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Certification Included
access
Lifetime
content
2.0 Hours
Description
As the demand for data is growing, a lot of domains are centering their operations around data. The finance industry is the most influenced by data, giving rise to a whole new FinTech industry where technology meets Finance. This course aims to keep you at the top of this emerging trend by teaching you how to use Python programming to perform your financial tasks. At the end of this Python to Finance Course, you will be able to implement your machine learning models to make predictions on financial data.
This course will guide you through everything you need to know about Python for Finance. It will start the journey with the fundamentals of Python. You will then proceed to learn about the core libraries used in the Py-Finance domain, including Numpy, Pandas, Matplotlib, and much more!
- Access 59 lectures & 2 hours of content 24/7
- Use Python programming to perform your financial tasks
- Understand the fundamentals of Python
- Learn about the core libraries used in the Py-Finance domain
- Implement your machine learning models to make predictions on financial data
Yoda Learning
4.3/5 Instructor Rating:
★ ★ ★ ★
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Yoda Learning is a team of 12+ industry professionals focused on developing project-based learning solutions, which allows students to accrue real-world experience and learn in an engaging and practical manner. Its courses encompass everything from web and mobile app development to data analytics and design.
Important Details
- Length of time users can access this course: lifetime
- Access options: desktop & mobile
- Certificate of completion included
- Redemption deadline: redeem your code within 30 days of purchase
- Updates included
- Experience level required: intermediate
Requirements
- Any device with basic specifications
Terms
- Unredeemed licenses can be returned for store credit within 30 days of purchase. Once your license is redeemed, all sales are final.
Course Outline
- Your First Program
- Section 01: Introduction
- Course Overview - 4:22
- Why Python for Finance? - 8:01
- Why Spyder for Python? - 4:16
- Environment Setup
- Feedback 01 | Python for Finance
- Section 02: Python Basics
- Python Introduction - 2:38
- Data Types - 8:54
- Functions - 4:15
- Conditions Loops - 6:57
- Python Assignment
- Feedback 02 | Python for Finance
- Section 03: Numpy
- What is Numpy? - 2:38
- How To Install Python Libraries? - 3:23
- Arrays - 4:26
- Indexing and Operations - 6:40
- Numpy Assignment
- Feedback 03 | Python for Finance
- Section 04: Pandas Data Handling
- What is Pandas? - 4:50
- Series - 3:38
- Dataframe - 5:13
- Moving Average - 5:14
- Pandas Assignment
- Feedback 04 | Python for Finance
- Section 05: Matplotlib Visual Analysis
- What is Matplotlib? - 3:09
- Types of Plots - 3:30
- Creating Plots for Python - Part 1 - 5:37
- Creating Plots for Python - Part 2 - 6:54
- Matplotlib Assignment
- Seaborn - 3:04
- Feedback 05 | Python for Finance
- Section 06: Scikit-learn Prediction
- What is Scikit-learn? - 2:26
- Types of Models - 3:27
- Prediction Models - 3:07
- What is Regression in Finance? - 2:22
- Linear Regression - 2:33
- Prediction - Data Preprocessing - 4:47
- Prediction - Regression - 4:56
- Scikit-learn Assignment
- Feedback 06 | Python for Finance
- Section 07: S&P-500 Price Prediction
- Overview - 2:55
- S&P 500 Stock Price Prediction
- Data Processing - 2:01
- Prediction Part 1 - 3:45
- Prediction Part 2 - 2:40
- Project Solution
- Feedback 07 | Python for Finance
- Section 08: Risk Analysis of Credit Card Defaulter
- Introduction - 3:16
- Risk Analysis of Credit Card Defaulter
- Data Processing - 3:36
- Prediction - 5:24
- Credit Card Defaulter Project Solution
- Feedback 08 | Python for Finance
- Section 09: Bonus Resources
- Applications in Industry - 4:03
- Guide to Resources - 2:27
- Tutorial Links
- Dataset Links
- Feedback 09 | Python for Finance
- Section 10: Conclusion
- Conclusion - 2:06
- Feedback 10 | Python for Finance
access
Lifetime
content
2.0 Hours