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Justin Flett
RapidMiner is a widely-used data science and machine learning platform. This course will teach you the basics of using RapidMiner, including basic setup, data visualization and preparation methods, and will briefly introduce some data analysis.
Read more
RapidMiner is a widely-used data science and machine learning platform. This course will teach you the basics of using RapidMiner, including basic setup, data visualization and preparation methods, and will briefly introduce some data analysis.
Read more
RapidMiner is a widely-used data science and machine learning platform. This course will teach you the basics of using RapidMiner, including basic setup, data visualization and preparation methods, and will briefly introduce some data analysis.
Data Science and Machine Learning are rapidly growing fields that use scientific methods and processes to extract useful knowledge and insights from data. In this course, RapidMiner: Getting Started, you will learn foundational knowledge of solving real-world data science problems. First, you will learn the basics of the software including setup, installation, interface, and data visualization options. Next, you will discover some commonly used data preparation methods. Finally, you will explore how to analyze data in order to come to useful conclusions and insights. When you’re finished with this course, you will have the skills and knowledge of RapidMiner needed to solve data science and machine learning problems.
Register for this course and see more details by visiting:
OpenCourser.com/course/c0ep1x/rapidminer
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What's inside
Syllabus
Course Overview
Learning the Basics of RapidMiner
Preparing the Data
Analyzing the Data and Further Learning
Read more
Syllabus
Course Overview
Learning the Basics of RapidMiner
Preparing the Data
Analyzing the Data and Further Learning
Read more
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Focuses on practical problem-solving with RapidMiner software
Guides beginners through essential RapidMiner skills
Provides foundational knowledge for solving real-world data science problems
Introduces data visualization, preparation, and analysis
Suitable for individuals seeking a practical introduction to data science and RapidMiner
Assumes no prior knowledge, making it beginner-friendly
Save this course
Save RapidMiner: Getting Started to your list so you can find it easily later:
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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 RapidMiner: Getting Started with these
activities:
Read 'Introduction to Data Science' by John Wiley & Sons
Show steps
Gain additional knowledge and insights by reading a foundational book on data science.
View
Biostatistics For Dummies
on Amazon
Show steps
-
Purchase or borrow the book.
-
Read the book thoroughly, taking notes and highlighting important passages.
-
Complete the exercises and assignments in the book to reinforce your learning.
Review Applied Linear Algebra
Show steps
Brush up on your linear algebra skills to strengthen your foundation for the course.
Browse courses on
Linear Algebra
Show steps
-
Watch a linear algebra video tutorial or series.
-
Solve practice problems and review proofs from a linear algebra textbook or online resources.
-
Consider attending a linear algebra workshop or course to refresh your knowledge.
Create a Course Study Guide
Show steps
Improve your retention by compiling a comprehensive study guide.
Browse courses on
Study Guide
Show steps
-
Gather your course notes, assignments, quizzes, and exams.
-
Organize and summarize the key concepts, definitions, and formulas.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Walkthrough RapidMiner Tutorials
Show steps
Enhance your understanding of RapidMiner through guided tutorials.
Browse courses on
RapidMiner
Show steps
-
Access the RapidMiner documentation and tutorials.
-
Work through the RapidMiner tutorial on data visualization and preparation.
-
Follow a tutorial that introduces basic data analysis techniques.
Explore Data Visualization Platforms
Show steps
Enhance your understanding of data visualization tools for effective communication.
Browse courses on
Data Visualization
Show steps
-
Explore online tutorials and resources for data visualization platforms like Tableau or Power BI.
-
Try out the platforms and create some basic visualizations using sample datasets.
Data Manipulation Exercises
Show steps
Sharpen your data manipulation skills for effective data analysis.
Browse courses on
Data Manipulation
Show steps
-
Utilize RapidMiner to perform data cleaning tasks, such as missing value handling and outlier removal.
-
Practice data transformation techniques, including data aggregation, feature engineering, and dimensionality reduction.
-
Complete coding challenges or online exercises that focus on data manipulation.
Develop a Data Analysis Project Plan
Show steps
Enhance your data analysis skills by creating a project plan.
Browse courses on
Data Analysis
Show steps
-
Define the business problem that you want to solve with data.
-
Gather and explore the relevant data sources.
-
Develop a plan for data analysis, including the techniques you will use and the metrics you will track.
Attend a RapidMiner Workshop
Show steps
Gain hands-on experience and expert insights by attending a RapidMiner workshop.
Browse courses on
RapidMiner
Show steps
-
Research and find a RapidMiner workshop that aligns with your interests and skill level.
-
Register for the workshop and make necessary arrangements.
-
Attend the workshop, actively participate in discussions, and ask questions.
Read 'Introduction to Data Science' by John Wiley & Sons
Show steps
Gain additional knowledge and insights by reading a foundational book on data science.
View
Biostatistics For Dummies
on Amazon
Show steps
Show steps
Show steps
- Purchase or borrow the book.
- Read the book thoroughly, taking notes and highlighting important passages.
- Complete the exercises and assignments in the book to reinforce your learning.
