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Axel Sirota
Data normalisation transforms our data to make it more tractable for downstream applications. This course will teach you the skills of data normalization in Pandas needed to make any dataset usable for machine learning.
Regardless of your line of work, data is everywhere. Nowadays we generate more data per second than ever before; however this data is usually raw, dirty and frequently unusable.
Read more
Data normalisation transforms our data to make it more tractable for downstream applications. This course will teach you the skills of data normalization in Pandas needed to make any dataset usable for machine learning.
Regardless of your line of work, data is everywhere. Nowadays we generate more data per second than ever before; however this data is usually raw, dirty and frequently unusable.
Read more
Data normalisation transforms our data to make it more tractable for downstream applications. This course will teach you the skills of data normalization in Pandas needed to make any dataset usable for machine learning.
Regardless of your line of work, data is everywhere. Nowadays we generate more data per second than ever before; however this data is usually raw, dirty and frequently unusable.
In this course, Normalize Data to Make It Appropriate for an Analysis with Pandas, you’ll gain the ability to normalize your data however you need - both to create amazing visualizations as well as feed it into your AI models.
First, you’ll explore what normalization and scaling really are.
Next, you’ll discover some simple techniques like simple scaling or min-max scaling.
Finally, you’ll learn how to perform Z-score scaling which presents your data into a normal distribution.
When you’re finished with this course, you’ll have the skills and knowledge of data normalization needed to make any type of dataset usable.
Register for this course and see more details by visiting:
OpenCourser.com/course/x9i1tg/normalize
What's inside
Syllabus
Course Overview
Why Normalize?
Some Simple Normalization Techniques
Gaussian Normalization
Read more
Syllabus
Course Overview
Why Normalize?
Some Simple Normalization Techniques
Gaussian Normalization
Read more
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Taught by Axel Sirota, who is an expert in data normalization on Pandas
Develops normalization skills for data preparation to use in machine learning
Applicable to a wide range of learners due to its broad scope of data normalization techniques
Covers essential techniques like simple scaling, min-max scaling, and Z-score scaling
เหมาะสำหรับผู้เรียนที่มีพื้นฐานในการวิเคราะห์ข้อมูล
Requires understanding of Pandas library
Save this course
Save Normalize Data to Make It Appropriate for an Analysis with Pandas 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 Normalize Data to Make It Appropriate for an Analysis with Pandas with these
activities:
Connect with Data Normalization Experts
Show steps
Reach out to experienced data professionals for guidance and insights on data normalization best practices.
Show steps
-
Identify individuals with expertise in data normalization.
-
Request informational interviews or mentorship opportunities.
-
Attend industry events and conferences to connect with potential mentors.
Read 'Data Manipulation with Pandas' by Wes McKinney
Show steps
Expand knowledge on data normalization techniques by reading a comprehensive reference book.
View
Python for Data Analysis: Data Wrangling with...
on Amazon
Show steps
-
Review the chapter on data normalization in Pandas
-
Understand the concepts and techniques explained in the book
-
Apply the knowledge gained to enhance data normalization skills
Review basic statistics concepts
Show steps
Prepare for data normalization concepts by reviewing basic statistics.
Browse courses on
Basic Statistics
Show steps
-
Revisit key concepts such as mean, median, mode, and standard deviation
-
Understand the concept of data distribution
-
Understand the importance of outliers in data normalization
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Peer Data Normalization Support Group
Show steps
Join or start a peer support group to discuss and troubleshoot data normalization challenges.
Show steps
-
Find or create a group of peers interested in data normalization.
-
Meet regularly to discuss challenges and share solutions.
-
Provide support and guidance to fellow group members.
Pandas Interview Questions & Solutions
Show steps
Solve interview questions that provide practical application of data normalization techniques.
Browse courses on
Pandas
Show steps
-
Go through a collection of interview questions related to Pandas normalization techniques.
-
Attempt to solve the questions on your own.
-
Review the provided solutions to check your answers.
Explore online tutorials on data normalization
Show steps
Supplement course knowledge by exploring expert tutorials on data normalization.
Browse courses on
Data Manipulation
Show steps
-
Search for online tutorials on data normalization in Pandas
-
Follow the tutorials and practice the techniques demonstrated
-
Refer to the tutorials for additional clarification and examples
Normalize data using different techniques
Show steps
Reinforce normalization techniques through hands-on practice.
Browse courses on
Normalization Techniques
Show steps
-
Apply min-max scaling to a dataset
-
Apply z-score scaling to a dataset
-
Compare the results and analyze the effect of different techniques
Data Normalization Workshop for Pandas Enthusiasts
Show steps
Attend a workshop dedicated to data normalization in Pandas, allowing for hands-on practice and expert guidance.
