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Janani Ravi
This course covers the important techniques of exploring data in order to find relationships between variables, including techniques to summarize and describe your data, and several powerful visualization tools to express relationships in that data.
Data science and data modeling are fast emerging as crucial capabilities that every enterprise and every technologist must possess these days. Increasingly, different organizations are using the same models and the same modeling tools, so what differs is how those models are applied to the data. Today, more than ever, it is really important that you know your data well.
Read more
This course covers the important techniques of exploring data in order to find relationships between variables, including techniques to summarize and describe your data, and several powerful visualization tools to express relationships in that data.
Data science and data modeling are fast emerging as crucial capabilities that every enterprise and every technologist must possess these days. Increasingly, different organizations are using the same models and the same modeling tools, so what differs is how those models are applied to the data. Today, more than ever, it is really important that you know your data well.
Read more
This course covers the important techniques of exploring data in order to find relationships between variables, including techniques to summarize and describe your data, and several powerful visualization tools to express relationships in that data.
Data science and data modeling are fast emerging as crucial capabilities that every enterprise and every technologist must possess these days. Increasingly, different organizations are using the same models and the same modeling tools, so what differs is how those models are applied to the data. Today, more than ever, it is really important that you know your data well.
In this course, Finding Relationships in Data with Python you will gain the ability to find relationships within your data that you can exploit to construct more complex models.
First, you will learn to summarize your data using univariate, bivariate and multivariate statistics. Next, you will discover how specific forms of visualization have evolved to identify and capture specific types of relationships. You will then learn some advanced tools such as the use of autocorrelation plots and KDE plots that help model probability distributions.
Finally, you will round out your knowledge by using four of these libraries - Matplotlib, Seaborn, Altair and Plotly to find relationships.
When you’re finished with this course, you will have the skills and knowledge to identify and exploit relationships that exist within your data, by efficiently exploring and visualizing that data.
Register for this course and see more details by visiting:
OpenCourser.com/course/0vva4x/finding
What's inside
Syllabus
Course Overview
Identifying and Visualizing Common Relationships in Data
New ModuleIdentifying and Visualizing Probabilistic and Statistical Relationships
Read more
Syllabus
Course Overview
Identifying and Visualizing Common Relationships in Data
New ModuleIdentifying and Visualizing Probabilistic and Statistical Relationships
Read more
Using Interactive Visualizations to Explore Relationships in Data
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Builds a strong foundation for beginners who want to understand how to find relationships within their data
Strengthens an existing foundation for intermediate learners whose existing knowledge may need refreshing or deeper examination
Develops professional skills and deep expertise in identifying and exploiting relationships that exist within data, which is useful in many industries
Taught by recognized instructors Janani Ravi, who is known for her work in exploring and visualizing complex data sets and extracting meaningful relationships
Offers hands-on labs and interactive materials, including exercises, quizzes, and activities that help learners practice finding relationships within data
Covers visualizing relationships in data using popular libraries, such as Matplotlib, Seaborn, Altair, and Plotly
Save this course
Save Finding Relationships in Data with Python 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 Finding Relationships in Data with Python with these
activities:
Review course materials and create a study guide
Show steps
Organize and summarize the key concepts covered in the course, promoting retention and understanding.
Show steps
-
Gather course notes, slides, and any provided resources
-
Summarize and condense the main points of each section
-
Identify key terms, definitions, and formulas
-
Organize the information logically and visually, using headings, bullet points, and diagrams
-
Review the study guide regularly, especially before tests or assessments
Compile a list of open-source data visualization libraries and tools
Show steps
Expand knowledge of available resources for data visualization and facilitate future project development.
Show steps
-
Research and identify popular open-source data visualization libraries and tools
-
Gather information on their features, strengths, and areas of application
-
Organize the information into a comprehensive list or table
-
Publish or share your compilation with others
Practice creating histograms and KDE plots to explore data distributions
Show steps
Reinforce understanding of data distributions and how to visualize them effectively using histograms and KDE plots.
