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Data Modeling for Qlik Sense Course Overview

Data Modeling for Qlik Sense Course Overview

The Data Modeling for Qlik Sense course is designed to equip learners with the skills necessary to effectively model data within the Qlik Sense environment. It provides an in-depth understanding of the features and Architecture of Qlik Sense, including Smart search and Responsive design, as well as the differences between Qlik Sense and QlikView. Learners will gain hands-on experience with Scripting essentials, loading data from various sources, and managing Complex data associations.

The course also covers the creation of Visualizations and the utilization of Qlik Sense's Associative engine to derive actionable insights. With modules on advanced topics such as Set analysis, Qlik Sense security, and Data model optimization, participants will learn best practices for building robust data models, including handling Synthetic keys and Circular references, and ensuring efficient performance.

By the end of the course, learners will have a comprehensive understanding of Qlik Sense editions, Architecture, and Data modeling techniques, positioning them to create powerful analytics applications that support data-driven decision-making within their organizations.

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Course Prerequisites

To ensure that you can successfully undertake training in the Data Modeling for Qlik Sense course, it is recommended that you meet the following minimum prerequisites:


  • Basic understanding of data structures and database concepts.
  • Familiarity with the fundamentals of SQL and relational databases.
  • Experience working with data in spreadsheets or other visualization tools.
  • Basic knowledge of business intelligence concepts and data analysis.
  • An understanding of the principles of data visualization.
  • Comfort with using a computer and navigating software interfaces.

Please note that these prerequisites are intended to provide a foundation for the course material, but we encourage individuals with a keen interest in learning Qlik Sense to enroll, even if they may not meet every single requirement listed above. Our course is designed to accommodate a range of technical backgrounds, and our instructors are skilled at helping participants bridge gaps in their knowledge.


Target Audience for Data Modeling for Qlik Sense

The Data Modeling for Qlik Sense course is designed for professionals seeking to master data visualization and analytics with Qlik Sense.


  • Data Analysts
  • Business Intelligence (BI) Analysts
  • Data Architects
  • Business Analysts
  • Data Engineers
  • Qlik Sense Developers
  • Database Administrators
  • IT Professionals looking to expand their skillset in data analytics
  • BI Consultants
  • Data Visualization Specialists
  • Report Developers
  • System Analysts
  • Data Science Enthusiasts with a focus on BI tools
  • Professionals working with data warehousing and business analytics


Learning Objectives - What you will Learn in this Data Modeling for Qlik Sense?

Introduction to Learning Outcomes:

The Data Modeling for Qlik Sense course equips learners with the expertise to build robust data models and create interactive visualizations, ensuring a comprehensive understanding of Qlik Sense's capabilities.

Learning Objectives and Outcomes:

  • Grasp the key features of Qlik Sense, including Smart Search and Responsive Design, and differentiate between Qlik Sense and QlikView.
  • Understand the various Qlik Sense editions and the underlying architecture to better align with organizational needs.
  • Master scripting essentials to efficiently load and manage data within Qlik Sense applications.
  • Develop skills to load data from multiple sources and associate data across tables, using data load scripts and the data model viewer.
  • Learn to handle complex data structures by creating and managing star and snowflake schemas, and optimizing data models for performance.
  • Gain proficiency in Qlik Sense's chart functions and script syntax for creating dynamic and responsive data visualizations.
  • Explore advanced data modeling techniques, including the use of QVDs, synthetic keys, and circular references, and learn best practices in data modeling.
  • Understand and apply Set Analysis to perform complex data comparisons and computations within Qlik Sense.
  • Acquire knowledge of Qlik Sense security features, including section access and dynamic data reduction, to maintain data governance and compliance.
  • Learn to troubleshoot common data modeling issues and optimize data models to enhance app performance and user experience.

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