In this lecture you will learn how to download and install QlikView Personal Edition. You will learn about the Qlik company and their major products used for personal data analysis.

In this lecture we will go through the application creation wizard and create the very first QlikView application. A simple sales application will be built from the sample data included with the installation. This will be your first introduction to working with QlikView.

The way our brain makes the associations so does QlikView. This is a core concept to any QlikView application and it will be explained here in detail. You will learn what the associated data means - the power of green - and what makes QlikView better in comparison to traditional top-to-bottom data analysis tools. A comparison to MS Excel will be done on the movies, film directors and actor analysis.

State and selections are important since data in QlikView changes as we make the selections. Through the Movies database application we will learn how to work with data selection and state navigation.

We will go over full example - Expense Management application. We will learn what makes the application easy and logical to use, basic design principles and how users can work with QlikView to identify hidden costs in the company, from alarming KPI-s to the detection of detailed transactional data that led to exorbitant costs and exceeding budget.

This is an introduction to the Shoe Sales Application, inspired by QlikView's own shoe sales example application. We will explain what we will learn and explain what can be learned in the process.

Recognizing data sources, building data model and visualizing that data are three steps to application building. We will go over explaining importance of good data sources, explain star schema design of the data model, how fact and dimension tables in traditional Business Intelligence tool are represented in QlikView. Shoe Sales application is built from various Excel and CSV files, like shoe sales data, shoe categories, stores etc. We explain how that data is integrated into QlikView model. Basics to table linking, script editor are explained and sheet objects, as a core concept related to building visualizations.

This lecture wraps all the data needed to build the example Shoe Sales QlikView application

We are not providing real shoe sales data, but we are generating an example from a website called Mockaroo. Since understanding data is important, I have added a chapter to learn how one is generated. Mockaroo can be used for any kind of data emulation for many different purposes.

We will learn how to load Excel files into Shoe Sales application by using the QlikView script editor. We will be interacting between changing script and checking the data model. Load statements will be explained.

In this lecture we explain how to load data from CSV file. We also explain what is automatic concatenation of tables, how to avoid them, and how QlikView links tables that have more than one common field.

We continue with explaining loading of inline data. We also explain the concept of forced data concatenation, where different tables are concatenated into one. Resident tables are introduced and concept is explain on the inline data loaded. Explained how drop table works.

Scripting functions are used for creating connection keys, repairing and cleaning data, creating new dimensions, changing field value and used extensively in the ETL process. We will explain scripting and operations in this chapter on our Shoe Sales Application.You will also learn how to use QlikView help guides efficiently.

We are demonstrating four types of joins - inner join, left join, right join and outer join. A new sample application is created to demonstrate the functionality.

In this lecture we use the knowledge about joining tables and connect data to build a proper model. We will show various concepts to join the tables, proper naming of keys, what to do in scenario where multiple keys are shared with a common table and learn how to drop fields from loaded tables.

Some final touch to the data model is to add variables. We use variables for easier code maintenance, and in our scenario to set the boundaries for cost calculation of shoes sold.

By changing some general application properties we can make the application look better and more readable. We explore changing the background picture, font size and general look and feel of sheet objects.