Review Applied Linear Algebra
Show steps
Brush up on your linear algebra skills to strengthen your foundation for the course.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Watch a linear algebra video tutorial or series.
- Solve practice problems and review proofs from a linear algebra textbook or online resources.
- Consider attending a linear algebra workshop or course to refresh your knowledge.
Create a Course Study Guide
Show steps
Improve your retention by compiling a comprehensive study guide.
Browse courses on
Study Guide
Show steps
Show steps
Show steps
- Gather your course notes, assignments, quizzes, and exams.
- Organize and summarize the key concepts, definitions, and formulas.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Walkthrough RapidMiner Tutorials
Show steps
Enhance your understanding of RapidMiner through guided tutorials.
Browse courses on
RapidMiner
Show steps
Show steps
Show steps
- Access the RapidMiner documentation and tutorials.
- Work through the RapidMiner tutorial on data visualization and preparation.
- Follow a tutorial that introduces basic data analysis techniques.
Explore Data Visualization Platforms
Show steps
Enhance your understanding of data visualization tools for effective communication.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Explore online tutorials and resources for data visualization platforms like Tableau or Power BI.
- Try out the platforms and create some basic visualizations using sample datasets.
Data Manipulation Exercises
Show steps
Sharpen your data manipulation skills for effective data analysis.
Browse courses on
Data Manipulation
Show steps
Show steps
Show steps
- Utilize RapidMiner to perform data cleaning tasks, such as missing value handling and outlier removal.
- Practice data transformation techniques, including data aggregation, feature engineering, and dimensionality reduction.
- Complete coding challenges or online exercises that focus on data manipulation.
Develop a Data Analysis Project Plan
Show steps
Enhance your data analysis skills by creating a project plan.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Define the business problem that you want to solve with data.
- Gather and explore the relevant data sources.
- Develop a plan for data analysis, including the techniques you will use and the metrics you will track.
Attend a RapidMiner Workshop
Show steps
Gain hands-on experience and expert insights by attending a RapidMiner workshop.
Browse courses on
RapidMiner
Show steps
Show steps
Show steps
- Research and find a RapidMiner workshop that aligns with your interests and skill level.
- Register for the workshop and make necessary arrangements.
- Attend the workshop, actively participate in discussions, and ask questions.
Career center
Learners who complete RapidMiner: Getting Started will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists work with data to uncover insights and apply them to decision-making. They may also be responsible for gathering data through surveys, interviews, and data mining, as well as cleaning, managing, and organizing data. As a RapidMiner user, you can prepare data, visualize it, and perform data analysis in order to provide insights from data. This course helps build a foundation for solving real-world data science problems with RapidMiner.
Data Analyst
Data Analyst
Data Analysts interpret and communicate data. This may involve generating data visualizations to summarize data and present insights, or creating interactive dashboards and reports to communicate data to stakeholders. RapidMiner offers data visualization tools, and this course will teach you how to use them. That makes this a good choice if you want to advance your career as a Data Analyst.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and deploy machine learning models. They may also design, develop, and maintain machine learning pipelines and systems, or perform tasks like model optimization and testing. This course introduces you to machine learning in RapidMiner, which includes data preparation, model selection, training, and evaluation.
Data Engineer
Data Engineer
Data Engineers design and implement data management systems. They may also be responsible for developing and maintaining data pipelines, or performing data modeling and prototyping. This course will teach you to prepare and analyze data with RapidMiner, which may help you enter or advance in a Data Engineering career.
Business Analyst
Business Analyst
Business Analysts use data and other techniques to analyze business processes and operations. Learning RapidMiner and the data analysis methods in this course may help you build a foundation for a Business Analyst career. You will learn techniques like data visualization and data preparation, which are helpful to Business Analysts who need to gather and understand data.
Data Visualization Analyst
Data Visualization Analyst
Data Visualization Analysts use tools and techniques to create visualizations that present data clearly and efficiently. This course introduces you to data visualization options in RapidMiner, and teaches you how to use them to summarize and present data. These are skills that will help launch or advance your Data Visualization Analyst career.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts build and implement quantitative models to analyze risk and guide investment decisions. They may also be involved in developing and testing trading strategies, or performing data analysis and research. This course introduces you to data analysis in RapidMiner, including data visualization, data preparation, and more. Completing this course may be a good first step in a Quantitative Analyst career.
Market Researcher
Market Researcher
Market Researchers gather data about consumer behavior, market trends, and other variables that influence supply and demand. This course will introduce you to data analysis in RapidMiner, including data visualization, data preparation, and more. These skills may help you get started or advance in a Market Researcher career.
Financial Analyst
Financial Analyst
Financial Analysts evaluate and make recommendations on companies, stocks, or other investments. This course introduces you to data analysis in RapidMiner, including data visualization, data preparation, and more. This may provide a useful foundation for individuals interested in entering or advancing in Financial Analysis.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data. They may use statistical methods to design experiments, analyze data, and draw conclusions. The data analysis training in this course may be helpful for aspiring Statisticians.
