Advanced analysis of outliers in R and Matlab from Udemy

Robust data analysis and outlier detection are crucial in Statistics, Data Analysis, Data Mining, Machine Learning, Artificial Intelligence, Pattern Recognition, Classification, Principal Components, Regression, Big Data, and any field related to the data.

With the course you will obtain the FREE BOOK ABOUT OUTLIERS with specific tips and tricks, and the summary of all the robust methods to detect them that will help you obtain accurate results and awsome data analysis.

Researchers, students, data analysts, and mostly anyone dealing with real data, should be aware of the problem with outliers (and outliers) and should know how to deal with this problem and what robust methods should be used. . The vast majority of Machine Learning algorithms are capable of detecting characteristics common to the majority of data, but many times they are confused or even ignore those atypical data, which should not be ignored in conditions where the security of people, such as the analysis of medical data, the world of the Internet of Things IoT, or risks and security in companies.

What would happen if a virus spread throughout the world because we ignored anomalous data? We would have a pandemic, like that of COVID19, which if the outlier signals detected by neural networks had not been ignored, we could have acted upon beforehand.
What would happen if we ignored any signal from a Smart City system? We could miss a gas leak.
What would happen if by ignoring an alarm, we miss a meteorite coming towards the earth? We would have to call Bruce Willis, to save us from Armageddon.

With this course you will be an expert in robust data analysis, in the detection and treatment of atypical data, both learning the theoretical concepts, and having at your disposal the algorithms implemented in a practical way with two different languages so that you can choose the one that best suits you: R-Studio and Matlab.

You will also have access to a community for questions, where all the students are and you can ask what you want about the analysis of outliers.

The example implementation codes are available to you in the open Github repository for you to download and use.

In addition, we have two sections of basic concepts that will help you to remember some notions necessary to understand atypical detection methods.

With this course you will be able to understand and know how to deal with one of the most important topics of today both academically, in the industry and in data analysis or machine learning. The examples will help you to visualize the importance of the analysis of outliers as well as a guide to carry out these analyzes yourself.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Can help you to maintain safety and security for yourself, others, and the environment

Covers a wide range of topics, including robust data analysis, outlier detection, and machine learning

Taught by experienced instructors with a strong reputation in the field

Provides a strong foundation for beginners and strengthens an existing foundation for intermediate learners

Offers hands-on labs and interactive materials to help you learn the material

Includes a community for questions where you can ask other students about the analysis of outliers

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 Advanced analysis of outliers in R and Matlab with these activities:

Join a Study Group or Online Forum on Outlier Detection

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Engaging with peers in discussions and activities can provide diverse perspectives and reinforce your understanding of the course material.

Browse courses on Outlier Detection

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Join a study group or online forum dedicated to outlier detection.
Participate in discussions and share your insights.

Create a Comprehensive Note Set

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Taking the time to organize your course materials into a comprehensive note set will improve your ability to find information quickly and effectively.

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Organize your notes by topic.
Include summaries of key concepts.
Use visual aids such as diagrams and charts to illustrate complex ideas.

Read 'Robust Data Analysis Techniques' by Andreas Alfons

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This book provides a comprehensive overview of robust data analysis techniques, which are essential for handling outliers and improving the accuracy of your results.

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Read the book thoroughly, making notes as you go.
Complete the exercises at the end of each chapter.
Apply the techniques you learn to a real-world dataset.

Seven other activities

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Learn how to identify and deal with outliers using R-Studio or Matlab

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Provides additional guidance and practice on outlier detection, a key topic covered in the course.

Browse courses on Outlier Detection

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Choose a tutorial on outlier detection in R-Studio or Matlab.
Follow the tutorial step-by-step to learn how to detect and handle outliers.
Apply the techniques learned in the tutorial to a dataset of your choice.
Compare the results using different outlier detection methods.
Document your findings and share them with others.

Practice Identifying Outliers Using R or MATLAB

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Hands-on practice identifying outliers using real-world data will strengthen your understanding and ability to apply these techniques in your own work.

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Find a dataset that contains outliers.
Use R or MATLAB to plot the data and identify the outliers.
Experiment with different outlier detection methods to see how they perform on different types of data.

Practice data analysis exercises

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Helps reinforce understanding of data analysis techniques covered in the course.

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Find a dataset that interests you and download it.
Clean the data by removing duplicates and correcting errors.
Explore the data to identify patterns and trends.
Perform data analysis using techniques learned in the course.
Draw conclusions and visualize your results.

Attend a Workshop on Robust Data Analysis Techniques

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Attending a workshop specifically focused on robust data analysis techniques will provide in-depth knowledge and practical experience.

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Research and find a workshop that aligns with your interests.
Attend the workshop and actively participate in discussions and exercises.

Create a Presentation on Outlier Detection Techniques

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Developing a presentation on outlier detection techniques will not only deepen your understanding of the topic but also improve your communication and presentation skills.

Browse courses on Outlier Detection

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Research different outlier detection techniques.
Identify the pros and cons of each technique.
Create a presentation that explains the techniques and their applications.

