I moved from my DevOps role in Cisco to Data Scientist profile at Oracle OCI team. I have taken few courses and some online material as well as focused more on the practice part because it's altogether a new stream for me. During this transition and tech stack change my mentor from Logicmojo classes helps in project development and how to answer questions during interviews. Initially I started with Ytube channel to build some foundation, especially in Python language. Mostly I had worked on Scripts in Devops. Once my foundation was strong I then started attending classes in Logicmojo. It's weekend classes morning time 9-12 PM and in almost every class you need to write code with the instructor. it's a good way of practicing. It starts developing an interest in the subject. Python is easy as I am aware of it from Ytube channel. Statistics I managed to understand as I was good in math. Math especially statistics used a lot in algorithms. Machine Learning which includes many algorithms like (supervised and unsupervised Learning etc) was completed but my instructor helped me with this. He explains to us even after classes finish. ML is the backbone of data science if you miss this topic, then you will face a lot of difficulty in DeepLearning DeepLearning, I believe the most complicated topic, It may include NLP and Neural also. I made my project on Deep learning only and included it in my resume. it's As its a new stream it demands practice. I prefer Kaggle and Github to store all my work. I made multiple sets of projects here, In case anyone is interested I will share that also. If you need any further Info, kindly DM me. More on reddit.com

funny how shelling out for something actually structured, like a proper coursera specialization, often beats just winging it with random free stuff when you're aiming for more than just a participation trophy, right? More on reddit.com

These might help: Open Source Society — Path to a free self-taught education in Data Science Python Data Science Handbook — learn to use Python libraries such as NumPy, Pandas, Matplotlib, Scikit-Learn, and related tools to effectively store, manipulate, and gain insight from data Python for Data Analysis — nuts and bolts of manipulating, processing, cleaning, and crunching data in Python Data Analysis with Python — online course offered by University of Helsinki, data analysis pipeline using Python and its data analysis ecosystem More on reddit.com

deploying models into production is definitly being taken over by engineers but I've seen several data scientists transition to that side of the house. data science, comp science, stats, engineering, it really doesn't matter that much. Your first job matters waaay more More on reddit.com