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[av_heading tag='h1' padding='10' heading='R Programming for Excel Users' color='' style='blockquote modern-quote modern-centered' custom_font='' size='' subheading_active='' subheading_size='15' custom_class=''][/av_heading]
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Topics:
Careers,
Data Science & Engineering,
Microsoft Excel,
R,
R Programming,
data sceintist,
data science,
Excel
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[av_heading heading='Text Analytics with R Programming' tag='h1' style='blockquote modern-quote modern-centered' size='' subheading_active='' subheading_size='15' padding='10' color='' custom_font=''][/av_heading]
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Topics:
Data Science & Engineering,
NLP,
NLTK,
openNLP,
R,
R Programming,
Text Analytics,
Text Mining,
Text Processing,
Tutorials,
data science,
Natural Language Processing
In February I had the privilege of traveling to Amsterdam to teach a great group of folks Data Science. As is so often the case, I felt I learned as much from the students as they learned from me. For example, one of the students asked for some R programming assistance in the area of Data Wrangling and Feature Engineering. The scenario in question really intrigued me. I knew how I could solve the problem using traditional non-functional programming techniques (e.g., using for loops), but I was looking for something more elegant.
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Topics:
Data Science & Engineering,
Feature Engineering,
Predictive Modeling,
R,
R Programming,
Data Wrangling,
dplyr,
Fundamentals
I started using R about 3 years ago. It was slow going at first. R had tricky and less intuitive syntax than languages I was used to, and it took a while to get accustomed to the nuances. It wasn’t immediately clear to me that the power of the language was bound up with the community and the diverse packages available.
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Topics:
R,
Big Data