Show steps
-
Generate sample data with different distribution patterns
-
Create histograms and KDE plots using Matplotlib or Seaborn to visualize the data distributions
-
Analyze the plots to identify shapes, central tendencies, and spread
Six other activities
Expand to see all activities and additional details
Show all nine activities
Practice drawing scatter plots to visualize data relationships
Show steps
Reinforce the use of scatter plots to reveal relationships between numeric variables and identify outliers.
Show steps
-
Generate sample data with varying levels of correlation and scatter
-
Create scatter plots using Matplotlib or Seaborn to visualize the data
-
Analyze the plots to identify patterns and correlations between variables
-
Identify potential outliers or influential points
Participate in a peer study group to discuss data visualization challenges and solutions
Show steps
Engage with peers to share knowledge, troubleshoot challenges, and gain new perspectives on data visualization techniques.
Show steps
-
Find or form a peer study group with fellow data science students
-
Select a specific data visualization topic or challenge for discussion
-
Contribute your own insights and experiences
-
Learn from the perspectives and approaches of others
Read "Data Visualization with Python and JavaScript" by Jake VanderPlas
Show steps
Gain in-depth knowledge of data visualization best practices and techniques using multiple programming languages.
View
Python Data Science Handbook: Essential Tools...
on Amazon
Show steps
-
Review the fundamentals of data visualization principles and techniques
-
Explore advanced visualization techniques, such as interactive plots and dashboards
-
Learn how to combine Python and JavaScript for effective data visualization
Explore online tutorials on advanced data visualization techniques
Show steps
Expand knowledge of data visualization tools and techniques beyond the scope of the course.
Show steps
-
Identify online platforms or resources offering tutorials on advanced data visualization
-
Select tutorials that align with specific areas of interest, such as interactive visualizations or multivariate analysis
-
Follow the tutorials step-by-step, experimenting with the provided examples and datasets
-
Apply the learned techniques to your own data analysis projects
Create a blog post or video tutorial on a specific data visualization technique
Show steps
Enhance understanding and solidify skills by teaching others a specific data visualization technique.
Show steps
-
Choose a specific data visualization technique that you have mastered
-
Develop a clear and concise explanation of the technique, including its benefits and applications
-
Create a blog post, video tutorial, or presentation that effectively conveys the technique
-
Publish or share your content with others
Develop a data visualization dashboard for a real-world dataset
Show steps
Apply data visualization skills to solve practical problems and present insights in a visually compelling way.
Show steps
-
Identify a real-world dataset that aligns with your interests or field of study
-
Explore the dataset and identify key relationships and insights
-
Design and develop an interactive data visualization dashboard using tools like Tableau or Power BI
-
Present your findings and insights to stakeholders or a target audience
Review course materials and create a study guide
Show steps
Organize and summarize the key concepts covered in the course, promoting retention and understanding.
Show steps
Show steps
Show steps
- Gather course notes, slides, and any provided resources
- Summarize and condense the main points of each section
- Identify key terms, definitions, and formulas
- Organize the information logically and visually, using headings, bullet points, and diagrams
- Review the study guide regularly, especially before tests or assessments
Compile a list of open-source data visualization libraries and tools
Show steps
Expand knowledge of available resources for data visualization and facilitate future project development.
Show steps
Show steps
Show steps
- Research and identify popular open-source data visualization libraries and tools
- Gather information on their features, strengths, and areas of application
- Organize the information into a comprehensive list or table
- Publish or share your compilation with others
Practice creating histograms and KDE plots to explore data distributions
Show steps
Reinforce understanding of data distributions and how to visualize them effectively using histograms and KDE plots.
Show steps
Show steps
Show steps
- Generate sample data with different distribution patterns
- Create histograms and KDE plots using Matplotlib or Seaborn to visualize the data distributions
- Analyze the plots to identify shapes, central tendencies, and spread
Six other activities
Expand to see all activities and additional details
Show all nine activities
Practice drawing scatter plots to visualize data relationships
Show steps
Reinforce the use of scatter plots to reveal relationships between numeric variables and identify outliers.
