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Organize Research Data: File Management

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Organize Research Data: File Management

This course is part of Conjoint to Cluster: Survey Design and Segmentation Specialization

Instructor: LearningMate

Included with

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2 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

2 hours to complete

Flexible schedule

Learn at your own pace

2 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

2 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Learners will apply a systematic approach to organize data by identifying data processing stages and applying standardized naming conventions.

Skills you'll gain

Details to know

Shareable certificate

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Build your subject-matter expertise

This course is part of the Conjoint to Cluster: Survey Design and Segmentation Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 2 modules in this course

This course builds essential data management foundations for market research professionals and anyone looking to bring order to their digital files. Learners will develop a strong understanding of data processing stages and master techniques for implementing standardized file organization systems. Through practical exercises, you will build the fundamental data governance skills needed to maintain well-organized research repositories that ensure data accessibility and integrity.

You'll learn to differentiate between raw, cleaned, and analyzed data and apply standardized naming conventions to keep your digital files organized and easily accessible. Through a series of case-driven videos, hands-on labs, and decision-focused projects, you'll explore the real-world implications of data organization, from preventing costly errors like the infamous Reinhart-Rogoff Excel mistake to ensuring supply chain efficiency at major retailers like Walmart. By the end of this course, you'll be able to implement a systematic approach to data organization, transforming chaotic file collections into valuable, well-documented knowledge repositories that support reliable, data-driven decisions.

Module details

This module introduces the foundational concepts of the data lifecycle. You will learn to distinguish between the different stages of data—raw, cleaned, and analyzed—and understand why this classification is crucial for accurate and reliable research. Through real-world examples and hands-on activities, you will develop the ability to identify and classify data, setting the stage for robust data management practices.

What's included

2 videos2 readings2 assignments

2 videosTotal 11 minutes

The Reinhart-Rogoff Error: A Cautionary Tale6 minutes
Spot the Difference: Identifying Data Stages in Clinical Trials5 minutes

2 readingsTotal 10 minutes

The Three Stages of Data: Raw, Cleaned, and Analyzed5 minutes
Career Focus: The Data-Savvy Professional5 minutes

2 assignmentsTotal 20 minutes

Hands-On Learning: Data Sherlock: Classifying Sample Files15 minutes
Knowledge Check: Data Stages Pop Quiz5 minutes

This module focuses on the practical application of data organization through standardized file naming conventions. You will learn how to create and apply a consistent naming system to your research files, ensuring they are easy to find, version, and share. By the end of this module, you will have the skills to transform a disorganized folder into a clean, professional, and efficient repository.