mindyng/gist:baca4114da1af15a43fca0458e40c147

## gistfile1.txt
  Last night, I got to meet a huge variety of people interested in data analytics/science! I really enjoyed meeting people who recently got their appetite wet in data science and people who are seasoned in the field since writing 1000-lines of code is a breeze for them and who talk about R as if they are more fluent in it than English. A neat surprise was the presence of Math and Economics professors adding in their input to the discussion on whether or not Peer Assisted Learning would help raise performance levels in STEM classes offered at Sacramento State University. This talk helped me learn that a result from an experiment can always be questioned. Experiment design can be reassessed even after the experiment has been completed. Therefore, even after drawing conclusions on my experiment, keeping an open mind for feedback would be advisable.

  The second talk was the one I had more interest in since I have been wondering about how to pick a model that best addressed my Capstone Project problem. I had an idea on what features to pick/inputs I would like to feed into a function, but which function/model would I choose to plug these into? This talk was about choosing the right model-explanatory versus predictive given what you are trying to do with the data. The talk itself did not give me a straight answer to my burning questions, but it definitely gave me some guiding principles on the direction I could take when choosing model/models. On top of that, I learned that the speaker is someone who practices A.I./M.L. professionally. This is exactly what I want to do as a professional data scientist! Next up for me: E-mail him to see how he got interested in these topics and any people he would advise me to get connected to if I were to start engaging with professionals who share the same excitement for A.I./M.L. as me!
	Last night, I got to meet a huge variety of people interested in data analytics/science! I really enjoyed meeting people who recently got their appetite wet in data science and people who are seasoned in the field since writing 1000-lines of code is a breeze for them and who talk about R as if they are more fluent in it than English. A neat surprise was the presence of Math and Economics professors adding in their input to the discussion on whether or not Peer Assisted Learning would help raise performance levels in STEM classes offered at Sacramento State University. This talk helped me learn that a result from an experiment can always be questioned. Experiment design can be reassessed even after the experiment has been completed. Therefore, even after drawing conclusions on my experiment, keeping an open mind for feedback would be advisable.

	The second talk was the one I had more interest in since I have been wondering about how to pick a model that best addressed my Capstone Project problem. I had an idea on what features to pick/inputs I would like to feed into a function, but which function/model would I choose to plug these into? This talk was about choosing the right model-explanatory versus predictive given what you are trying to do with the data. The talk itself did not give me a straight answer to my burning questions, but it definitely gave me some guiding principles on the direction I could take when choosing model/models. On top of that, I learned that the speaker is someone who practices A.I./M.L. professionally. This is exactly what I want to do as a professional data scientist! Next up for me: E-mail him to see how he got interested in these topics and any people he would advise me to get connected to if I were to start engaging with professionals who share the same excitement for A.I./M.L. as me!