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Kirsten Gokay, Meeta Dash, Alyssa Simpson-Rochwerger, Andrea Butkovic, and Kiran Vajapey
Build a custom data annotation job to create novel datasets.

What's inside

Syllabus

Learn how data can affect the performance of a machine learning model and see how to create your own labeled dataset using Appen's annotation platform.
Given a dataset and business goal, design your own data labeling job using Appen’s platform.

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Examines industry-standard techniques for data annotation and labeling
Emphasizes real-life scenarios by utilizing Appen's platform for data labeling
Provides hands-on experience in designing and implementing data labeling jobs
Led by industry experts with extensive experience in data annotation
This course may require access to a computer with specific software

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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 Creating A Dataset with these activities:
Organize and review course-related materials
Enhance your learning experience by organizing and reviewing course materials proactively.
Show steps
  • Gather and organize lecture notes, assignments, and other course materials.
  • Review the materials regularly to reinforce your understanding of the concepts.
  • Identify areas where you need additional clarification or support.
Review foundational concepts in data analysis
Strengthen your foundation in data analysis to support your learning in data annotation.
Browse courses on Data Analysis
Show steps
  • Review key concepts in statistics and data analysis.
  • Refresh your knowledge of common data analysis techniques.
  • Complete practice problems or exercises to test your understanding.
Participate in a peer-mentoring program
Engage with peers to enhance your understanding of data annotation concepts and techniques through mentoring and support.
Show steps
  • Find a peer who is more experienced in data annotation.
  • Set up regular meetings or communication channels.
  • Share knowledge, ask questions, and provide feedback to each other.
  • Collaborate on practice data annotation tasks.
One other activity
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Show all four activities
Complete annotated data labeling tasks on Appen's platform
Reinforce your understanding of the data annotation job design process by completing practice tasks using Appen's annotation platform.
Show steps
  • Create your Appen account and set it up.
  • Familiarize yourself with the Appen interface and its tools.
  • Review the annotation guidelines provided for the data labeling job.
  • Start annotating data on the platform.

Career center

Learners who complete Creating A Dataset will develop knowledge and skills that may be useful to these careers:
Data Annotation Specialist
Data Annotation Specialists prepare data for machine learning models. They label data so that models can learn to recognize patterns. This course may be helpful for Data Annotation Specialists by teaching them how to create a custom data annotation job. This can help them to improve the quality of the data that they label.
Machine Learning Engineer
Machine Learning Engineers build and maintain machine learning models. They use data to train models that can make predictions or perform other tasks. This course may be helpful for Machine Learning Engineers by teaching them how to create a custom data annotation job to create novel datasets. This can help them to build better models with more accurate predictions.
Data Architect
Data Architects design and build data systems. They work with data engineers and data scientists to ensure that data is available and accessible. This course may be helpful for Data Architects by teaching them how to create a custom data annotation job to create novel datasets. This can help them to build better data systems that meet the needs of the business.
Data Scientist
Data Scientists use data to solve business problems. They collect, clean, and analyze data to build models that can make predictions or perform other tasks. This course may be helpful for Data Scientists by teaching them how to create a custom data annotation job to create novel datasets. This can help them to build better models with more accurate predictions.
Data Engineer
Data Engineers build and maintain data pipelines. They ensure that data is collected, cleaned, and stored in a way that makes it accessible to data analysts and data scientists. This course may be helpful for Data Engineers by teaching them how to create a custom data annotation job to create novel datasets. This can help them to build better data pipelines that improve the quality of the data that is available for analysis.
AI Engineer
AI Engineers design, develop, and maintain artificial intelligence systems. They use data to train models that can make predictions or perform other tasks. This course may be helpful for AI Engineers by teaching them how to create a custom data annotation job to create novel datasets. This can help them to build better models with more accurate predictions.
Data Management Analyst
Data Management Analysts manage data for organizations. They ensure that data is accurate, consistent, and accessible. This course may be helpful for Data Management Analysts by teaching them how to create a custom data annotation job to create novel datasets. This can help them to improve the quality of the data that they manage.
Risk Analyst
Risk Analysts identify and assess risks to organizations. They develop and implement plans to mitigate risks. This course may be helpful for Risk Analysts by teaching them how to create a custom data annotation job to create novel datasets. This can help them to better identify and assess risks to organizations.
Software Engineer
Software Engineers design, develop, and maintain software applications. They use data to build models that can make predictions or perform other tasks. This course may be helpful for Software Engineers by teaching them how to create a custom data annotation job to create novel datasets. This can help them to build better software applications with more accurate predictions.
Database Administrator
Database Administrators manage and maintain databases. They ensure that data is stored securely and efficiently. This course may be helpful for Database Administrators by teaching them how to create a custom data annotation job to create novel datasets. This can help them to better manage and maintain databases.
Business Analyst
Business Analysts use data to improve business processes. They collect, clean, and analyze data to identify trends and make recommendations. This course may be helpful for Business Analysts by teaching them how to create a custom data annotation job to create novel datasets. This can help them to better analyze data and extract valuable insights.
Data Analyst
Data Analysts get insights from data to improve company strategy and decision making. Data Analysts collect, clean, and analyze data to build data models. This course may be helpful for Data Analysts by teaching them how to build a custom data annotation job to create novel datasets. This can help them to better analyze data and extract valuable insights.
IT Auditor
IT Auditors evaluate the security of computer systems and networks. They identify vulnerabilities and make recommendations to improve security. This course may be helpful for IT Auditors by teaching them how to create a custom data annotation job to create novel datasets. This can help them to better evaluate the security of computer systems and networks.
Information Security Analyst
Information Security Analysts protect data from unauthorized access, use, disclosure, disruption, modification, or destruction. This course may be helpful for Information Security Analysts by teaching them how to create a custom data annotation job to create novel datasets. This can help them to better protect data from unauthorized access and use.
Product Manager
Product Managers manage the development of products. They work with engineers, designers, and marketers to bring products to market. This course may be helpful for Product Managers by teaching them how to create a custom data annotation job to create novel datasets. This can help them to better understand the needs of their customers and develop better products.

Reading list

We've selected ten 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 Creating A Dataset.
A comprehensive introduction to machine learning for data scientists, covering a wide range of topics from data preparation to model deployment. It provides a solid foundation for those looking to start applying machine learning in their work.
A comprehensive introduction to deep learning for natural language processing, covering a wide range of topics from word embeddings to sequence-to-sequence models. It provides a valuable resource for those looking to apply deep learning to natural language processing tasks.
A comprehensive introduction to natural language processing with Python, covering a wide range of topics from text preprocessing to machine learning models. It provides a valuable resource for those looking to get started with natural language processing in Python.
A comprehensive introduction to data science for business, covering a wide range of topics from data collection to model deployment. It provides a valuable resource for those looking to apply data science to business problems.
A classic textbook on statistical learning, covering a wide range of topics from linear regression to support vector machines. It provides a solid foundation for those looking to understand the theoretical underpinnings of machine learning.
A practical guide to data science from the ground up, covering a wide range of topics from data cleaning to model deployment. It provides a valuable resource for those looking to get started with data science in Python.
A practical guide to machine learning with Python, covering a wide range of topics from data preparation to model deployment. It provides a valuable resource for those looking to get started with machine learning in Python.
A practical guide to machine learning with Python, covering a wide range of topics from data preparation to model deployment. It provides a valuable resource for those looking to get started with machine learning in Python.
A gentle introduction to machine learning, covering a wide range of topics from data preparation to model deployment. It provides a valuable resource for those looking to get started with machine learning without any prior experience.

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