3. Cybersecurity for Critical Urban Infrastructure An introduction course for students seeking to serve as cybersecurity consultants to understand, help prevent and manage cyberattacks on vulnerable communities across America. Check here 👇

MIT University just released free online courses. No application or fee required. Here are 10 courses you don't want to miss in 2024:

1. Introduction to Computational Thinking and Data Science This course is aimed at students who want to get experience by learning Python and to enhance knowledge of computational complexity. Check here 👇

2. Introduction to Computer Science and Programming Using Python An introduction to computer science as a tool to solve real-world analytical problems using Python 3.5 Check here 👇

4. Machine Learning with Python: from Linear Models to Deep Learning An in-depth introduction to the field of machine learning, from linear models to deep learning and reinforcement learning, through hands-on Python projects. Check here 👇

5. Data Analysis: Statistical Modeling and Computation in Applications A hands-on introduction to the interplay between statistics and computation for the analysis of real data. Check here 👇

6. Startup Success: How to Launch a Technology Company in 6 Steps This course discusses the lessons learned by Michael Stonebraker and Andy Palmer during their start-up endeavors over a 30-year period. Check here 👇

7. Becoming an Entrepreneur Learn the business skills and startup mindset needed to embark on your entrepreneurial path from the premier program for aspiring entrepreneurs, MIT Launch. Check here 👇

8. MITx: Foundations of Modern Finance Topics: • Interpolation methods and their impact on model convergence • Numerical integration techniques • Procedures for numerical differentiation • Solving linear and nonlinear equations 🔗

9. The Secret of Life Topics: • How to describe the building blocks of life and how their interactions dictate structure and function in biology • How to predict genotypes and phenotypes given genetics data and more 🔗

10. The Science of Uncertainty and Data Topics: • The basic structure and elements of probabilistic models • Random variables, their distributions, means, and variances • Probabilistic calculations • Inference methods and more 🔗

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