Data Processing and Feature Engineering with MATLAB

This course is part of Practical Data Science with MATLAB Specialization

Instructors: Michael Reardon

Access provided by Indonesia Cyber Education Institute

16,037 already enrolled

5 modules

Gain insight into a topic and learn the fundamentals.

351 reviews

Intermediate level

Some related experience required

Flexible schedule

2 weeks at 10 hours a week

Learn at your own pace

96%

Most learners liked this course

5 modules

Gain insight into a topic and learn the fundamentals.

351 reviews

Intermediate level

Some related experience required

Flexible schedule

2 weeks at 10 hours a week

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

Skills you'll gain

Tools you'll learn

Matlab

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 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.

These skills are valuable for those who have domain knowledge and some exposure to computational tools, but no programming background. To be successful in this course, you should have some background in basic statistics (histograms, averages, standard deviation, curve fitting, interpolation) and have completed Exploratory Data Analysis with MATLAB. Throughout the course, you will merge data from different data sets and handle common scenarios, such as missing data. In the last module of the course, you will explore special techniques for handling textual, audio, and image data, which are common in data science and more advanced modeling. By the end of this course, you will learn how to visualize your data, clean it up and arrange it for analysis, and identify the qualities necessary to answer your questions. You will be able to visualize the distribution of your data and use visual inspection to address artifacts that affect accurate modeling.

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

10 videos Total 46 minutes

Practical Data Science with MATLAB 4 minutes
Overview of Data Processing and Feature Engineering 4 minutes
Instructor Introduction 3 minutes
Introduction to Module 1 1 minute
Introduction to the Flights Dataset 7 minutes
Exploring the Flights Dataset 6 minutes
Describing Distributions 6 minutes
Examples of Distributions 5 minutes
Visualizing Multidimensional Data 9 minutes
Summary of Module 1: Surveying Your Data 2 minutes

4 readings Total 90 minutes

Access MATLAB 15 minutes
Data and Code Files 15 minutes
Variables in the Flights Dataset 15 minutes
Practice Visualizing Multidimensional Data 45 minutes

2 assignments Total 50 minutes

Module 1 Quiz 30 minutes
Understanding the Flights Dataset 20 minutes

1 discussion prompt Total 15 minutes

Feeling unlucky with delays? 15 minutes

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

8 videos Total 34 minutes

Introduction to Module 2: Organizing Your Data 2 minutes
Working with Strings 7 minutes
Working with Dates and Times 5 minutes
Importing Multiple Data Files 4 minutes
Combining Data 6 minutes
Joining Tables 5 minutes
Sorting Your Data 4 minutes
Summary of Module 2: Organizing Your Data 2 minutes

2 readings Total 45 minutes

Practice Working with Strings 15 minutes
Practice Using Dates and Times 30 minutes

2 assignments Total 75 minutes

Quiz 2: Organizing Your Data 60 minutes
Practice Working with Strings 15 minutes

1 app item Total 30 minutes

Create Datetime Variables 30 minutes

1 discussion prompt Total 20 minutes

How many direct flights can you make? 20 minutes

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

9 videos Total 44 minutes

Introduction to Module 3: Cleaning Your Data 2 minutes
Identifying Missing Data 7 minutes
Handling Missing Data 5 minutes
Identifying Outliers 5 minutes
Investigating Outliers 6 minutes
Normalizing Data 5 minutes
Examples of Normalizing Data 5 minutes
Smoothing Data 7 minutes
Summary of Module 3: Cleaning Your Data 2 minutes

2 readings Total 90 minutes

Practice Working with Outliers 60 minutes
Cleaning Data: Analyzing Flight Volume 30 minutes

2 assignments Total 55 minutes

Quiz 3: Cleaning Your Data 40 minutes
Practice Quiz: Putting it all Together 15 minutes

1 app item Total 15 minutes

Normalizing Data with z-scores 15 minutes

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

7 videos Total 35 minutes

Introduction to Module 4: Finding Features that Matter 2 minutes
Introduction to Feature Engineering 6 minutes
Introduction to Unsupervised Learning 4 minutes
Introduction to Clustering Algorithms 7 minutes
Evaluating Features 8 minutes
Introduction to Dimensionality Reduction and PCA 6 minutes
Summary of Module 4: Finding Features that Matter 2 minutes

5 readings Total 150 minutes

Examples of Feature Engineering 30 minutes
Practice Clustering Data 30 minutes
K-means Clustering 15 minutes
Applying Filter Methods 30 minutes
Applying PCA 45 minutes

1 assignment Total 45 minutes

Quiz 4: Finding Features that Matter 45 minutes

1 app item Total 15 minutes

Feature Generation Through Discretization 15 minutes

1 discussion prompt Total 10 minutes

How many features does your data have? 10 minutes

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

11 videos Total 69 minutes

Introduction to Module 5: Domain-Specific Feature Engineering 3 minutes
Feature Engineering Workflow 3 minutes
Synchronizing Data with Timetables 9 minutes
Summary Statistics as Features 9 minutes
Finding Peaks 9 minutes
Optional: Recording Sensor Data with MATLAB Mobile 3 minutes
Feature Engineering and Clustering with Images 13 minutes
Feature Engineering with Text 7 minutes
Feature Engineering with Storm Events 7 minutes
Summary of Module 5: Domain-Specific Feature Engineering 2 minutes
Summary of Data Processing and Feature Engineering 4 minutes

5 readings Total 103 minutes

Grading Information for Week 5 3 minutes
Practice using Summary Stats as Features 20 minutes
Practice Working with Images 30 minutes
Modeling Using Qualitative Descriptions 45 minutes
Provide Feedback on Your Course Experience 5 minutes

4 assignments Total 130 minutes

Signal Processing Graded Quiz 45 minutes
Image Processing Graded Quiz 25 minutes
Text Processing Graded Quiz 40 minutes
Practice Finding Peaks 20 minutes

1 app item Total 30 minutes

Practice Segmenting Images 30 minutes

1 discussion prompt Total 10 minutes

What other features could you use? 10 minutes

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Instructors

Instructor ratings

(109 ratings)

Michael Reardon

MathWorks

4 Courses 55,609 learners

Maria Gavilan-Alfonso

MathWorks

4 Courses 55,609 learners

Offered by

MathWorks

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Showing 3 of 351

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).

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 !

Reviewed on Nov 30, 2020

A very beautiful course based on the content. Just rushes with the content in the last 3 weeks. A week could be added to evenly distribute the content and not rush through it.

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