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Alexander Byzov
In this online course, you will learn about the next big thing in applied analytics – text analysis. This course is self-contained: you will learn everything from basic programming skills to advanced natural language modelling for topic discovery. This course is designed around a problem-oriented approach, meaning that we will not spend too much time learning theoretical concepts but instead focus on applying them to practical problems.
a. The goal of this online course is to equip students with the necessary knowledge and skills for analysing text data with R programming language.
b. We do not assume any specific prerequisites...
Read more
In this online course, you will learn about the next big thing in applied analytics – text analysis. This course is self-contained: you will learn everything from basic programming skills to advanced natural language modelling for topic discovery. This course is designed around a problem-oriented approach, meaning that we will not spend too much time learning theoretical concepts but instead focus on applying them to practical problems.
a. The goal of this online course is to equip students with the necessary knowledge and skills for analysing text data with R programming language.
b. We do not assume any specific prerequisites...
Read more
In this online course, you will learn about the next big thing in applied analytics – text analysis. This course is self-contained: you will learn everything from basic programming skills to advanced natural language modelling for topic discovery. This course is designed around a problem-oriented approach, meaning that we will not spend too much time learning theoretical concepts but instead focus on applying them to practical problems.
a. The goal of this online course is to equip students with the necessary knowledge and skills for analysing text data with R programming language.
b. We do not assume any specific prerequisites for this course. However, some knowledge of natural language processing or R programming might ease the dive into the course materials.
c. Each week on the course is accompanied by tests, gradable and non-gradable programming assignments, and links to additional material for those who want to dig deeper into the course material. At the end of the course, you’ll have to complete a project and then review your peers' projects.
d. R (programming language), RStudio
e. This course is heavily tilted toward practical skills. During this course, students will dive into the basics of R for text analysis, tidy text approach, regular expressions, different algorithms for topic modelling and text classification with machine learning and deep learning approaches, and many more. Various synthetic and real-world databases will help participants see how to apply these techniques to extract insights from user reviews, social media posts, short descriptions of the products. This distance learning opportunity is brought to you by HSE University, one of the top think tanks in Russia, by instructors experienced in using text analysis for business-oriented projects.
The online course consists on short pre-recorded lectures, 5 to 15 minutes in length.
Each week will have a graded test with 10 to 15 questions. At the end of the last week, students will have to complete a project utilising the skills learned in the course, and then review and grade the projects of their peers.
The course gives students an opportunity to learn the methods on natural language processing (NLP) and then apply these methods to problems in students’ own areas of interest.
Register for this course and see more details by visiting:
OpenCourser.com/course/5vo1d8/introduction
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Enriches skill sets in the trending and in-demand area of natural language processing (NLP) and application thereof in various sectors
Practical course structure enables immediate hands-on experience
Provides learners with an enriched toolkit for leveraging R programming language for text-based data analysis
Includes learning material variety through lectures, assessments, hands-on projects, and peer reviews
Suitable for adaptable learners who can keep pace with the course's brisk delivery
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Reviews summary
Text mining basics
This course covers the basics of text analysis using the R programming language. It assumes no prior knowledge of natural language processing or R programming, but some experience in either field would be helpful. The course consists of short pre-recorded lectures, weekly graded tests, and a final project. Students may experience some issues with code transparency and package availability, but overall, this course provides a solid foundation in text mining techniques.
Some packages needed for assignments were unavailable.
"At the beginning of Week 4, there was a package that it was no longer possible to download (this issue had also been flagged up for some time on the forum without any response from the provider)."
Instructor could share code from lectures to enhance transparency.
"Há alguns probleminhas de transparência de código. Talvez o professor pudesse liberar o código que fez em aula para não gerar problemas de não compartilhamento de alguma etapa."
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 Introduction to Text Mining with R with these
activities:
Read Introduction to Natural Language Processing, 2nd Edition
Show steps
Provides a comprehensive introduction to natural language processing concepts and techniques, complementing the course material.
View
Foundations of Statistical Natural Language...
on Amazon
Show steps
-
Read assigned chapters or sections to supplement lecture material
-
Take notes, highlight key concepts, and summarize main ideas
-
Complete practice exercises or review questions
-
Discuss key points with peers or instructors for clarification
Compile Course Materials
Show steps
Helps organize notes, assignments, quizzes, and exams for later review before or during the course.
Show steps
-
Collect course materials such as notes, assignments, quizzes, and exams
-
Review materials to identify areas of importance
-
Organize materials into a logical system
-
Store materials in a central location for easy access
Follow Online Tutorials
Show steps
Provides additional practice and reinforcement of text analysis concepts covered in lectures.