Show steps
Show steps
Show steps
- Generate sample data with varying levels of correlation and scatter
- Create scatter plots using Matplotlib or Seaborn to visualize the data
- Analyze the plots to identify patterns and correlations between variables
- Identify potential outliers or influential points
Participate in a peer study group to discuss data visualization challenges and solutions
Show steps
Engage with peers to share knowledge, troubleshoot challenges, and gain new perspectives on data visualization techniques.
Show steps
Show steps
Show steps
- Find or form a peer study group with fellow data science students
- Select a specific data visualization topic or challenge for discussion
- Contribute your own insights and experiences
- Learn from the perspectives and approaches of others
Read "Data Visualization with Python and JavaScript" by Jake VanderPlas
Show steps
Gain in-depth knowledge of data visualization best practices and techniques using multiple programming languages.
View
Python Data Science Handbook: Essential Tools...
on Amazon
Show steps
Show steps
Show steps
- Review the fundamentals of data visualization principles and techniques
- Explore advanced visualization techniques, such as interactive plots and dashboards
- Learn how to combine Python and JavaScript for effective data visualization
Explore online tutorials on advanced data visualization techniques
Show steps
Expand knowledge of data visualization tools and techniques beyond the scope of the course.
Show steps
Show steps
Show steps
- Identify online platforms or resources offering tutorials on advanced data visualization
- Select tutorials that align with specific areas of interest, such as interactive visualizations or multivariate analysis
- Follow the tutorials step-by-step, experimenting with the provided examples and datasets
- Apply the learned techniques to your own data analysis projects
Create a blog post or video tutorial on a specific data visualization technique
Show steps
Enhance understanding and solidify skills by teaching others a specific data visualization technique.
Show steps
Show steps
Show steps
- Choose a specific data visualization technique that you have mastered
- Develop a clear and concise explanation of the technique, including its benefits and applications
- Create a blog post, video tutorial, or presentation that effectively conveys the technique
- Publish or share your content with others
Develop a data visualization dashboard for a real-world dataset
Show steps
Apply data visualization skills to solve practical problems and present insights in a visually compelling way.
Show steps
Show steps
Show steps
- Identify a real-world dataset that aligns with your interests or field of study
- Explore the dataset and identify key relationships and insights
- Design and develop an interactive data visualization dashboard using tools like Tableau or Power BI
- Present your findings and insights to stakeholders or a target audience
Career center
Learners who complete Finding Relationships in Data with Python will develop knowledge and skills
that may be useful to these careers:
Statistician
Statistician
A Statistician uses data to make inferences about the world. This course will help you build the skills you need to find relationships in data that can be used to make informed decisions.
Data Scientist
Data Scientist
A Data Scientist uses data to build models that can predict future outcomes. This course will help you build the skills you need to find relationships in data that can be used to create accurate models.
Data Analyst
Data Analyst
A Data Analyst uses data to improve business processes. You will learn to find patterns and trends in data that can be used to make better decisions. This course will help you build the skills you need to identify relationships in data that can be used to solve business problems.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer builds and deploys machine learning models. This course will help you build the skills you need to find relationships in data that can be used to create powerful machine learning models.
Market Researcher
Market Researcher
A Market Researcher uses data to understand consumer behavior. This course will help you build the skills you need to find relationships in data that can be used to develop effective marketing campaigns.
Business Analyst
Business Analyst
A Business Analyst uses data to improve business processes. This course will help you build the skills you need to find relationships in data that can be used to make better decisions.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses data to improve the efficiency of operations. This course will help you build the skills you need to find relationships in data that can be used to optimize processes.
Financial Analyst
Financial Analyst
A Financial Analyst uses data to make investment decisions. This course will help you build the skills you need to find relationships in data that can be used to make sound investment decisions.
