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Ahmad Varasteh
By the end of this project you will learn how to analyze high-dimensional data using different visualization techniques. We are going to learn how to implement Scatterplot Matrix and Parallel coordinate plots (PCP) in python. and We will learn how to use these two high-dimensional data visualization techniques to analyze our data by solving three tasks: Outlier Detection, Correlation Analysis and Cluster analysis. we will also talk about Data reduction techniques. we will learn how to sample our data to reduce the number of the data points for a better visualization. We will also learn about the Dimensionality reduction technique to reduce the number of dimensions in our dataset and how it can help us for a better analysis.
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What's inside
Syllabus
Project Overview
By the end of this project you will learn how to analyze high-dimensional data using different visualization techniques. We are going to learn how to implement Scatterplot Matrix and Parallel coordinate plots (PCP) in python. and We will learn how to use these two high-dimensional data visualization techniques to analyze our data by solving three tasks: Outlier Detection, Correlation Analysis and Cluster analysis. we will also talk about Data reduction techniques. we will learn how to sample our data to reduce the number of the data points for a better visualization. We will also learn about the Dimensionality reduction technique to reduce the number of dimensions in our dataset and how it can help us for a better analysis.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops important techniques for exploratory data analysis, a core skillset for data analysts
Provides opportunities to gain hands-on experience with visualization tools, enhancing practical skills
Covers advanced techniques like Dimensionality Reduction, catering to learners interested in deeper understanding
Involves retrieving data from a credible repository, ensuring learners gain experience with real-world scenarios
Exploration of multiple data visualization techniques enhances learner's toolkit and strengthens their ability to communicate data insights
Assumes some prior knowledge of Python, limiting accessibility for complete beginners
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Activities
Be better prepared
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your course. Deepen your understanding
during
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after
it. Supplement your coursework and achieve mastery of the topics covered
in High-dimensional Data visualization techniques using python with these
activities:
Connect with Experts in High-Dimensional Data Analysis
Show steps
Connecting with experts will provide you with valuable insights and guidance in high-dimensional data analysis.
Show steps
-
Attend industry events or conferences
-
Reach out to professors or researchers in the field
-
Join online communities or forums
Organize Course Materials
Show steps
Organizing your course materials will help you stay on top of the content and make it easier to review later.
Show steps
-
Create a system for storing and organizing notes, assignments, and quizzes
-
Review materials regularly to reinforce learning
Review Basic Probability
Show steps
Understanding basic probability will help prepare you to understand how to detect outliers.
Browse courses on
Probability
Show steps
-
Review lecture notes from introductory statistics or probability course
-
Complete practice problems on basic probability
Six other activities
Expand to see all activities and additional details
Show all nine activities
Solve Data Sampling Problems
Show steps
Solving data sampling problems will reinforce your understanding of how to select a representative sample.
Browse courses on
Data Sampling
Show steps
-
Review different data sampling techniques
-
Solve practice problems on determining sample size and sampling methods
Create a Scatterplot Matrix
Show steps
Creating a scatterplot matrix from scratch will help you understand how to use it for outlier detection.
Show steps
-
Gather data with at least 5 dimensions
-
Create a scatterplot matrix using Python or R
-
Identify and analyze potential outliers
Discuss Outlier Detection Methods
Show steps
Discussing outlier detection methods with peers will help you gain different perspectives and identify potential blind spots.
Browse courses on
Outlier Detection
Show steps
-
Join a study group or online forum
-
Present your own approach to outlier detection
-
Discuss and evaluate different outlier detection methods
Explore PCA for Dimensionality Reduction
Show steps
Exploring PCA will help you understand how to reduce the number of dimensions in your dataset.
Browse courses on
Dimensionality Reduction
Show steps
-
Review the concept of Principal Component Analysis
-
Find a tutorial on how to implement PCA in Python
-
Apply PCA to a dataset with many dimensions
Build a Data Visualization Dashboard
Show steps
Building a data visualization dashboard will allow you to apply your knowledge of high-dimensional data visualization to a practical project.
