# ChatGPT — raw output, 2026-09-13 22:08 (+07), free plan, default model, **temporary chat with "Without personalization"** (memory excluded), one message, no follow-ups Searched the web ("Поиск в интернете"). Interface Russian; answer in English. Copied via the chat's copy button (citation footnotes preserved). Temporary chats are not kept; the tab was left open. --- You have a **very plausible route into data analytics**. Your biggest advantage is not the SQL you already know—it’s the combination of **8 years of bookkeeping + Excel + business/financial data experience**. I would position you as a *finance/business data analyst transitioning from accounting*, rather than as a generic beginner. I checked current 2026 course/certification information and Lisbon salary data. One caveat: salary sites have surprisingly small samples, so treat the numbers as ranges rather than precise market truth. ## The target By month 6, I would want you to be able to demonstrate: * **Excel:** advanced formulas, PivotTables, Power Query * **SQL:** SELECT, filtering, aggregation, JOINs, CASE, CTEs, window functions * **Power BI:** Power Query, data modelling, DAX, dashboards * **Analytics:** KPIs, descriptive statistics, explaining findings to non-technical people * **Portfolio:** 2–3 polished projects, ideally including finance/accounting data * **Job applications:** already underway by months 4–6 I would **not** spend your six months trying to become a data scientist, learn Python deeply, or collect five certificates. --- # Your 6-month plan — 8 hours/week ### Month 1 — SQL foundation + analytics thinking **Goal:** become comfortable querying relational data. Spend roughly: * 4 h/week SQL * 2 h/week analytics concepts * 2 h/week building something **Course:** *SQL for Data Science* — University of California, Davis/Coursera. It covers filtering, sorting, aggregation, joins, subqueries, CASE, dates and basic data modelling. You can enroll for free; the certificate requires the paid experience, and Coursera says financial aid may be available. ([Coursera][1]) [SQL for Data Science — Coursera](https://www.coursera.org/learn/sql-for-data-science?utm_source=chatgpt.com) **Practice:** use SQLBolt or HackerRank/LeetCode-style SQL exercises alongside it. By the end of month 1, you should be able to answer questions like: ```text Which customers generated the most revenue? What was revenue by month? Which products have declining sales? What percentage of transactions are overdue? ``` Don't just watch videos. **Write queries every week.** --- # Month 2 — Excel → analytical Excel You're already comfortable with Excel, so **do not spend 40 hours learning Excel basics again**. Instead, learn the parts that translate directly into analyst work: * XLOOKUP / INDEX-MATCH * SUMIFS / COUNTIFS * PivotTables * Power Query * cleaning messy data * dates * conditional logic * basic statistics * creating management-ready reports A good structured option is Microsoft's *Data for Analysis with Microsoft Excel* on Coursera. It specifically covers preparing Excel data for analysis, formulas/functions and preparing data for Power BI. ([Coursera][2]) [Microsoft — Data for Analysis with Excel](https://www.coursera.org/learn/data-for-analysis-with-microsoft-excel?utm_source=chatgpt.com) **Your project:** take a fictional/general ledger or accounts-receivable dataset and build: > Revenue → expenses → margins → overdue receivables → monthly trends → management dashboard. This is where your bookkeeping experience becomes an advantage. --- # Month 3 — Power BI This is the most important month. Install **Power BI Desktop**, which Microsoft provides as a downloadable desktop application. ([Microsoft Learn][3]) [Download Power BI Desktop — Microsoft](https://learn.microsoft.com/en-us/power-bi/fundamentals/desktop-get-the-desktop?utm_source=chatgpt.com) Follow Microsoft's free learning material: **Prepare and visualize data with Microsoft Power BI** It covers getting data, Power Query, cleaning/transforming data, modelling and report design. ([Microsoft Learn][4]) [Power BI learning path — Microsoft Learn](https://learn.microsoft.com/en-us/training/paths/prepare-visualize-data-power-bi/?utm_source=chatgpt.com) Then move into: **Model data with Power BI** This is where you learn relationships, semantic models and DAX. ([Microsoft Learn][5]) [Model data with Power BI — Microsoft Learn](https://learn.microsoft.com/en-us/training/paths/model-data-power-bi/?utm_source=chatgpt.com) ### Your deliverable Build a proper **finance dashboard**: * Revenue * Gross margin * Operating expenses * AR/AP * month-over-month change * budget vs actual * top customers * drill-down by month/customer/category Don't make it a pretty collection of charts. Make it answer **business questions**. --- # Month 4 — SQL to intermediate level + portfolio Now return to SQL. You want to move beyond: `SELECT → WHERE → GROUP BY` and become comfortable with: * INNER/LEFT JOIN * subqueries * CTEs * CASE * date manipulation * window functions * `ROW_NUMBER` * `RANK` * `LAG/LEAD` * calculating percentages and running totals The current Microsoft Data Analysis with SQL, Excel & Power BI specialization is particularly aligned with your target stack: SQL Server, Excel and Power BI, and its current estimated workload is about 7 hours/week over five months. ([Coursera][6]) [Microsoft