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.
Este curso faz parte do Programa de cursos integrados Practical Data Science with MATLAB
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Informações sobre o curso
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Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Nível intermediário
Aprox. 20 horas para completar
Inglês
Legendas: Árabe, Francês, Portuguese (European), Italiano, Vietnamita, Alemão, Russo, Inglês, Espanhol
Habilidades que você terá
- Feature Engineering
- Matlab
- Data Cleansing
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Nível intermediário
Aprox. 20 horas para completar
Inglês
Legendas: Árabe, Francês, Portuguese (European), Italiano, Vietnamita, Alemão, Russo, Inglês, Espanhol
oferecido por
MathWorks
Accelerating the pace of discovery, innovation, development, and learning in engineering and science.
Programa - O que você aprenderá com este curso
3 horas para concluir
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 horas para concluir
10 vídeos (Total 46 mín.), 4 leituras, 2 testes
3 horas para concluir
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 horas para concluir
8 vídeos (Total 34 mín.), 2 leituras, 3 testes
3 horas para concluir
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 horas para concluir
9 vídeos (Total 44 mín.), 2 leituras, 3 testes
4 horas para concluir
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 horas para concluir
7 vídeos (Total 35 mín.), 5 leituras, 2 testes
Avaliações
- 5 stars76,29%
- 4 stars19,15%
- 3 stars2,92%
- 1 star1,62%
Principais avaliações do DATA PROCESSING AND FEATURE ENGINEERING WITH MATLAB
por DM7 de abr de 2020
Big step up from the previous course but good introduction into data science, what it is and what MATLAB can do to help you get the most out of your analysis. Very good !
por AL28 de ago de 2020
Great overview. Very useful exercises and tests. However, Week 5 was a little overwhelming since it is a little bit of everything (signals, images, texts).
por SE25 de fev de 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.
por SA11 de ago de 2021
Good course, teaches you how to process data to extract features in preparation for application of supervised learning algorithms.
Sobre Programa de cursos integrados Practical Data Science with MATLAB
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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