- Data analytics, statistics, data analysis - Fundamental knowledge of statistics, data analysis (including correlation, regression and segmentation) and forecasting methods - Growth (decay), index calculations
- Modeling, simulation, scenarios - Modeling, simulation, scenarios
- Spreadsheets for finance (Excel ) - Mastery of spreadsheets for finance
Target audience
- Internal auditors
- Bankers / Account managers
- Financial managers and controllers
- Chartered accountants
- Treasurers
2 day
Prerequisites
Proficiency in Excel is required. No prior knowledge of advanced statistics, programming, or artificial intelligence is necessary.
Objectives
◗ Interpret the statistical fundamentals relevant to financial analysis: distributions, correlation, and regression.
◗ Analyze how the main AI algorithms work (regression, classification, time series forecasting).
◗ Use advanced Excel tools to calculate, visualize, and interpret statistical indicators.
◗ Compare Excel results with AI tool outputs to validate the analyses and ensure their reliability.
◗ Apply statistical and predictive methods to real-world corporate finance scenarios (sales forecasting, customer segmentation, scoring).
Training program
This program has been updated to reflect recent developments in AI, with a stronger emphasis on hands-on practice and experimentation during sessions.
◗ Identify data and variable types
– Distinguishing between categorical and numerical variables
– Understanding the nature of a population, an individual, and the associated variables
– Visualizing distributions and identifying the shape of the data
✔ UNDERSTAND | Illustration: Classification of variables and types of distributions
✔ EVALUATE | Quiz: What type are the following variables?
◗ Describe data using simple statistics
– Measures of central tendency: mean, median, mode
– Measures of dispersion: range, standard deviation, interquartile range
– Visual representations: histogram, box-and-whisker plot, distribution curve, …
✔ APPLY | Excel practice exercise: Calculate and interpret the statistical indicators for a customer dataset
✔ EXPERIMENT | AI workshop: Have an AI tool analyze the same dataset and compare the results with the Excel calculations
✔ EVALUATE | Quiz: In your opinion… When is the median more reliable than the mean?
◗ Measuring Relationships Between Variables: Correlation and Regression
– Correlation: Pearson’s coefficient, chi-square (χ²), Cramer’s V – calculation and interpretation
– Simple linear regression: regression line, slope, R², coefficient of determination
– Multiple regression: incorporating multiple explanatory variables
✔ APPLY | Excel case study: build a simple regression model to predict revenue
✔ EXPERIMENT | AI workshop: use an AI tool to project sales forecasts and compare them with Excel projections
✔ EVALUATE | Quiz: Does a strong correlation always imply a causal relationship?
◗ AI Algorithms Applied to Finance: Classification and Clustering
– Bayesian logic: conditional probabilities, belief updating (scoring, customer risk)
– Decision trees: how they work and how to read a tree for finance
– Clustering (unsupervised segmentation): k-means, distances, interpreting clusters
✔ APPLY | Excel case study: segmenting customers by payment behavior and order type
✔ UNDERSTAND | Illustration: Diagram of Bayesian reasoning applied to project delay forecasting
✔ EVALUATE | Quiz: Why is the cosine distance more suitable for certain types of financial data?
◗ Time Series and Financial Forecasting with AI
– Decomposition of a time series: trend, seasonality, residual
– Smoothing methods: moving averages, exponential smoothing, Holt-Winters
– Excel forecasting functions
– Comparing Excel forecasts with the output of a conversational AI tool
✔ APPLY | Practical exercise: Forecast monthly sales over 6 months using each method and compare the results
✔ EXPERIMENT | AI workshop: Ask an AI tool to comment on the results and suggest the method best suited to the context
✔ EVALUATE | Quiz: How can you tell if a forecasting model is well-fitted?
Why choose this course?
A basic understanding of statistics and related mathematical concepts is necessary to get started with data analytics.
This course covers these concepts in a fun and visual way to make them accessible to everyone. Most of the courses in the FinHarmony Data Analytics training program build on the knowledge gained during these two days.
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
1,860 EXCL.
Testimonials
Data fundamentals training
Data fundamentals training
Data fundamentals training
Jonathan C.
Company
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