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.

Octave vs matlab