Browse courses on
Data Visualization
Show steps
-
Choose a dataset with multiple dimensions
-
Design the layout and visualizations for the dashboard
-
Implement the dashboard using a data visualization tool or library
Analyze High-Dimensional Data for a Real-World Problem
Show steps
Working on a real-world project will allow you to apply your skills to a practical problem and gain valuable experience.
Browse courses on
High-Dimensional Data Analysis
Show steps
-
Identify a problem or dataset that involves high-dimensional data
-
Gather and preprocess the data
-
Apply appropriate visualization techniques to analyze the data
-
Develop and implement a model or solution
-
Evaluate the results and communicate your findings
Connect with Experts in High-Dimensional Data Analysis
Show steps
Connecting with experts will provide you with valuable insights and guidance in high-dimensional data analysis.
Show steps
Show steps
Show steps
- Attend industry events or conferences
- Reach out to professors or researchers in the field
- Join online communities or forums
Organize Course Materials
Show steps
Organizing your course materials will help you stay on top of the content and make it easier to review later.
Show steps
Show steps
Show steps
- Create a system for storing and organizing notes, assignments, and quizzes
- Review materials regularly to reinforce learning
Review Basic Probability
Show steps
Understanding basic probability will help prepare you to understand how to detect outliers.
Browse courses on
Probability
Show steps
Show steps
Show steps
- Review lecture notes from introductory statistics or probability course
- Complete practice problems on basic probability
Six other activities
Expand to see all activities and additional details
Show all nine activities
Solve Data Sampling Problems
Show steps
Solving data sampling problems will reinforce your understanding of how to select a representative sample.
Browse courses on
Data Sampling
Show steps
Show steps
Show steps
- Review different data sampling techniques
- Solve practice problems on determining sample size and sampling methods
Create a Scatterplot Matrix
Show steps
Creating a scatterplot matrix from scratch will help you understand how to use it for outlier detection.
Show steps
Show steps
Show steps
- Gather data with at least 5 dimensions
- Create a scatterplot matrix using Python or R
- Identify and analyze potential outliers
Discuss Outlier Detection Methods
Show steps
Discussing outlier detection methods with peers will help you gain different perspectives and identify potential blind spots.
Browse courses on
Outlier Detection
Show steps
Show steps
Show steps
- Join a study group or online forum
- Present your own approach to outlier detection
- Discuss and evaluate different outlier detection methods
Explore PCA for Dimensionality Reduction
Show steps
Exploring PCA will help you understand how to reduce the number of dimensions in your dataset.
Browse courses on
Dimensionality Reduction
Show steps
Show steps
Show steps
- Review the concept of Principal Component Analysis
- Find a tutorial on how to implement PCA in Python
- Apply PCA to a dataset with many dimensions
Build a Data Visualization Dashboard
Show steps
Building a data visualization dashboard will allow you to apply your knowledge of high-dimensional data visualization to a practical project.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Choose a dataset with multiple dimensions
- Design the layout and visualizations for the dashboard
- Implement the dashboard using a data visualization tool or library
Analyze High-Dimensional Data for a Real-World Problem
Show steps
Working on a real-world project will allow you to apply your skills to a practical problem and gain valuable experience.
Browse courses on
High-Dimensional Data Analysis
Show steps
Show steps
Show steps
- Identify a problem or dataset that involves high-dimensional data
- Gather and preprocess the data
- Apply appropriate visualization techniques to analyze the data
- Develop and implement a model or solution
- Evaluate the results and communicate your findings
Career center
Learners who complete High-dimensional Data visualization techniques using python will develop knowledge and skills
that may be useful to these careers:
Data Visualization Specialist
Data Visualization Specialist
Data Visualization Specialists use their knowledge of visual design and data analysis to create visualizations that communicate insights from data. This course, High-dimensional Data visualization techniques using python, can provide a strong foundation for this role by teaching you how to visualize and analyze high-dimensional data, a common challenge in the field. By learning how to use visualization techniques like Scatterplot Matrix and Parallel coordinate plots, you'll be able to create clear and concise visualizations that effectively communicate insights.