Show steps
-
Register for the workshop.
-
Attend the workshop and actively participate in the activities.
-
Implement the learned techniques in your own projects.
Discuss normalization challenges with peers
Show steps
Gain diverse perspectives and insights by discussing normalization challenges with peers.
Browse courses on
Data Transformation
Show steps
-
Identify common challenges faced during data normalization
-
Share experiences and discuss best practices for overcoming these challenges
-
Engage in peer-to-peer learning and knowledge exchange
Improve Your Z-Score Calculations: Additional Resources
Show steps
Explore more resources to enhance understanding of Z-score calculations and their relevance to data normalization.
Browse courses on
Normalization Techniques
Show steps
-
Search for tutorials and articles on advanced Z-score calculation methods.
-
Review the resources and work through the provided examples.
-
Apply the techniques to practice datasets to solidify your understanding.
Attend a workshop on advanced data normalization techniques
Show steps
Enhance skills and gain exposure to cutting-edge techniques through a specialized workshop.
Browse courses on
Data Transformation
Show steps
-
Identify and register for a relevant workshop
-
Attend the workshop and actively participate in discussions
-
Implement the learned techniques in practical applications
Normalize a dataset for a machine learning project
Show steps
Apply normalization techniques to real-world data for practical implementation.
Show steps
-
Choose a dataset relevant to a machine learning project
-
Normalize the dataset using appropriate techniques
-
Evaluate the normalized dataset and compare it to the original dataset
Data Normalization Portfolio
Show steps
Create a portfolio of data normalization projects to showcase your skills and understanding.
Browse courses on
Data Normalization
Show steps
-
Gather datasets that require normalization.
-
Apply different normalization techniques to the datasets.
-
Create visualizations and reports to demonstrate the effectiveness of your normalization methods.
-
Compile your projects into a portfolio.
Contribute to open-source projects related to data normalization
Show steps
Enhance practical skills and contribute to the data science community by participating in open-source projects.
Browse courses on
Data Manipulation
Show steps
-
Identify open-source projects that focus on data normalization
-
Review the project documentation and codebase
-
Contribute to the project by fixing bugs, adding features, or improving documentation
Connect with Data Normalization Experts
Show steps
Reach out to experienced data professionals for guidance and insights on data normalization best practices.
Show steps
Show steps
Show steps
- Identify individuals with expertise in data normalization.
- Request informational interviews or mentorship opportunities.
- Attend industry events and conferences to connect with potential mentors.
Read 'Data Manipulation with Pandas' by Wes McKinney
Show steps
Expand knowledge on data normalization techniques by reading a comprehensive reference book.
View
Python for Data Analysis: Data Wrangling with...
on Amazon
Show steps
Show steps
Show steps
- Review the chapter on data normalization in Pandas
- Understand the concepts and techniques explained in the book
- Apply the knowledge gained to enhance data normalization skills
Review basic statistics concepts
Show steps
Prepare for data normalization concepts by reviewing basic statistics.
Browse courses on
Basic Statistics
Show steps
Show steps
Show steps
- Revisit key concepts such as mean, median, mode, and standard deviation
- Understand the concept of data distribution
- Understand the importance of outliers in data normalization
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Peer Data Normalization Support Group
Show steps
Join or start a peer support group to discuss and troubleshoot data normalization challenges.
Show steps
Show steps
Show steps
- Find or create a group of peers interested in data normalization.
- Meet regularly to discuss challenges and share solutions.
- Provide support and guidance to fellow group members.
Pandas Interview Questions & Solutions
Show steps
Solve interview questions that provide practical application of data normalization techniques.
Browse courses on
Pandas
Show steps
Show steps
Show steps
- Go through a collection of interview questions related to Pandas normalization techniques.
- Attempt to solve the questions on your own.
- Review the provided solutions to check your answers.
Explore online tutorials on data normalization
Show steps
Supplement course knowledge by exploring expert tutorials on data normalization.
Browse courses on
Data Manipulation
Show steps
Show steps
Show steps
- Search for online tutorials on data normalization in Pandas
- Follow the tutorials and practice the techniques demonstrated
- Refer to the tutorials for additional clarification and examples
Normalize data using different techniques
Show steps
Reinforce normalization techniques through hands-on practice.
Browse courses on
Normalization Techniques
Show steps
Show steps
Show steps
- Apply min-max scaling to a dataset
- Apply z-score scaling to a dataset
- Compare the results and analyze the effect of different techniques
Data Normalization Workshop for Pandas Enthusiasts
Show steps
Attend a workshop dedicated to data normalization in Pandas, allowing for hands-on practice and expert guidance.
