Data Processing and Feature Engineering with MATLAB

This course is part of Practical Data Science with MATLAB Specialization

Instructors: Michael Reardon

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

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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 videosTotal 46 minutes

Practical Data Science with MATLAB3 minutesPreview module
Overview of Data Processing and Feature Engineering3 minutes
Instructor Introduction2 minutes
Introduction to Module 11 minute
Introduction to the Flights Dataset7 minutes
Exploring the Flights Dataset5 minutes
Describing Distributions6 minutes
Examples of Distributions5 minutes
Visualizing Multidimensional Data8 minutes
Summary of Module 1: Surveying Your Data2 minutes

4 readingsTotal 90 minutes

Access MATLAB15 minutes
Data and Code Files15 minutes
Variables in the Flights Dataset15 minutes
Practice Visualizing Multidimensional Data45 minutes

2 assignmentsTotal 50 minutes

Module 1 Quiz30 minutes
Understanding the Flights Dataset20 minutes

1 discussion promptTotal 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 videosTotal 34 minutes

Introduction to Module 2: Organizing Your Data1 minutePreview module
Working with Strings7 minutes
Working with Dates and Times4 minutes
Importing Multiple Data Files4 minutes
Combining Data5 minutes
Joining Tables5 minutes
Sorting Your Data3 minutes
Summary of Module 2: Organizing Your Data2 minutes

2 readingsTotal 45 minutes

Practice Working with Strings15 minutes
Practice Using Dates and Times30 minutes

2 assignmentsTotal 75 minutes

Quiz 2: Organizing Your Data60 minutes
Practice Working with Strings15 minutes

1 app itemTotal 30 minutes

Create Datetime Variables30 minutes

1 discussion promptTotal 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 videosTotal 44 minutes

Introduction to Module 3: Cleaning Your Data1 minutePreview module
Identifying Missing Data6 minutes
Handling Missing Data5 minutes
Identifying Outliers5 minutes
Investigating Outliers6 minutes
Normalizing Data4 minutes
Examples of Normalizing Data4 minutes
Smoothing Data7 minutes
Summary of Module 3: Cleaning Your Data1 minute

2 readingsTotal 90 minutes

Practice Working with Outliers60 minutes
Cleaning Data: Analyzing Flight Volume30 minutes

2 assignmentsTotal 55 minutes

Quiz 3: Cleaning Your Data40 minutes
Practice Quiz: Putting it all Together15 minutes

1 app itemTotal 15 minutes

Normalizing Data with z-scores15 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 videosTotal 34 minutes

Introduction to Module 4: Finding Features that Matter2 minutesPreview module
Introduction to Feature Engineering5 minutes
Introduction to Unsupervised Learning3 minutes
Introduction to Clustering Algorithms6 minutes
Evaluating Features8 minutes
Introduction to Dimensionality Reduction and PCA5 minutes
Summary of Module 4: Finding Features that Matter1 minute

5 readingsTotal 150 minutes

Examples of Feature Engineering30 minutes
Practice Clustering Data30 minutes
K-means Clustering15 minutes
Applying Filter Methods30 minutes
Applying PCA45 minutes

1 assignmentTotal 45 minutes

Quiz 4: Finding Features that Matter45 minutes

1 app itemTotal 15 minutes

Feature Generation Through Discretization15 minutes

1 discussion promptTotal 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 videosTotal 69 minutes

Introduction to Module 5: Domain-Specific Feature Engineering2 minutesPreview module
Feature Engineering Workflow2 minutes
Synchronizing Data with Timetables9 minutes
Summary Statistics as Features9 minutes
Finding Peaks9 minutes
Optional: Recording Sensor Data with MATLAB Mobile3 minutes
Feature Engineering and Clustering with Images13 minutes
Feature Engineering with Text6 minutes
Feature Engineering with Storm Events7 minutes
Summary of Module 5: Domain-Specific Feature Engineering2 minutes
Summary of Data Processing and Feature Engineering3 minutes

5 readingsTotal 103 minutes

Grading Information for Week 53 minutes
Practice using Summary Stats as Features20 minutes
Practice Working with Images30 minutes
Modeling Using Qualitative Descriptions45 minutes
Provide Feedback on Your Course Experience5 minutes

4 assignmentsTotal 130 minutes

Signal Processing Graded Quiz45 minutes
Image Processing Graded Quiz25 minutes
Text Processing Graded Quiz40 minutes
Practice Finding Peaks20 minutes

1 app itemTotal 30 minutes

Practice Segmenting Images30 minutes

1 discussion promptTotal 10 minutes

What other features could you use?10 minutes

Instructors

Instructor ratings

4.7 (108 ratings)

Michael Reardon

MathWorks

4 Courses52,597 learners

Maria Gavilan-Alfonso

MathWorks

4 Courses52,597 learners

Offered by

MathWorks

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Reviewed on Aug 29, 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 8, 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 Mar 15, 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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