Data Scientist
Data Scientist
Data Scientists use their knowledge of math, statistics, and computer science to extract insights from data. This course, High-dimensional Data visualization techniques using python, can help you build a foundation for this role by teaching you how to visualize and analyze high-dimensional data. By learning how to use visualization techniques like Scatterplot Matrix and Parallel coordinate plots, you'll be able to identify patterns, detect outliers, and perform cluster analysis, all of which are essential skills for Data Scientists.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers use their knowledge of machine learning algorithms to develop and deploy machine learning models. This course, High-dimensional Data visualization techniques using python, can help you build a foundation for this role by teaching you how to visualize and analyze high-dimensional data, a common challenge in the field. By learning how to use visualization techniques like Scatterplot Matrix and Parallel coordinate plots, you'll be able to identify patterns, detect outliers, and perform cluster analysis, all of which are essential skills for Machine Learning Engineers.
Data Analyst
Data Analyst
A Data Analyst analyzes data to extract meaningful insights and trends. This course, High-dimensional Data visualization techniques using python, can provide a strong foundation for this role by teaching you how to visualize and analyze high-dimensional data, a common challenge in the field. By learning how to use visualization techniques like Scatterplot Matrix and Parallel coordinate plots, you'll be able to identify patterns, detect outliers, and perform cluster analysis, all of which are essential skills for Data Analysts.
Statistician
Statistician
Statisticians use their knowledge of statistics to collect, analyze, and interpret data. This course, High-dimensional Data visualization techniques using python, can provide a strong foundation for this role by teaching you how to visualize and analyze high-dimensional data, a common challenge in the field. By learning how to use visualization techniques like Scatterplot Matrix and Parallel coordinate plots, you'll be able to identify patterns, detect outliers, and perform cluster analysis, all of which are essential skills for Statisticians.
Data Engineer
Data Engineer
Data Engineers design and build systems that collect, store, and process data. This course, High-dimensional Data visualization techniques using python, can help you build a foundation for this role by teaching you how to visualize and analyze high-dimensional data, a common challenge in the field. By learning how to use visualization techniques like Scatterplot Matrix and Parallel coordinate plots, you'll be able to identify patterns, detect outliers, and perform cluster analysis, all of which are essential skills for Data Engineers.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts use their knowledge of business and data analysis to help businesses make informed decisions. This course, High-dimensional Data visualization techniques using python, can help you build a foundation for this role by teaching you how to visualize and analyze high-dimensional data, a common challenge in the field. By learning how to use visualization techniques like Scatterplot Matrix and Parallel coordinate plots, you'll be able to identify patterns, detect outliers, and perform cluster analysis, all of which are essential skills for Business Intelligence Analysts.
User Experience Researcher
User Experience Researcher
User Experience Researchers use their knowledge of human behavior and research methods to design and evaluate user experiences. This course, High-dimensional Data visualization techniques using python, can help you build a foundation for this role by teaching you how to visualize and analyze high-dimensional data, a common challenge in the field. By learning how to use visualization techniques like Scatterplot Matrix and Parallel coordinate plots, you'll be able to identify patterns, detect outliers, and perform cluster analysis, all of which are essential skills for User Experience Researchers.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use their knowledge of mathematics, statistics, and computer science to solve complex problems in business and industry. This course, High-dimensional Data visualization techniques using python, can help you build a foundation for this role by teaching you how to visualize and analyze high-dimensional data, a common challenge in the field. By learning how to use visualization techniques like Scatterplot Matrix and Parallel coordinate plots, you'll be able to identify patterns, detect outliers, and perform cluster analysis, all of which are essential skills for Operations Research Analysts.
Financial Analyst
Financial Analyst
Financial Analysts use their knowledge of finance and data analysis to help businesses make informed decisions about investments and financial planning. This course, High-dimensional Data visualization techniques using python, can help you build a foundation for this role by teaching you how to visualize and analyze high-dimensional data, a common challenge in the field. By learning how to use visualization techniques like Scatterplot Matrix and Parallel coordinate plots, you'll be able to identify patterns, detect outliers, and perform cluster analysis, all of which are essential skills for Financial Analysts.
