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Brandon Armstrong,
Michael Reardon,
Isaac Bruss,
Maria Gavilan-Alfonso,
Matt Rich,
Heather Gorr,
Erin Byrne,
and
Adam Filion
Like most subjects, practice makes perfect in Data Science. In the capstone project, you will apply the skills learned across courses in the Practical Data Science with MATLAB specialization to explore, process, analyze, and model data. You will choose your own pathway to answer key questions with the provided data.
Read more
Like most subjects, practice makes perfect in Data Science. In the capstone project, you will apply the skills learned across courses in the Practical Data Science with MATLAB specialization to explore, process, analyze, and model data. You will choose your own pathway to answer key questions with the provided data.
Read more
Like most subjects, practice makes perfect in Data Science. In the capstone project, you will apply the skills learned across courses in the Practical Data Science with MATLAB specialization to explore, process, analyze, and model data. You will choose your own pathway to answer key questions with the provided data.
To complete the project, you must have mastery of the skills covered in other courses in the specialization. The project will test your ability to import and explore your data, prepare the data for analysis, train a predictive model, evaluate and improve your model, and communicate your results.
Register for this course and see more details by visiting:
OpenCourser.com/course/ksx2e7/data
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What's inside
Syllabus
Import and Explore the Data
In this module you'll be introduced to the goals of the capstone project. You will complete the initial task of preparing a data set and performing an exploratory analysis.
Read more
Syllabus
Import and Explore the Data
In this module you'll be introduced to the goals of the capstone project. You will complete the initial task of preparing a data set and performing an exploratory analysis.
Read more
Create and Evaluate Features
In this module you'll perform feature engineering. You'll create a response variable and investigate the relationships between features and the response variable.
Apply Machine Learning
In this module you will perform machine learning. You'll train and customize various models. Using validation data and common evaluation metrics you'll choose the most appropriate model for the problem.
Communicate Your Results
In this module, you'll learn a framework for creating a data science story and the importance of crafting your narrative for the intended audience, along with tips for creating meaningful visualizations.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops highly sought-after skills in the industry, such as data exploration, feature engineering, machine learning, and data visualization
Provides a comprehensive overview of the data science workflow, from data acquisition to model deployment
Led by instructors with extensive experience in the field, ensuring high-quality content and practical guidance
Taught using MATLAB, a powerful programming language widely used in data science and engineering
Requires prior knowledge of MATLAB and data science concepts, making it suitable for intermediate learners
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Save Data Science Project: MATLAB for the Real World to your list so you can find it easily later:
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Reviews summary
Matlab for data science projects
Learners say that this course is excellent and hands-on with practical project-based assignments. The instructors are helpful and the material is top-notch. However, it is important to note that the course requires significant time dedication.
The course focuses on practical, hands-on learning.
"Hands-on with Data Science"
"practical final project"
"top-notch"
Learners largely enjoyed the course and praised the instructors.
"What an amazing course"
"I am very happy"
"One of the best in Coursera"
Learners should be prepared to dedicate a significant amount of time to this course.
"but it requires a lot more of time dedication"
"I recommend to clarify how the data is cleaned"
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 Science Project: MATLAB for the Real World with these
activities:
Review Linear Algebra and Calculus
Show steps
Review linear algebra and calculus to strengthen your mathematical foundation for data science.
Browse courses on
Linear Algebra
Show steps
-
Find online resources
-
Read textbooks or articles
-
Solve practice problems
Data Science Tutorials
Show steps
Watch tutorials to learn about data science concepts and techniques.
Browse courses on
Data Science
Show steps
-
Find tutorials online
-
Watch a tutorial
-
Take notes
Read 'Data Science for Business'
Show steps
Read 'Data Science for Business' to gain insights into the business applications of data science.
View
Data Science for Business: What You Need to...
on Amazon
Show steps
-
Purchase the book
-
Read the book
-
Take notes
Five other activities
Expand to see all activities and additional details
Show all eight activities
Attend Data Science Meetups
Show steps
Attend data science meetups to network with other data scientists and learn about the latest trends.
Browse courses on
Data Science
Show steps
-
Find data science meetups in your area
-
Attend a meetup
-
Introduce yourself to others
Data Science Coding Challenges
Show steps
Solve coding challenges to practice your data science skills.
Browse courses on
Data Science
Show steps
-
Find coding challenges online
-
Choose a challenge and solve it
-
Review your solution
Data Science Study Group
Show steps
Join a study group to discuss data science concepts and work on projects together.
Browse courses on
Data Science
Show steps
-
Find a study group
-
Attend study group meetings
-
Participate in discussions
Contribute to Open Source Data Science Projects
Show steps
Contribute to open source data science projects to gain experience and give back to the community.
