![]() If you wish to change your preferences, click this link to launch our preference portal: You may opt out from Google Analytics by following the instructions on the following URL.Ĭalifornia Consumer Privacy Act (“CCPA”): Under CCPA, Californian residents have the right to declare their preferences on the sale of data for advertising and marketing purposes. Note that this information is NOT personally identifiable. Google Analytics may use Google AdSense cookies in your browser to record demographics about the users of Octave Online, including, but not limited to, age and gender. Your activity in Octave Online is recorded by Google Analytics. ![]() To inquire about full deletion, open a support ticket or send an email as described below. The information may be stored indefinitely.ĭeleting a file from your account on your own may not constitute full deletion from our servers. Additional copies of the data may be stored as backups in other physical locations and not necessarily in the Rackspace network. The above information is stored on Google Cloud Platform based on Council Bluffs, Iowa, USA. When using Google+ Sign In: your email address, name, and basic Google account information, including gender and locale. When using Email Sign In: your email address. ![]() Historical transcripts of Octave commands and output, which may be associated with your account or IP address. Scripts and other files you upload to or edit in Octave Online. In order to provide excellent software, we collect and save the following information: The views expressed in this article are my personal views and do not reflect the views of my employer.Octave Online LLC values the privacy of our users. PS: I'm a fan of R and Python for data analytics activities respectively. I haven't used MATLAB (it's statistical toolbox) and Octave yet. "It's always good to have more weapons in your armory." If you want to develop new mathematical models quickly, you can use Octave or MATLAB. If you need to use data structures and integrate with external applications, use Python. Now, let's look at the winner from the type of data science activity that you want to pursue - If the data needs to try several different algorithms, choose R as it has huge CRAN package base. If you are tech enthusiast and love exploring/learning new things, you can learn Julia - the killer feature being the speed of execution. Because, to build a product in an enterprise scenario you need interact with multiple entities which may talk different language. Later, when you have MATLAB access, you can use your Octave skills! If you are an employee, I suggest to master both Python and R. If you are a research scholar, good to start with R and explore Octave. IMO: If you are a graduate student, it's good to start with Python - as you get the advantages of general purpose language. However, the winner is kind of subjective to the phase you are in the career. It may seem evident from the comparison table that "Python leads the way, but R is pretty powerful" if you are willing to put that extra effort of going through the learning curve. To find out a winner, I have assigned points (on a scale of 0 to 5) to each programming language in the following categories: the speed of execution, learning curve involved, it's data analytics capabilities, visualization support, development tools (IDEs, dev/build/deployment, etc), ease of integration with other applications/languages and the job opportunities in the Industry. Analytical solutions such as Excel, Stata and SAS are not compared as they are not programming-oriented. Programming languages - R, Python, Octave, MATLAB, Octave, Julia, etc provide the capabilities to perform data analytics operations in a much better way than traditional programming languages - Java, C++, C, etc as they offer rapid prototyping, machine learning classifiers and regressors straightaway. This becomes even difficult if you are starting off and wondering which programming language to learn. It's always a challenge when it comes to choosing a particular programming language that comes out as a winner, especially in the field of Data Science.
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