Volunteer as a Mentor for Students Learning about Outlier Detection

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Sharing your knowledge and experience with others will not only help them learn but also reinforce your own understanding of the material.

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Join an organization or platform that connects mentors with students.
Provide guidance and support to students learning about outlier detection.

Participate in a Data Science Competition

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Participating in a data science competition will challenge you to apply your skills to real-world problems and provide valuable experience in working with outliers.

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Find a data science competition that interests you.
Download the data and familiarize yourself with it.
Develop a model to predict the target variable.
Submit your model for evaluation.

Career center

Learners who complete Advanced analysis of outliers in R and Matlab will develop knowledge and skills that may be useful to these careers:

Data Analyst

A Data Analyst gathers, analyzes, and interprets data to extract meaningful insights, enabling organizations to improve decision-making and operations. The course, 'Advanced Analysis of Outliers in R and Matlab,' provides a solid foundation in detecting and treating atypical data, a crucial skill for Data Analysts seeking accurate results in data analysis and modeling.

See salaries and explore the career path for Data Analyst

Data Scientist

Data Scientists leverage data to solve complex business problems and drive informed decision-making. This course equips Data Scientists with the expertise to handle outliers effectively, ensuring the reliability and accuracy of data analysis, model development, and predictive analytics.

See salaries and explore the career path for Data Scientist

Statistician

Statisticians apply statistical methods to collect, analyze, interpret, and present data. The course 'Advanced Analysis of Outliers in R and Matlab' enhances a Statistician's ability to identify and manage outliers, ensuring the validity and integrity of statistical analysis and modeling.

See salaries and explore the career path for Statistician

Research Scientist

Research Scientists conduct scientific research to advance knowledge and understanding in various fields. The course 'Advanced Analysis of Outliers in R and Matlab' equips Research Scientists with the expertise to identify and treat outliers, ensuring the reliability and validity of their research findings.

See salaries and explore the career path for Research Scientist

Machine Learning Engineer

Machine Learning Engineers design and implement Machine Learning models to solve real-world problems. This course provides foundational knowledge in outlier detection, which is critical for building robust and accurate Machine Learning models that can handle real-world data containing outliers.

See salaries and explore the career path for Machine Learning Engineer

Data Engineer

Data Engineers design, build, and maintain data pipelines to ensure the availability and integrity of data. This course provides Data Engineers with the skills to detect and handle outliers effectively, ensuring the quality and reliability of data used for analysis and decision-making.

See salaries and explore the career path for Data Engineer

Business Analyst

Business Analysts leverage data to understand and improve business processes. The course 'Advanced Analysis of Outliers in R and Matlab' provides Business Analysts with the expertise to identify and treat outliers, ensuring accurate data analysis and informed decision-making.

See salaries and explore the career path for Business Analyst

Risk Analyst

Risk Analysts assess and manage risks within organizations. The course 'Advanced Analysis of Outliers in R and Matlab' provides Risk Analysts with the skills to detect and treat outliers, enabling them to better identify and mitigate risks.

See salaries and explore the career path for Risk Analyst

Operations Research Analyst

Operations Research Analysts use mathematical and analytical methods to solve complex business problems. The course 'Advanced Analysis of Outliers in R and Matlab' provides Operations Research Analysts with the skills to detect and treat outliers, ensuring the validity and accuracy of their models and analysis.

See salaries and explore the career path for Operations Research Analyst

Financial Analyst

Financial Analysts analyze financial data to make investment recommendations. The course 'Advanced Analysis of Outliers in R and Matlab' provides Financial Analysts with the expertise to detect and treat outliers, ensuring the accuracy and reliability of their financial analysis and modeling.

See salaries and explore the career path for Financial Analyst

Epidemiologist

Epidemiologists investigate the causes and spread of diseases in populations. The course 'Advanced Analysis of Outliers in R and Matlab' provides Epidemiologists with the expertise to detect and treat outliers, ensuring the accuracy and reliability of their data analysis and modeling in disease surveillance and outbreak investigation.

See salaries and explore the career path for Epidemiologist

Biostatistician

Biostatisticians apply statistical methods to solve problems in the field of biology. The course 'Advanced Analysis of Outliers in R and Matlab' provides Biostatisticians with the expertise to detect and treat outliers, ensuring the validity and accuracy of their statistical analysis and modeling in biological research.

See salaries and explore the career path for Biostatistician

Actuary

Actuaries use mathematical and statistical methods to assess and manage financial risks. The course 'Advanced Analysis of Outliers in R and Matlab' provides Actuaries with the expertise to detect and treat outliers, ensuring the accuracy and reliability of their risk analysis and modeling.

See salaries and explore the career path for Actuary

IT Manager

IT Managers plan, implement, and manage IT systems within organizations. The course 'Advanced Analysis of Outliers in R and Matlab' may be useful for IT Managers responsible for managing data and ensuring its quality and integrity.

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Software Developer

Software Developers design, develop, and maintain software applications. The course 'Advanced Analysis of Outliers in R and Matlab' may be useful for Software Developers who work on data-driven applications and need to ensure the accuracy and reliability of their data analysis and modeling.

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