Computer Programmer
Computer Programmer
Computer Programmers write and maintain code for software applications and systems. RapidMiner is a software application, and learning RapidMiner teaches you a bit about programming. If you are interested in programming, this course could be a good place to start.
IT Analyst
IT Analyst
IT Analysts plan and design computer systems and networks. They may also be responsible for troubleshooting and resolving technical issues, or providing technical support. This course teaches you the basics of RapidMiner, including installation, interface, and data visualization options. This training may be useful for those looking to enter or advance in an IT Analyst role.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications and systems. This course teaches you the basics of RapidMiner, including installation, interface, and data visualization options. This foundational knowledge may prove useful for aspiring Software Engineers.
Database Administrator
Database Administrator
Database Administrators implement and maintain databases. They may also be responsible for data security, performance tuning, and disaster recovery. This course may prove helpful for those looking to enter or advance in a Database Administrator role, as it teaches the basics of data visualization and data preparation in RapidMiner.
Information Security Analyst
Information Security Analyst
Information Security Analysts plan and implement security measures to protect information systems and networks. They may also be responsible for monitoring and analyzing security events, or conducting security audits and risk assessments. The data analysis methods taught in this course may prove useful for Information Security Analysts.
For more career information including salaries, visit:
OpenCourser.com/course/c0ep1x/rapidminer
Reading list
We've selected 17 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
RapidMiner: Getting Started.
Provides a comprehensive overview of statistical learning and data mining. It covers a wide range of topics, including linear regression, logistic regression, decision trees, and support vector machines, offering a theoretical foundation for machine learning.
Comprehensive guide to reinforcement learning, covering the basics of reinforcement learning, as well as advanced topics such as deep reinforcement learning. It provides a theoretical foundation for reinforcement learning, and includes case studies and examples.
Save
Comprehensive guide to data mining, covering the basics of data mining, as well as advanced topics such as data mining algorithms and applications. It provides a theoretical foundation for data mining, and includes case studies and examples.
Comprehensive guide to pattern recognition and machine learning, covering a wide range of topics from data preparation to model evaluation. It provides a theoretical foundation for pattern recognition and machine learning, and includes case studies and examples.
Comprehensive guide to machine learning from a probabilistic perspective, covering a wide range of topics from data preparation to model evaluation. It provides a theoretical foundation for machine learning, and includes case studies and examples.
Comprehensive guide to Bayesian data analysis, covering a wide range of topics from data preparation to model evaluation. It provides a theoretical foundation for Bayesian data analysis, and includes case studies and examples.
Save
Comprehensive guide to deep learning, covering the basics of deep learning, as well as advanced topics such as recurrent neural networks and generative adversarial networks. It provides a theoretical foundation for deep learning, and includes case studies and examples.
Save
Complete guide to data mining using RapidMiner. It covers all the essential concepts and techniques, from data preparation to model building and evaluation. It valuable resource for anyone who wants to learn how to use RapidMiner for data mining.
Provides a practical guide to machine learning with Python libraries such as Scikit-Learn, Keras, and TensorFlow. It covers a wide range of machine learning algorithms and techniques, offering a deeper dive into the practical applications of machine learning.
Save
Collection of recipes for RapidMiner. It covers a wide range of topics, from data preparation to model building. It valuable resource for anyone who wants to learn how to use RapidMiner for specific tasks.
Provides a comprehensive introduction to data science from scratch, using Python. It covers the fundamentals of data science, including data cleaning, feature engineering, model building, and evaluation, offering a solid foundation for learners.
Provides a practical introduction to deep learning with Python. It covers the fundamentals of deep learning, including neural networks, convolutional neural networks, and recurrent neural networks, offering a deeper dive into the practical applications of deep learning.
Provides a comprehensive overview of natural language processing with Python. It covers a wide range of topics, including text classification, sentiment analysis, and machine translation, offering a deeper dive into the practical applications of natural language processing.
Provides a practical introduction to computer vision with Python. It covers a wide range of topics, including image classification, object detection, and image segmentation, offering a deeper dive into the practical applications of computer vision.
Comprehensive guide to machine learning with Python, covering a wide range of topics from data preparation to model evaluation. It provides practical guidance on how to use Python for machine learning, and includes case studies and examples.
Provides an introduction to data science with Python. It covers data exploration, data cleaning, data visualization, and machine learning, offering a complementary perspective on data science using a different programming language.
Save
Provides a critical perspective on data science, and discusses the ethical and social implications of using data to make decisions. It covers a wide range of topics, from data collection to model deployment.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/c0ep1x/rapidminer
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Time
83 hours
Level
Introductory
Via
Pluralsight
Instructor
Justin Flett
Language
English
Good to know
Focuses on practical problem-solving with RapidMiner software
Guides beginners through essential RapidMiner skills
Provides foundational knowledge for solving real-world data science problems
Introduces data visualization, preparation, and analysis
Suitable for individuals seeking a practical introduction to data science and RapidMiner
Assumes no prior knowledge, making it beginner-friendly
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