TIDES-009: Nine (9) Data Science Concepts and Resources for Learning

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As the name says, the idea is to focus on the five topics that make up the acronym. Please reach out with your recommendations for the inclusion of suitable posts in this newsletter.

This week, we shall cover nine resources/inputs related to data science content. We shall get have content on engineering in the next post.

Hope you enjoy the article and always stay updated!

Thanks and best wishes

Kalilur Rahman

?1. List of 5 Brilliant Machine Learning Cheatsheets

These 5 cheatsheets cover all the essentials of machine learning in a compressed, simple and efficient format.

2. Neural Network Cheatsheets

Two brilliant resources from the Asimov Institute for Neural networks!

Asimov Institute's "The Neural Network Zoo" has a wonderful visualization of the neural network.

3. Machine Learning Algorithms Cheatsheet

i. Microsoft Azure Machine Learning Cheatsheet - Part-1

ii. Microsoft Azure Machine Learning Cheatsheet - Part-2

iii. SAS Machine Learning Cheatsheet -1

iv. SAS Machine Learning Cheatsheet -2

v. Scikit-Learn Machine Learning Algorithms Cheatsheet

4. Machine Learning Algorithm Types

A Simple mind-map of machine learning techniques and methods

5. Data Scientist Skills Building Roadmap - In a Tube/Metro Map format

A nice tube map style representation of how one can master the art of becoming a good data scientist.

6. Machine Learning and AI Toolset Landscape

Machine Learning and AI Toolset landscape from Bloomberg - Covering Machine intelligence 3.0

7. Machine Learning and Data Science Courses - Mostly free resources

Github based curation lists

i. Awesome Deep Learning (https://github.com/ChristosChristofidis/awesome-deep-learning)

ii. A nice curated list of Applied ML Courses and Data Science topics

iii. A List of Computer Science Courses with Video Lectures (https://github.com/Developer-Y/cs-video-course)

iv. Stanford-Tensorflow-Tutorials with Video Lectures (https://github.com/chiphuyen/stanford-tensorflow-tutorials)

v. D2L.ai: Interactive Deep Learning Book with Multi-Framework Code, Math, and Discussions (https://github.com/d2l-ai/d2l-en)

vi. Deep Learning Papers Reading Roadmap (https://github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap)

vii. NVIDIA Deep Learning Examples for Tensor Cores(https://github.com/NVIDIA/DeepLearningExamples)

viii. ML YouTube Courses (https://github.com/dair-ai/ML-YouTube-Courses)

8. Data Science and Machine Learning Courses from leading universities

i. Learning from Data - Caltech

ii. Deep Learning for Natural language processing (NLP) - Stanford University

iii. Algorithms for Big Data - Harvard University

iv. Advanced Robotics - University of Berkeley, California

v. Machine Learning - Cornell University

vi. Machine Learning for Data Science - Cornell University

vi. Deep Learning - NYU (New York University)

vii. Advanced Big Data Analytics - Columbia University

viii. Deep Learning for Computer Vision and NLP (Natural language Processing) - Columbia University

9. Application of Data Science on an Industry - Computational Finance - Some good resources

Following are some nice resources on Computational Finance by leading Universities and MOOC resources

• COMP510 - Computational Finance - Steven Skiena - 2007 HKUST

• MOOC - Mathematical Methods for Quantitative Finance, University of Washington/Coursera)

• 18.S096 Topics in Mathematics with Applications in Finance, MIT OCW

• Computational Finance - Universit?t Leipzig

• Machine Learning for Trading | Udacity

• ACT 460 / STA 2502 – Stochastic Methods for Actuarial Science - University of Toronto

• MMF1928H / STA 2503F – Pricing Theory I / Applied Probability for Mathematical Finance - University of Toronto

• STA 4505H – High Frequency & Algorithmic trading - University of Toronto

• Mathematical Finance - IIT Guwahati

• Quantitative Finance - IIT Kanpur
• Financial Derivatives & Risk Management - IIT Roorkee
• Financial Mathematics - IIT Roorkee

There are tons of resources available. I have shared my curated list and prioritization of the content I have used or come across over the past few years for your perusal. I hope you find them useful and apply them in your lifelong learning!

I hope you enjoyed this as much as I did writing it. I am all ears to hear from you. Caring is sharing. Feel free to like, share or comment on what you think! Please tag me if you forward this for relevance.

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