Python Programming Bootcamp 2.0
Skillwise
Course Summary
Python can be especially useful in finance as it has powerful analytical and predictive modeling capabilities. In this course, you'll learn just how Python can be applied in the world of finance. Even if you know absolutely nothing about coding, you'll learn in-demand, real-world skills that can make your resume jump out or help you make a difference in your own financial portfolio. Access 104 lectures & 6.5 hours of content 24/7 Build correlations between stocks, estimate risk & rate of return Explore the difference between diversifiable & non-diversifiable risk Perform regression analysis Measure a regression's explanatory power w/ R^2 Use Monte Carlo in a corporate finance context for options & stock pricing Apply the Black Scholes formula
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Course Description
Python can be especially useful in finance as it has powerful analytical and predictive modeling capabilities. In this course, you'll learn just how Python can be applied in the world of finance. Even if you know absolutely nothing about coding, you'll learn in-demand, real-world skills that can make your resume jump out or help you make a difference in your own financial portfolio.
- Access 104 lectures & 6.5 hours of content 24/7
- Build correlations between stocks, estimate risk & rate of return
- Explore the difference between diversifiable & non-diversifiable risk
- Perform regression analysis
- Measure a regression's explanatory power w/ R^2
- Use Monte Carlo in a corporate finance context for options & stock pricing
- Apply the Black Scholes formula
- Length of time users can access this course: lifetime
- Access options: web streaming, mobile streaming
- Certification of completion not included
- Redemption deadline: redeem your code within 30 days of purchase
- Experience level required: all levels
- Internet required
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Course Syllabus
- Introduction
- Language Fundamentals
- Functions & Classes
- Exception Handling
- Introduction
- Theory Basics
- Application
- Conclusion
- Introduction
- Image Processing Basics
- Security Camera
- Raspberri Pi
- Conclusion
- Course Introduction
- Projects
- Course Conclusion
- Introduction to Python
- Veriable
- Operators
- Statement and Loop
- Number
- List
- Tuple
- Dictionary
- Function
- Home Assignment # Gause the Number
- Model
- Exception Handling
- Python Package
- Getting started with python
- Basics of python
- Variables
- Input and print statement
- String
- List
- Tuples and set
- Dictionary
- Decision statements
- Conditional Statements
- Control Statements
- Functions
- Exceptions
- Modules
- Code along - Project 1
- Code along - Project 2
- Answers for the Exercises
- Course Overview
- Getting Started
- Variables and Operators
- Simple Data Types
- Making your Program Interactive
- Lists
- Dictionaries
- Statements and Loops
- Functions
- Working with Files
- Modules
- Course Introduction
- Machine Learning Concepts
- First ML Application
- Data Analysis
- Linear Algebra
- Natural Language Processing
- Clustering
- Welcome! Course Introduction
- Introduction to Programming with Python
- Python Variables and Data Types
- Basic Python Syntax
- Python Operators Continued
- Conditional Statements
- Python Functions
- Sequences
- Using Iterations in Python
- Useful Tools
- PART II Finance: Calculating and Comparing Rates of Return in Python
- PART II Finance: Measuring Investment Risk
- PART II Finance: Using Regressions for Financial Analysis
- PART II Finance: Markowitz Portfolio Optimization
- PART II Finance: The Capital Asset Pricing Model
- PART II Finance: Multivariate Regression Analysis
- PART II Finance: Monte Carlo Simulations as a Decision-Making Tool
