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Seonmin Park
In this course, students will understand characteristics of language through big data. Students will learn how to collect and analyze big data, and find linguistic features from the data. A number of approaches to the linguistic analysis of written and spoken texts will be discussed.
Register for this course and see more details by visiting:
OpenCourser.com/course/w41ena/big
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What's inside
Syllabus
Scientific Approaches
Analysis Tools I – BNC
Analysis Tools II – COCA, ANTCONC & TagAnt
Read more
Syllabus
Scientific Approaches
Analysis Tools I – BNC
Analysis Tools II – COCA, ANTCONC & TagAnt
Read more
Considerations of Big Data and Language
Project
In the final projects, students are required to conduct their research on linguistics. The research topic can be chosen by students and the linguistic data should be collected according to. The material for this course can be helpful. Through the project, students can learn how to collect data, process data and analyze data for their purposes.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops skills in analyzing and interpreting big data for linguistic research
Learners are introduced to various tools and approaches used in linguistic analysis, such as BNC, COCA, ANTCONC and TagAnt
Taught by Seonmin Park, who is recognized in the field of linguistics
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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 Big data and Language 2 with these
activities:
Review methodology and principles of linguistics
Show steps
Begin learning about linguistics theory by reviewing basic principles and methodologies of linguistics.
Browse courses on
Methodology
Show steps
-
Read articles in linguistics academic journals.
-
Summarize the main points of the articles in your own words.
Organize course materials for easy reference
Show steps
Helps students stay organized and focused on the course content.
Show steps
-
Create a system for organizing notes, assignments, and other course materials.
-
Keep digital and physical materials organized.
-
Review organized materials regularly.
Review previous linguistics courses
Show steps
Brings foundational knowledge to the forefront of memory.
Browse courses on
Linguistics
Show steps
-
Review notes and assignments from previous linguistics courses.
-
Identify key concepts and theories.
-
Read relevant articles and books.
12 other activities
Expand to see all activities and additional details
Show all 15 activities
Read 'The Cambridge Handbook of Linguistics'
Show steps
Provides a comprehensive overview of linguistics, including big data analysis techniques.
View
The Cambridge Dictionary of Linguistics
on Amazon
Show steps
-
Read the chapters on big data and linguistic analysis.
-
Summarize the key concepts and theories presented in the book.
-
Apply the knowledge gained to the course material.
Use BNC and COCA tools for linguistic analysis
Show steps
Learn to understand characteristics and use of BNC and COCA as text analysis tools and as sources of data for linguistics.
Browse courses on
Text Analysis
Show steps
-
Watch video tutorials provided by the BNC and COCA websites.
-
Obtain a text you would like to analyze from BNC and COCA.
-
Apply the tutorials to actually analyze the text using the tools.
BNC Tutorial
Show steps
Enhance understanding of the BNC tool for linguistic analysis.
Browse courses on
Big Data
Show steps
-
Follow an online tutorial or documentation on the BNC (British National Corpus).
-
Explore the BNC interface, search for text data, and download relevant corpora.
-
Familiarize yourself with the BNC data structure and annotation conventions.
Follow online tutorials on big data analysis
Show steps
Provides students with hands-on experience in analyzing linguistic data.
Browse courses on
Big Data Analysis
Show steps
-
Identify reliable online tutorials.
-
Follow the tutorials step-by-step.
-
Apply the techniques learned to real-world linguistic data.
Complete online exercises on linguistic data analysis
Show steps
Provides opportunities for students to practice their data analysis skills.
Browse courses on
Data Analysis
Show steps
-
Find online exercises or platforms that offer linguistic data analysis exercises.
-
Complete the exercises regularly.
-
Review your results and identify areas for improvement.
Practice Analyzing Text Data
Show steps
Practice analyzing text data to enhance understanding of linguistic characteristics.
Browse courses on
Data Analysis
Show steps
-
Collect text data from various sources (e.g., news articles, social media posts, online forums).
-
Identify linguistic features of interest (e.g., word frequency, sentence length, part-of-speech distribution).
-
Apply analytical techniques (e.g., descriptive statistics, machine learning algorithms) to extract insights from the data.
-
Interpret results and draw conclusions about the characteristics of the language used in the text.
Engage in online discussion forums
Show steps
Facilitates exchange of ideas, questions, and insights on course topics.
Show steps
-
Join online discussion forums related to linguistics and big data analysis.
