Caution before taking this course:This course does not make you expert in R programming rather it will teach you concepts which will be more than enough to be used in machine learning and natural language processing models.About the course:In this practical, hands-on course you’ll learn how to program in R and how to use R for effective data analysis, visualization and how to make use of that data in a practical manner. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language.Our main objective is to give you the education not just to understand the ins and outs of the R programming language, but also to learn exactly how to become a professional Data Scientist with R and land your first job.This course covers following topics:1. R programming concepts: variables, data structures: vector, matrix, list, data frames/ loops/ functions/ dplyr package/ apply() functions2. Web scraping: How to scrape titles, link and store to the data structures3. NLP technologies: Bag of Word model, Term Frequency model, Inverse Document Frequency model4. Sentimental Analysis: Bing and NRC lexicon5. Text miningBy the end of the course you’ll be in a journey to become Data Scientist with R and confidently apply for jobs and feel good knowing that you have the skills and knowledge to back it up.
What you'll learn
Understand basic R programming concepts
Utilize data structures like vectors, matrices, lists, and data frames
Perform web scraping to collect data
Apply NLP technologies such as Bag of Words and Term Frequency models
Conduct sentiment analysis using Bing and NRC lexicons
Engage in text mining techniques
Course objectives
To familiarize students with R programming for data analysis
To equip students with skills for practical application in machine learning
To prepare students for entry-level Data Scientist positions