IT07A07: DATA MODELING WITH QLIK SENSE (SF)
DATA MODELING WITH QLIK SENSE (SF)
Course Duration
Mode of Assessment
Written Questions and Practical Performance
Who Should Attend
- Business and/or IT working professionals who wants to build a structured data model for self-service analytics.
- Job functional roles that would be suitable to take up this course include Database Administrator and Developer, Business/Data Analyst, Data Engineer, Data Architect, Data Scientists etc.
What's In It for Me
- Learn to develop a coherent data model in Qlik Sense by loading and transforming multiple data sources
- Acquire deep skills to cleanse and transform source data, resolving data model issues and optimize performance
- Use Set Analysis to optimize query
Course Overview
This course will provide learners with the necessary skills to develop a coherent data model in Qlik Sense by loading and transforming multiple data sources as well as to optimize query with Set Analysis and resolving data model issues. With information, tools, techniques, and exercises, this course includes topics dealing with maintaining data connections, cleansing and transforming source data, architecting data models. Optimising for performance and application development on Qlik Sense.
Course Schedule
Next available schedule
Course Objectives
- Utilise data modelling tools and techniques to create data models to deliver business value
- Use the Data Load Editor and the Data Manager effectively and efficiently for data transformation and optimisation to portray trends and findings
- Resolve data modelling issues such as synthetic keys and circular references for the presentation of data to portray critical trends and patterns
- Generate data using techniques for better dashboard visualisation and performance
- Combine tables to customize data requirements from the data model to enable better analytics capabilities
- Handle advanced modelling challenges for the mapping of data for better data displays to address the questions of stakeholders
- Apply concepts to develop and debug data scripts from the data model to suit the data visualisations
- Use the Set Analysis to optimize data queries for data visualisations with display features to align interpretation and presentation of data analytics findings
- Work with server, data and object security for performance considerations with considerations for good user experience and strategic visualisations
Pre-requisites
The admission requirements are:
- Creating Visualizations with Qlik Sense (advantageous)
- Database and SQL query knowledge
- Read, write, and speak English at WPL Level 4
- Manipulate numbers at WPN Level 4
- Hardware & Software
- This course will be conducted as a Virtual Live Class (VLC) via Zoom platform
- Participants must own a Zoom account and have a laptop or a desktop with “Zoom Client for Meetings” installed. Download from zoom.us/download.
System Requirement |
Must-have:
Good-to-have:
Not recommended: |
Course Outline
Module 1: Modelling Data With Qlik Sense
- Qlik Sense deployment architecture
- Data sources and data structures
- Qlik Sense Platform and Qlik Sense App
- Create a new app
- Data load editor and script sections
Module 2: Sourcing & Loading Data
- Data connections
- Extract data from a database
- Data model viewer
- Loading file data
Module 3: Resolving Common Modelling Issues
- Synthetic keys
- Counting table records
- Circular references
- Basic data transformations
Module 4: Generating Data
- Adding Calculated Fields to a table
- Limiting and re-using data
- Creating composite keys
- Master calendar
Module 5: Combining Data
- Mapping Table
- Preceding load on preceding load
- Joining Tables
- Concatenation
Module 6: Handling Advanced Modelling Challenges
- Aggregation Tables
- Cross Tables
- Link Tables
- Data Classification
Module 7: Developing and Debugging
- Control script execution
- Reusing script
- Script variables
- Debugging scripts
Module 8: Applying Finishing Touches
- Defining and Working With Data Sets in Expressions
- Aggregation Functions
- Reusable Items and Object Library
- Data Islands
- QVD Files
- Performance Considerations
Module 9: Exploring Security and Advance Concepts
- Big Data with Qlik Sense
- Managing Security with Section Access
- Profiling Data
- Reloading from the Hub
- Evaluating App Performance
- Advance Analytics Integration in Qlik Sense
Certificate Obtained and Conferred by
- Upon meeting 75% attendance and passing the assessment, participants will be awarded with a digital Statement of Attainment (SOA), accredited by SkillsFuture Singapore. SOA will be reflected as [ICT-DIT-4006-1.1 Data Visualisation].
- Upon meeting 75% attendance and passing the assessment, participants will be awarded with a digital Certificate of Completion from NTUC LearningHub.
- External Certification Exams
This course prepares trainees for the Qlik Sense Data Architect Certification exam. Upon passing the exam, the participants will receive Qlik Sense Data Architect certification.
- Certificate of completion from NTUC LearningHub
Upon meeting at least 75% attendance and passing the assessment(s), participants will receive a Certificate of Completion from NTUC LearningHub.
Additional Details
Medium of Instruction: English
Trainer to trainee ratio: 1:20
Mode of Delivery: <Virtual Live Class (VLC) via Zoom> or <Physical class>
Price
Course Fee and Government Subsidies |
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Individual Sponsored |
Company Sponsored |
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Non-SME |
SME |
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Before GST |
After GST |
Before GST |
After GST |
Before GST |
After GST |
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Full Course Fee |
$3,000.00 |
$3,270.00 |
$3,000.00 |
$3,270.00 |
$3,000.00 |
$3,270.00 |
For Singapore Citizens aged 39 years and below |
$900.00 |
$981.00 |
$900.00 |
$981.00 |
$300.00 |
$381.00 |
For Singapore Citizens aged 40 years and above |
$300.00 |
$381.00 |
$300.00 |
$381.00 |
$300.00 |
$381.00 |
Funding Eligibility Criteria
Individual Sponsored Trainee |
Company Sponsored Trainee |
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Remarks
Individual Sponsored Trainee |
Company Sponsored Trainee |
SkillsFuture Credit:
UTAP:
PSEA:
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Absentee Payroll (AP) Funding:
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Terms & Conditions apply. NTUC LearningHub reserves the right to make changes or improvements to any of the products described in this document without prior notice.
Prices are subject to other LHUB miscellaneous fees.
Batch ID | Course Period | Course Title | Funding Available |
Duration (Hours) |
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