sw-ot-ashishpatel/Notes

Notes from different sources such as Harvard CS109 course, Springboard's Data Science Interview questions, Elements of Programming Interview book, etc.

Notes (work in progress)

• databases

  • Databases and SQL for Data Science, IBM, Coursera

• deep_learning

  • "TensorFlow for Deep Learning (O'Reilly)", Bharath Ramsundar & Reza Bosagh Zadeh
  • "Fundamentals of Deep Learning (O'Reilly)", Nikhil Buduma with contributions by Nicholas Locascio

• interview_questions.

  • 109 Commonly Asked Data Science Interview Questions
  • The Springboard Data Science Career Track's main units are wrapped up with interview practice questions. I am collecting the answeres in the file springbrd_interview_practice.ipynb. View in jupyter nbviewer:

• linear_algebra. Some introductory concepts and python representations.

• machine_learning.

  • CS109 Data Science, Harvard University
  • Supervised Learning with scikit-learn, DataCamp
  • Advanced Machine Learning with TensorFlow on Google Cloud Platform Specialization, Coursera
  • Machine Learning with TensorFlow on Google Cloud Platform Specialization, Coursera

• programming.

  • MIT 6.006 Introduction to Algorithms, Fall 2011, https://www.youtube.com/watch?v=HtSuA80QTyo&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb&index=1
  • data_structures_and_algorithms
    • "Programming in Python 3, A Complete Introduction to the Python Language", Mark Summerfield
    • "Elements of Programming Interview in Python", Adnan Aziz, Tsung-Hsien Lee, Amit Prakash
    • Python3 documentation
    • Learn to Program: Crafting Quality Code, University of Toronto, Coursera
  • interview_practice
    • leetcode
    • mock interviews
  • python_programmer_track_datacamp
    • Python Programmer Track, DataCamp

• statistics.

  • Random Variables, Sampling Distributions, Confidence Intervals, Khan Academy
  • Statistical Thinking in Python (Part 1), DataCamp

• quick_notes.ipynb

  • Random notes