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.
This course is part of the Practical Data Science with MATLAB Specialization
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Course 2 of 4 in the
Intermediate Level
Approx. 20 hours to complete
English
Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish
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Try Coursera for BusinessSkills you will gain
- Feature Engineering
- Matlab
- Data Cleansing
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 2 of 4 in the
Intermediate Level
Approx. 20 hours to complete
English
Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish
Could your company benefit from training employees on in-demand skills?
Try Coursera for Business
Offered by
MathWorks
Accelerating the pace of discovery, innovation, development, and learning in engineering and science.
Syllabus - What you will learn from this course
3 hours to complete
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.
3 hours to complete
10 videos (Total 46 min), 4 readings, 2 quizzes
3 hours to complete
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.
3 hours to complete
8 videos (Total 34 min), 2 readings, 3 quizzes
3 hours to complete
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.
3 hours to complete
9 videos (Total 44 min), 2 readings, 3 quizzes
4 hours to complete
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.
4 hours to complete
7 videos (Total 35 min), 5 readings, 2 quizzes
Reviews
- 5 stars76.12%
- 4 stars19.03%
- 3 stars3.22%
- 1 star1.61%
TOP REVIEWS FROM DATA PROCESSING AND FEATURE ENGINEERING WITH MATLAB
by WSJan 6, 2021
This is a great course from Matlab. It is very well designed and easy to follow. I could apply such big concepts with easiness and get a good understanding.
by SEFeb 25, 2020
The course content and delivery are top-notch. I like the practicals, quizzes and exams which help to deepen understanding. For me, they'll always be a treasure load of reference materials.
by SKMar 14, 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!
by ATJul 9, 2020
The course was easy to follow and the sections in the week 5 module showed some real life applications of the methods learnt throughout the course.
About the Practical Data Science with MATLAB Specialization
Do you find yourself in an industry or field that increasingly uses data to answer questions? Are you working with an overwhelming amount of data and need to make sense of it? Do you want to avoid becoming a full-time software developer or statistician to do meaningful tasks with your data?
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