Try: Data Science Except: Monty’s Bayes Example

Great first day at Zipfian. Definitely a different experience starting from Hackbright but some similarities. Granted there are the obvious differences of the content focus on data science vs. web application development as well as 20% women in the class vs. 100%. Plus I’m not the oldest or the youngest of the group. We have a really nice mix of people from various parts of the country and a myriad of backgrounds. Though there is a lot PhDs and/or engineering backgrounds. It was a much quieter energy to the start of the class even though you could tell there was some nervousness.

I know there is no way I could do this program if I was where I was at last Feb in my software experience. Out the gate today we were working on forking, cloning, branching and running pull requests through Github. We were also learning how to use sha’s to move in and out of previous commits (esp. ones you no longer wanted). It took me a couple weeks to even understand what Github was when I started Hackbright, and I stuck pretty close to add and commit for the longest time. And for the actual exercises we were doing today, we were coding with list comprehensions, try / except and lambda’s which I was still learning how to apply those python concepts after I graduated Hackbright. So it was definitely hit the ground running.

We are also doing pair programming for 5 weeks which does make me groan a little even though I do understand and see the value. I did have a really great experience my first day out pairing again. My partner was coding in C prior to class and helped me understand some just great best practices in programming fundamentals. While my knowledge of Python was a little stronger, and I was able to help guide us in the direction on how to code our ideas for solutions.

Patience and communication are still key skills that make pairing successful. I want to expand on this to say that its also really important to make sure not to discount someone’s capabilities if s/he lacks knowledge in certain subjects out the gate. You will be amazed at what you can learn from someone who is also learning if you are receptive and respectful. Just don’t write off everything that person has to say. On the flip side of that, don’t shut down if you are uncertain about concepts initially and push on with questions because this is the space to learn and make mistakes.

We ended the day working through the Monty Hall Bayes example. Talk about a bit of a brain teaser. There were a number of us crowded around the whiteboard talking through it. It took a little time, but we got there and it was actually really cool to see us all working together to try to get clear on the concepts. This is definitely going to go fast and it will be as intense as I expected if not more so.

One thought on “Try: Data Science Except: Monty’s Bayes Example

  1. Pingback: #WomenShiftDigital – from books to ‘data based science’ and ‘Cyber Arts’ ?! | Sabine K McNeill

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