Section 1 : Course Introduction, Success Tips and Key Learning Outcomes
Section 2 : PART #1 PYTHON PROGRAMMING FUNDAMENTALS
Section 3 : Python 101 Variables Assignment, Math Operation, Precedence and PrintGet
Section 4 : Python 101 Data Types
Section 5 : Python 101 Comparison Operators, Logical Operators, and Conditional Statements
Section 6 : Python 101 Loops
Section 7 : Python 101 Functions
Section 8 : Python 101 Files Operations
Section 9 : Python 101 Data Science Python Libraries for Data Analysis (Numpy)
Section 10 : Python 101 Data Science Python Libraries for Data Analysis (Pandas)
Section 11 : Python 101 Data Visualization with Matplotlib
Section 12 : Python 101 Data Visualization with Seaborn
Section 13 : PART #2 PYTHON FOR FINANCIAL ANALYSIS
Section 14 : Stocks Data Analysis and Visualization in Python
Section 15 : Asset Allocation and Statistical Data Analysis
Section 16 : Capital Asset Pricing Model (CAPM)
Section 17 : Monte Carlo Simulation, Portfolio Optimization, and Trading with Momentum
Section 18 : PART #3 MACHINE AND DEEP LEARNING IN FINANCE
Section 19 : Predict Stocks Future Prices Using Machine and Deep Learning
Section 20 : Perform Bank Market Segmentation Using Unsupervised Machine Learning Techniques
Section 21 : Perform Sentiment Analysis On Stocks Data Using Natural Language Processing