Course Overview

This comprehensive course integrates Python programming with finance principles, equipping participants with the practical skills needed to analyze financial data, build models, and make informed decisions in real-world financial scenarios. Leveraging the foundation established in the Introduction to Python course, this program delves deeper into finance-specific applications, including Monte Carlo simulation, Efficient Frontier analysis, and Value at Risk (VaR) modeling.

 

Prerequisites

  • Trainees should be proficient in Python programming
  • Introduction to Python (for Trainees without python background)

Target Audience

This course is tailored for individuals with prior Python programming knowledge who aim to apply it within the finance industry. It is suitable for:

  • Professionals in finance-related roles.
  • Individual who aim to develop their practical programming skills

Course Highlights

  • Practical integration of Python programming with finance concepts.
  • Real-world case studies and applications in financial analysis and modelling.
  • Emphasis on Monte Carlo simulation, Efficient Frontier analysis, and Value at Risk modelling.
  • Hands-on projects and exercises designed to simulate real-world financial scenarios.
  • Preparing participants for data-driven decision-making in finance roles.

Course Objectives

Upon completion of the course, participants will be able to:

  • Implement Monte Carlo simulation techniques to analyze financial risk and uncertainty.
  • Construct Efficient Frontier portfolios to optimize risk-return trade-offs in investment portfolios.
  • Calculate Value at Risk (VaR) to quantify the potential loss in financial investments.
  • Apply Python programming skills to financial data analysis, modeling, and visualization.
  • Evaluate and interpret the results of financial models to make informed decisions in real-world financial scenarios.

 

Course Outline

  • Understanding the basics of Python programming language (variables, data types, operators).
  • Introduction to Jupyter Notebook for interactive coding (installation, setup).
  • Understanding basics of code execution in Jupyter Notebooks, using Google Collab.
  • Usage of pre-built libraries to familiarize with how to call functions in them.
  • Understanding of command line basics in the Jupyter environment.
  • Installing NumPy and basic functions
  • Installing and importing Pandas library
  • Loading and exploring financial datasets
  • Data cleaning and preprocessing techniques
  • Filtering, sorting, and summarizing financial data, with introduction to basics of statistics
  • Introduction to Matplotlib and Seaborn for data visualization.
  • Creating line plots, bar charts, and scatter plots for financial data analysis.
  • Introduction to key financial ratios.
  • Performing mathematical operations on financial data.
  • Calculating descriptive statistics and financial metrics.
  • Mini Lab: Intra sector analysis of companies with financial ratios.
  • Introduction to time series data and its applications in finance.
  • Handling time series data with Pandas and calculating custom metrics for analysis.
  • Visualizing time series data (trends, seasonality, volatility), linking it to business forecasting.
  • Common analysis techniques for data with visualization.
  • Understanding the principles of analysing correlations and statistical significance.
  • Building basic regression models.
  • Understanding proper use cases for regression, and pitfalls in analysis.
  • Introduction to key financial ratios.
  • Performing mathematical operations on financial data.
  • Calculating descriptive statistics and financial metrics.
  • Mini Lab: Intra sector analysis of companies with financial ratios.
  • Introduction to fundamental of database design.
  • Efficient querying, command line basics.
  • Best practices for working with unstructured data.
  • Introduction to processing outliers and normalizing data.
  • Using qualitative analysis to understand which parameters to use for regression analysis.
  • Avoiding overfitting, testing model robustness.
  • Introduction to processing outliers and normalizing data.
  • Using qualitative analysis to understand which parameters to use.
  • Avoiding overfitting, testing model robustness.

Certification

A certificate of completion will be awarded upon successful completion of the course.

Course Fees

Course fee $788 $488

Python for Finance

Master Python for Finance and Make Data-Driven Financial Decisions

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Master Python for Finance and Make Data-Driven Financial Decisions

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