Introduction to Text Mining with R from Coursera

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.

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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

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

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Provides a comprehensive introduction to natural language processing concepts and techniques, complementing the course material.

View Foundations of Statistical Natural Language... on Amazon

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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

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Helps organize notes, assignments, quizzes, and exams for later review before or during the course.

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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

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Provides additional practice and reinforcement of text analysis concepts covered in lectures.

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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

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Solve Topic Exercises

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Reinforces understanding of text analysis concepts and algorithms through repetitive practice.

Browse courses on Regular Expressions

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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

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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

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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

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Provides practical experience applying text analysis techniques to real-world data sets.

Browse courses on Social Media Posts

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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

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.

See salaries and explore the career path for Natural Language Processing Engineer

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.

See salaries and explore the career path for Data Scientist

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.

See salaries and explore the career path for Business 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.

See salaries and explore the career path for Quantitative Analyst

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.

See salaries and explore the career path for Data Visualization Specialist

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.

See salaries and explore the career path for Technical Writer

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.

See salaries and explore the career path for Market Researcher

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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

See salaries and explore the career path for Product Manager

Introduction to Text Mining with R

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Text mining basics

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