-
Participate in discussions, asking questions and sharing perspectives.
-
Collaborate with peers on group projects or assignments.
Develop a glossary of linguistic terms related to big data
Show steps
Helps students build a strong foundation in linguistic terminology.
Show steps
-
Identify key linguistic terms related to big data.
-
Define each term clearly and concisely.
-
Organize the terms into a logical structure.
Create a summary of ANTCONC & TagAnt
Show steps
Summarize and explain the purpose and uses of ANTCONC and TagAnt.
Show steps
-
Create a compare-contrast table of ANTCONC and TagAnt.
-
Provide at least one example use case for each tool.
Linguistic Feature Presentation
Show steps
Develop strong presentation skills and showcase understanding of linguistic features.
Show steps
-
Identify and research a specific linguistic feature.
-
Collect and analyze data to illustrate the feature.
-
Design and create visual aids (e.g., charts, graphs, examples) to present the data effectively.
-
Practice presenting the feature and its implications to an audience.
Design a research proposal on linguistic analysis of big data
Show steps
Allows students to apply their knowledge to a specific research project.
Browse courses on
Research Proposal
Show steps
-
Identify a research question and develop a hypothesis.
-
Design a data collection plan using big data sources.
-
Outline the analysis methods that will be used.
-
Create a timeline and budget for the project.
Develop a mini-project using big data and linguistics
Show steps
Apply methodologies of this course to design and carry out a smaller scale research project involving big data and linguistics.
Browse courses on
Big Data
Show steps
-
Choose a topic for your research.
-
Collect data from a corpus.
-
Analyze the data using a linguistics approach.
-
Write a report on your findings.
Review methodology and principles of linguistics
Show steps
Begin learning about linguistics theory by reviewing basic principles and methodologies of linguistics.
Browse courses on
Methodology
Show steps
Show steps
Show steps
- Read articles in linguistics academic journals.
- Summarize the main points of the articles in your own words.
Organize course materials for easy reference
Show steps
Helps students stay organized and focused on the course content.
Show steps
Show steps
Show steps
- Create a system for organizing notes, assignments, and other course materials.
- Keep digital and physical materials organized.
- Review organized materials regularly.
Review previous linguistics courses
Show steps
Brings foundational knowledge to the forefront of memory.
Browse courses on
Linguistics
Show steps
Show steps
Show steps
- Review notes and assignments from previous linguistics courses.
- Identify key concepts and theories.
- Read relevant articles and books.
12 other activities
Expand to see all activities and additional details
Show all 15 activities
Read 'The Cambridge Handbook of Linguistics'
Show steps
Provides a comprehensive overview of linguistics, including big data analysis techniques.
View
The Cambridge Dictionary of Linguistics
on Amazon
Show steps
Show steps
Show steps
- Read the chapters on big data and linguistic analysis.
- Summarize the key concepts and theories presented in the book.
- Apply the knowledge gained to the course material.
Use BNC and COCA tools for linguistic analysis
Show steps
Learn to understand characteristics and use of BNC and COCA as text analysis tools and as sources of data for linguistics.
Browse courses on
Text Analysis
Show steps
Show steps
Show steps
- Watch video tutorials provided by the BNC and COCA websites.
- Obtain a text you would like to analyze from BNC and COCA.
- Apply the tutorials to actually analyze the text using the tools.
BNC Tutorial
Show steps
Enhance understanding of the BNC tool for linguistic analysis.
Browse courses on
Big Data
Show steps
Show steps
Show steps
- Follow an online tutorial or documentation on the BNC (British National Corpus).
- Explore the BNC interface, search for text data, and download relevant corpora.
- Familiarize yourself with the BNC data structure and annotation conventions.
Follow online tutorials on big data analysis
Show steps
Provides students with hands-on experience in analyzing linguistic data.
Browse courses on
Big Data Analysis
Show steps
Show steps
Show steps
- Identify reliable online tutorials.
- Follow the tutorials step-by-step.
- Apply the techniques learned to real-world linguistic data.
Complete online exercises on linguistic data analysis
Show steps
Provides opportunities for students to practice their data analysis skills.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Find online exercises or platforms that offer linguistic data analysis exercises.
- Complete the exercises regularly.
- Review your results and identify areas for improvement.
