Description

Introduction

This training teaches participants how to load, transform, and organize data in Qlik Sense. It covers load script development, data modeling, table associations, data quality, and performance optimization. Participants gain practical experience in building reliable data models for business intelligence applications.

Prerequisites

1. Basic knowledge of databases and data analysis.

2. Understanding of tables, fields, and data relationships.

3. Familiarity with spreadsheets and structured data.

4. Basic SQL knowledge is recommended.

Recommended Training Duration

• Beginner: 3 days

• Intermediate: 5 days

• Advanced: 7 days

• Enterprise: 8 days, including practical exercises and a project.

Training Lab Setup Specifications

1. A computer with a supported operating system.

2. Access to a Qlik Sense training environment.

3. Sample CSV, Excel, and database files.

4. Access to a supported relational database for integration exercises.

5. Permissions to create applications, data connections, and load scripts.

6. A stable internet connection for online training.

7. Sample datasets for data modeling and validation.

TABLE OF CONTENTS

Module 1: Introduction to Qlik Sense Scripting and Data Modeling

1.1 Qlik Sense Fundamentals

1.1.1 Overview of the Qlik Sense platform

1.1.2 Role of scripting in data preparation

1.1.3 Principles of data modeling in business intelligence

1.2 Data Architecture Basics

1.2.1 Understanding data sources and data structures

1.2.2 Overview of tables, fields, and keys

1.2.3 Understanding the Qlik associative data model

Module 2: Data Connections and Source Preparation

2.1 Connecting to Data Sources

2.1.1 Connecting to Excel and CSV files

2.1.2 Connecting to relational databases

2.1.3 Configuring and managing data connections

2.2 Preparing Source Data

2.2.1 Reviewing field names and data types

2.2.2 Identifying missing and duplicate values

2.2.3 Validating source data before loading

Module 3: Qlik Sense Load Script Development

3.1 Creating Load Scripts

3.1.1 Understanding the Data Load Editor

3.1.2 Using LOAD statements to import data

3.1.3 Renaming fields and defining variables

3.2 Controlling Data Loads

3.2.1 Filtering records with conditions

3.2.2 Loading selected fields and records

3.2.3 Using script comments and reusable sections

Module 4: Data Transformation and Script Functions

4.1 Transforming Data

4.1.1 Using string and text functions

4.1.2 Applying date and time functions

4.1.3 Creating calculated fields and expressions

4.2 Managing Data Values

4.2.1 Handling null and missing values

4.2.2 Converting data types and formats

4.2.3 Applying conditional logic and mapping functions

Module 5: Data Modeling Fundamentals

5.1 Designing Data Models

5.1.1 Understanding dimensions and measures

5.1.2 Designing fact and dimension tables

5.1.3 Applying star and snowflake schema concepts

5.2 Creating Table Associations

5.2.1 Identifying common fields between tables

5.2.2 Establishing associations through field names

5.2.3 Validating relationships in the data model viewer

Module 6: Advanced Data Loading Techniques

6.1 Combining Tables

6.1.1 Joining tables using common fields

6.1.2 Concatenating tables with compatible structures

6.1.3 Using mapping tables for lookups

6.2 Managing Advanced Loads

6.2.1 Using resident loads

6.2.2 Applying preceding loads

6.2.3 Implementing incremental loading concepts

Module 7: Data Model Validation and Troubleshooting

7.1 Identifying Data Model Issues

7.1.1 Understanding synthetic keys

7.1.2 Identifying and resolving circular references

7.1.3 Preventing unintended table associations

7.2 Validating Data Accuracy

7.2.1 Comparing loaded data with source records

7.2.2 Checking record counts and field values

7.2.3 Troubleshooting script errors and failed reloads

Module 8: Script and Data Model Optimization

8.1 Improving Load Script Performance

8.1.1 Reducing unnecessary fields and records

8.1.2 Reviewing script execution time

8.1.3 Improving transformation efficiency

8.2 Optimizing Data Models

8.2.1 Reducing unnecessary table associations

8.2.2 Managing data volume and memory usage

8.2.3 Improving reload performance and model maintainability

Module 9: Data Security, Governance, and Maintenance

9.1 Managing Data Access

9.1.1 Understanding data connection permissions

9.1.2 Applying available data access controls

9.1.3 Protecting sensitive business information

9.2 Maintaining Data Models

9.2.1 Documenting scripts and field definitions

9.2.2 Managing changes to source data

9.2.3 Maintaining reliable data reload processes

Module 10: Practical Project and Skills Assessment

10.1 Building a Qlik Sense Data Model

10.1.1 Connecting multiple data sources

10.1.2 Developing scripts for data transformation

10.1.3 Creating table associations and validating results

10.2 Testing and Optimizing the Solution

10.2.1 Identifying and correcting data model issues

10.2.2 Improving script and reload performance

10.2.3 Presenting the completed data preparation solution

Conclusion

Participants will learn to develop Qlik Sense load scripts, transform data, create effective data models, and troubleshoot common issues. Hands-on exercises reinforce practical skills in data preparation, validation, and performance optimization.

For more inputs on Qlik Sense you can connect here.
Contact the L&D Specialist at Locus IT

Locus Academy has more than a decade experience in delivering the training/staffing on Qlik Sense Scripting  for corporates across the globe. The participants for the training/staffing on Qlik Sense  are extremely satisfied and are able to implement the learnings in their on going projects.

Other useful references

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