Mailman 3 March 2023 - Edu-sig

Part-time opportunities for Python instructors
by Merav Yuravlivker May 7, 2024

May 7, 2024

Hi there, My name is Merav Yuravlivker, and I'm the CEO of Data Society - we deliver data science academies to Fortune 500 companies, government agencies, and other international organizations. We're currently looking for part-time Python instructors and TAs, and my friend Jackie Kazil recommended I reach out to you and your list serv. All of these opportunities can be available for people who are employed full-time, professors, or grad students. We pay well and provide all the materials for the instructor, as well as instructor training and support. If possible, would you please be able to share the following blurb? Please let me know if there is anything else you need from me. Much appreciated! Best, Merav --- Data Society, a fast-growing data science training company, is looking for awesome Python instructors and TAs! We deliver data academies to Fortune 500 companies, government agencies, and international organizations. All of our content is built in-house by an expert team of data scientists and instructional designers, so you can focus on what you do best - teach professionals how to find new insights and make their jobs easier. We currently have a few openings for TAs, as well as part-time instructors - all of these opportunities can be available for people who are employed full-time, professors, or grad students. We pay competitively, have a great support team, and provide amazing opportunities for additional projects if you're interested. To learn more, please visit our page for current opportunities <https://t.sidekickopen10.com/s2t/c/5/f18dQhb0S7lM8dDMPbW2n0x6l2B9nMJN7t5X-F…>, or simply reach out to Merav at merav(a)datasociety.com. -- Schedule a time to meet <https://t.sidekickopen10.com/s1t/c/5/f18dQhb0S7lM8dDMPbW2n0x6l2B9nMJN7t5X-F…> Merav Yuravlivker Data Society, Chief Executive Officer and Co-founder 777 6th Street NW, 11th Floor Washington, D.C., 20001 Enterprise: solutions.datasociety.com Consumer: datasociety.com

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ChatGPT for py teaching
by Jurgis Pralgauskis March 24, 2023

March 24, 2023

Hi, happy NY! ChatGPT can create, fix and explain code https://openai.com/blog/chatgpt/#samples Anyone tried to incorporate it into teaching process? Or have ideas/doubts how it ciuld help?

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Python on background for data analyst track
by kirby urner March 1, 2023

March 1, 2023

As some of us will have experienced, quite a few budding data analysts in training will come to Python via numpy and pandas, heading towards seaborn and dash, perhaps from past experience in R or Matlab, perhaps not. They've never yet had a core Python class, but are likely to have one in the near future, if they decide to stay within the Python ecosystem and/or stick with data science. I've got such a cohort coming my way and thought this time I would develop some more explicit background content on Python, the core language, which I'm calling "warm-up notebooks" and so far doing in Jupyter. https://github.com/4dsolutions/clarusway_data_analysis/tree/main/python_war… Here are some pedagogical features I incorporate that might be of interest to others in a similar position of needing to develop curriculum: 1. mention numpy and pandas early There's nothing conceptually that hard about a rectangle of numbers, addressable by row and column. The mechanisms of installing 3rd party packages may be addressed. Introduce range, then why not arange and linspace for compare and contrast purposes. I go straight to numpy and pandas in notebook 2. To see the sequence, see here (home base for the class): https://github.com/4dsolutions/clarusway_data_analysis/blob/main/DAwPy_S1_(… I'm still adding notebooks. 2. functions are just another type of object In exploring the various built-in types, with Python allowing us to make new types, it becomes useful to see individual functions as instances of the function type, where list, tuple, int, str.... function are all built-in types. We don't use keyword class to define a function, but keyword def (or maybe lambda). What makes function instances stand out is they're callable. But so are instances of classes implementing __call__. [1] def f(x): return x * x # return x times itself [2] type(f) function [3] issubclass(type(f), object) True [4] isinstance(f, type(f)) True 3. Use "composition of functions" to motivate drive a decorator syntax intro I've been doing this for a while now. Most students will remember composition of functions from high school, and if not, it's an easy concept. def f(x): return 2 * x def g(x): return x**2 print("f after g:", f(g(arg))) print("g after f:", g(f(arg))) Output: f after g: 200 g after f: 400 What's not so easy, an may require active tutoring, is passing a function into Composer type like this: # where decorator syntax will come in handy f = Composer(f) g = Composer(g) print("f after g:", (f * g)(arg)) print("g after f:", (g * f)(arg)) Output (same answers): f after g: 200 g after f: 400 In other words, we repurpose the __mul__ operator (*) to become the compose operator, which in LaTeX is usually \circ. Kirby

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