In this course, you will build on the skills learned in Exploratory Data Analysis with MATLAB to lay the foundation required for predictive modeling. This intermediate-level course is useful to anyone who needs to combine data from multiple sources or times and has an interest in modeling.
Data Processing and Feature Engineering with MATLAB
Data Processing and Feature Engineering with MATLAB
This course is part of Practical Data Science with MATLAB Specialization
Instructors: Michael Reardon
15,411 already enrolled
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Gain insight into a topic and learn the fundamentals.
4.7
(342 reviews)
Intermediate level
Some related experience required
Flexible schedule
Approx. 20 hours
Learn at your own pace
96%
Most learners liked this course
What you'll learn
Prepare data for further analysis by removing noise, identifying outliers, & merging data from multiple sources
Create and evaluate features for machine learning applications
Explore special techniques for handling textual, audio, & image data
Perform unsupervised machine learning
Details to know
Shareable certificate
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Assessments
11 assignments
Taught in English
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This course is part of the Practical Data Science with MATLAB Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 5 modules in this course
In this module you'll apply the skills gained in Exploratory Data Analysis with MATLAB on a new dataset. You'll explore different types of distributions and calculate quantities like the skewness and interquartile range. You'll also learn about more types of plots for visualizing multi-dimensional data.
What's included
10 videos4 readings2 assignments1 discussion prompt
In this module you'll learn to prepare data for analysis. Often data is not recorded as required. You'll learn to manipulate string variables to extract key information. You'll create a single datetime variable from date and time information spread across multiple columns in a table. You'll efficiently load and combine data from multiple files to create a final table for analysis.
What's included
8 videos2 readings2 assignments1 app item1 discussion prompt
In this module you'll clean messy data. Missing data, outliers, and variables with very different scales can obscure trends in the data. You'll find and address missing data and outliers in a data set. You'll compare variables with different scales by normalizing variables.
What's included
9 videos2 readings2 assignments1 app item
In this module you'll create new features to better understand your data. You'll evaluate features to determine if a feature is potentially useful for making predictions.
What's included
7 videos5 readings1 assignment1 app item1 discussion prompt
In this module you'll apply the concepts from Modules 1 through 4 to different domains. You'll create and evaluate features using time-based signals such as accelerometer data from a cell phone. You'll use Apps in MATLAB to perform image processing and create features based on segmented images. You'll also use text processing techniques to find features in unstructured text.
What's included
11 videos5 readings4 assignments1 app item1 discussion prompt
Instructors
Instructor ratings
4.7 (108 ratings)Offered by
Recommended if you're interested in Data Analysis
Status: [object Object]
Duke University
Duke University
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Learner reviews
4.7
342 reviews
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Showing 3 of 342
AL
4
Reviewed on Aug 28, 2020
Great overview. Very useful exercises and tests. However, Week 5 was a little overwhelming since it is a little bit of everything (signals, images, texts).
DM
4
Reviewed on Apr 7, 2020
Big step up from the previous course but good introduction into data science, what it is and what MATLAB can do to help you get the most out of your analysis. Very good !
SK
5
Reviewed on Mar 14, 2020
Highly recommended for people with patience, and deep interest in Data Processing and Feature Engineering. It is not easy. However, MATLAB instructional tools make this process so much simplified!
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Frequently asked questions
Yes. A free license to MATLAB Online is available to learners enrolled in the course. You can view the supported browsers here.
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.