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I have taken the same classes, and they get much more interesting. Chances are that you could complete the work for the first courses in a couple hours without
by rz2k 11y ago
I have taken the same classes, and they get much more interesting. Chances are that you could complete the work for the first courses in a couple hours without the lectures, or maybe just skimming the notes. I recommend doing that quickly and moving on to the later material, so that you don't get discouraged thinking that you are completely wasting your time.
- nl 11y agoDo they really get better? I'm either going to jump straight to the (R) Statistical Inference[1] course from JHU, or switch to the Berkeley/EdX Spark course[2]. I use a lot more Spark in my day job than R, but I really should learn statistics more formally. [1] https://www.coursera.org/course/statinference https://www.coursera.org/course/statinference [2] https://www.edx.org/course/scalable-machine-learning-uc-berkeleyx-cs190-1x https://www.edx.org/course/scalable-machine-learning-uc-berk...
- rz2k 11y agoI thought they got better compared to the first few classes, but they do really revolve around R. For a rigorous treatment of the subject matter, the MITx course on Probability is really good. [1] You could also take a look at the two JHU "Mathemtical Biostatistics Bootcamp"[2] courses. Those are also quick compared to the MITx course, but a little more careful about the math than the courses in the data science specialization are. I haven't ever used Spark, and I like R, but I am going to take the Berkeley/EdX course. [1] https://www.edx.org/course/introduction-probability-science-mitx-6-041x-0 https://www.edx.org/course/introduction-probability-science-... [2] https://www.coursera.org/course/biostats https://www.coursera.org/course/biostats & https://www.coursera.org/course/biostats2 https://www.coursera.org/course/biostats2