Practice Analyzing Text Data
Show steps
Practice analyzing text data to enhance understanding of linguistic characteristics.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Collect text data from various sources (e.g., news articles, social media posts, online forums).
- Identify linguistic features of interest (e.g., word frequency, sentence length, part-of-speech distribution).
- Apply analytical techniques (e.g., descriptive statistics, machine learning algorithms) to extract insights from the data.
- Interpret results and draw conclusions about the characteristics of the language used in the text.
Engage in online discussion forums
Show steps
Facilitates exchange of ideas, questions, and insights on course topics.
Show steps
Show steps
Show steps
- Join online discussion forums related to linguistics and big data analysis.
- Participate in discussions, asking questions and sharing perspectives.
- Collaborate with peers on group projects or assignments.
Develop a glossary of linguistic terms related to big data
Show steps
Helps students build a strong foundation in linguistic terminology.
Show steps
Show steps
Show steps
- Identify key linguistic terms related to big data.
- Define each term clearly and concisely.
- Organize the terms into a logical structure.
Create a summary of ANTCONC & TagAnt
Show steps
Summarize and explain the purpose and uses of ANTCONC and TagAnt.
Show steps
Show steps
Show steps
- Create a compare-contrast table of ANTCONC and TagAnt.
- Provide at least one example use case for each tool.
Linguistic Feature Presentation
Show steps
Develop strong presentation skills and showcase understanding of linguistic features.
Show steps
Show steps
Show steps
- Identify and research a specific linguistic feature.
- Collect and analyze data to illustrate the feature.
- Design and create visual aids (e.g., charts, graphs, examples) to present the data effectively.
- Practice presenting the feature and its implications to an audience.
Design a research proposal on linguistic analysis of big data
Show steps
Allows students to apply their knowledge to a specific research project.
Browse courses on
Research Proposal
Show steps
Show steps
Show steps
- Identify a research question and develop a hypothesis.
- Design a data collection plan using big data sources.
- Outline the analysis methods that will be used.
- Create a timeline and budget for the project.
Develop a mini-project using big data and linguistics
Show steps
Apply methodologies of this course to design and carry out a smaller scale research project involving big data and linguistics.
Browse courses on
Big Data
Show steps
Show steps
Show steps
- Choose a topic for your research.
- Collect data from a corpus.
- Analyze the data using a linguistics approach.
- Write a report on your findings.
Career center
Learners who complete Big data and Language 2 will develop knowledge and skills
that may be useful to these careers:
Fraud Analyst
Fraud Analyst
Fraud Analysts use data to detect and prevent fraud. They collect, analyze, and interpret data to identify fraudulent activity. KAIST's Big Data and Language 2 offers students hands-on experience with data collection and analysis techniques, which are essential for Fraud Analysts. The skills learned in this course can help build a foundation for students wishing to enter this field.
Software Engineer
Software Engineer
Software Engineers design, develop, and test software applications. They use data to improve the performance and efficiency of their applications. KAIST's Big Data and Language 2 offers students hands-on experience with data collection and analysis techniques, which are essential for Software Engineers. The skills learned in this course can help build a foundation for students wishing to enter this field.
Database Administrator
Database Administrator
Database Administrators design, implement, and maintain databases. They use data to improve the performance and efficiency of their databases. KAIST's Big Data and Language 2 offers students hands-on experience with data collection and analysis techniques, which are essential for Database Administrators. The skills learned in this course can help build a foundation for students wishing to enter this field.
Web Developer
Web Developer
Web Developers design and develop websites. They use data to improve the performance and efficiency of their websites. KAIST's Big Data and Language 2 offers students hands-on experience with data collection and analysis techniques, which are essential for Web Developers. The skills learned in this course can help build a foundation for students wishing to enter this field.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use data to make investment decisions. They collect, analyze, and interpret data to identify investment opportunities. KAIST's Big Data and Language 2 offers students hands-on experience with data collection and analysis techniques, which are essential for Quantitative Analysts. The skills learned in this course can help build a foundation for students wishing to enter this field.
Actuary
Actuary
Actuaries use data to assess risk and uncertainty. They collect, analyze, and interpret data to help businesses make better decisions about risk management. KAIST's Big Data and Language 2 offers students hands-on experience with data collection and analysis techniques, which are essential for Actuaries. The skills learned in this course can help build a foundation for students wishing to enter this field.