Marketing Analyst
Marketing Analyst
Marketing Analysts use their knowledge of marketing and data analysis to help businesses understand their customers and make informed decisions. This course, High-dimensional Data visualization techniques using python, can help you build a foundation for this role by teaching you how to visualize and analyze high-dimensional data, a common challenge in the field. By learning how to use visualization techniques like Scatterplot Matrix and Parallel coordinate plots, you'll be able to identify patterns, detect outliers, and perform cluster analysis, all of which are essential skills for Marketing Analysts.
Risk Analyst
Risk Analyst
Risk Analysts use their knowledge of risk management and data analysis to help businesses identify and mitigate risks. This course, High-dimensional Data visualization techniques using python, can help you build a foundation for this role by teaching you how to visualize and analyze high-dimensional data, a common challenge in the field. By learning how to use visualization techniques like Scatterplot Matrix and Parallel coordinate plots, you'll be able to identify patterns, detect outliers, and perform cluster analysis, all of which are essential skills for Risk Analysts.
Product Manager
Product Manager
Product Managers use their knowledge of business, technology, and design to develop and manage products. This course, High-dimensional Data visualization techniques using python, can help you build a foundation for this role by teaching you how to visualize and analyze high-dimensional data, a common challenge in the field. By learning how to use visualization techniques like Scatterplot Matrix and Parallel coordinate plots, you'll be able to identify patterns, detect outliers, and perform cluster analysis, all of which are essential skills for Product Managers.
Quantitative Researcher
Quantitative Researcher
Quantitative Researchers use their knowledge of mathematics, statistics, and computer science to develop and test financial models. This course, High-dimensional Data visualization techniques using python, can help you build a foundation for this role by teaching you how to visualize and analyze high-dimensional data, a common challenge in the field. By learning how to use visualization techniques like Scatterplot Matrix and Parallel coordinate plots, you'll be able to identify patterns, detect outliers, and perform cluster analysis, all of which are essential skills for Quantitative Researchers.
Data Architect
Data Architect
Data Architects design and build data systems that meet the needs of businesses. This course, High-dimensional Data visualization techniques using python, can help you build a foundation for this role by teaching you how to visualize and analyze high-dimensional data, a common challenge in the field. By learning how to use visualization techniques like Scatterplot Matrix and Parallel coordinate plots, you'll be able to identify patterns, detect outliers, and perform cluster analysis, all of which are essential skills for Data Architects.
For more career information including salaries, visit:
OpenCourser.com/course/4tbik2/high
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
High-dimensional Data visualization techniques using python.
Provides a comprehensive introduction to high-dimensional data analysis. It covers a wide range of topics, including the different types of high-dimensional data, dimensionality reduction techniques, and clustering algorithms.
This classic textbook on statistical learning. It covers a wide range of topics, including supervised learning, unsupervised learning, and model selection.
Provides a gentle introduction to machine learning for data science, helping you get started with Python and common data science frameworks. Background in linear regression, naive Bayes and various clustering algorithms.
Provides a thorough introduction to information theory, inference, and learning algorithms. It valuable resource for anyone interested in learning more about the foundations of machine learning.
Provides a comprehensive overview of pattern recognition and machine learning.
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Provides an introduction to data mining techniques.
Provides a comprehensive guide to machine learning in Python.
Provides a comprehensive guide to machine learning in R.
Provides a comprehensive guide to data analysis in Python.
Provides a comprehensive guide to data visualization in Python.
Provides a comprehensive guide to programming in R.
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Provides a comprehensive guide to R for data science.
Provides a comprehensive guide to machine learning in Python using Scikit-Learn, Keras, and TensorFlow.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/4tbik2/high
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Ahmad Varasteh
Language
English
Good to know
Develops important techniques for exploratory data analysis, a core skillset for data analysts
Provides opportunities to gain hands-on experience with visualization tools, enhancing practical skills
Covers advanced techniques like Dimensionality Reduction, catering to learners interested in deeper understanding
Involves retrieving data from a credible repository, ensuring learners gain experience with real-world scenarios
Exploration of multiple data visualization techniques enhances learner's toolkit and strengthens their ability to communicate data insights
Assumes some prior knowledge of Python, limiting accessibility for complete beginners
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