1. 𝐁𝐞𝐜𝐨𝐦𝐢𝐧𝐠 𝐚𝐧 𝐄𝐧𝐭𝐫𝐞𝐩𝐫𝐞𝐧𝐞𝐮𝐫 Topics: • Overcoming the top myths of entrepreneurship • Identifying business opportunities • Performing market research and choosing your target customer 🔗 http://edx.sjv.io/Kj6PEv

2. 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐭𝐨 𝐂𝐨𝐦𝐩𝐮𝐭𝐞𝐫 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐚𝐧𝐝 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 𝐔𝐬𝐢𝐧𝐠 𝐏𝐲𝐭𝐡𝐨𝐧. Topics: • A Notion of computation • Python programming language • Some simple algorithms • Data Structures and more 🔗 http://edx.sjv.io/DK6BXG

3. 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐏𝐲𝐭𝐡𝐨𝐧 Topics: • ML problem principles • Implement & analyze models • ML project implementation: Training, validation, tuning, and feature engineering 🔗 http://edx.sjv.io/1rY2q6

𝟒. 𝐒𝐮𝐩𝐩𝐥𝐲 𝐂𝐡𝐚𝐢𝐧 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 Topics: • Basic analytical methods • How to apply basic probability models • Statistics in supply chains • Formulating and solving optimization models 🔗 http://edx.sjv.io/ZQGmR0

𝟓. 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐭𝐡𝐞 𝐖𝐨𝐫𝐥𝐝 𝐓𝐡𝐫𝐨𝐮𝐠𝐡 𝐃𝐚𝐭𝐚. Topics: • Python programming and the Colab notebook programming environment • Relationships between data using linear and polynomial regression models And more 🔗 http://edx.sjv.io/B03Gk0

𝟔. 𝐁𝐞𝐜𝐨𝐦𝐢𝐧𝐠 𝐚𝐧 𝐄𝐧𝐭𝐫𝐞𝐩𝐫𝐞𝐧𝐞𝐮𝐫 Topics: • Overcoming the top myths of entrepreneurship • Identifying business opportunities • Performing market research and choosing your target customer 🔗 http://edx.sjv.io/Kj6PEv

𝟕. 𝐂𝐨𝐦𝐩𝐮𝐭𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐓𝐡𝐢𝐧𝐤𝐢𝐧𝐠 𝐟𝐨𝐫 𝐌𝐨𝐝𝐞𝐥𝐢𝐧𝐠 𝐚𝐧𝐝 𝐒𝐢𝐦𝐮𝐥𝐚𝐭𝐢𝐨𝐧 Topics: • Interpolation methods and their impact on model convergence • Numerical integration techniques • Solving linear and nonlinear equations 🔗 http://edx.sjv.io/baBqgm

𝟖. 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐬 𝐨𝐟 𝐌𝐨𝐝𝐞𝐫𝐧 𝐅𝐢𝐧𝐚𝐧𝐜𝐞 Topics: • Valuation of fixed income securities and common stocks • Risk analysis, APT, Efficient Market Hypothesis • Introduction to corporate finance and capital budgeting and more 🔗 http://edx.sjv.io/VmkQdJ

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 🔗 http://edx.sjv.io/XYar5y

𝟏𝟎. 𝐓𝐡𝐞 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐨𝐟 𝐔𝐧𝐜𝐞𝐫𝐭𝐚𝐢𝐧𝐭𝐲 𝐚𝐧𝐝 𝐃𝐚𝐭𝐚 Topics: • The basic structure and elements of probabilistic models • Random variables, their distributions, means, and variance • Inference methods and more 🔗 http://edx.sjv.io/B03GY0

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