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Daniel Moura updated their profile
Sep 5, 2019
Daniel Moura posted a blog post

Julia vs R: Freeing the data scientist mind from the curse of vectoRization

Nowadays, most data scientists use either Python or R as their main programming language. That was also my case until I met Julia earlier this year. Julia promises performance comparable to statically typed compiled languages (like C) while keeping the rapid development features of interpreted languages (like Python, R or Matlab). This performance is achieved by just-in-time (JIT) compilation. Instead of interpreting code, Julia compiles code in runtime. While JIT compilation has been around…See More
Aug 22, 2019
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Daniel Moura's blog post was featured

Julia vs R: Freeing the data scientist mind from the curse of vectoRization

Nowadays, most data scientists use either Python or R as their main programming language. That was also my case until I met Julia earlier this year. Julia promises performance comparable to statically typed compiled languages (like C) while keeping the rapid development features of interpreted languages (like Python, R or Matlab). This performance is achieved by just-in-time (JIT) compilation. Instead of interpreting code, Julia compiles code in runtime. While JIT compilation has been around…See More
Aug 22, 2019
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Profile Information

Short Bio
Computer Scientist and Engineer passionate about Data and Algorithms
Field of Expertise
Data Science, Machine Learning, AI, BI
Professional Status
Technical
Years of Experience:
20
Your Job Title:
Head of Data
How did you find out about DataScienceCentral?
Twitter
Interests:
Contributing, Networking, Other

@daniel_c_moura

linkedin: dmoura

vimeo: danielcmoura

Daniel Moura's Blog

Julia vs R: Freeing the data scientist mind from the curse of vectoRization

Posted on August 22, 2019 at 1:00pm 0 Comments

Nowadays, most data scientists use either Python or R as their main programming language. That was also my case until I met Julia earlier this year. Julia promises performance comparable to statically typed compiled languages (like C) while keeping the rapid development features of interpreted languages (like Python, R or Matlab). This performance is achieved by just-in-time (JIT) compilation. Instead of interpreting code, Julia compiles code in runtime. While JIT compilation has…

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