Risk Analyst
Risk Analyst
A Risk Analyst uses data to identify and mitigate risks. This course will help you build the skills you need to find relationships in data that can be used to protect organizations from financial and operational risks.
Product Manager
Product Manager
A Product Manager uses data to make decisions about product development. This course will help you build the skills you need to find relationships in data that can be used to create products that meet customer needs.
Software Engineer
Software Engineer
A Software Engineer uses data to build software applications. This course will help you build the skills you need to find relationships in data that can be used to create efficient and effective software.
Database Administrator
Database Administrator
A Database Administrator manages and maintains databases. This course will help you build the skills you need to find relationships in data that can be used to optimize database performance.
Information Security Analyst
Information Security Analyst
An Information Security Analyst protects organizations from cyber threats. This course will help you build the skills you need to find relationships in data that can be used to identify and mitigate security risks.
Data Engineer
Data Engineer
A Data Engineer builds and maintains the infrastructure that stores and processes data. This course will help you build the skills you need to find relationships in data that can be used to optimize data pipelines.
Actuary
Actuary
An Actuary uses data to assess and manage risk. This course will help you build the skills you need to find relationships in data that can be used to develop insurance products and pricing.
For more career information including salaries, visit:
OpenCourser.com/course/0vva4x/finding
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
Finding Relationships in Data with Python.
Covers the fundamentals of Python for data analysis, including data manipulation, visualization, and modeling. It valuable resource for those who want to learn the basics of data analysis in Python.
Provides a comprehensive overview of machine learning techniques and algorithms. It covers a wide range of topics, from supervised learning to unsupervised learning and deep learning.
Teaches the fundamentals of data science from scratch. It covers a wide range of topics, from data cleaning and wrangling to machine learning and deep learning.
Classic textbook on statistical learning. It covers a wide range of topics, from supervised learning to unsupervised learning and deep learning.
More advanced textbook on statistical learning. It covers a wide range of topics, from supervised learning to unsupervised learning and deep learning.
Save
Comprehensive textbook on deep learning. It covers a wide range of topics, from the basics of deep learning to more advanced topics such as convolutional neural networks and recurrent neural networks.
Classic textbook on pattern recognition and machine learning. It covers a wide range of topics, from the basics of machine learning to more advanced topics such as Bayesian inference and kernel methods.
More advanced textbook on machine learning. It covers a wide range of topics, from the basics of machine learning to more advanced topics such as graphical models and Bayesian inference.
Textbook on Bayesian statistics. It covers a wide range of topics, from the basics of Bayesian statistics to more advanced topics such as hierarchical models and Markov chain Monte Carlo.
Textbook on causal inference. It covers a wide range of topics, from the basics of causal inference to more advanced topics such as structural equation modeling and counterfactuals.
Textbook on econometrics. It covers a wide range of topics, from the basics of econometrics to more advanced topics such as time series analysis and panel data.
Save
Textbook on data mining. It covers a wide range of topics, from the basics of data mining to more advanced topics such as machine learning and data visualization.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/0vva4x/finding
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Visualizing Data with Python
Time
124 hours
Level
Introductory
Via
Pluralsight
Instructor
Janani Ravi
Language
English
This course belongs to a
collection
called
Interpreting Data with Python. It's comprised of three courses:
- Finding Relationships in Data with Python (viewing)
- Interpreting Data Using Statistical Models with Python
- Interpreting Data Using Descriptive Statistics with Python
Good to know
Builds a strong foundation for beginners who want to understand how to find relationships within their data
Strengthens an existing foundation for intermediate learners whose existing knowledge may need refreshing or deeper examination
Develops professional skills and deep expertise in identifying and exploiting relationships that exist within data, which is useful in many industries
Taught by recognized instructors Janani Ravi, who is known for her work in exploring and visualizing complex data sets and extracting meaningful relationships
Offers hands-on labs and interactive materials, including exercises, quizzes, and activities that help learners practice finding relationships within data
Covers visualizing relationships in data using popular libraries, such as Matplotlib, Seaborn, Altair, and Plotly
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