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Maria Gavilan-Alfonso,
Heather Gorr,
Michael Reardon,
Erin Byrne,
Isaac Bruss,
Brandon Armstrong,
Brian Buechel,
Nikola Trica,
Adam Filion,
Matt Rich,
Cris LaPierre,
and
Amanda Wang
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.
Read more
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.
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/x5buqh/data
What's inside
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.
Read more
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.
Read more
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.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Useful for those who need to combine data from multiple sources or times and have an interest in modeling
Provides a solid foundation for predictive modeling
Requires no programming background
Suitable for those with domain knowledge and exposure to computational tools
Assumes some background in basic statistics
Completion of Exploratory Data Analysis with MATLAB is recommended
Save this course
Save Data Processing and Feature Engineering with MATLAB to your list so you can find it easily later:
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Reviews summary
Feature engineering with matlab
Learners say this MATLAB course is well-received and well-organized, enabling them to practice data processing with hands-on exercises. It covers topics such as signal, image, and text processing, along with feature engineering. While some sections are found to be challenging, learners appreciate the engaging assignments and helpful instructors.
Learners express appreciation for the in-depth coverage of feature engineering concepts.
"I must say it is a great course with a lots of practical applications. However, I think the course contents are too rich to be covered in just 5 weeks."
"This course Data Processing and Feature Engineering with MATLAB is excellent for learners. It covers the important domain of Feature Engineering such as signal, image, and text preprocessing."
Instructors are rated highly for their clear and intuitive explanations.
"Builds up on the introductory concepts from Course 1 to perform inferential statistics and derive the most meaningful features from data in multiple forms."
"The course on Data Processing and Feature Engineering with MATLAB charms me extremely . It covers all the area , like image, signal and text processing with feature engineering."
Learners find the content challenging, requiring careful study.
"Great course! Thanks."
"It was an amazing course,looking forward to the next courses"
"MATLAB provides the best courses on coursera after Andrew. Loved and enjoyed the learning process."
"I am impressed with the instructors lectures and I am very satisfied with what I have been able to accomplish with my programming and machine learning skills"
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Data Processing and Feature Engineering with MATLAB with these
activities:
Review Statistics Concepts
Show steps
Reinforce understanding of statistical principles to prepare for more advanced modeling concepts.
Browse courses on
Statistics
Show steps
-
Review basic concepts of probability and distributions.
-
Organize key equations and formulas for easy reference during the course.
Follow MATLAB Tutorials
Show steps
Expand your knowledge by following tutorials that cover advanced MATLAB techniques and concepts relevant to this course.
Browse courses on
MATLAB Programming
Show steps
-
Search for and select tutorials from reputable sources or the official MATLAB documentation.
-
Go through the tutorials at your own pace, practicing the examples and experimenting with the code.
Explore MATLAB Features
Show steps
Gain proficiency in MATLAB functions and commands essential for data manipulation and analysis.
Show steps
-
Follow tutorials on data import, cleaning, and visualization.
-
Practice using MATLAB functions for statistical analysis and plotting.
Nine other activities
Expand to see all activities and additional details
Show all 12 activities
Solve MATLAB Coding Exercises
Show steps
Reinforce your understanding of MATLAB coding by solving practice exercises that cover topics relevant to this course.
Show steps
-
Find online resources or textbooks with MATLAB coding exercises.
-
Work through the exercises, focusing on understanding the logic and implementation.
Join Study Groups or Discussion Forums
Show steps
Collaborate with peers to discuss course concepts, share insights, and solve problems together.
Show steps
-
Join online study groups or participate in discussion forums related to MATLAB and data analysis.
-
Engage in discussions, ask questions, and provide answers to support your peers.
Write a MATLAB Script or Function
Show steps
Enhance your understanding of MATLAB by creating your own MATLAB script or function to perform a specific data analysis task.
Browse courses on
MATLAB Programming
Show steps
-
Identify a specific task or problem that you want to solve with MATLAB.
-
Design and implement your script or function using MATLAB's built-in functions and syntax.
-
Test and debug your script or function to ensure it performs as expected.
Data Manipulation Exercises
Show steps
Enhance data manipulation skills through targeted exercises, reinforcing essential techniques for predictive modeling.
Show steps
-
Complete practice problems on merging datasets.
-
Work through exercises on handling missing data and outliers.
-
Conduct simulations to evaluate different data normalization methods.
Analyze Real-World Datasets
Show steps
Apply your data analysis skills by working with real-world datasets to identify trends and patterns.
Browse courses on
Data Analysis Techniques
Show steps
-
Find publicly available datasets related to a topic of your interest.
-
Import the data into MATLAB and explore its distribution and characteristics.
-
Use statistical techniques to analyze the data and draw meaningful conclusions.
