No-Code Data Science Made Easy: 1 Day Upskill Training in Sydney

Bring your team and save:

1. Groups of three or more receive a 10% discount

2. Organizations hosting an in-house session with 10+ participants enjoy a 15% discount.

For more information on venue address, reach out to "[email protected]"

Duration: 1 Full Day (8 Hours)

Delivery Mode: Classroom (In-Person)

Language: English

Credits: 8 PDUs / Training Hours

Certification: Course Completion Certificate

Refreshments: Lunch, snacks, and beverages will be provided during the session

If you would like weekend training sessions, kindly reach out to us at [email protected] for availability and scheduling.

This program introduces participants to the intersection of no-code development and data science concepts, enabling them to understand how data is structured, analyzed, and used in decision-making within applications. The course emphasizes conceptual understanding of data workflows, logic, and analytics while leveraging no-code environments.

Learning Objectives:

By the end of this training, you will be able to:

● Understand how data flows within applications

● Apply data science concepts in no-code environments

● Interpret and structure data for business insights

● Develop a data-driven mindset without coding

Target Audience:

● Mastering Business Writing Skills

● Core Management Skills

● Coaching Skills for Workplace Success

● Mastering the Art of Public Speaking

● Mastering Emotional Intelligence

● Stress Management and Resilience Training

This course provides a unique combination of no-code development concepts and data science fundamentals, helping participants build a strong data-driven mindset without needing programming experience. It simplifies complex technical concepts into practical, business-focused learning that professionals from any background can understand and apply. Participants will gain valuable knowledge on how data flows through applications, how automation and logic improve business processes, and how insights can be used for smarter decision-making.

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Want to train your entire team?

This course is available as a fully customizable in-house program, tailored to your organization’s branding, goals, and team needs, ensuring relevant, practical learning with real-time application and improved design consistency across teams.

Contact us today to schedule a customized in-house, face-to-face session: [email protected]

Info: ● Understanding no-code and low-code ecosystems
● Introduction to data science lifecycle (data collection → processing → analysis → insights)
● Role of data in modern applications
● Key concepts: structured vs unstructured data, data-driven decision-making
● Relationship between no-code tools and data science workflows

Info: ● Translating business problems into data problems
● Identifying data requirements for applications
● Types of data: categorical, numerical, time-series
● Introduction to data-driven design thinking
● Understanding inputs, outputs, and expected insights

Info: ● Concept of data structures in applications (tables, records, fields)
● Basics of data modeling (entities, relationships, normalization – conceptual level)
● Understanding databases in no-code platforms
● Data integrity and consistency concepts
● Introduction to relational vs non-relational thinking

Info: ● Sources of data (manual input, APIs, external tools)
● Concept of data cleaning (missing values, duplicates, inconsistencies)
● Data transformation basics (filtering, sorting, formatting)
● Importance of data quality in analysis
● Ethical considerations in data collection

Info: ● Introduction to logical thinking in workflows
● Conditional logic (IF/THEN scenarios) in decision-making
● Concept of automation as data processing pipelines
● Understanding triggers, rules, and events
● Mapping workflows to basic analytical processes

Info: ● Introduction to descriptive analytics (what happened?)
● Basic metrics: counts, averages, trends
● Concept of dashboards and data visualization
● Communicating insights through visual elements
● Understanding patterns and simple interpretations

Info: ● Building applications with a data-first approach
● Role of data in improving user experience
● Concepts of data security, privacy, and governance
● Bias in data and ethical implications
● Future trends: AI integration in no-code and data science

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