Building Features from Nominal Data from Pluralsight

The quality of preprocessing the numeric data is subjected to the important determinant of the results of machine learning models built using that data. In this course, Building Features from Nominal Data, you will gain the ability to encode categorical data in ways that increase the statistical power of models. First, you will learn the different types of continuous and categorical data, and the differences between ratio and interval scale data, and between nominal and ordinal data. Next, you will discover how to encode categorical data using one-hot and label encoding, and how to avoid the dummy variable trap in linear regression. Finally, you will explore how to implement different forms of contrast coding - such as simple, Helmert, and orthogonal polynomial coding, so that regression results closely mirror the hypotheses that you wish to test. When you’re finished with this course, you will have the skills and knowledge of encoding categorical data needed to increase the statistical power of linear regression that includes such data.

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Enhances statistical power of linear regression including categorical data, crucial for building robust machine learning models

Instructors Janani Ravi bring expertise in data preprocessing and machine learning

Provides practical techniques for encoding categorical data, addressing real-world challenges in data analysis

Covers various coding methods, including one-hot encoding, label encoding, and contrast coding, equipping learners with diverse approaches

Builds a solid foundation in categorical data encoding, enabling learners to enhance the accuracy and interpretability of machine learning models

Designed for individuals seeking to advance their skills in machine learning and data analysis

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Learners who complete Building Features from Nominal Data will develop knowledge and skills that may be useful to these careers:

Quantitative Analyst

Quantitative Analysts use mathematical and statistical methods to analyze financial data. The Building Features from Nominal Data course can be beneficial for Quantitative Analysts who want to improve their skills in data preprocessing and feature engineering. This course covers various techniques for encoding categorical data, which is a common challenge in financial data analysis. By understanding how to encode categorical data effectively, Quantitative Analysts can prepare data that is suitable for building more accurate and interpretable models.

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Statistician

Statisticians use statistical methods to collect, analyze, interpret, and present data. The Building Features from Nominal Data course can be useful for Statisticians who specialize in developing statistical models for various applications. This course provides a foundation in encoding categorical data, which is a crucial step in data preprocessing for statistical modeling. By mastering these techniques, Statisticians can enhance the accuracy and interpretability of their models.

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Data Analyst

Data Analysts collect, clean, and analyze data to extract meaningful insights. The Building Features from Nominal Data course can be helpful for Data Analysts who want to improve their skills in data preprocessing and feature engineering. This course covers various techniques for encoding categorical data, which is a common challenge in data analysis. By understanding how to encode categorical data effectively, Data Analysts can prepare data that is suitable for building more accurate and interpretable models.

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Actuary

Actuaries use mathematical and statistical methods to assess and manage financial risks. The Building Features from Nominal Data course may be useful for Actuaries who want to enhance their skills in data analysis and modeling. This course provides a foundation in encoding categorical data, which is a critical step in preparing data for actuarial analysis and modeling. By mastering these techniques, Actuaries can improve the accuracy and interpretability of their models, leading to better risk management decisions.

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Risk Analyst

Risk Analysts assess and manage financial risks. The Building Features from Nominal Data course may be useful for Risk Analysts who want to enhance their skills in data analysis and modeling. This course provides a foundation in encoding categorical data, which is a crucial step in preparing data for risk analysis and modeling. By mastering these techniques, Risk Analysts can improve the accuracy and interpretability of their models, leading to better risk management decisions.

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Machine Learning Engineer

Machine Learning Engineers design, develop, and deploy machine learning models. The Building Features from Nominal Data course can be beneficial for Machine Learning Engineers who want to improve their ability to prepare and process data for machine learning algorithms. This course covers various techniques for encoding categorical data, which is a common challenge in machine learning. By understanding how to encode categorical data effectively, Machine Learning Engineers can build more accurate and robust models.

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Market Researcher

Market Researchers collect and analyze data to understand consumer behavior and market trends. The Building Features from Nominal Data course may be useful for Market Researchers who want to enhance their skills in data analysis and modeling. This course provides a foundation in encoding categorical data, which is a common challenge in market research. By understanding how to encode categorical data effectively, Market Researchers can build more accurate and robust models, leading to better insights and decision-making.

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Operations Research Analyst

Operations Research Analysts use mathematical and statistical methods to solve business problems. The Building Features from Nominal Data course may be useful for Operations Research Analysts who want to enhance their skills in data analysis and modeling. This course provides a foundation in encoding categorical data, which is a critical step in preparing data for analysis and modeling. By mastering these techniques, Operations Research Analysts can improve the accuracy and interpretability of their models, leading to better decision-making.

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Business Intelligence Analyst

Business Intelligence Analysts use data to identify and solve business problems. The Building Features from Nominal Data course may be useful for Business Intelligence Analysts who want to enhance their skills in data analysis and modeling. This course provides a foundation in encoding categorical data, which is a critical step in preparing data for analysis and modeling. By mastering these techniques, Business Intelligence Analysts can improve the accuracy and interpretability of their findings, leading to better decision-making.

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Data Engineer

Data Engineers design and build data pipelines. The Building Features from Nominal Data course may be useful for Data Engineers who want to enhance their skills in data integration and data quality management. This course provides a foundation in encoding categorical data, which is a common challenge in data integration. By understanding how to encode categorical data effectively, Data Engineers can build data pipelines that are more accurate and reliable.

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Data Architect

Data Architects design and implement data management solutions. The Building Features from Nominal Data course may be useful for Data Architects who want to enhance their skills in data modeling and data quality management. This course provides a foundation in encoding categorical data, which is a crucial step in data modeling and ensuring data quality. By mastering these techniques, Data Architects can design data solutions that are more accurate and reliable.

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Business Analyst

Business Analysts use data to identify and solve business problems. The Building Features from Nominal Data course may be useful for Business Analysts who want to enhance their skills in data analysis and modeling. This course provides a foundation in encoding categorical data, which is a critical step in preparing data for analysis and modeling. By mastering these techniques, Business Analysts can improve the accuracy and interpretability of their findings, leading to better decision-making.

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Data Scientist

Data Scientists are responsible for developing and maintaining models that analyze and interpret data. The Building Features from Nominal Data course may be useful for Data Scientists who wish to enhance their skills in encoding categorical data, which is crucial for increasing the statistical power of models that include such data. This course can help Data Scientists build a foundation in different encoding techniques, including one-hot encoding, label encoding, and contrast coding, which are essential for effective data preprocessing and model building.

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Database Administrator

Database Administrators manage and maintain databases. The Building Features from Nominal Data course may be useful for Database Administrators who want to enhance their skills in data management and data quality control. This course provides a foundation in encoding categorical data, which is a common challenge in data management. By understanding how to encode categorical data effectively, Database Administrators can improve the efficiency and accuracy of data storage and retrieval.

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Software Engineer

Software Engineers design, develop, and maintain software systems. The Building Features from Nominal Data course may be useful for Software Engineers who work on developing machine learning applications. This course provides a foundation in encoding categorical data, which is a common challenge in machine learning. By understanding how to encode categorical data effectively, Software Engineers can develop more accurate and robust machine learning models.

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Building Features from Nominal Data

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