Show steps
-
Search for online tutorials related to specific text analysis techniques
-
Select reputable sources and follow the instructions provided
-
Try out different algorithms and tools to broaden understanding
-
Apply the learned techniques to practice exercises or small projects
Three other activities
Expand to see all activities and additional details
Show all six activities
Solve Topic Exercises
Show steps
Reinforces understanding of text analysis concepts and algorithms through repetitive practice.
Browse courses on
Regular Expressions
Show steps
-
Review lecture materials and notes on relevant topics
-
Attempt topic exercises related to the studied material
-
Compare solutions with provided answers and identify areas for improvement
-
Revise notes and seek additional resources to strengthen understanding of concepts
Develop a Data Visualization
Show steps
Enhances communication and presentation skills by requiring students to convey insights gained from text analysis in a visually appealing manner.
Browse courses on
Data Visualization
Show steps
-
Identify key findings or insights from text analysis results
-
Choose appropriate visualization techniques to represent the data clearly and effectively
-
Design and create visually appealing data visualizations using tools such as Tableau or Python libraries
-
Present the visualizations to peers or instructors for feedback
Text Analysis Project
Show steps
Provides practical experience applying text analysis techniques to real-world data sets.
Browse courses on
Social Media Posts
Show steps
-
Choose a specific domain or topic of interest
-
Gather and pre-process a relevant data set
-
Apply text analysis algorithms to extract insights from the data
-
Present findings and insights in a clear and concise manner
Read Introduction to Natural Language Processing, 2nd Edition
Show steps
Provides a comprehensive introduction to natural language processing concepts and techniques, complementing the course material.
View
Foundations of Statistical Natural Language...
on Amazon
Show steps
Show steps
Show steps
- Read assigned chapters or sections to supplement lecture material
- Take notes, highlight key concepts, and summarize main ideas
- Complete practice exercises or review questions
- Discuss key points with peers or instructors for clarification
Compile Course Materials
Show steps
Helps organize notes, assignments, quizzes, and exams for later review before or during the course.
Show steps
Show steps
Show steps
- Collect course materials such as notes, assignments, quizzes, and exams
- Review materials to identify areas of importance
- Organize materials into a logical system
- Store materials in a central location for easy access
Follow Online Tutorials
Show steps
Provides additional practice and reinforcement of text analysis concepts covered in lectures.
Show steps
Show steps
Show steps
- Search for online tutorials related to specific text analysis techniques
- Select reputable sources and follow the instructions provided
- Try out different algorithms and tools to broaden understanding
- Apply the learned techniques to practice exercises or small projects
Three other activities
Expand to see all activities and additional details
Show all six activities
Solve Topic Exercises
Show steps
Reinforces understanding of text analysis concepts and algorithms through repetitive practice.
Browse courses on
Regular Expressions
Show steps
Show steps
Show steps
- Review lecture materials and notes on relevant topics
- Attempt topic exercises related to the studied material
- Compare solutions with provided answers and identify areas for improvement
- Revise notes and seek additional resources to strengthen understanding of concepts
Develop a Data Visualization
Show steps
Enhances communication and presentation skills by requiring students to convey insights gained from text analysis in a visually appealing manner.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Identify key findings or insights from text analysis results
- Choose appropriate visualization techniques to represent the data clearly and effectively
- Design and create visually appealing data visualizations using tools such as Tableau or Python libraries
- Present the visualizations to peers or instructors for feedback
Text Analysis Project
Show steps
Provides practical experience applying text analysis techniques to real-world data sets.
Browse courses on
Social Media Posts
Show steps
Show steps
Show steps
- Choose a specific domain or topic of interest
- Gather and pre-process a relevant data set
- Apply text analysis algorithms to extract insights from the data
- Present findings and insights in a clear and concise manner
Career center
Learners who complete Introduction to Text Mining with R will develop knowledge and skills
that may be useful to these careers:
Natural Language Processing Engineer
Natural Language Processing Engineer
A Natural Language Processing Engineer designs and develops systems to understand and generate human language. This course provides an in-depth understanding of text analysis with R, which is a key skill for this role.
Data Scientist
Data Scientist
Data Scientists study, clean, model, and interpret data. This course gives students the opportunity to apply text analysis techniques to problems in their own areas of interests. A Data Scientist may use text analysis to accurately model and predict trends, patterns, and outcomes.
