Analysis
    1. Case Study: Profit Analysis
    2. Connecting Tableau with the Dataset
    3. Use a Horizontal Bar Chart
    4. Individual Customer Profitability with a Scatterplot
    5. Filled Maps Chart
    6. Full Interactivity Between the Charts
    7. Making Dashboard Tablet-Friendly
  7. Chapter 7 : Advanced Tableau Visualizations
    1. Case Study: Titanic Disaster – Sankey Diagram
    2. Connecting Tableau with the Dataset
    3. Data Preparation
    4. Building Nested Table Calculation
    5. Building Calculated Fields
    6. Building Core Chart
    7. Complete Sankey Diagram Chart
    8. Sankey Diagram Dashboard

Product information

  • Title: Tableau 2019.1 for Data Scientists
  • Author(s): Manja Bogicevic
  • Release date: February 2019
  • Publisher(s): Packt Publishing
  • ISBN: 9781789958249