Data Analysis Training - Adding value to your data

Prerequisites

You must have a solid understanding of statistical analysis and Excel, or have completed the courses “Exploring Fundamental Data Calculations” [FHDATC] and “AI and Excel for Finance” [FHEXCA].

Objectives

◗ Develop a solid understanding of data: semantics, Big Data, structured/unstructured data, data roles, APIs.

◗ Structure a business use case using the CRISP-DM model.

◗ Interpret the main artificial intelligence models and algorithms used in predictive analytics.

◗ Create 1- and 3-year financial forecasts by combining Excel with a conversational AI tool.

◗ Apply analytical and forecasting methods to real-world challenges in the finance function.

Training program

Updating content to strengthen in-session practice in light of recent AI changes.

◗ The Basics of Data Culture

– Semantics and Key Concepts in Data
– Big Data and Illustrations for the Finance Department
– Task Distribution in Data
– From the Data Warehouse to the Lakehouse: History and Architectural Challenges
✔ UNDERSTAND | Illustration: Mapping the Data Ecosystem of a Typical Finance Department (ERP, BI, open data, data warehouse, etc.)
✔ EVALUATE | Quiz: What types of data are present in your organization?

◗ Identify a business use case using CRISP-DM

– Introduction to the CRISP-DM model
– Translating a business question into a data problem: from “I want to forecast my revenue” to a structureduse case
– Identifying available data and the scope’s limitations
✔ APPLY | Practical exercise: Apply CRISP-DM to a financial use case and structure the information needed to implement it
✔ EXPERIMENT | Individual or pair workshop: Each participant identifies a use case from their own daily life and structures it using the CRISP-DM method
✔ EVALUATE | Quiz: What best practices should be systematically included in any data project?

◗ Understanding How AI Works

– From Statistics to AI: Regression, Classification, Clustering
– Predictive Models: How They Work, Training, Validation, Overfitting
– Bayesian Logic in Management: Conditional Probabilities, Belief Updates
– Key Algorithms: Decision Trees, Random Forests, Neural Networks, etc.
✔ UNDERSTAND | Illustration: Diagram of Bayesian reasoning applied to forecasting project delays in finance
✔ APPLY | Practical case study: Using the Goal Seek tool and Excel Solver to optimize a delivery schedule and forecast sales
✔ EVALUATE | Quiz: What is the difference between a supervised and an unsupervised model?

◗ Applying predictive models to management

– Simple and logistic regression, decision trees, etc.
– Business use cases for finance: sales forecasting, erroneous order cancellations, customer targeting
– Interpreting results: reliability, thresholds, expected gains
✔ APPLY | Practical exercise: predicting erroneous orders using a decision tree
✔ EXPERIMENT | AI workshop: using an AI tool to interpret results and propose alternative hypotheses
✔ EVALUATE | Quiz: In your opinion, which variable has the greatest influence on the prediction?

◗ Creating Financial Forecasts with Excel and AI

– Build a 1-year and 3-year forecasting model: assumptions, scenarios, sensitivities
– Use an AI tool to generate the model structure, document the assumptions, and write comments
– Validate and audit the model: consistency of assumptions, bounds, robustness tests
– Present the forecast to the Executive Committee: key metrics, relevant charts, key messages
✔ APPLY | Hands-on exercise: Build a regression model to forecast sales over the next 6 months using leading indicators (sales pipeline, seasonal history)
✔ EXPERIMENT | Workshop: Use AI to generate an executive summary of the forecast, then format it for an executive committee presentation
✔ EVALUATE | Final quiz: How can you make a forecasting model defensible, auditable, and understandable to a non-finance professional?

Why choose this course?

Finance departments are facing growing expectations: they are no longer expected merely to consolidate past data, but to use available data to shed light on the future.

This 3-day training program offers a gradual and coherent progression in skills: data literacy on the first day, understanding AI on the second, and practical application to financial forecasting on the third. It is deliberately designed to move away from an overly theoretical approach: each half-day focuses on a practical case study rooted in the day-to-day work of a finance professional.

Teaching and assessment methods

Before the training: Submit your expectations via your online portal 15 days before the training begins and complete a self-assessment of your skills.

Teaching Methods: A variety of teaching methods to cater to different learning styles, encourage active participation, and reinforce learning through experimentation and practice.

Educational materials: presentation materials, reference materials (summary sheets, tables, etc.), and other resources available in your account.

During the training: theoretical concepts are alternated with examples drawn from real-life cases. Participants are given the opportunity to engage in role-playing exercises. Numerous practical exercises allow participants to demonstrate their mastery of the skills as the training progresses.

Training monitoring and evaluation: attendance sheet and certificate of completion. Immediate and post-training evaluations conducted using the LearnEval platform.

Price

2,750 EXCL. TAX

Testimonials

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