Notes (work in progress)

Current focus is on programming -> elements_prog_interview

• 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

  • harvard_cs109.ipynb: CS109 Data Science, Harvard University
  • sup_learning_scikit_learn.ipynb: Supervised Learning with scikit-learn, DataCamp
  • advanced_machine_learning_tensorflow_gcp: Advanced Machine Learning with TensorFlow on Google Cloud Platform Specialization, Coursera
  • machine_learning_tensorflow_gcp:Machine Learning with TensorFlow on Google Cloud Platform Specialization, Coursera
  • aws_ml_path_data_science: AWS Machine Learning Path, Data Scientist

• programming

  • 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
  • elements_prog_interview: Chapter overview + coding exercises of Elements of Programming Interviews in Python by Adnan Aziz, Tsung-Hsien Lee, Amit Prakash
  • interview_practice: leetcode, mock interviews
  • python_programmer_track_datacamp: Python Programmer Track, DataCamp
  • debugging_testing_profiling.ipynb: Notes from Crafting Quality Code, University of Toronto, Coursera: https://www.coursera.org/learn/program-code/home/welcome
  • mit_datastructures_algos.ipynb: MIT 6.006 Introduction to Algorithms, Fall 2011, https://www.youtube.com/watch?v=HtSuA80QTyo&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb&index=1

• statistics

  • foundations_of_statistics.ipynb: Random Variables, Sampling Distributions, Confidence Intervals, Khan Academy
  • infer_statistics_python.ipynb: Statistical Thinking in Python (Part 1), DataCamp

• quick_notes.ipynb

  • Random notes