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Data Processing and Feature Engineering with MATLAB

MathWorks via Coursera


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.


  • 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.

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4.6 rating, based on 27 reviews

Start your review of Data Processing and Feature Engineering with MATLAB

  • 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 code...
  • 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 both...
  • Anonymous
    The course was very helpful for me, because I've never worked with MATLAB before and it gives me a great overview of what I could do with MATLAB. Nevertheless, it feels like a little bit of promoting all the features: "Look, MATLAB can this and this and...
  • Anonymous
    A great course, but a bit on the challenging side. I was pretty familiar with the subject matter, and definitely struggled in places. One of the "45 minute" readings I'm pretty sure contained my entire 2nd year statistics course I haven't seen in about 17 years. The audio signal processing section would have completely killed me had I not already done some work with fourrier transforms.

    I'm giving it a 3 stars, though I could have gone 4. I don't want this review to discourage people from taking the course, take the course! I just hope they would consider slowing a few sections down and explaining some of the background out a little farther. The most challenging mooc I've taken to date.
  • 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.
  • Anonymous
    The user should have more experience with actually programming in MATLAB before attempting this course. I found myself having to look back through basic matrix operations to answer questions on the quizzes. I was very frustrated at several points during the course trying to figure out what was needed to even attempt the questions.

    I would add a MATLAB programming fundamentals course to the beginning of the specialization.
  • Profile image for 邦邦来



    但是同样还有不尽如人意的地方,比如,MATLAB中的text features engineer 里并没有支持汉语,挺令人遗憾的。
  • Anonymous
    I highly recommend the course. It covers all the important topics and is quite informative about feature engineering and its implementations on different domains. Pace is perfect as well. It also provides an unique mindset and skills in MATLAB. Instructors are helpful and nice. Keep in mind that the course might be a bit difficult unless you are familiar with basics of data science.
  • Anonymous
    Generally a good course, but..

    This course requires a lot of self learning and prior knowledge in statistics and basic signal processing

    Some topics covered very briefly by instructors. I expect from instructors to explain better each function and options how to use it and when , and not just give reference to matlab help pages.

  • Anonymous
    The course is well explained and comes along with useful exercises. Although I would like to have a more 'hands on' experience where I had the chance to program more and get more involved with the algorithms and theory provided. Certainly is a good course to have a general grasp about the topic and learn about MATLABS in-built functions.
  • Anonymous
    I really enjoyed it, as an engineering student with no data science background I felt the pace was great. All provided examples are related to real life, and there is variety so that people from different fields can see its application. Really looking forward to the next 2 courses in the specialization.
  • Anonymous
    Very good Course finally comes to an end. Really enjoyed the course and learn about data science, especially feature engineering. The videos were small enough and discussed every minor detail. If something missed, taking a quiz forced the learner to go around. Thank you, Mathworks, Thank you Coursera
  • Anonymous
    The examples are detailed, and the videos are short; however, it is necessary to know the Matlab environment beforehand. The suggested times are very optimistic, and in reality, it may take more time to solve the quizzes.
  • Anonymous
    Very practical, well structured and easy to follow. This course is exactly what I need. The materials summarizes key points of feature engineering theory and there are a lot of combination with real world application examples.
  • Profile image for Ronald Escobar
    Ronald Escobar
    Very grateful to MathWorks for this course. I am taking my first steps in the world of data science and this course opened my mind a lot and I was able to learn useful skills to achieve my objectives.
  • Anonymous
    This course " Data Processing and Feature Engineering with MATLAB "is awesome. Its covers important topics on various technique on Feature Engineering such as signal, image, and text preprocessing.
  • Anonymous
    Nice overview of data analysis capabilities in Matlab and hands-on exercises. Each week is closed with a quiz and accompanying live script give suggestions for further reading
  • Anonymous
    Such a brilliant app for learning, it's all courses are useful and easy to learn . We can give quizs to improve knowledge about course and we can achieve certificate also
  • Anonymous
    Pretty good, good explanations and further reading resources are given by MATLAB documentation. TBH I learned more stuff than I hoped for which is already good.
  • Anonymous
    I learned Data feauture and analysis. I have seen y learned some of the most powerful functions to me, which are bagofWords, tokenizedDocuments and bagOfNgrams.

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