Book Image

Hands-On Business Intelligence with Qlik Sense

By : Pablo Labbe, Clever Anjos, Kaushik Solanki, Jerry DiMaso
Book Image

Hands-On Business Intelligence with Qlik Sense

By: Pablo Labbe, Clever Anjos, Kaushik Solanki, Jerry DiMaso

Overview of this book

Qlik Sense allows you to explore simple-to-complex data to reveal hidden insights and data relationships to make business-driven decisions. Hands-On Business Intelligence with Qlik Sense begins by helping you get to grips with underlying Qlik concepts and gives you an overview of all Qlik Sense’s features. You will learn advanced modeling techniques and learn how to analyze the data loaded using a variety of visualization objects. You’ll also be trained on how to share apps through Qlik Sense Enterprise and Qlik Sense Cloud and how to perform aggregation with AGGR. As you progress through the chapters, you’ll explore the stories feature to create data-driven presentations and update an existing story. This book will guide you through the GeoAnalytics feature with the geo-mapping object and GeoAnalytics connector. Furthermore, you’ll learn about the self-service analytics features and perform data forecasting using advanced analytics. Lastly, you’ll deploy Qlik Sense apps for mobile and tablet. By the end of this book, you will be well-equipped to run successful business intelligence applications using Qlik Sense's functionality, data modeling techniques, and visualization best practices.
Table of Contents (18 chapters)
Free Chapter
Section 1: Qlik Sense and Business Intelligence
Section 2: Data Loading and Modeling
Section 3: Building an Analytical Application
Section 4: Additional Features

Using a Python extension

Within the scope of this chapter, we've prepared a Python module (simple and unprepared to use in production) that implemented three different models for linear regression. Even though linear regression is often inappropriate for time-series analysis, this method is well-known and easy to understand, so it serves as the following:

  • Simple: Runs the linear regression expression y=Wx + b
  • Estimator: Uses TensorFlow's built-in estimator to help automate training, testing, and predicting
  • Polynomial: Uses TensorFlow (this time without an Estimator) to run training and predict a polynomial linear regression expression

We can find it on the GitHub repository at and download it as a ZIP file to your computer using the green Clone or download button.

Unzip the package at any location on the computer...