MathWorks - Data Processing and Feature Engineering with MATLAB
- Offered byCoursera
Data Processing and Feature Engineering with MATLAB at Coursera Overview
Duration | 18 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Data Processing and Feature Engineering with MATLAB at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 2 of 4 in the Practical Data Science with MATLAB Specialization
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Intermediate Level
- Approx. 18 hours to complete
- English Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish
Data Processing and Feature Engineering with MATLAB at Coursera Course details
- 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.
Data Processing and Feature Engineering with MATLAB at Coursera Curriculum
Surveying Your Data
Practical Data Science with MATLAB
Overview of Data Processing and Feature Engineering
Instructor Introduction
Introduction to Module 1
Introduction to the Flights Dataset
Exploring the Flights Dataset
Describing Distributions
Examples of Distributions
Visualizing Multi-Dimensional Data
Summary of Module 1: Surveying Your Data
Download and Install MATLAB
Data and Code Files
Variables in the Flights Dataset
Practice Visualizing Multidimensional Data
Understanding the Flights Dataset
Module 1 Quiz
Organizing Your Data
Introduction to Module 2: Organizing Your Data
Working with Strings
Working with Dates and Times
Importing Multiple Data Files
Combining Data
Joining Tables
Sorting Your Data
Summary of Module 2: Organizing Your Data
Practice Working with Strings
Practice Using Dates and Times
Practice Working with Strings
Quiz 2: Organizing Your Data
Cleaning Your Data
Introduction to Module 3: Cleaning Your Data
Identifying Missing Data
Handling Missing Data
Identifying Outliers
Investigating Outliers
Normalizing Data
Examples of Normalizing Data
Smoothing Data
Summary of Module 3: Cleaning Your Data
Practice Working with Outliers
Cleaning Data: Analyzing Flight Volume
Practice Quiz: Putting it all Together
Quiz 3: Cleaning Your Data
Finding Features that Matter
Introduction to Module 4: Finding Features that Matter
Introduction to Feature Engineering
Introduction to Unsupervised Learning
Introduction to Clustering Algorithms
Evaluating Features
Introduction to Dimensionality Reduction and PCA
Summary of Module 4: Finding Features that Matter
Examples of Feature Engineering
Example of K-means Clustering
Applying Filter Methods
Applying PCA
Quiz 4: Finding Features that Matter
Domain-Specific Feature Engineering
Introduction to Module 5: Domain-Specific Feature Engineering
Feature Engineering Workflow
Synchronizing Data with Timetables
Summary Statistics as Features
Finding Peaks
Feature Engineering and Clustering with Images
Feature Engineering with Text
Summary of Module 5: Domain-Specific Feature Engineering
Summary of Data Processing and Feature Engineering
Practice using Summary Stats as Features
Practice Working with Images
Modeling Using Qualitative Descriptions
Provide Feedback on Your Course Experience
Practice Finding Peaks
Quiz 5: Domain-Specific Feature Engineering
Data Processing and Feature Engineering with MATLAB at Coursera Admission Process
Important Dates
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