Hands-On Data Analysis with R: 1 Day Workshop in Perth
Description
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About This Course
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 provided
Course Overview
This 1 Day beginner-to-intermediate program introduces you to practical data analysis using R. The focus is on understanding how to structure data, clean it efficiently, explore patterns, generate insights, and apply analytical techniques using real datasets. Instead of covering deep programming or complex statistical theory, this workshop prioritizes applied analytics, reproducible workflows, and essential R techniques used in industry.
You will learn how analysts use R to transform messy data, explore relationships, summarize trends, and interpret insights clearly. Throughout the day, you’ll work through conceptual demonstrations, guided exercises, and scenario-based activities to gain a strong, hands-on understanding of R-powered data analysis.
Learning Objectives
By the end of the course, you will be able to:
Understand R workflows used for practical data analysis.
Import, clean, and manage datasets effectively.
Apply essential transformation techniques for analysis.
Explore data using structured EDA approaches.
Interpret simple visualizations created with R.
Identify patterns and generate insight-driven summaries.
Create a basic analysis plan using R-based workflows.
Target Audience
Data beginners moving toward analytical roles
Business analysts & reporting professionals
Students entering data analytics
Researchers working with small-to-medium datasets
Teams needing practical, non-technical analytics skills
Professionals who want to understand R at a functional level
📧 Contact us today to schedule a customized in-house session: info@mangates.com
Why Choose This Course?
This training focuses on practical R usage without overwhelming you with programming fundamentals or advanced statistics. The trainer brings experience in applied analytics, data preparation, exploratory analysis, and insight communication—ensuring the session remains hands-on, beginner-friendly, and industry-relevant. You leave with a clear understanding of how R supports real-world data decisions.
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FAQs
- 1. Is this course suitable for beginners?
Yes, the course is designed for beginners moving into intermediate-level concepts. - 2. Do I need programming experience for this course?
No, the workshop focuses on practical analysis rather than coding complexity. - 3. Is this training industry-specific?
No, the concepts apply to all industries working with structured data. - 4. Can this course help me transition to a data role?
Yes, it builds foundational skills needed for analytical responsibilities. - 5. Are customized or corporate sessions available?
Yes, in-house sessions can be fully tailored to your industry or team. - 6. Does the course cover data cleaning workflows?
Yes, a full module covers practical transformation and data preparation. - 7 Will I learn how to perform exploratory data analysis?
Yes, you will practice EDA techniques using real-world scenarios. - 8. Does the course include visualization concepts?
Yes, basic visualization principles are introduced at a beginner-intermediate level. - 9. Will I learn how to structure insights from analysis?
Yes, the session teaches how to interpret and present findings clearly. - 10. Is the training fully hands-on?
Yes, each module includes activities, guided exercises, and applied examples.
Agenda
Module 1: R for Data Analysis – Foundations & Workflow
• Understanding R’s analytical ecosystem and workflow patterns • Key objects, data structures & beginner-friendly R concepts • How analysts use scripts for reproducible analysis • Icebreaker Activity
Module 2: Importing & Managing Data in R
• Reading CSV, Excel, and structured datasets into R • Handling missing values, formatting issues, and data types • Organizing datasets for smooth analytical workflows • Activity
Module 3: Data Cleaning & Transformation Essentials
• Filtering, sorting, aggregating, and reshaping datasets • Using beginner-friendly transformation workflows • Identifying data quality issues and fixing inconsistencies • Role Play
Module 4: Exploratory Data Analysis (EDA) Concepts
• Understanding distributions, trends, and variable relationships • Generating summary statistics for insights • Using EDA techniques to guide decision-making • Case Study
Module 5: Introductory Visualization Concepts with R
• Visualizing patterns using clean, simple R plotting approaches • Choosing the right plot to answer analytical questions • Interpreting visual outputs for insights • Simulation
Module 6: Basic Statistical Thinking for Data Analysis
• Understanding simple correlation, variation, and trends • Applying basic hypothesis concepts without heavy math • Interpreting results to support insights • Group Brainstorm Activity
Module 7: Analytical Reporting & Insight Communication
• Turning analysis into meaningful, decision-ready insights • Structuring interpretation and summarizing findings • Building a simple, actionable analysis plan • Action Plan Review
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