- Intro to Deep Learning
- How to AI (Almost) Anything
- Driving Innovation with Generative AI
- Minds and Machines
- Artificial Intelligence: Implications for Business Strategy
- Workplace Analytics, AI, and Ethics
- Deep Learning: Mastering Neural Networks
- Agentic AI for Organizational Transformation
- Ethics of AI: Building Responsible AI, Machine Learning, and GPTs
- Designing and Building AI Products and Services
25 August 2025
Top MIT Courses on AI
17 July 2022
Non-Degree Finance Courses
MIT OpenCourseware:
- Behavior Economics and Finance
- Blockchain and Money
- Competitive Decision Making and Negotiation
- Early Stage Capital
- Economic Analysis for Business Decisions
- Entrepreneurial Finance
- Entrepreneurial Marketing
- Entrepreneurial Sales
- Financial Accounting
- Financial and Managerial Accounting
- Financial Management
- Financial Theory 1
- Financial Theory 2
- Fintech: Shaping The Financial World
- Investments
- Negotiation and Conflict Management
- New Enterprises
- Nuts and Bolts of Business Plans
- Practice of Finance: Advanced Corporate Risk Management
- Pricing
- Technology Strategy
- The Analytics Edge
- Topics in Mathematics with Applications in Finance
- Strategic Management 1
- Strategic Management 2
- Business Analysis and Financial Statements
Non-Degree Law Courses
MIT OpenCourseware:
- Law for Corporate Finance and Financial Markets
- Law for Mergers and Acquisitions
- Constitutional Law: Structures of Power and Individual Rights
- Ethics and The Law on The Electronic Frontier
- Introduction to Copyright Law
- Patents, Copyrights, and Law of Intellectual Rights
- Law for Entrepreneur and Manager
20 June 2021
Why Pure Theoretical Degrees Are Useless
Theoretical degrees are utterly useless in the practical world. However, they may be useful for teaching. Reason being they have zero element for practical reasoning. If one can't apply theory into practice then what is the point of such degrees. Math degrees teach concepts, they provide the formula and they provide the problem. In physics, they provide the problem and they provide the formula to solve for that problem. Such degrees amplify little in application. When such people enter the practical world, they need to be taught how to do literally everything. One wonders where they forgot to think along the way of attaining such a theoretical degree in being able to apply themselves. In real world, one has to find the problem then find a way to solve for that problem, and this is the case ninety nine percent of the time. The only way one can combine such excessive theory is to add an element of engineering to it. In biology, chemistry, and other such courses the degree is transformed into application for medicine, pharmacology and life science disciplines. Any degree that only provides an element of theory is pointless, as it is only good for academic purposes. In practical world one has to be taught how to apply such theory into practice to be productive and useful in society. Increasingly, universities are failing to combine theory with practice, because they aim to meet numbers for educational measures and indicators so as to achieve more academic funding. The biggest mistake employers can make in the IT world is recruit graduates with purely theoretical backgrounds to do practical AI work. Don't hire a math, physics, or statistics graduate to build a machine learning model they will require a lot of mentoring and training. Majority of practical and theoretical AI work requires a computer science background where such material is formally taught in the degree course.
2 June 2020
13 May 2019
9 January 2017
16 October 2014
Birkbeck College
22 January 2014
Online Data Science Programs
A Sample Online Course Set:
Lower Division:
DS101 Statistics One
DS102 Computing for Data Analysis
DS103 Data Analysis
DS104 Introduction to Data Science
Upper Division:
DS201 Machine Learning I
DS202 Machine Learning II
DS203 Neural Networks for Machine Learning
Graduate Level:
DS301 Learning from Data
DS302 Machine Learning III
Coursera Options:
Data Science
Artificial Intelligence
Machine Learning
Natural Language Processing
Game Theory
Recommender Systems
Networks
High Performance Computing
Algorithms
Information
Databases
Finance
Business
Programming
Cloud
Big Data