Data Analysis with SQL, Excel & Power BI — Coursera](https://www.coursera.org/specializations/microsoft-data-analysis-sql-excel-powerbi?utm_source=chatgpt.com) **However:** don't automatically buy the entire specialization. You've already got Excel experience. At your 8-hours/week limit, I'd use the relevant SQL/Power BI portions rather than religiously completing every lesson. ### Portfolio project #2 Build something **non-financial**, e.g.: **E-commerce analysis** SQL: > 100k orders → clean → join customers/products → calculate customer revenue, retention, AOV and monthly trends. Power BI: > executive dashboard + customer/product analysis. Put the SQL queries on GitHub and the dashboard/screenshots/project explanation somewhere recruiters can see them. --- # Month 5 — Certification + job preparation At this point I'd consider the **Microsoft Certified: Power BI Data Analyst Associate (PL-300)**. [Microsoft Power BI Data Analyst Associate — Microsoft](https://learn.microsoft.com/en-us/credentials/certifications/data-analyst-associate/?utm_source=chatgpt.com) Microsoft's current headline price is **US$165**, with the actual price depending on the country/region where the exam is proctored. ([Microsoft Learn][7]) So I would budget roughly **€150–€170**, but check the actual Portugal price at booking rather than treating €165 as a guaranteed Portuguese price. The important part: **don't take it just because it's a certificate.** Take it after you've built dashboards and worked through Microsoft's free PL-300 material. Microsoft itself provides the relevant learning paths and a practice assessment. ([Microsoft Learn][8]) ### At the same time: start applying Don't wait until month 6. Start targeting: * Junior Data Analyst * Data Analyst * BI Analyst * Reporting Analyst * Business Analyst — Data * Finance Data Analyst * FP&A/Data Analyst * Operations Analyst * Revenue Analyst And particularly: > **Finance + Data** That's where your eight years of experience can differentiate you from someone who has merely completed a bootcamp. --- # Month 6 — Portfolio → interviews This month should be approximately: **3 h/week:** applications/networking **3 h/week:** portfolio/interview practice **2 h/week:** technical revision Create a third project if you have time. I'd make it **accounts/finance related**, because it creates a coherent story: ### Project 3 — "Financial Performance Analytics" Dataset: * invoices * customers * payment dates * expenses * revenue * budget Questions: * Which customers are driving revenue? * Which are chronically late? * What is DSO? * How does revenue vary by month? * Which expense categories are growing fastest? * Actual vs budget? * What explains the biggest changes? Then write a one-page executive summary: > **Finding:** DSO increased 11% in Q3. > **Driver:** three customer segments account for 72% of the increase. > **Recommendation:** prioritize collection activity on X segment. That final step is what separates an analyst from someone who knows Power BI. --- # What I'd spend You have a **€500 ceiling**, but I don't think you need to spend anywhere close to it. | Item | Cost | | ------------------------------- | -----------------------------------------------------------------------------------------------: | | Microsoft Learn | €0 | | Power BI Desktop | €0 | | SQL course/material | €0 possible | | Excel/Power BI Coursera courses | €0 if using free/no-certificate options; paid certificate pricing varies by Coursera plan/locale | | PL-300 exam | ~€150–€170 budget | | Portfolio/GitHub | €0 | | **Recommended total** | **~€150–€250** | Coursera's current pages allow free enrollment on several relevant courses, while certificates require the paid experience; financial aid is also offered on eligible programs. ([Coursera][1]) **I would keep the remaining €250+ in reserve.** You don't need to spend your budget to become employable. --- # What I'd skip This is important. ### ❌ A €1,000–€5,000 data analytics bootcamp You don't need it. You already have professional experience, Excel and some SQL. Your bottleneck is **demonstrable analyst capability**, not classroom access. ### ❌ A second degree Not for this transition. A degree may help later with some employers, but spending several years obtaining one is not necessary for the specific transition you're describing. ### ❌ Python for now Learn Python **after** you're employable. For your first Lisbon analyst role, I'd rather see: > SQL + Excel + Power BI + business judgement than: > SQL + Python + pandas + NumPy + machine learning with no strong dashboard or business analysis. ### ❌ Tableau Don't learn Power BI *and* Tableau simultaneously. For your situation, Power BI makes particularly good sense because you're coming from Excel and finance. ### ❌ Machine learning Completely unnecessary for your first job. ### ❌ Collecting certificates One meaningful certificate + good projects beats six random certificates. --- # What salary should you realistically expect? There is some noisy data here. Indeed currently reports an average Data Analyst salary in Lisbon of **€21,468/year**, based on five reported salaries, updated August 29, 2026. ([Indeed][9]) Glassdoor currently reports a much higher Lisbon figure: **€32,000/year median total pay**, with a reported total range of €27k–€40k and base pay of roughly €25k–€36k. ([Glassdoor][10]) The discrepancy tells you something important: **don't interpret either figure as "the salary for a junior career changer."