Business Analyst
Business Analyst
A Business Analyst combines knowledge of business processes and information technology to solve business problems. With this course, students can apply techniques such as topic modeling and text classification to extract insights from user reviews and other unstructured data, enhancing their ability to identify patterns and trends that inform decision-making.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses mathematical and statistical models to analyze data. This course provides problem-oriented applications and may enhance the foundational knowledge needed to be successful in this field.
Data Visualization Specialist
Data Visualization Specialist
A Data Visualization Specialist creates visual representations of data. This course may be useful for building a foundation in text analysis, which is becoming increasingly important in the field of data visualization.
Technical Writer
Technical Writer
A Technical Writer creates and maintains technical documentation. This course may be useful for building a foundation in text analysis, which can enhance their ability to understand and organize complex technical information.
Market Researcher
Market Researcher
A Market Researcher gathers and interprets data on consumer behavior. This course may be useful in building a foundation for success in this role, particularly in the area of analyzing unstructured text data such as customer feedback and social media posts.
Information Architect
Information Architect
An Information Architect designs and organizes information systems. This course may be useful in building a foundation for success in this role, particularly in the area of analyzing and structuring text-based information.
Social Media Manager
Social Media Manager
A Social Media Manager plans and executes social media campaigns. This course may be useful for building a foundation in text analysis, which is a key aspect of this role, particularly in understanding customer sentiment and optimizing social media content.
Content Strategist
Content Strategist
A Content Strategist plans and executes content creation strategies. This course may be useful in building a foundation for success in this role, particularly in the area of analyzing and optimizing text content for various platforms.
Digital Marketing Manager
Digital Marketing Manager
A Digital Marketing Manager plans and executes digital marketing campaigns. This course may be useful for building a foundation in text analysis, which can enhance their ability to analyze and optimize online content and campaigns.
User Experience Researcher
User Experience Researcher
A User Experience Researcher studies and evaluates user experience. This course may be useful for building a foundation in text analysis, which can enhance their ability to analyze and interpret user feedback.
Search Engine Optimizer
Search Engine Optimizer
A Search Engine Optimizer improves the visibility of websites in search engine results. This course may be useful for building a foundation in text analysis, which is a key aspect of this role.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer optimizes algorithms and machine learning models. Text analysis through R will very likely be a part of their projects. This course may be useful in building a foundation for success in this role.
Data Analyst
Data Analyst
A Data Analyst plans, develops, and deploys systems to store and retrieve data for business intelligence purposes. The knowledge of text mining with R provides an edge in collecting and analyzing unstructured text data. This course may help build a foundation for success in this role.
Product Manager
Product Manager
A Product Manager plans and manages the development and launch of new products. This course may be useful for building a foundation in text analysis, which can enhance their ability to understand customer needs and make data-driven decisions.
For more career information including salaries, visit:
OpenCourser.com/course/5vo1d8/introduction
Reading list
We've selected six 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
Introduction to Text Mining with R .
Save
Provides a comprehensive overview of the text mining process, from data collection and preparation to analysis and visualization.
Provides a comprehensive overview of text analysis techniques in Python, including data preprocessing, feature engineering, and model evaluation.
Provides a comprehensive overview of natural language processing with Python, covering topics such as tokenization, stemming, and part-of-speech tagging.
Provides a comprehensive overview of machine learning techniques for text analysis, including supervised and unsupervised learning, feature engineering, and model evaluation.
Provides a comprehensive overview of natural language processing with Python, covering topics such as tokenization, stemming, and part-of-speech tagging.
Introduces the reader to the basics of deep learning for natural language processing, covering topics such as word embeddings, recurrent neural networks, and attention mechanisms.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/5vo1d8/introduction
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Text Mining and Natural Language Processing in R
Time
24 hours
Via
Coursera
Institution
HSE University
Instructor
Alexander Byzov
This course belongs to a
collection
called
Network Analytics for Business. It's comprised of four courses:
- Network Analysis
- Introduction to Text Mining with R (viewing)
- Business Analytics: Diversity of Practical Applications
- Contemporary Data Analysis: Survey and Best Practices
Good to know
Enriches skill sets in the trending and in-demand area of natural language processing (NLP) and application thereof in various sectors
Practical course structure enables immediate hands-on experience
Provides learners with an enriched toolkit for leveraging R programming language for text-based data analysis
Includes learning material variety through lectures, assessments, hands-on projects, and peer reviews
Suitable for adaptable learners who can keep pace with the course's brisk delivery
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