Udemy, Coursera, 2U/edX Face Lawsuits Over Meta Pixel Use

Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Coursera

Data Processing and Feature Engineering with MATLAB

MathWorks via Coursera

Go to class Write review

Overview

Class Central Tips
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.

Syllabus

  • Surveying Your Data
    • 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.
  • Organizing Your Data
    • 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.
  • Cleaning Your Data
    • 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.
  • Finding Features that Matter
    • 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.
  • Domain-Specific Feature Engineering
    • 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.

Taught by

Michael Reardon, Maria Gavilan-Alfonso, Erin Byrne, Amanda Wang, Cris LaPierre, Matt Rich, Brandon Armstrong, Adam Filion, Isaac Bruss, Nikola Trica, Brian Buechel, and Heather Gorr

Reviews

4.7 rating, based on 50 Class Central reviews

4.7 rating at Coursera based on 331 ratings

Start your review of Data Processing and Feature Engineering with MATLAB

  • Profile image for Sufi Shah Hamid Jalali
    Sufi Shah Hamid Jalali @sufham
    A super course from Mathworks! The course is a valuable source for data science used in industry. I recommend it to all who want to get a specialty in data science with the powerful computation engine of MATLAB.
  • David Cliffe
    Some disclosure: I took this course after 'Exploratory Data Analysis with MATLAB', work as an engineer, and use MATLAB frequently in my work. I am also completing the 'Practical Data Science with MATLAB' specialization. I found this course was bot…
  • Anonymous
    Brilliant videos and tasks focused to practical aspect of everyday workflow. Helpful also for future tuning of the gained skills.
  • Anonymous
    I would give it a 3.5/5 if I could. The course is very interesting and very goal-oriented and the video aren't too long. However, the learning curve is all over the place. One minute they are guiding you with baby steps the next there is a page of…
  • Anonymous
    good for practice in feature extraction and feature engineering using Matlab and know to process the data into insight for me and other people
  • Anonymous
    Good continuation of the Exploratory Data Analysis course. I will probably continue with the Predictive Modeling.
  • Profile image for Suman Ray Pramanik
    Suman Ray Pramanik
    An excellent course with concise and appealing content. The graded and ungraded assignments are beneficial, exciting, and a great way to enhance learning. This course touches on feature engineering for different fields and makes each one as exciting as the other. Lastly, the provided codes are a gift from heaven. I am sure these codes will help us advance toward the journey as professional coders in MATLAB.
  • Anonymous
    I found this course very interesting and challenging. The examples provided are very good. However, the estimated completion times are too optimistic, and the approximated 20 hours estimation to complete the course are clearly insufficient, especially if you are not familiarized with some of the data processing data techniques referred (e.g. Fourier analyse).
    So I would give it a 4.5/5 if I could.
  • Anonymous
    I think it is a great course. The reason giving 4 stars, is due to the fast rhythm of the last module that talks about Text feature engineering. Also, it has many functions that requires significant time to learn on our own. Would recommend to explain those functions, and slow down the rhythm similar to the Feature engineering with Signals.
  • Anonymous
    Good course on feature extraction for machine learning if you are using Matlab. Examples from three domains are covered: Signals (typically, parameters logged as a function of time), Images (detecting a STOP sign), and text (natural language processing). The course version of Matlab provides all the appropriate toolboxes, which is good since Matlab has a tendency to create more toolboxes (more revenue!) instead of adding functionality to an existing toolbox such as a Machine Learning toolbox. The course walks you through the important steps in data processing -- loading and preprocessing the data, extracting features, cleaning the data, and evaluating/assessing the features to see if they are important.
  • Anonymous
    This course is designed and organized very well. It is beneficial for a person who needs to analyze large datasets. For a person new to data science and data analysis, this course provides an overview of how to proceed and interpret the results. An advance course must be floated for more in-depth knowledge.
  • Anonymous
    a good kick off for clustering and data processing, although it has explained only the very basics of clustering and feature engineering but this glimpse is very helpful for the next course in the machine learning specialization
  • Anonymous
    Very interesting course. I am already experienced with using MatLab but, still, taking the course was infinitely useful and I learnt how to optimize many preprocessing steps as well as to analyze images and text.
  • Anonymous
    Great work from Mathworks! . The course is a valuable source for data science used in industry. I recommend it to all who want to get a specialty in data science with the powerful computation engine of MATLAB.
  • Anonymous
    This course was considerably more rigorous than the first, Exploratory Data Analysis, however, it presented an opportunity to advance my skills. I am looking forward to the next course in this series.
  • Anonymous
    very high quality course. The guys explained difficult concept in good working ways. The course is great since it teaches the live examples, which we can apply in our domain.
  • Anonymous
    Complete course focused on Data Engineering. It teaches how to manipulate, organize, clean and engineer different types of data, in a proper manner. Definetely would recommend it.
  • Anonymous
    Excellent course. I recommend it for people who wants, like me, learn data science and using Matlab at the same time.
  • Anonymous
    I've learnt a lot from this course and the material provided is really valuable and comprehensive. The videos are very well done and are not lengthy.

    I'm actually a Python user and enrolled in this course for the content. I tried replicating the work in Python but not all of it could be done with ease so I still had to dabble in MATLAB for this course.

    My only grouse is that the MATLAB license provided did not come with the 'text analytics toolbox' so I couldn't do the hands-on for these lessons.
  • Anonymous
    I learned a lot about MATLAB in this lesson Data Processing and Feature Engineering with MATLAB, this course had tought us some knowledge extends beyond processing tables to processing images, signals, and text, and it provides a lot of examples for our follow-up review. The downside of this lecture is that I want to be able to go into some of the functions in more detail, because just writing them out would make some of the arguments really hard to understands for our follow-up review.
Go to class

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Sign up for free
Someone learning on their laptop while sitting on the floor.