You have six hours this week to work on your finance skills. Should you spend them on Excel, Python, or the latest AI tool?
The pressure to learn AI is real. In a September 10 ICIMS report, 47% of 1,000 surveyed US job seekers said they had worked on their AI skills in the previous six months.
For someone aiming at IB, equity research, or FP&A, my starting order is simple: Excel foundations first, AI alongside your learning, and Python when your work calls for repeatable data processing. Let me show you what that means with one small project.
A practical starting point for IB, equity research, and FP&A learners. Quantitative and data-focused roles may call for Python earlier.
The 60-Second Version
Excel: learn to connect assumptions, calculations, and an answer.
AI: use it early to explain, challenge, and help draft work you can verify.
Python: bring it in when repeatable data work makes automation worthwhile.
Build, Check, Repeat: finish one useful analysis before expanding your toolkit.
1. Build something small enough to understand
Start with the business logic. Imagine a hypothetical business that sells 1,000 units a month for $50 each. Variable cost is $30 per unit, and monthly fixed costs are $15,000.
Put those four inputs in separate Excel cells. Calculate revenue, variable costs, and operating profit. Keep the formulas linked to the inputs so you can change an assumption without rebuilding the analysis.
Your first result should be $5,000 of operating profit: $50,000 of revenue, less $30,000 of variable costs and $15,000 of fixed costs.
Now increase volume by 10%, keeping the selling price, unit cost, and fixed costs unchanged. Before touching the spreadsheet, write down what you expect to happen. This is the useful skill: understanding which costs move with sales.
2. Let AI help you interrogate the answer
Operating profit rises to $7,000. Revenue becomes $55,000, variable costs become $33,000, and fixed costs stay at $15,000. A 10% volume increase produces a 40% profit increase under these assumptions.
Hypothetical monthly example. Price: $50; variable cost: $30 per unit; fixed costs: $15,000. Assumes capacity for the additional volume without extra fixed costs.
Ask an AI assistant to explain why. Then ask it to identify which assumptions could fail. Extra staff, overtime, discounts, or capacity constraints could change the result.
Try this prompt: Explain my calculation step by step. Identify the assumptions, and suggest three checks I can run in Excel. Treat any missing inputs as unknown.
Recalculate the answer yourself. You can use AI from day one as a tutor, formula helper, and reviewer. Use this hypothetical data or information approved for the tool you are using.
3. Add Python when repetition becomes the problem
One tidy worksheet is a manageable task. Now imagine receiving monthly sales files from five regions, each with slightly different date formats, product names, and missing fields.
This is where a repeatable workflow becomes valuable. Start by defining the steps: load files, standardise fields, identify duplicates, group sales, and reconcile the result to the original totals.
Excel Power Query may handle that comfortably. Python becomes attractive when you need more flexible processing, larger workflows, or connections to other systems. The choice depends on the task and the tools your team can maintain.
If AI helps write the code, check a small sample by hand and make the reconciliation visible. A script that silently drops a region can still produce an impressive chart.
Our Elite Membership brings together finance, Excel, Python, and AI learning. You can build the foundation and then follow the next skill your project needs.
4. Let the role change the emphasis
The same starting point can lead to different priorities. Look at the work expected in the roles you are actually applying for.
Investment banking: prioritise accounting, Excel modeling, valuation, and a clear presentation of your assumptions.
Equity research: practise forecasts, valuation, and explaining an investment view. Add Python sooner if repeated data collection is central to the role.
FP&A: focus on budgets, variance analysis, and forecasts. Learn to turn an explanation into an action a business manager can take.
For a quantitative research or finance-data role, Python may deserve a place at the beginning. Existing skills matter too. If you already build reliable models, repeating beginner Excel lessons is unlikely to be your best next step.
FOLLOW THE SKILL YOU NEED NEXT
Build your finance toolkit one project at a time
Explore finance, Excel, Python, and AI learning in one membership. Start with the skills your next piece of work requires.
5. Spend the six hours on one finished project
Here is how I would use that study time. Adjust the pace to your starting point; the goal is a finished piece of work.
Build, two hours: create the small operating model, label the assumptions, and write a three-sentence explanation.
Check, two hours: change volume and costs. Ask AI for challenges, verify its suggestions, and record which checks caught a mistake.
Repeat, two hours: add a second month. Try Power Query or a small Python exercise if you are ready; otherwise practise the update and document each step.
You should finish with a workbook, a few checks, and an explanation you can discuss. That gives you evidence about what to learn next. Our financial analyst roadmap can help you place the next project in a broader learning sequence.
Reply with the role you are aiming for and one task you would like to do better. A specific task tells me much more than a list of tools you want to learn.
Best regards,
Dheeraj Vaidya, CFA, FRM
Ex-JPMorgan & CLSA
Co-founder, WallStreetMojo
P.S. If you want a structured place to build these skills, take a look at Elite Membership. Choose one relevant learning path and put it to work on a project. The newsletter stays free either way.
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