'More': below you'll find an (increasing) assortment of useful and interesting links on data science (and related topics)... Please go through them - you'll get extra 'context' for the lecture material, and, there might be exam questions based on these.

Data science - a growth area: https://datasciencedegree.wisconsin.edu/data-science/data-science-careers/

https://theconversation.com/chatgpt-and-other-language-ais-are-nothing-without-humans-a-sociologist-explains-how-countless-hidden-people-make-the-magic-211658

https://www.freecodecamp.org/news/data-science-vs-data-engineering/

https://www.forbes.com/sites/elijahclark/2023/08/18/unveiling-the-dark-side-of-artificial-intelligence-in-the-job-market/amp/

https://venturebeat.com/ai/gartner-hype-cycle-places-generative-ai-on-the-peak-of-inflated-expectations/

https://www.linkedin.com/advice/3/how-do-you-simplify-structure-solutions-algorithm-design

https://www.lesswrong.com/posts/4XRjPocTprL4L8tmB/science-in-a-high-dimensional-world - relevant to data science (related to looking for the most useful 'features' (columns) in data)

This is about data collection, modeling, etc.

data (collect, organize...) -> process (analyze, visualize...) -> do something IRL (about/with the results), eg: https://www.sfchronicle.com/us-world/article/sf-overdose-deaths-tracker-18207169.php?sid=640df996f2cfbd3916069504

Hardware acceleration: https://sungkim11.medium.com/ai-landscape-is-shifting-from-gpu-to-ai-accelerator-5dc1aeaffdc

https://www.aboutamazon.com/news/retail/generative-ai-trains-amazon-one-palm-scanning-technology

https://towardsdatascience.com/its-time-to-say-goodbye-to-pd-read-csv-and-pd-to-csv-27fbc74e84c5

Excellent, comprehensive, free! https://biabl.org/enigma-u/

https://keck.usc.edu/the-future-of-ai-in-medicine/

https://www.defense.gov/News/Releases/Release/Article/3531768/deputy-secretary-of-defense-kathleen-hicks-announces-238m-chips-and-science-act/

'Neural stochastic motion texture': https://generative-dynamics.github.io/#demo

boba!

Great (VALUABLE!) lessons: https://www.linkedin.com/pulse/why-did-i-leave-google-stay-so-long-noam-bardin/

https://www.businessinsider.com/indeed-ceo-ai-chatgpt-could-make-college-skills-obsolete-2023-9

Python libs to master: this [text notes: this]

Do this!!

A fantastic way to 'peek' into the workings of your Python (or C++ etc) program: https://pythontutor.com/

This is one way to use ChatGPT to learn DS...

https://www.chartr.co/newsletters/2023-10-09

https://github.com/ByteByteGoHq/system-design-101

Building and deploying an ML model: see this and this.

https://www.kaggle.com/code/lusfernandotorres/convolutional-neural-network-from-scratch

https://www.cnn.com/videos/opinions/2023/10/24/tiktok-teachers-pandemic-learning-gaps-contd-orig-dp.cnn [look at the scores graph at 0:45 on]

https://venturebeat.com/ai/datagpt-launches-ai-analyst-to-allow-any-company-to-talk-directly-to-their-data/

https://www.cnn.com/videos/business/2023/10/26/sam-altman-openai-artificial-intelligence-vault-orig-jc.cnn

Data/tech/market/business analysis/accounting companies:

• Deloitte

• Bloomberg

• PwC

• Ernst & Young

• KPMG

• CB Insights

• Forrester

• more: https://builtin.com/big-data/big-data-consulting

This is a current internship ad from Ford...

Tons of MATLAB resources (ML etc): https://bit.ly/MATLAB_USC

https://www.decipad.com/ and https://app.decipad.com/n/Chartr-x-Decipad-Tesla-Average-Selling-Prices-vs-Revenue%3AKFslqJX94q-hbFdFjuaBf

https://dcai.csail.mit.edu/

GenAI Against Humanity: https://arxiv.org/pdf/2310.00737.pdf

A thorough walkthrough: https://ai.plainenglish.io/how-i-deployed-a-machine-learning-model-for-the-first-time-b82b9ea831e0

https://roadmap.sh/ai-data-scientist

This stats book is an actually entire course on data science (with code snippets which you can run in a Jupyter notebook or a simple IDE), and, true to the '50+' in the title, has a LOT of concepts!

Here is a simple interface to try out MANY stats functions: https://bytes.usc.edu/~saty/tools/xem/run.html?x=simplestats