Browse courses on
Data Science
Show steps
-
Find open source data science projects
-
Choose a project to contribute to
-
Read the project documentation
-
Make a contribution
-
Submit a pull request
Data Science Portfolio
Show steps
Create a portfolio of data science projects to showcase your skills and knowledge.
Browse courses on
Data Science
Show steps
-
Gather your work
-
Create a website or online portfolio
-
Write up your projects
-
Get feedback on your portfolio
-
Publish your portfolio
Review Linear Algebra and Calculus
Show steps
Review linear algebra and calculus to strengthen your mathematical foundation for data science.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Find online resources
- Read textbooks or articles
- Solve practice problems
Data Science Tutorials
Show steps
Watch tutorials to learn about data science concepts and techniques.
Browse courses on
Data Science
Show steps
Show steps
Show steps
- Find tutorials online
- Watch a tutorial
- Take notes
Read 'Data Science for Business'
Show steps
Read 'Data Science for Business' to gain insights into the business applications of data science.
View
Data Science for Business: What You Need to...
on Amazon
Show steps
Show steps
Show steps
- Purchase the book
- Read the book
- Take notes
Five other activities
Expand to see all activities and additional details
Show all eight activities
Attend Data Science Meetups
Show steps
Attend data science meetups to network with other data scientists and learn about the latest trends.
Browse courses on
Data Science
Show steps
Show steps
Show steps
- Find data science meetups in your area
- Attend a meetup
- Introduce yourself to others
Data Science Coding Challenges
Show steps
Solve coding challenges to practice your data science skills.
Browse courses on
Data Science
Show steps
Show steps
Show steps
- Find coding challenges online
- Choose a challenge and solve it
- Review your solution
Data Science Study Group
Show steps
Join a study group to discuss data science concepts and work on projects together.
Browse courses on
Data Science
Show steps
Show steps
Show steps
- Find a study group
- Attend study group meetings
- Participate in discussions
Contribute to Open Source Data Science Projects
Show steps
Contribute to open source data science projects to gain experience and give back to the community.
Browse courses on
Data Science
Show steps
Show steps
Show steps
- Find open source data science projects
- Choose a project to contribute to
- Read the project documentation
- Make a contribution
- Submit a pull request
Data Science Portfolio
Show steps
Create a portfolio of data science projects to showcase your skills and knowledge.
Browse courses on
Data Science
Show steps
Show steps
Show steps
- Gather your work
- Create a website or online portfolio
- Write up your projects
- Get feedback on your portfolio
- Publish your portfolio
Career center
Learners who complete Data Science Project: MATLAB for the Real World will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use their knowledge of statistics, programming, and machine learning to analyze data and extract insights from it. They then use these insights to help businesses make better decisions. Machine learning is a key part of data science, and the skills you learn in this course will help you build a foundation in this area. This course will help you to master the skills you need to become a successful Data Scientist.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design and develop machine learning models. They also work to improve the performance of these models. This course will help you to develop the skills you need to become a successful Machine Learning Engineer. You will learn about the different types of machine learning models, how to train them, and how to evaluate their performance.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data. They then use this data to identify trends and patterns. This course will help you to develop the skills you need to become a successful Data Analyst. You will learn about the different types of data analysis techniques, how to use them, and how to interpret the results.
Business Analyst
Business Analyst
Business Analysts use data to help businesses make better decisions. They work with stakeholders to understand their business needs, and then use data to identify opportunities and solve problems. This course will help you to develop the skills you need to become a successful Business Analyst. You will learn about the different types of business analysis techniques, how to use them, and how to communicate your findings to stakeholders.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. They use this data to draw conclusions about the world around us. This course will help you to develop the skills you need to become a successful Statistician. You will learn about the different types of statistical techniques, how to use them, and how to interpret the results.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical models to solve complex problems. They work in a variety of industries, including healthcare, finance, and manufacturing. This course will help you to develop the skills you need to become a successful Operations Research Analyst. You will learn about the different types of mathematical models, how to use them, and how to interpret the results.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical techniques to analyze financial data. They use this data to make investment decisions. This course will help you to develop the skills you need to become a successful Quantitative Analyst. You will learn about the different types of financial analysis techniques, how to use them, and how to interpret the results.
Market Researcher
Market Researcher
Market Researchers collect and analyze data about consumers. They use this data to help businesses make better decisions about their products and services. This course will help you to develop the skills you need to become a successful Market Researcher. You will learn about the different types of market research techniques, how to use them, and how to interpret the results.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They work in a variety of industries, including healthcare, finance, and manufacturing. This course will help you to develop the skills you need to become a successful Software Engineer. You will learn about the different types of software engineering techniques, how to use them, and how to test and debug software.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data systems. They work in a variety of industries, including healthcare, finance, and manufacturing. This course will help you to develop the skills you need to become a successful Data Engineer. You will learn about the different types of data engineering techniques, how to use them, and how to build and maintain data systems.