Participate in Kaggle Competitions
Show steps
Challenge skills in a competitive environment, promoting problem-solving, collaboration, and continuous learning.
Show steps
-
Join Kaggle competitions related to data manipulation and predictive modeling.
-
Collaborate with other participants and learn from shared insights.
-
Present findings in discussion forums and network with industry professionals.
Develop a Data Analysis Project
Show steps
Solidify your learning by applying your skills to a data analysis project that involves data exploration, modeling, and interpretation.
Browse courses on
Predictive Modeling
Show steps
-
Identify a problem or topic of interest that you want to analyze.
-
Gather and prepare a dataset relevant to your topic.
-
Explore the data, identify patterns, and build a predictive model.
-
Evaluate your model's performance and draw meaningful conclusions.
Build a Decision Tree Model
Show steps
Apply predictive modeling techniques to a real-world dataset, enhancing understanding of the modeling process.
Show steps
-
Select a dataset and formulate a modeling problem.
-
Prepare data for modeling, including feature engineering and data normalization.
-
Build a decision tree model using MATLAB's TreeBagger function.
-
Validate the model's accuracy and performance metrics.
-
Present findings in a project report or technical write-up.
Develop a Predictive Analytics Dashboard
Show steps
Gain hands-on experience in translating predictive models into actionable insights and visualizations.
Show steps
-
Integrate a predictive model into a MATLAB App.
-
Design an interactive dashboard for data visualization and model exploration.
-
Deploy the dashboard for use in a real-world scenario.
Review Statistics Concepts
Show steps
Reinforce understanding of statistical principles to prepare for more advanced modeling concepts.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Review basic concepts of probability and distributions.
- Organize key equations and formulas for easy reference during the course.
Follow MATLAB Tutorials
Show steps
Expand your knowledge by following tutorials that cover advanced MATLAB techniques and concepts relevant to this course.
Browse courses on
MATLAB Programming
Show steps
Show steps
Show steps
- Search for and select tutorials from reputable sources or the official MATLAB documentation.
- Go through the tutorials at your own pace, practicing the examples and experimenting with the code.
Explore MATLAB Features
Show steps
Gain proficiency in MATLAB functions and commands essential for data manipulation and analysis.
Show steps
Show steps
Show steps
- Follow tutorials on data import, cleaning, and visualization.
- Practice using MATLAB functions for statistical analysis and plotting.
Nine other activities
Expand to see all activities and additional details
Show all 12 activities
Solve MATLAB Coding Exercises
Show steps
Reinforce your understanding of MATLAB coding by solving practice exercises that cover topics relevant to this course.
Show steps
Show steps
Show steps
- Find online resources or textbooks with MATLAB coding exercises.
- Work through the exercises, focusing on understanding the logic and implementation.
Join Study Groups or Discussion Forums
Show steps
Collaborate with peers to discuss course concepts, share insights, and solve problems together.
Show steps
Show steps
Show steps
- Join online study groups or participate in discussion forums related to MATLAB and data analysis.
- Engage in discussions, ask questions, and provide answers to support your peers.
Write a MATLAB Script or Function
Show steps
Enhance your understanding of MATLAB by creating your own MATLAB script or function to perform a specific data analysis task.
Browse courses on
MATLAB Programming
Show steps
Show steps
Show steps
- Identify a specific task or problem that you want to solve with MATLAB.
- Design and implement your script or function using MATLAB's built-in functions and syntax.
- Test and debug your script or function to ensure it performs as expected.
Data Manipulation Exercises
Show steps
Enhance data manipulation skills through targeted exercises, reinforcing essential techniques for predictive modeling.
Show steps
Show steps
Show steps
- Complete practice problems on merging datasets.
- Work through exercises on handling missing data and outliers.
- Conduct simulations to evaluate different data normalization methods.
Analyze Real-World Datasets
Show steps
Apply your data analysis skills by working with real-world datasets to identify trends and patterns.
Browse courses on
Data Analysis Techniques
Show steps
Show steps
Show steps
- Find publicly available datasets related to a topic of your interest.
- Import the data into MATLAB and explore its distribution and characteristics.
- Use statistical techniques to analyze the data and draw meaningful conclusions.
Participate in Kaggle Competitions
Show steps
Challenge skills in a competitive environment, promoting problem-solving, collaboration, and continuous learning.
Show steps
Show steps
Show steps
- Join Kaggle competitions related to data manipulation and predictive modeling.
- Collaborate with other participants and learn from shared insights.
- Present findings in discussion forums and network with industry professionals.
Develop a Data Analysis Project
Show steps
Solidify your learning by applying your skills to a data analysis project that involves data exploration, modeling, and interpretation.