Show steps
Show steps
Show steps
- Register for the workshop.
- Attend the workshop and actively participate in the activities.
- Implement the learned techniques in your own projects.
Discuss normalization challenges with peers
Show steps
Gain diverse perspectives and insights by discussing normalization challenges with peers.
Browse courses on
Data Transformation
Show steps
Show steps
Show steps
- Identify common challenges faced during data normalization
- Share experiences and discuss best practices for overcoming these challenges
- Engage in peer-to-peer learning and knowledge exchange
Improve Your Z-Score Calculations: Additional Resources
Show steps
Explore more resources to enhance understanding of Z-score calculations and their relevance to data normalization.
Browse courses on
Normalization Techniques
Show steps
Show steps
Show steps
- Search for tutorials and articles on advanced Z-score calculation methods.
- Review the resources and work through the provided examples.
- Apply the techniques to practice datasets to solidify your understanding.
Attend a workshop on advanced data normalization techniques
Show steps
Enhance skills and gain exposure to cutting-edge techniques through a specialized workshop.
Browse courses on
Data Transformation
Show steps
Show steps
Show steps
- Identify and register for a relevant workshop
- Attend the workshop and actively participate in discussions
- Implement the learned techniques in practical applications
Normalize a dataset for a machine learning project
Show steps
Apply normalization techniques to real-world data for practical implementation.
Show steps
Show steps
Show steps
- Choose a dataset relevant to a machine learning project
- Normalize the dataset using appropriate techniques
- Evaluate the normalized dataset and compare it to the original dataset
Data Normalization Portfolio
Show steps
Create a portfolio of data normalization projects to showcase your skills and understanding.
Browse courses on
Data Normalization
Show steps
Show steps
Show steps
- Gather datasets that require normalization.
- Apply different normalization techniques to the datasets.
- Create visualizations and reports to demonstrate the effectiveness of your normalization methods.
- Compile your projects into a portfolio.
Contribute to open-source projects related to data normalization
Show steps
Enhance practical skills and contribute to the data science community by participating in open-source projects.
Browse courses on
Data Manipulation
Show steps
Show steps
Show steps
- Identify open-source projects that focus on data normalization
- Review the project documentation and codebase
- Contribute to the project by fixing bugs, adding features, or improving documentation
Career center
Learners who complete Normalize Data to Make It Appropriate for an Analysis with Pandas will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts are responsible for collecting, cleaning, and analyzing data to identify trends and patterns. They use their findings to make recommendations that can help businesses improve their operations. This course can help you develop the skills you need to become a successful Data Analyst by teaching you how to normalize data, which is an essential step in the data analysis process.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models. They use their expertise in data normalization to ensure that their models are accurate and reliable. This course can help you develop the skills you need to become a successful Machine Learning Engineer by teaching you how to normalize data in Pandas.
Data Scientist
Data Scientist
Data Scientists use their skills in data normalization to clean and prepare data for analysis. They then use this data to develop models that can help businesses make better decisions. This course can help you develop the skills you need to become a successful Data Scientist by teaching you how to normalize data in Pandas.
Statistician
Statistician
Statisticians use their skills in data normalization to ensure that their data is accurate and reliable. They then use this data to develop models that can help businesses make better decisions. This course can help you develop the skills you need to become a successful Statistician by teaching you how to normalize data in Pandas.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use their skills in data normalization to develop models that can help businesses make better decisions. They use their knowledge of data normalization to ensure that their models are accurate and reliable. This course can help you develop the skills you need to become a successful Quantitative Analyst by teaching you how to normalize data in Pandas.
Business Analyst
Business Analyst
Business Analysts use their skills in data normalization to identify trends and patterns in data. They then use this information to make recommendations that can help businesses improve their operations. This course can help you develop the skills you need to become a successful Business Analyst by teaching you how to normalize data in Pandas.
Data Engineer
Data Engineer
Data Engineers are responsible for building and maintaining the infrastructure that stores and processes data. They use their skills in data normalization to ensure that data is stored and processed in a way that makes it easy to analyze. This course can help you develop the skills you need to become a successful Data Engineer by teaching you how to normalize data in Pandas.
Software Engineer
Software Engineer
Software Engineers use their skills in data normalization to develop software that can process and analyze data. They use their knowledge of data normalization to ensure that their software is accurate and reliable. This course can help you develop the skills you need to become a successful Software Engineer by teaching you how to normalize data in Pandas.
Database Administrator
Database Administrator
Database Administrators are responsible for managing and maintaining databases. They use their skills in data normalization to ensure that data is stored in a way that makes it easy to retrieve and analyze. This course can help you develop the skills you need to become a successful Database Administrator by teaching you how to normalize data in Pandas.