Database Administrator
Database Administrator
Database Administrators design, build, and maintain databases. They work in a variety of industries, including healthcare, finance, and manufacturing. This course will help you to develop the skills you need to become a successful Database Administrator. You will learn about the different types of database technologies, how to use them, and how to build and maintain databases.
Systems Analyst
Systems Analyst
Systems Analysts design, build, and maintain computer systems. They work in a variety of industries, including healthcare, finance, and manufacturing. This course will help you to develop the skills you need to become a successful Systems Analyst. You will learn about the different types of computer systems, how to use them, and how to build and maintain them.
Network Administrator
Network Administrator
Network Administrators design, build, and maintain networks. They work in a variety of industries, including healthcare, finance, and manufacturing. This course will help you to develop the skills you need to become a successful Network Administrator. You will learn about the different types of networks, how to use them, and how to build and maintain them.
Computer Scientist
Computer Scientist
Computer Scientists design, develop, and analyze computer systems. They work in a variety of industries, including healthcare, finance, and manufacturing. This course will help you to develop the skills you need to become a successful Computer Scientist. You will learn about the different types of computer systems, how to use them, and how to design and analyze them.
Information Security Analyst
Information Security Analyst
Information Security Analysts design, implement, and maintain security systems. They work in a variety of industries, including healthcare, finance, and manufacturing. This course will help you to develop the skills you need to become a successful Information Security Analyst. You will learn about the different types of security systems, how to use them, and how to design and implement them.
For more career information including salaries, visit:
OpenCourser.com/course/ksx2e7/data
Reading list
We've selected 12 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 Science Project: MATLAB for the Real World.
Classic textbook on statistical learning and data mining. It provides a comprehensive overview of the field, including topics such as regression, classification, and clustering. It can be used as a reference for the statistical modeling module of the course.
Provides a comprehensive overview of causal inference, a statistical approach to understanding the causal relationships between variables. It can be used as a reference for the causal inference module of the course.
Provides a comprehensive overview of data science concepts and techniques, using Python as the programming language. It can be used as a supplement to the course, providing additional depth and breadth.
Provides a comprehensive overview of machine learning, using a probabilistic perspective. It can be used as a reference for the machine learning module of the course.
Covers the basics of MATLAB for data analysis, including data import, cleaning, exploration, and visualization. It can be used as a companion to the course, providing additional examples and exercises.
Save
Provides a comprehensive overview of deep learning, a subfield of machine learning that has gained significant attention in recent years. It can be used as a reference for the deep learning module of the course.
Provides a comprehensive overview of Bayesian data analysis, a statistical approach that uses Bayes' theorem to make inferences about unknown quantities. It can be used as a reference for the Bayesian statistics module of the course.
Provides a comprehensive overview of Bayesian statistics, using R and Stan as the programming languages. It can be used as a reference for the Bayesian statistics module of the course.
Provides a comprehensive overview of reinforcement learning, a subfield of machine learning that deals with learning optimal behavior in sequential decision-making problems. It can be used as a reference for the reinforcement learning module of the course.
Save
Provides a practical introduction to programming and problem solving using MATLAB. It can serve as a starting point for the course, especially for those with no prior programming experience.
Provides a practical introduction to data analysis using Python. It covers topics such as data import, cleaning, and visualization. It can be used as a companion to the course, providing additional examples and exercises in Python.
Provides a practical introduction to data science for business professionals. It covers topics such as data collection, analysis, and visualization. It can be used as a supplement to the course, providing additional insights into the business applications of data science.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ksx2e7/data
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Level
Intermediate
Via
Coursera
Institution
MathWorks
Instructors
Brandon Armstrong
Michael Reardon
Isaac Bruss
Maria Gavilan-Alfonso
Matt Rich
Heather Gorr
Erin Byrne
Adam Filion
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
- Exploratory Data Analysis with MATLAB
- Data Science Project: MATLAB for the Real World (viewing)
Good to know
Develops highly sought-after skills in the industry, such as data exploration, feature engineering, machine learning, and data visualization
Provides a comprehensive overview of the data science workflow, from data acquisition to model deployment
Led by instructors with extensive experience in the field, ensuring high-quality content and practical guidance
Taught using MATLAB, a powerful programming language widely used in data science and engineering
Requires prior knowledge of MATLAB and data science concepts, making it suitable for intermediate learners
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