ディーパック

Do your duty and dissappear!

[Uday👨‍💻 @uday_devops]

📈 Data Analyst

Stage 1: Excel
Stage 2: SQL
Stage 3: Python
Stage 4: Statistics
Stage 5: Data Cleaning
Stage 6: Power BI / Tableau
Stage 7: Data Visualization
Stage 8: Dashboards
Stage 9: Business Analytics
Stage 10: Storytelling with Data
Stage 11: Build Portfolio Projects
Stage 12: Analyze Real-World Datasets

🏆 Data Analyst unlocked ��

[ディーパック @dpk0106]

@Sanjeeb2412 Sehwag

[ディーパック @dpk0106]

@MohdSha66621889 Allan Lamb

[ディーパック @dpk0106]

@funnyman00167 Somewhere between a perfect parabola and hyperbola. IYKYK 🥵

[ディーパック @dpk0106]

@xzx_slipknot Mitch Johnson

[ディーパック @dpk0106]

@BabajiSutar3 Aiith Wadekar?

[ディーパック @dpk0106]

@OversLife Vaas

[ディーパック @dpk0106]

@balaji25_t Kukkalu morgutuntayi brother. You're doing good job and keep continuing.

[Becky_Data Analyst @BukolaSalau2]

What's the difference?

Data Analyst → Answers "What happened?" by analyzing historical data and identifying trends.

Business Intelligence (BI) Analyst → Builds dashboards and reports that help businesses monitor KPIs and make data-driven decisions.

Business Analyst → Identifies business problems, gathers requirements, and recommends process improvements.

Data Scientist → Uses statistics and machine learning to predict future outcomes and solve complex problems.

Different roles. Different goals.

Save this if you're exploring a career in data.

[ディーパック @dpk0106]

@Sanjeeb2412 Errapalli Prasanna, forms the major trio of spin bowling Prasanna, Chandrashekhar, Bishan Bedi ji.

[ディーパック @dpk0106]

@bagga_buntyy1 Roger Binny

[ディーパック @dpk0106]

@NewsAlgebraIND Bhai, then shut your mouth because you're living in a Hindu majority country, accept the fact and law as well.

[ディーパック @dpk0106]

@BabajiSutar3 Ajay Ratra

[ディーパック @dpk0106]

@rajatjain1107 Interested

[Rajat Jain - Data Analyst @rajatjain1107]

Hi I am doing Free Live Power BI Workshop for my Followers so if you want to learn analytics with me
Comment Interested to get link and follow me

[Shiva🧘 @Dagam_offl]

Must watch for anyone trying to change jobs

[Rajat Jain - Data Analyst @rajatjain1107]

Non IT to Data Analytics Roadmap
Save this roadmap

[Victor Ugwu | Excel Automation for Logistics @_VictorUgwu]

LinkedIn Profile Optimization Prompt for Data Analysts

Act as a LinkedIn Top Voice, personal branding strategist, recruiter, and hiring manager who has reviewed thousands of data analyst profiles.

Your task is to optimize my LinkedIn profile so it attracts recruiters, hiring managers, founders, and potential clients.

About Me

Before writing anything, ask me questions about:

• My experience level
• My industry
• My technical skills
• My projects
• My certifications
• My achievements
• My target role
• My location
• My preferred industries
• Whether I want a full time job, freelance clients, or both

Do not write anything until you have enough information.

Optimise Every Section

Help me rewrite and improve:

1. Profile Headline

Create 10 headline options using relevant keywords that recruiters search for.

2. About Section

Write a compelling story that includes:

Who I help

What problems I solve

My technical expertise

Business impact

Tools I use

Industries I understand

Call to action

Make it conversational, confident, and easy to read.

3. Featured Section

Recommend what should appear in my Featured section and explain why.

4. Experience

Rewrite each experience entry using measurable achievements instead of responsibilities.

Use this format:

Challenge

Action

Result

Include numbers wherever possible.

5. Projects

Rewrite every project like a business case study.

Include:

Business problem

Dataset

Tools used

Analysis performed

Key insights

Recommendations

Business impact

Technologies

Keywords

6. Skills

Recommend the top 50 skills I should add based on my target role.

Separate them into:

Technical Skills

Business Skills

Industry Knowledge

AI Skills

Soft Skills

7. Certifications

Recommend certifications that strengthen my credibility.

Prioritize Microsoft, Google, IBM, SQL, Power BI, Excel, Python, Tableau, Fabric, Azure, AWS, and AI certifications.

8. Keywords

Generate the top recruiter keywords that should naturally appear throughout my profile.

9. Creator Mode Content Strategy

Create a 30 day LinkedIn content plan covering:

Educational posts

Storytelling posts

Career lessons

Project breakdowns

Excel tips

Power BI tips

SQL tips

Python tips

AI for Data Analytics

Case studies

Industry insights

Personal branding

Job search advice

10. Banner Recommendation

Suggest text, colours, layout, and a strong value proposition for my LinkedIn banner.

11. Profile Photo Tips

Provide recommendations that improve credibility and professionalism.

12. Open to Work Strategy

Explain how to configure my profile so recruiters can easily discover me.

13. SEO Audit

Review every section and identify:

Missing keywords

Weak descriptions

Areas to improve

Recruiter red flags

Missed opportunities

14. Networking Strategy

Create a strategy to gain over 500 targeted connections in my niche.

Include:

Who to connect with

Connection request templates

Follow up messages

Relationship building strategy

15. LinkedIn Growth Strategy

Create a practical 90 day plan to help me:

Increase profile views

Receive more recruiter messages

Generate inbound opportunities

Build authority in data analytics

Grow followers

Position myself as an expert

Writing Style

Use a professional but human tone.

Avoid buzzwords and clichés.

Focus on business impact rather than listing tools.

Write naturally so every section feels authentic.

Use recruiter friendly keywords without keyword stuffing.

Every recommendation should include an explanation of why it improves visibility, credibility, and search ranking on LinkedIn.

[Becky_Data Analyst @BukolaSalau2]

10 Habits of Great Data Analysts

• They ask questions
• They validate data
• They document work
• They communicate clearly
• They automate repetitive tasks
• They understand the business
• They keep learning
• They test assumptions
• They stay curious
• They think critically

[Becky_Data Analyst @BukolaSalau2]

Data Tip

Learn the difference between Metrics and KPIs.

A Metric is any measurable value that tells you what's happening.

Examples: • Total Sales • Number of Customers • Website Visits • Revenue

A KPI (Key Performance Indicator) is a metric that's directly tied to a specific business objective.

Examples: • Increase monthly sales by 15% • Reduce customer churn to below 5% • Achieve 95% customer satisfaction

Think of it this way:
📌 Metrics measure performance.
📌 KPIs measure progress toward a business goal.

Every KPI is a metric, but not every metric is important enough to be a KPI.

Example:
A company tracks:
Website Visits = Metric
Conversion Rate = KPI (because the goal is to increase sales)

Always ask yourself:
"How does this metric help the business achieve its goal?"

If it doesn't, it's probably just a metric, not a KPI.

Save this for your next Data Analytics interview.