I am not leaving finance for a completely different field. I am adding mathematics, statistics and programming to the financial experience I already have. My goal is to understand financial questions more deeply and test ideas more carefully.
This process is still underway. I continue to work in finance while preparing for further study and building the skills I will need for quantitative research.
Why finance came first
I completed a professional bachelor’s degree in Finance Management at the University of Latvia. The programme taught me how financial statements, investments, planning and business operations are connected.
That way of thinking is still central to my work. A financial model is not just a set of formulas. It shows how assumptions about revenue, costs, investment, financing and cash flow affect the final result.
My bachelor thesis examined UCITS ETFs using moving averages. I received the highest grade for the defence. Working on the thesis taught me that even a familiar topic becomes difficult when you have to define a method, use it consistently and explain what the results do and do not show.
What spreadsheets do well and where they become limiting
Spreadsheets remain one of the main tools in my work as a Finance Analyst. I use them to build models, prepare forecasts, compare plan with actual results and create reports.
They work well when the task is clearly defined and the logic is easy to follow. Problems appear when the amount of data grows, the same steps must be repeated many times or the model becomes difficult to audit. Manual work also increases the risk of errors.
This made me more interested in the structure behind the model. I wanted to understand which variables matter, how uncertainty should be represented and how the same analysis can be repeated on different data.
Why I need mathematics and statistics
Mathematics helps describe relationships clearly. Statistics helps decide what can reasonably be learned from incomplete or noisy data.
In finance, producing a number is often easier than explaining what it means. A forecast can be internally consistent and still be unreliable. A pattern can appear in historical data and disappear later. A model can fit past observations without being useful for future decisions.
This is one reason I am preparing to study Mathematics and Data Science and Financial Economics. The first programme will deepen my mathematical, statistical and computational skills. The second will keep that work connected to economic and financial questions.
Why I am learning Python and R
I am strengthening Python and R alongside mathematics and statistics. The aim is not to collect programming languages. I want to turn an analysis into a process that can be checked, repeated and improved.
Python is useful for preparing data, automating calculations and building analytical workflows. R is especially useful for statistical analysis and visualisation. In both cases, the quality of the method matters more than the syntax.
Real data is rarely clean. Definitions differ, observations are missing and several sources may record the same variable in different ways. Cleaning and checking the data are therefore part of the analysis itself.
Combining finance with quantitative methods
Financial experience provides context. It helps explain whether a pattern may be related to accounting rules, market structure, financing decisions or a temporary economic situation.
Context alone is not enough. A plausible explanation still needs to be tested. Quantitative methods require me to define the variables, assumptions and rules clearly. The result may support the original idea, change it or show that the evidence is weak.
The approach I am working toward is simple. Finance helps me choose useful questions. Mathematics helps describe them. Statistics helps evaluate the evidence. Programming makes the work easier to repeat and verify.
What I am doing now
I continue to work as a Finance Analyst, building financial models, forecasts, reports and analytical tools. At the same time, I am preparing for further study in Mathematics and Data Science and Financial Economics.
My long-term interests include quantitative research, statistics, machine learning, algorithmic trading and mathematical finance. I am still building the knowledge needed for those areas, so I prefer to separate completed work from ideas that are still being developed.
What comes next
The next step is continued study and practice. I want to apply new methods to real problems, review weaknesses in my work and improve the way I document assumptions and results.
A result is not useful simply because it looks impressive. It must also be tested without hindsight, based on data that would have been available at the time and explained together with its limitations.
What I have learned so far
- Start with a field you already understand. Existing experience helps you ask better questions.
- Do not skip the basics. Mathematics and statistics determine which conclusions an analysis can support.
- Learn programming through real problems. Python and R become easier to understand when they are used for actual data and analysis.
- Use spreadsheets where they work well. Choose another workflow when the data or logic becomes too difficult to maintain.
- Be clear about what is finished. It is better to describe ongoing work honestly than to claim expertise too early.
- Show the uncertainty. Assumptions, limitations and sensitivity are part of the result.
Finance remains the starting point of my work. Mathematics, statistics and programming are helping me examine the same questions more carefully. This transition is still in progress, and I plan to document it as I learn.
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