Financial Analyst
Financial Analyst
Financial Analysts use their skills in data normalization to analyze financial data and make recommendations to investors. They use their knowledge of data normalization to ensure that their analysis is accurate and reliable. This course can help you develop the skills you need to become a successful Financial Analyst by teaching you how to normalize data in Pandas.
Marketing Analyst
Marketing Analyst
Marketing Analysts use their skills in data normalization to analyze marketing data and make recommendations to businesses. They use their knowledge of data normalization to ensure that their analysis is accurate and reliable. This course can help you develop the skills you need to become a successful Marketing Analyst by teaching you how to normalize data in Pandas.
Operations Analyst
Operations Analyst
Operations Analysts use their skills in data normalization to analyze operational data and make recommendations to businesses. They use their knowledge of data normalization to ensure that their analysis is accurate and reliable. This course can help you develop the skills you need to become a successful Operations Analyst by teaching you how to normalize data in Pandas.
Risk Analyst
Risk Analyst
Risk Analysts use their skills in data normalization to analyze risk data and make recommendations to businesses. They use their knowledge of data normalization to ensure that their analysis is accurate and reliable. This course can help you develop the skills you need to become a successful Risk Analyst by teaching you how to normalize data in Pandas.
Actuary
Actuary
Actuaries use their skills in data normalization to analyze insurance data and make recommendations to insurance companies. They use their knowledge of data normalization to ensure that their analysis is accurate and reliable. This course may help you develop the skills you need to become a successful Actuary by teaching you how to normalize data in Pandas.
Economist
Economist
Economists use their skills in data normalization to analyze economic data and make recommendations to governments and businesses. They use their knowledge of data normalization to ensure that their analysis is accurate and reliable. This course may help you develop the skills you need to become a successful Economist by teaching you how to normalize data in Pandas.
For more career information including salaries, visit:
OpenCourser.com/course/x9i1tg/normalize
Reading list
We've selected 15 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
Normalize Data to Make It Appropriate for an Analysis with Pandas.
Provides a comprehensive guide to data science. It covers all the essential topics, from data preparation and feature engineering to model evaluation and deployment.
Provides an end-to-end guide to building machine learning models with Python. It covers a wide range of topics, from data preparation and feature engineering to model evaluation and deployment.
Teaches you how to build machine learning models from scratch using Python. It covers all the essential concepts, from data preparation and feature engineering to model evaluation and deployment.
Provides a comprehensive guide to machine learning with Python, including a section on data normalization. It covers a wide range of topics, including data preprocessing, model selection, and evaluation.
Save
Provides a comprehensive introduction to deep learning. It covers all the essential concepts, from neural networks and deep learning architectures to training and evaluation methods.
Provides a concise and accessible introduction to deep learning. It covers all the essential concepts, from neural networks and deep learning architectures to training and evaluation methods.
Provides a comprehensive introduction to statistical learning. It covers all the essential concepts, from supervised and unsupervised learning to model evaluation and selection.
Provides a comprehensive introduction to pattern recognition and machine learning. It covers all the essential concepts, from supervised and unsupervised learning to model evaluation and selection.
Provides a practical introduction to machine learning. It covers all the essential concepts, from supervised and unsupervised learning to model evaluation and selection.
Provides a collection of recipes for solving common machine learning problems with Python. It covers a wide range of topics, from data preparation and feature engineering to model evaluation and deployment.
Provides a practical introduction to machine learning for people with a non-technical background. The authors explain how to build machine learning models using Python and provide real-world examples.
Save
Provides a comprehensive introduction to machine learning. It covers all the essential concepts, from supervised and unsupervised learning to model evaluation and selection.
Provides a comprehensive overview of data science, including a section on data normalization. It covers a wide range of topics, including data collection, data analysis, and data visualization.
Provides a comprehensive overview of statistical methods for machine learning, including a section on data normalization. It covers a wide range of topics, including probability, inference, and regression.
Save
Provides a comprehensive overview of data mining, including a section on data normalization. It covers a wide range of topics, including data preprocessing, data clustering, and data classification.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/x9i1tg/normalize
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Time
47 hours
Level
Intermediate
Via
Pluralsight
Instructor
Axel Sirota
Language
English
Good to know
Taught by Axel Sirota, who is an expert in data normalization on Pandas
Develops normalization skills for data preparation to use in machine learning
Applicable to a wide range of learners due to its broad scope of data normalization techniques
Covers essential techniques like simple scaling, min-max scaling, and Z-score scaling
เหมาะสำหรับผู้เรียนที่มีพื้นฐานในการวิเคราะห์ข้อมูล
Requires understanding of Pandas library
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