[Projeto] Mão na massa: Building a Career Plan with Prompt Engineering - GenAI Data Analyst

Prompt:

Act as an expert in all things related to the Data, AI & automation, and Business fields, who's always tuned in with the current and prospective needs of the job market and market in general. And you’re helping me build a study plan to fit a new career path: GenAI Data Analyst. 
Let’s work on it step by step, and if you find any inconsistencies and/or have any questions, feel free to ask me and we’ll fix it. 
In all the steps, consider real-life answers and statistics, include your sources of information (in hyperlink format, if using web tools) and reasonings, and, when it comes up, use USD as the standard currency, please.


Step 1: General market
Research the Data and the AI markets for questions like:
    • How’s each market’s prospect for the future globally, and in Brazil? Specially regarding job openings, possible salary range (in USD), level of difficulty for entrance, companies’ needs for this kind of service and professional
    • Are these markets truly merging? Explain your reasoning, please.
    • What skills does one need to work in this area (or these areas, in case they’re not merging)? Including hard and soft skills
    • What are the most common employment statuses (full-time, part-time, fixed-term contract, temporary, seasonal, or casual) for these professionals? 


Step 2: Studies
Based on the answers from Step 1, research courses and programs that would be helpful for someone changing to this career path from a Banking background, with some knowledge in web development, relational databases, Microsoft Office and Google Workspace. 

Here, we’re going to split into three sub-steps: 

    Sub-step 1: General content
        o Search for helpful subjects (e.g. Python, SQL)
        o List them in order of development (from beginner to advanced)
    
    Sub-step 2: Specific
        o Search for actual courses and programs that fit into the subjects from Sub-step 1
        o Show, at least, 2 options for each subject and give preference to platforms and programs that offer a fully structured learning path (like Alura, for example)
        o List them in order of development (from beginner to advanced)
        o Preferences: remote, in English and/or Portuguese, from widely recognized schools/platforms/teachers
    
    Sub-step 3: Financial
        o Create a simple table with cost estimates for each one and the total


Step 3: Timeline
Based on the previous steps’ answers, create a full-path timeline, please. It should account for some studying-only time at first (as preparation to join the market), and 20 hours of study per week (which might gradually decrease as the acquired experience becomes more important than heavy studying).
The timeline could be something like:

Phase 1 – Study
Phase 2 – Study + job search
Phase 3 – Study + Work (more study than experience)
Phase 4 – Work + Study (more experience than study)

With a Gantt-style breakdown at the end, please.

Devido ao limite de caracteres do fórum, pedi ao ChatGPT uma versão resumida da resposta gerada:

Step 1 – Market Overview:

Data and AI job markets are growing globally, with AI-related roles expected to increase by over 35% annually.

In Brazil, demand for Data Analysts and AI specialists is rising, though salary ranges are still 40–50% below the U.S. average (USD 45k–100k/year).

These fields are merging fast through automation, data-driven decision-making, and generative AI tools.



Step 2 – Studies:

| Subject | Suggested Courses | Platforms | Est. Cost (USD) | Level |
| Python | Python for Data Science | Alura, Coursera | 30–50 | Beginner |
| SQL     | SQL for Data Analysis     | Alura, Google     | 30–60 | Intermediate |
| Power BI | Data Visualization | Alura, Udemy | 40–70 | Intermediate |
| GenAI Tools | Prompt Engineering & AI Integration | Coursera, DeepLearning.AI | 0–80 | Advanced |
                

Step 3 – Timeline:

| Phase | Duration | Focus | Weekly Hours |
| 1. Study |4–6 months | Foundations (Python, SQL) | 20h |
| 2. Study + Job Search     | 2 months     | Power BI, AI basics     | 15h |
| 3. Study + Work     | 6 months     | GenAI projects     | 10h |
| 4. Work + Study     | Continuous     | Upskilling     | 5h |



Gantt Summary:
▣ Phase 1 → Foundations → 6 months
▣ Phase 2 → Transition → 2 months
▣ Phase 3 → Integration → 6 months
▣ Phase 4 → Maintenance → ongoing

Nesta atividade, pude colocar em prática alguns dos conhecimentos adquiridos no decorrer das aulas, como:

• Few-Shot e Chain-of-Thought Prompting
• Estruturar o prompt com instruções claras, organizadas em etapas
• Definir o papel a ser seguido pelo modelo, a fim de obter respostas mais consistentes com o esperado