This is the difference between beginners analysts and senior data analysts #dataanalytics #dataanalyst

You're not learning—you’re repeating.⁠.Solve your own problems, and the skills will stick.⁠.#DataProjects #SQL #DataCleaning #AnalyticsTips

How to crack the data analyst interview #dataanalytics #dataanalyst

Common Data Analyst mistakes. #dataanalytics #dataanalyst

Ask the right questions. Spot the gaps. Propose solutions.⁠.That’s how you close clients.⁠.#DataFreelancing #BusinessAnalytics #DataScienceTips #ClientPitch

—.Found this helpful? ♻️ Repost to help someone start their journey #DataAnalytics #CareerChange #DataSkills #SQL.

Difference 3: Course strategy .Beginner: Bookmarks 47 courses, completes zero projects. Pro: Picks ONE course or program, builds projects while learning.

Difference 2: Learning approach .Beginner: "I need to learn everything before applying" .Pro: "I'll learn the basics of SQL and Excel, and I'll learn what I need based on what job I get".

Difference 1: Skill expectations .Beginner: Sees "Must know SQL, Python, R, Tableau, Power BI, Excel, Machine Learning" and panics. Pro: Realizes most analysts use 2-3 tools daily, master others as needed.

90% of aspiring data analysts quit before they start.(here are 3 reasons why).

How to fix bad data visualization.#dataanalytics #dataanalyst

I used real-time tweets to map positive vs. negative sentiment—by location.⁠.Data doesn’t lie.⁠.#SentimentAnalysis #DataAnalytics #TwitterData

These are some of the BEST resources to learn SQL.#learnsql #sql #dataanalytics #dataanalyst

Skip the leetcode grind—show them a project they want to implement. ⁠.Let your work speak for you.⁠.#SQLJobs #PythonForDataScience #DataCareerTips

-----.If you found this helpful, repost ♻️ to help another analyst out. #dataanalytics #dataanalyst.

That I could take raw data, ask real questions, and communicate findings clearly. The hiring manager didn't care about my code's elegance. They cared that I could solve their actual business problems.

One was a sentiment analysis project with twitter data for different seltzer brands. The other was a basic dashboard. But you know what they showed?.

but, companies are hiring analysts who build projects that drive impact. So when does a portfolio become "good enough"?. Here's my story: I landed my first data role with exactly 2 projects on my resume. Not 10. Not even 5. Two.

Here's what I see happening:.1) You're watching videos online and copying the exact project;.2) You are using the same kaggle datasets everyone uses;.3) Researching "What makes a portfolio stand out?".

When is your data portfolio "good enough" to start applying?. Because I'm watching very talented analysts spend tons of time on their portfolios without ACTUALLY applying.