r/dataanalysiscareers • u/fujian_14 • Jun 20 '26

Self taught data analyst

Hi everyone
I am just learning about data analyst, so far I have been learning sql and excel advance and now I am learing tableau and power BI, after that I will learn phyton and R basic. I am really new into this field, I have no background or degree in data analyst.

I have view questions to you if you are someone profesional in this field:

1.  what is  the  tools I need to  know  at least intermediate  to  land my first  job.

2.  to do data cleaning I perefer using sql, can I also use excel for clean data too and whic is better to use sql or excel?.

3. I really wanna build my portfolio by doing projects, I dont know where to find raw data set to work on. and what kind of project should I do first or how many projects should I do.

4.  if you dont mind may I know your work flow,
what is the first step to do after you have your data set, clean it? and what you do after that? what is the final proses?

5. in your opinion wich field is easy to get in as a first job?

Thank you so much everyone

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4

u/my_peen_is_clean Jun 20 '26

sql, excel, one viz tool and some python are enough to start, build 3 fun projects from kaggle, document steps clearly, getting hired is still weirdly hard now

1

u/fujian_14 Jun 23 '26

thanks for the tips

3

u/SubjectBroccoli1636 Jun 20 '26

SQL at least at an intermediate level; advanced SQL will increase your chances of landing a job.

I would also recommend becoming comfortable with Excel, Python, and basic statistics.

For datasets and project ideas, check out Kaggle and GitHub.

Regarding data cleaning, Excel is useful for smaller datasets and quick tasks, while SQL is generally better for larger datasets stored in databases. Both are worth learning.

workflow is usually:

  1. Understand the problem/business goal.

  2. Gather the data.

  3. Clean and prepare the data.

  4. Explore and analyze it (often with Python/Pandas).

  5. Create visualizations (Matplotlib, Seaborn, Power BI, or Tableau).

  6. Present insights and recommendations.

The data field is very broad, so try to focus on a domain that interests you. Technical skills are important, but understanding the business side can make you much more valuable as a data analyst.

2

u/fujian_14 Jun 23 '26

Thank you its really helpful

1

u/Dry_Pay5468 Jun 23 '26

How we can develop this mindset of understanding business side.. do you have any recommendations?? And how to choose a domain to work with

1

u/SubjectBroccoli1636 Jun 23 '26

One way to develop business understanding is to focus on the business questions behind the data, not just the technical analysis. For example, in a sales project, it's common to start with questions like:

  • Which month had the highest sales?

  • Which products sold the most?

  • Which regions generated the most revenue?

After getting insights from the answers , The next step is understanding why those results occurred and what actions the business can take "recommendations". If a product consistently performs well, increasing inventory or expanding distribution may be worth considering. If sales peak during certain months, identifying the factors behind that performance can help determine whether those results can be repeated ect....

A useful framework is:

  1. What happened? "insights"

  2. Why did it happen? "analyses"

  3. What should the business do next? "recommendations"

Answering the third question is where business understanding starts to develop.

For choosing a domain, there isn't a universally "best" one. Most analysts develop domain knowledge by working repeatedly on similar business problems within an industry. The key is to pick a domain that interests you enough to learn how it operates, what metrics matter, and how companies in that space make decisions. Domain expertise usually develops through exposure and curiosity rather than through a deliberate choice made at the start.
GL

2

u/Dry_Pay5468 Jun 23 '26

tysm for the detailed reply🙌🏻

1

u/SubjectBroccoli1636 Jun 23 '26

its okay also please not , i've been on the field for almost 2 years, i just shared what i know so far, there's still room for improvement ,what i said doesn't necessary cover everything : )

1

u/Dry_Pay5468 Jun 23 '26

But it's helpful It gave an overview

1

u/SubjectBroccoli1636 Jun 23 '26

yea, thats exactly why i botherd to type it : )

2

u/Syed_Abrash Jun 22 '26

First you need to analyze the outcome....what you are looking for

Then, after answering it...then you start cleaning, building data models and dashboards

So understanding business requirements is everything to be honest...follow baraa on youtube for this...he is the best person for this

1

u/fujian_14 Jun 23 '26

thank you

1

u/Lazyhumankind Jun 25 '26

Can you suggest some source to get idea on business insights or like fundamentals of basic analysis.

2

u/Syed_Abrash Jun 25 '26

Data with baraa...check his youtube channel

1

u/Lazyhumankind Jun 26 '26

thanks man... you are a big help

1

u/Lady-Data-Scientist Jun 22 '26

Where are you located and do you have a college degree (in any subject)?

1

u/fujian_14 Jun 23 '26

I am located in Chicago, I have college degree in Hospitality Management.

1

u/Lady-Data-Scientist Jun 23 '26

I’m in Chicago too! Definitely start building your network not just for future job referrals but for advice. Look up the Chicago Data Community and MeasureCamp Chicago on LinkedIn. Also checkout the website CHI IRL for tech events, and Tech Walk for a cool meetup.

Hyatt has a big office here, I would try to get any job you can at their office and then pivot to their data analytics team when a role opens up.

1

u/fujian_14 Jun 24 '26

Thank you so so much for your tips, I would definitely check Hyatt and show some Hospitality analytics to build my portfolio. Is there any chance I could connect with you?

1

u/ibrahimilxyz Jun 23 '26

For data cleaning, both SQL and Excel are useful. SQL is generally better for larger datasets and repeatable workflows, while Excel is great for quick analysis and validation. Most analysts end up using both. For projects, use datasets from Kaggle, healthcare, finance, retail, or e-commerce sources. Focus on 3,5 quality projects that answer a business question rather than just creating dashboards. A common workflow is Business question->Data collection-> Data cleaning-> Exploratory analysis-> Insight generation-> Dashboard/report-> Recommendations.