Spending on data science and artificial intelligence has grown faster than the financial discipline around it. Cloud compute is billed by the hour or the token, pilots multiply without clear funding rules, and boards ask what all the investment has returned. At the same time, a model's value depends on the decisions it changes, so the accuracy metrics data scientists usually report say little about profit, savings or risk avoided. Organisations need people who can connect the two worlds.

The Financial Management for Data Science & Machine Learning in Practice Training Course covers the full financial life of an ML initiative. Days one to five establish the foundations: cost structures and lifecycle costing, budgeting and forecasting for data teams, investment appraisal, the choice to build, buy or partner, and expressing model performance in monetary terms. Days six to ten deepen the practice with cloud FinOps for training and inference, the economics of generative AI applications, accounting and capitalisation, contracts and financial risk, benefits realisation and portfolio funding, closing with an integrated investment case.

Sessions alternate between finance workshops and Python and spreadsheet labs. Participants build a cost model, a cash flow appraisal and a profit-curve analysis for a case model, and leave with templates they can reuse in their next budget round.

After ten days, participants will be able to:

• Estimate the total cost of ownership of a machine learning system, from data acquisition and labelling to retraining and decommissioning.
• Prepare budgets and rolling forecasts for data science teams, platforms and cloud compute.
• Appraise ML investments using NPV, IRR, payback and scenario analysis.
• Translate confusion matrices and forecast errors into money using cost-sensitive thresholds, expected value and profit curves.
• Apply FinOps practices to allocate, monitor and reduce the cost of training and inference workloads.
• Evaluate build, buy and API options, including the per-token economics of large language models.
• Explain the accounting treatment of model development costs and the financial risks in AI vendor contracts.
• Track benefits realisation and report the financial performance of a data science portfolio.

The course brings together the finance and technical sides of machine learning investment. It suits:

• Heads of data science, analytics and AI who own budgets
• Finance business partners and management accountants supporting technology functions
• Financial controllers and CFO office analysts reviewing AI proposals
• IT finance, FinOps and cloud cost management practitioners
• Procurement and vendor managers buying data, AI platforms and model APIs
• Programme managers and grant accountants running donor-funded data projects
• Investment committee members in banks, insurers and telecom operators
• Digital transformation leads in government and parastatals

Participants leave with practical tools, including:

• A total cost of ownership template covering people, data, compute, licences and maintenance.
• A twelve-month budget and forecast model for a data science team.
• A Python notebook that converts model outputs into expected profit and selects the most valuable threshold.
• A cloud and LLM cost allocation scheme with tags, unit metrics and budget alerts.
• A build, buy or partner decision matrix with weighted financial and strategic criteria.
• A benefits register and realisation tracker for models already in production.
• A complete, board-ready investment case for an ML initiative.

Finance theory is always tied to a live calculation, using a mix of:

• Worked case files with cost data, invoices and model outputs from banking, retail, telecoms and donor-funded programmes
• Spreadsheet modelling sessions for TCO, budgets and discounted cash flow
• Python labs in Jupyter that convert model results into expected value and profit curves
• Cloud billing exports used to practise cost allocation and optimisation
• Mixed teams pairing finance and technical participants to challenge each other's assumptions
• A final investment case reviewed by a mock finance committee

Completion of all ten days, the spreadsheet and Python labs, and the final investment case earns participants a CPD-accredited Certificate of Completion issued by Vision Reach Global Consultancy.

Alongside the course name, dates and delivery format, the certificate confirms practical work that finance and data leaders can cite, while the cost models and business case templates remain available for future budget rounds.