Financial Data Analytics with Python

INTRODUCTION

In today’s data-driven financial world, the ability to process, analyse, and interpret large financial datasets is a critical skill for professionals across banking, investment, risk management, and corporate finance. Python has become the leading tool for financial analytics, offering powerful libraries for data manipulation, statistical modelling, machine learning, and predictive analysis.

The Financial Data Analytics with Python training course provides a hands-on approach to financial data science, covering essential concepts such as data wrangling, exploratory data analysis (EDA), financial modelling, and machine learning applications in finance. This training course is designed to help delegates leverage data analytics to drive financial decision-making, risk assessment, and investment strategies.

Participants will learn how to clean and preprocess financial data, build forecasting models, implement risk assessment frameworks, and optimise portfolios using Python. The training course will also introduce advanced techniques such as algorithmic trading, sentiment analysis, and big data analytics to equip professionals with the skills needed to stay competitive in modern finance.

This GLOMACS training course will use real-world financial datasets and case studies to demonstrate the application of Python in finance. Delegates will gain practical experience through hands-on exercises, developing their ability to extract insights, visualise trends, and apply machine learning models to financial data.

The Financial Data Analytics with Python training course will highlight:

• Key Python tools for financial data analysis, including NumPy, pandas, and Matplotlib
• Exploratory data analysis (EDA) and financial modelling for investment and risk assessment
• Machine learning applications in finance, such as predictive analytics and portfolio optimisation
• Advanced topics, including algorithmic trading, sentiment analysis, and big data analytics
• Ethical considerations and regulatory compliance in financial data analytics

Objectives

By the end of this Financial Data Analytics with Python training course, participants will be able to:

• Use Python for financial data analysis, including data cleaning, processing, and visualisation
• Apply statistical and machine learning techniques to financial forecasting and risk assessment
• Develop and interpret financial models, including valuation, portfolio optimisation, and scenario analysis
• Leverage big data and sentiment analysis for financial decision-making and market predictions
• Implement algorithmic trading strategies and understand ethical and regulatory considerations in financial data analytics

Training Methodology

This Financial Data Analytics with Python training course will be delivered using a practical, hands-on approach with:

• Interactive lectures covering key concepts in financial analytics and Python programming
• Hands-on coding sessions using real-world financial datasets for data analysis and modelling
• Case studies and industry examples to illustrate financial data analytics in action
• Step-by-step guided exercises to apply machine learning and predictive modelling techniques
• Group discussions and Q&A sessions to enhance understanding and real-world application

Organisational Impact

Participants of this Financial Data Analytics with Python training course will bring valuable skills to their organisation, enhancing data-driven decision-making and financial analysis. The benefits include:

• Improved financial data analysis capabilities for better strategic planning and investment decisions
• Enhanced risk assessment techniques using machine learning and predictive analytics
• Optimised financial modelling and forecasting, leading to better portfolio and risk management
• The ability to extract insights from big data and sentiment analysis, providing a competitive edge in financial markets
• Stronger compliance and ethical understanding in financial data analytics, aligning with industry regulations

Personal Impact

By attending this training course, participants will develop:

• Hands-on experience with Python for financial data processing and analysis
• Practical skills in machine learning and AI-driven financial forecasting
• A deeper understanding of financial modelling, valuation, and risk assessment
• Confidence in applying data-driven insights to improve investment strategies
• The ability to implement algorithmic trading and portfolio optimisation techniques

WHO SHOULD ATTEND?

This GLOMACS Financial Data Analytics with Python training course is designed for professionals looking to enhance their financial data analytics skills. It is most suitable for:

• Finance professionals and analysts seeking to integrate data analytics into their work
• Investment and risk managers looking to leverage Python for portfolio and risk assessment
• Data analysts and IT professionals working with financial datasets
• Traders and investment professionals interested in algorithmic trading and forecasting.
• Consultants and decision-makers aiming to enhance financial insights with data analytics