Browse courses on
Predictive Modeling
Show steps
Show steps
Show steps
- Identify a problem or topic of interest that you want to analyze.
- Gather and prepare a dataset relevant to your topic.
- Explore the data, identify patterns, and build a predictive model.
- Evaluate your model's performance and draw meaningful conclusions.
Build a Decision Tree Model
Show steps
Apply predictive modeling techniques to a real-world dataset, enhancing understanding of the modeling process.
Show steps
Show steps
Show steps
- Select a dataset and formulate a modeling problem.
- Prepare data for modeling, including feature engineering and data normalization.
- Build a decision tree model using MATLAB's TreeBagger function.
- Validate the model's accuracy and performance metrics.
- Present findings in a project report or technical write-up.
Develop a Predictive Analytics Dashboard
Show steps
Gain hands-on experience in translating predictive models into actionable insights and visualizations.
Show steps
Show steps
Show steps
- Integrate a predictive model into a MATLAB App.
- Design an interactive dashboard for data visualization and model exploration.
- Deploy the dashboard for use in a real-world scenario.
Career center
Learners who complete Data Processing and Feature Engineering with MATLAB will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers use the skills taught in the course *Data Processing and Feature Engineering with MATLAB* to develop and deploy machine learning models. They use machine learning algorithms to train models that can make predictions or recommendations. The course can help you develop the skills needed to succeed as a Machine Learning Engineer by teaching you how to clean and prepare data for modeling, identify and address data quality issues, and develop machine learning models.
Data Scientist
Data Scientist
Data Scientists use the skills taught in the course *Data Processing and Feature Engineering with MATLAB* to extract insights from data. They use machine learning and statistical techniques to develop predictive models. The course can help you develop the skills needed to succeed as a Data Scientist by teaching you how to clean and organize data, prepare data for analysis, identify and address data quality issues, and develop machine learning models.
Quant Analyst
Quant Analyst
Quant Analysts use the skills taught in the course *Data Processing and Feature Engineering with MATLAB* to develop and implement quantitative trading models. They use statistical and machine learning techniques to develop models that can predict financial market movements. The course can help you develop the skills needed to succeed as a Quant Analyst by teaching you how to clean and organize data, prepare data for modeling, identify and address data quality issues, and develop machine learning models.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers use the skills taught in the course *Data Processing and Feature Engineering with MATLAB* to design and develop AI systems. They use machine learning and deep learning techniques to develop systems that can learn from data and make predictions or recommendations. The course can help you develop the skills needed to succeed as an Artificial Intelligence Engineer by teaching you how to clean and organize data, prepare data for modeling, identify and address data quality issues, and develop machine learning models.
Fraud Analyst
Fraud Analyst
Fraud Analysts use the skills taught in the course *Data Processing and Feature Engineering with MATLAB* to detect and investigate fraudulent activities. They use data analysis techniques to identify patterns and trends in data that may indicate fraud. The course can help you develop the skills needed to succeed as a Fraud Analyst by teaching you how to clean and organize data, prepare data for analysis, identify and address data quality issues, and develop data-driven fraud detection models.
Business Analyst
Business Analyst
Business Analysts use the skills taught in the course *Data Processing and Feature Engineering with MATLAB* to analyze business data and identify opportunities for improvement. They use data analysis techniques to identify trends, patterns, and relationships in data. The course can help you develop the skills needed to succeed as a Business Analyst by teaching you how to clean and organize data, prepare data for analysis, identify and address data quality issues, and develop data-driven recommendations.
Statistician
Statistician
Statisticians leverage the skills taught in the course *Data Processing and Feature Engineering with MATLAB* to collect, analyze, interpret and present data. They use statistical methods to develop models and make predictions. Data Processing and Feature Engineering with MATLAB can help you develop the skills needed to succeed as a Statistician by teaching you how to analyze data, create visualizations, and develop statistical models.
Data Architect
Data Architect
Data Architects use the skills taught in the course *Data Processing and Feature Engineering with MATLAB* to design and implement data management solutions. They use data modeling techniques to create data structures that can support business intelligence and analytics applications. The course can help you develop the skills needed to succeed as a Data Architect by teaching you how to clean and organize data, prepare data for modeling, identify and address data quality issues, and develop data-driven solutions.
Risk Analyst
Risk Analyst
Risk Analysts use the skills taught in the course *Data Processing and Feature Engineering with MATLAB* to identify and assess risks. They use data analysis techniques to quantify risks and develop mitigation strategies. The course can help you develop the skills needed to succeed as a Risk Analyst by teaching you how to clean and organize data, prepare data for analysis, identify and address data quality issues, and develop data-driven risk assessment models.
