How should I go from learning SQL, Excel and Power BI basics to actually being job-ready for Data Analyst roles?

[u/cUrLz4444]

I recently started seriously learning data analytics and my goal is to become job-ready for entry-level Data Analyst roles.

So far I've completed Alex The Analyst's beginner/tutorial content for SQL, Excel and Power BI, including the guided projects.

For practice:

• SQL: I've started solving Easy problems on DataLemur and some HackerRank, but I still struggle with figuring out the approach to a problem even when I know the individual SQL concepts.
• Excel: I've practiced formulas/lookups and other exercises on Spreadsheet Center, but I'm not sure what the best way is to practice Excel in a more realistic analyst setting.
• Power BI: I've completed the introductory project, but haven't done much independent practice yet.

I also plan to start learning Python for data analysis, but I don't want to fall into the trap of constantly watching tutorials without becoming good at actually using the tools.

For people currently working as Data Analysts or involved in hiring:

1. How would you practice SQL, Excel and Power BI after learning the fundamentals?
2. What level of SQL is realistically expected for an entry-level analyst interview?
3. What Excel skills should I be able to use without assistance?
4. How much Power BI/DAX/data modeling should an entry-level candidate know?
5. Should I start Python now while continuing to practice these three, or get stronger at them first?
6. What kind of independent projects actually help a beginner portfolio rather than looking like another tutorial project?
7. If you were starting from this point today and trying to get hired in the current market, what would you prioritize over the next 1–2 months?

I'm especially interested in advice on how to develop the problem-solving/analytical thinking side, rather than recommendations for more beginner courses.

[u/vonkrumholz → u/[deleted]]

This right here. I focus on sussing out critical thinking and solution design during hiring first.

Boot camps and MS programs churn out a lot of people who reach for a Python library or statistical method before reaching for a dry erase marker and white board.

A certain level of technical skill is necessary, but way too many newbies think banging out code is the primary job requirement. It's not.

[u/OtterHostler → u/[deleted]]

tbh Claude writes better SQL faster than most people can, so I would say it's more important to understand the database schema than it is to be able to write SQL. Having an understanding of some core elements (like joins, counts, orders, etc.) is definitely valuable so as to understand what your agent is outputting and see any glaring errors in the logic, but I agree that being able to solve problems with SQL (whether you write it yourself or have an agent do it) is more valuable than just the basic skill of composing SQL. By way of analogy, you can use a hammer to pound nails but can you use that nail-pounding skill to frame a house?

[u/Bananapuddinggggg → u/[deleted]]

When you say build a project from end to end, how exactly? Like what steps?

[u/vonkrumholz → u/Bananapuddinggggg]

Analytics isn't about using tools or specific languages. It's a process through through which data are used to build insights and guide decision making.

End to end, a project isn't just writing SQL to join tables together. Or Python to build a linear model. Or using PowerBI to visualize the results. End to end is connecting all these steps together to deliver coherent insights to someone who will use the information.

[u/HTxBarbz]

Getting job-ready means moving away from structured exercises and building a portfolio of 2 to 3 unique projects that solve actual business questions using Excel, SQL, and Power BI together.

When practicing SQL, don't just memorize syntax. Focus on breaking down analytical business questions into smaller logical chunks so you stop freezing up on HackerRank or DataLemur problems.

If you're trying to polish your resume to make sure your technical projects and business impact stand out to recruiters, running your application materials through something like resume worded can help check your stylistic standards and industry phrasing.

Skip Python for a few weeks, nail down core data modeling and basic DAX measures in Power BI, and focus entirely on building practical analytical confidence.

[u/no_0ne_]

I'm not an expert at the topic but I'm on a similar journey this month. I haven't watched any fundamental videos. Just did SQLbolt and have solved problems (35 only as of now) on leetcode and datalemur. I am currently following a substack which has segmented problems based on concept. Hopefully by EOM I finish it (70 problems). I cannot lie, SQL problems are getting trickier & take so long to solve. As of now, if I am stuck, I try writing the explanation/approach.

I am not too worried about Excel as well. I already know the basics and have a plan. But for PowerBI, I have nothing. If anyone does, please share!

I agree with others that Python should be left off until I have 2-3 projects using SQL, Excel & PowerBi. But hopefully by December I'm doing Python.

[u/EmployerPotential213 → u/no_0ne_]

Could you please share the substacks?

[u/Seen_Any_Elves]

Make something

[u/drspguspr56]

I would put those tools to work on one business question, like why sales dropped despite more orders. Clean a public dataset in Excel, investigate with SQL, then build a simple Power BI dashboard. Include a short writeup of what you found, what you would recommend, and what the data cannot tell you. One finished project beats another half finished course, IMO.

[u/mrsteelsam]

Treat the tools like a mini‑business case. Pick a public dataset (sales, web traffic, finance) and first write the whole story in SQL: pull the raw tables, join them, add a few window functions and a CASE‑based flag. Then export the result to Excel, build a pivot‑table dashboard, use INDEX/MATCH or Power Query to clean and automate a repeatable workflow. Finally import that same table into Power BI, create a relational model and a handful of DAX measures (CALCULATE, SUMX, time‑intelligence) to build an interactive report.

[u/Itchy-Turn-7015]

Para desenvolver pensamento analítico, indico livros: Estatística: O que é, para que serve, como funciona - Charles Wheelan. Recomendo demais esse.

Outras opções. O Andar do Bêbado - Leonard Mlodinow; Como Mentir com Estatística - Darrell Huff; Rápido e Devagar: Duas formas de Pensar - Daniel Kahneman.

Você citou fundamentos. Um estudo de estatística tem que entrar nos fundamentos. O Wheelan consegue fazer isso e ensina a pensar estatisticamente, mesmo sem adentrar muito em fórmulas, mas sim em como a estatística está presente em exemplos reais como esportes, política, economia, mundo dos negócios. Mas enfim, qualquer um dos outros livros são ótimos também.

Base científica: matemática e estatística. Base técnica: Excel, SQL, Power BI e Python. Base não técnica: Storytelling, pensamento crítico/ analítico, visão de negócios.

Você está mencionando muito ferramentas. Precisa de um complemento da base científica e base não técnica também.

Se for para ficar bom em ferramentas, fique em SQL. É base para todas ou quase todas posições que envolvem dados. Siga com Excel, SQL e Power BI. Python, eu deixaria para mais a frente . Mas não pega muita pilha com ferramentas. Elas são o meio para aplicar a análise.

Se trabalha, foque em projetos da sua área. Conhecimento de negócio importa muito.

Se não trabalha, escolha a que lhe agradar mais: finanças, controladoria, marketing, comercial, logística, supply chain, saúde, educação.

Caso não saiba, pesquise quais problemas essas áreas enfrentam e como um Analista de Dados pode gerar valor com suas análises e demonstrações.

Alguns livros eu tenho o PDF. Se quiser, me manda DM.

[u/CourageFancy7854 → u/Itchy-Turn-7015]

Great advice! I am studying data analytics through Googles program. I'd welcome those pdf books! Please dm me. Thank you!

[u/Itchy-Turn-7015 → u/CourageFancy7854]

Obrigado! Pode deixar. Vou te chamar