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image for course Clean and Prepare Data for Analysis

Clean and Prepare Data for Analysis

Get your data ready for analysis with some of the most common data preprocessing techniques including outlier removal, normalization, interpolation, smoothing, and detrending.

Course modules

Introduction

Get acquainted with data preprocessing and the course.

Lessons:
  • Course Overview
  • Course Outline and Prerequisites

Remove Outliers from Data

Clean data by performing outlier removal.

Lessons:
  • What Is an Outlier?
  • Remove Outliers from Data

Shift and Scale Data

Normalize data by shifting and scaling values.

Lessons:
  • Introduction to Data Normalization
  • Normalize Electricity Usage Data

Work with Missing Data

Locate, standardize, and remove missing data.

Lessons:
  • Introduction to Missing Data
  • Work with Missing Data

Interpolate Missing Data

Fill in and interpolate missing data.

Lessons:
  • Introduction to Data Interpolation
  • Interpolate Missing Data

Smooth Noisy Data

Reduce noise with data smoothing.

Lessons:
  • Introduction to Data Smoothing
  • Techniques for Smoothing Data

Remove Unwanted Trends in Data

Detrend data to isolate important features and reveal underlying patterns.

Lessons:
  • Introduction to Detrending Data
  • Detrend Electricity Usage Data

Projects

Practice what you learned in the course.

Lessons:
  • World Population Data
  • International Gasoline Prices

Conclusion

Learn next steps and give feedback on the course.

Lessons:
  • Summary
  • Additional Resources
  • Survey

Format:Self-paced

Language:English

Language

  • Hands-on exercises with automated feedback
  • Access to MATLAB through your web browser
  • Shareable progress report and course certificate

MATLAB Onramp

Get started quickly with the basics of MATLAB.

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