Data Engineer
Data Engineer
Data Engineers use the skills taught in the course *Data Processing and Feature Engineering with MATLAB* to build and maintain data pipelines. They use data integration and data transformation techniques to move data between different systems and applications. The course can help you develop the skills needed to succeed as a Data Engineer by teaching you how to clean and organize data, prepare data for modeling, identify and address data quality issues, and develop data pipelines.
Actuary
Actuary
Actuaries use the skills taught in the course *Data Processing and Feature Engineering with MATLAB* to assess and manage financial risks. They use statistical and financial modeling techniques to develop insurance and pension plans. The course can help you develop the skills needed to succeed as an Actuary by teaching you how to clean and organize data, prepare data for modeling, identify and address data quality issues, and develop data-driven financial models.
Software Engineer
Software Engineer
The skills taught in the course *Data Processing and Feature Engineering with MATLAB* are commonly used by *Software Engineers* to extract and transform data from multiple sources into a format that can be used by software applications. This course can help you develop the skills needed to succeed as a Software Engineer by teaching you how to clean and organize data, as well as how to develop data-driven applications.
Information Security Analyst
Information Security Analyst
Information Security Analysts use the skills taught in the course *Data Processing and Feature Engineering with MATLAB* to protect data from unauthorized access, use, disclosure, disruption, modification, or destruction. They use data security techniques to implement and maintain security measures. The course can help you develop the skills needed to succeed as an Information Security Analyst by teaching you how to identify and address data security risks, develop data security policies and procedures, and implement data security measures.
Database Administrator
Database Administrator
Database Administrators use the skills taught in the course *Data Processing and Feature Engineering with MATLAB* to create and maintain databases. They manage data and ensure that it is accurate, secure, and accessible. This course can help you develop the skills needed to succeed as a Database Administrator by teaching you how to clean and organize data, as well as how to identify and address data quality issues.
Data Analyst
Data Analyst
Data Analysts use the skills taught in the course *Data Processing and Feature Engineering with MATLAB* to manipulate and prepare data for analysis. They find patterns and trends in data to help businesses improve decision-making, customer relationships, and product development.
For more career information including salaries, visit:
OpenCourser.com/course/x5buqh/data
Reading list
We've selected 13 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Data Processing and Feature Engineering with MATLAB.
Focuses specifically on feature engineering for machine learning, providing a step-by-step guide to understanding, selecting, and creating features that improve model performance.
Provides a comprehensive introduction to data science concepts, including data preparation, feature engineering, and modeling techniques. Covers essential tools and libraries in Python.
Explores data science concepts and techniques from a business perspective. Emphasizes practical applications and case studies of successful data science initiatives in various industries.
Complements the course by providing a comprehensive guide to exploratory data analysis techniques in MATLAB. Covers data visualization, statistical summaries, and outlier detection.
Provides a thorough introduction to data manipulation and transformation techniques in R. Covers data cleaning, reshaping, and merging operations.
Provides a comprehensive overview of statistical concepts and techniques relevant to data science. Covers probability, hypothesis testing, and regression analysis.
Provides a practical guide to using Python for data analysis. Covers data manipulation, visualization, and modeling techniques.
Offers a gentle introduction to machine learning concepts and techniques. Useful for those with no prior experience in machine learning.
Save
Provides a foundational understanding of probability and statistics concepts. Useful for those with a technical background.
A comprehensive reference guide to statistical learning methods, including supervised and unsupervised learning algorithms. Suitable for advanced readers.
Save
Covers a wide range of data mining techniques, including data preprocessing, feature selection, and classification and clustering algorithms.
Provides a practical introduction to machine learning with Python. Covers various supervised and unsupervised learning algorithms.
Save
A comprehensive reference guide to deep learning theory and techniques. Suitable for advanced readers with a strong background in machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/x5buqh/data
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Level
Intermediate
Via
Coursera
Institution
MathWorks
Instructors
Maria Gavilan-Alfonso
Heather Gorr
Michael Reardon
Erin Byrne
Isaac Bruss
Brandon Armstrong
Brian Buechel
Nikola Trica
Adam Filion
Matt Rich
Cris LaPierre
Amanda Wang
Language
English
Transcripts
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آذربایجان دیلی
This course belongs to a
collection
called
Practical Data Science with MATLAB. It's comprised of four courses:
- Predictive Modeling and Machine Learning with MATLAB
- Data Processing and Feature Engineering with MATLAB (viewing)
- Exploratory Data Analysis with MATLAB
- Data Science Project: MATLAB for the Real World
Good to know
Useful for those who need to combine data from multiple sources or times and have an interest in modeling
Provides a solid foundation for predictive modeling
Requires no programming background
Suitable for those with domain knowledge and exposure to computational tools
Assumes some background in basic statistics
Completion of Exploratory Data Analysis with MATLAB is recommended
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