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.
Overview
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, Cris LaPierre, Matt Rich, Brandon Armstrong, Adam Filion, Nikola Trica, Isaac Bruss, Brian Buechel, and Heather Gorr
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Reviews
4.7 rating, based on 53 Class Central reviews
4.7 rating at Coursera based on 339 ratings
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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.
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Malik BilalI recently completed the [Course Name] and found it to be an invaluable experience. The content was well-structured and covered key concepts in-depth, making it easy to grasp even for beginners. The instructor was knowledgeable and engaging, providing real-world examples that enhanced the learning process. I particularly appreciated the hands-on assignments, which allowed me to apply what I learned immediately. Additionally, the supportive online community fostered great discussions and collaboration. Overall, this course exceeded my expectations and has equipped me with practical skills that I can apply in my career. Highly recommended for anyone looking to enhance their knowledge! -
Excellent level of content and explanations. This is a course to really dedicate some time into all you can possibly do with Matlab for data science.
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This course is a real deal, You have to take it if you're data analyst and specially working with matlab environment
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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…
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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…
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Suman Ray PramanikAn 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. -
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.
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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.
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good for practice in feature extraction and feature engineering using Matlab and know to process the data into insight for me and other people
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Brilliant videos and tasks focused to practical aspect of everyday workflow. Helpful also for future tuning of the gained skills.
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Good continuation of the Exploratory Data Analysis course. I will probably continue with the Predictive Modeling.
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Excellent course. I recommend it for people who wants, like me, learn data science and using Matlab at the same time.
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This is a challenging course especially to those who are new to Matlab. But, the video lectures are very helpful so you won't feel lost or left behind. You really must sit down, read and do the sample mlx scripts so you have a better grasp of the course which will eventually help you in the quizzes. I feel like I'm a better data processor now because of the examples and the scripts provided. Highly recommended!
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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. -
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.
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AKRAM MUBARAKIt was a valuable experience to attend this course. Through its five modules, I acquired a wide range of common and domain specific skills for cleaning and smoothing data and engineering features. look forward for the next session.. -
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
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This course covers major elements in Data Preprocessing, engineering and cleaning Features, and then, Evaluating them. It helped alot on comprehend what I judge a must before to a Machine Learning Course. Thanks MathWorks.
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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.