Auditor
Auditor
Auditors use data to assess the financial health of businesses. They collect, analyze, and interpret data to identify financial risks and opportunities. KAIST's Big Data and Language 2 offers students hands-on experience with data collection and analysis techniques, which are essential for Auditors. The skills learned in this course can help build a foundation for students wishing to enter this field.
Business Analyst
Business Analyst
Business Analysts use data to help businesses make better decisions. They collect, analyze, and interpret data to identify problems and opportunities. KAIST's Big Data and Language 2 offers students hands-on experience with data collection and analysis techniques, which are essential for Business Analysts. The skills learned in this course can help build a foundation for students wishing to enter this field.
Data Architect
Data Architect
Data Architects design, build, and maintain data architectures. They use data to improve the performance and efficiency of their data architectures. KAIST's Big Data and Language 2 offers students hands-on experience with data collection and analysis techniques, which are essential for Data Architects. The skills learned in this course can help build a foundation for students wishing to enter this field.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers use data to train machine learning models, which can be used to solve a variety of problems, such as image recognition, natural language processing, and speech recognition. KAIST's Big Data and Language 2 offers students hands-on experience with data collection and analysis techniques, which are essential for Machine Learning Engineers. The skills learned in this course can help build a foundation for students wishing to enter this field.
Data Scientist
Data Scientist
Data Scientists use data to solve problems and make informed decisions. They collect, analyze, and interpret data to help businesses, governments, and organizations make better decisions. KAIST's Big Data and Language 2 offers students hands-on experience with data collection and analysis techniques. The skills learned in this course can help build a foundation for students wishing to enter this field.
Data Analyst
Data Analyst
Data Analysts use data to solve problems and make informed decisions. They collect, analyze, and interpret data to help businesses, governments, and organizations make better decisions. KAIST's Big Data and Language 2 offers students hands-on experience with data collection and analysis techniques. The skills learned in this course can help build a foundation for students wishing to enter this field.
Statistician
Statistician
Statisticians use data to solve problems and make informed decisions. They collect, analyze, and interpret data to help businesses, governments, and organizations make better decisions. KAIST's Big Data and Language 2 offers students hands-on experience with data collection and analysis techniques. The skills learned in this course can help build a foundation for students wishing to enter this field.
Copywriter
Copywriter
For those interested in persuasive writing, Copywriters use their in-depth understanding of the written word to create advertisements. With the use of data, Copywriters adjust the tone and style of their text based on the preferences of their audience. KAIST's Big Data and Language 2 course can teach someone about the many ways to analyze data, a skill which helps one to understand a target audience. This knowledge can help build a foundation for students wishing to enter this field.
Market Researcher
Market Researcher
Market Researchers study the market to determine what products customers are interested in, what new products to launch, and what marketing strategies work best. Data analysis is a crucial part of this job. With their strong analytical skills, Market Researchers are able to use data to predict trends, understand customer behavior, and develop marketing campaigns. KAIST's Big Data and Language 2 can help build a foundation in data collection and analysis techniques, which are essential for Market Researchers.
For more career information including salaries, visit:
OpenCourser.com/course/w41ena/big
Reading list
We've selected seven 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
Big data and Language 2.
This comprehensive handbook provides an in-depth overview of the field of natural language processing, covering a wide range of topics from理论to applications.
This classic textbook provides a comprehensive introduction to speech and language processing, covering topics such as phonetics, phonology, morphology, syntax, semantics, and pragmatics.
Provides a comprehensive overview of the field of computational semantics, which involves the use of computer science techniques to represent and reason about meaning in natural language.
Save
Provides a comprehensive overview of quantitative methods used in linguistics, including statistical techniques, natural language processing, and machine learning.
Introduces the fundamental concepts of natural language processing, with a focus on practical applications using Python.
Provides a practical overview of corpus analysis techniques, with a focus on applications in language studies.
Introduces the fundamental concepts of data science, with a focus on applications in business.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/w41ena/big
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Effort
2-3 hours/week
Level
Introductory
Via
Coursera
Institution
Korea Advanced Institute of Science and Technology(KAIST)
Instructor
Seonmin Park
Language
English
Transcripts
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中文
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Good to know
Develops skills in analyzing and interpreting big data for linguistic research
Learners are introduced to various tools and approaches used in linguistic analysis, such as BNC, COCA, ANTCONC and TagAnt
Taught by Seonmin Park, who is recognized in the field of linguistics
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