Hello everyone. I am joining graduate school come Fall. I would be studying NLP and ML. I reduced my commitment from my current job to part time, so that I can focus on skill development prior to joining graduate school. I want to spend time developing skills to become a good data scientist in general and not just for grad school. Though am not a complete noob to ML, but there are many things that I dont know. So my question is:
- What relevant theory should I read and master before graduate school. Things on mind are Bayesian Non-Parametrics, Probabilistic Graphical Models. What more would you suggest?
- What languages should I develop coding skills in? I am fairly good at python. Should I focus on R, Octave?
- Related to both above, what toolkits should I be well acquainted with? Besides Weka.
- What are the best tools for data visualization in your experience?
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