** The datasets are small and include people with very different experience levels. For **you specifically**, I'd use this as a realistic first-job target: ### **€22k–€28k gross/year** That's roughly **€1,570–€2,000 gross/month if paid over 14 salaries**, before considering the details of the Portuguese compensation package. I'd regard: * **€20–22k:** low, but potentially worth accepting if it gets you genuine analyst experience * **€23–28k:** realistic target * **€28–32k:** very good first transition * **€32k+:** possible, but don't build your plan around getting it immediately Interestingly, Indeed's current company data includes Randstad at around **€27k/year** for Data Analyst roles in Lisbon. ([Indeed][9]) Your existing eight years of experience could help you outperform a pure entry-level applicant—but employers may still discount some of it because it's bookkeeping rather than analytics. --- # Your odds — honestly If you told me: > "I'm 34, no degree, no technical experience, 8 hours/week" I'd say the transition is difficult. But that's **not actually your profile**. You have: **+** 8 years professional experience **+** substantial Excel exposure **+** some SQL already **+** accounting/financial domain knowledge **+** realistic study commitment **+** six months of focused preparation **+** a clear target market Your disadvantages are: **−** no degree **−** no formal analyst title **−** limited SQL experience **−** limited Power BI experience **−** only ~200 hours of study available over six months So my honest assessment: ### If you simply complete courses: **Low-to-moderate odds.** A certificate isn't going to magically turn you into a data analyst. ### If you complete the plan + build 2–3 genuinely good projects: **Moderate-to-good odds of getting interviews.** ### If you additionally exploit your finance background and apply for finance/BI/reporting analyst roles: **I'd consider the transition quite realistic within 12 months.** I would **not** promise you a job within six months. That's too optimistic. But a **data/BI/financial analyst transition within 12 months is absolutely plausible**, particularly if you're willing to accept that your first role might be a lateral move rather than a huge salary increase. --- ## The strategy I'd use in your shoes Don't market yourself as: > "Bookkeeper trying to become a data analyst." Market yourself as: > **Finance professional with 8 years of experience in financial data, transitioning into data analytics, with hands-on SQL, Excel and Power BI skills.** That's a much stronger story. And I'd specifically look for companies where the job description says things like: > "Finance / Accounting / Business degree preferred" or > "Experience with financial data" or > "Reporting / BI / FP&A" because your bookkeeping background suddenly becomes **relevant experience rather than unrelated experience**. ### Your ideal skill stack after six months **Excel → SQL → Power Query → Power BI → DAX → business/financial analysis** That's enough. You don't need to know everything. You need to be **good enough at those six things to solve a business problem and explain your answer**. If you can do that convincingly in an interview, the absence of a degree becomes much less important. ([Microsoft Learn][8]) **Bottom line:** I'd do this transition. Your constraints are tight but realistic, and I'd spend **far less than €500**. The biggest risk isn't your age or lack of degree; it's spending six months consuming courses instead of producing a portfolio and applying for jobs. [1]: https://www.coursera.org/learn/sql-for-data-science?utm_source=chatgpt.com "SQL for Data Science | Coursera" [2]: https://www.coursera.org/learn/data-for-analysis-with-microsoft-excel?utm_source=chatgpt.com "Data for Analysis with Microsoft Excel | Coursera" [3]: https://learn.microsoft.com/en-us/power-bi/fundamentals/desktop-get-the-desktop?utm_source=chatgpt.com "Download Power BI Desktop - Power BI | Microsoft Learn" [4]: https://learn.microsoft.com/en-us/training/paths/prepare-visualize-data-power-bi/?utm_source=chatgpt.com "Prepare and visualize data with Microsoft Power BI DP-605T00 - Training | Microsoft Learn" [5]: https://learn.microsoft.com/en-us/training/paths/model-data-power-bi/?utm_source=chatgpt.com "Model Data with Power BI - Training | Microsoft Learn" [6]: https://www.coursera.org/specializations/microsoft-data-analysis-sql-excel-powerbi?utm_source=chatgpt.com "Microsoft Data Analysis with SQL, Excel & Power BI | Coursera" [7]: https://learn.microsoft.com/en-us/credentials/certifications/data-analyst-associate/?utm_source=chatgpt.com "Microsoft Certified: Power BI Data Analyst Associate - Certifications | Microsoft Learn" [8]: https://learn.microsoft.com/en-us/training/career-paths/data-analyst?utm_source=chatgpt.com "Training for Data Analysts | Microsoft Learn" [9]: https://pt.indeed.com/career/analista-de-dados/salaries/Lisboa?campaignid=serp-more&fromjk=2a463e2eac4c0c74&utm_source=chatgpt.com "Salário de Analista de dados em Lisboa" [10]: https://www.glassdoor.com/Salaries/lissabon-data-analyst-salary-SRCH_IL.0%2C8_IM1121_KO9%2C21.htm?utm_source=chatgpt.com "Salary: Data Analyst in Lisbon, Portugal 2026 | Glassdoor"