R Programming for Data Analysis – 1 Day Workshop | Surrey

Group Discounts:

• Save 10% when registering 3 or more participants
• Save 15% when registering 10 or more participants

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

Duration: 1 Full Day (9:00 AM – 5:00 PM)
Delivery Mode: Classroom (In-Person)
Language: English
Credits: 8 PDUs / Training Hours
Certification: Course Completion Certificate
Refreshments: Lunch, beverages, and light snacks included

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

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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Want to train your entire team in practical data analysis using R?
Our in-house sessions are fully customizable based on your datasets, reporting needs, and organizational challenges. Your team can work with tailored examples, guided scenarios, or industry-specific exercises to strengthen analytical confidence. Custom modules can be added for data cleaning, reporting workflows, or domain-specific analytics.

📧 Contact us today to schedule a customized in-house, face-to-face session:

Info: • 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

Info: • Reading CSV, Excel, and structured datasets into R
• Handling missing values, formatting issues, and data types
• Organizing datasets for smooth analytical workflows
• Activity

Info: • Filtering, sorting, aggregating, and reshaping datasets
• Using beginner-friendly transformation workflows
• Identifying data quality issues and fixing inconsistencies
• Role Play

Info: • Understanding distributions, trends, and variable relationships
• Generating summary statistics for insights
• Using EDA techniques to guide decision-making
• Case Study

Info: • Visualizing patterns using clean, simple R plotting approaches
• Choosing the right plot to answer analytical questions
• Interpreting visual outputs for insights
• Simulation

Info: • Understanding simple correlation, variation, and trends
• Applying basic hypothesis concepts without heavy math
• Interpreting results to support insights
• Group Brainstorm Activity

Info: • 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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