Do I need to know Python before taking this course?

The course works through the Python code step by step, starting from installing the libraries, so you do not need to be an experienced programmer. A general comfort with data and financial concepts will help you get the most from it.

What will I actually build during the course?

You will build models that predict a company's share price and stock return using logistic regression, linear regression and an LSTM deep-learning model, plus a panel regression model that relates financial factors to ESG performance.

What data and tools does the course use?

It uses Python with libraries such as yfinance, pandas, NumPy, Matplotlib, Seaborn and scikit-learn, working with ten years of Microsoft share-price data and an ESG dataset covering multiple companies.

How many CPD units does this course carry?

Completing the course earns you 8 CPD units, equivalent to 8 hours of CPD.

Artificial IntelligenceIntermediate

Improving Financial Decision-Making with AI-Powered Analytics (8 CPD Units)

Build and interpret real predictive models in Python to forecast company financial and ESG performance.

8 CPD credits on completion

Enrol Now

Certificate on completion

Downloadable resources

Self-paced learning

About This Course

This hands-on CPD course takes AI from theory into practice. You will use Python to perform predictive analytics, using historical data to forecast how a company's financial and ESG performance may change over time.

You will work in two parts. First, you will predict corporate performance, using ten years of Microsoft share-price data pulled with the yfinance library to build a logistic regression model for gain or loss, a multiple linear regression to predict closing price, and an LSTM deep-learning model for time-series forecasting.

Second, you will predict ESG performance across multiple companies using panel data, applying a panel regression to see how factors such as return on assets, return on equity, leverage and earnings per share relate to a firm's ESG score, and how to read the coefficients and p-values that result.

Throughout, you will use Python libraries including pandas, NumPy, Matplotlib, Seaborn and scikit-learn to load, clean, visualise and model real financial and ESG datasets.

By the end, you will be able to use AI and analytics to improve forecasting, support investment decisions, and make more informed, data-driven financial decisions. Successfully finishing this course will earn you 8 CPD units (equivalent to 8 hours).

What You Will Learn

Explain how predictive analytics uses historical data to forecast a company's financial and ESG performance.
Load and prepare real financial data in Python using the yfinance library, pandas and NumPy.
Build and evaluate logistic and linear regression models to predict stock return and share price.
Apply an LSTM deep-learning model to forecast share price from time-series data.
Use panel regression to model how financial factors relate to a company's ESG performance.
Interpret model coefficients and p-values to judge the direction, size and significance of each factor.
Visualise financial and ESG data using Matplotlib and Seaborn to support decision-making.

Who This Course Is For

Accountants and finance professionals who want to apply AI to forecasting and analysis.
Analysts who want practical, hands-on experience with predictive modelling rather than theory.
Finance professionals exploring how to use ESG data to assess company performance.
Anyone in finance who wants an introduction to Python for data analysis and machine learning.

Frequently Asked Questions

Course Details

CPD Credits8

LevelIntermediate

CertificateYes

Need Help?

Our team is here to help you choose the right course for your CPD needs.

Pricing

Invest in your professional development with flexible payment options.

Everything included in your subscription:

CPD certificate on completion