I help entry-level, non-technical professionals get their first data analyst role | Data Analyst @RBC | Data Career Coach

I help immigrants and career-switchers land analyst roles—without burning out on certificates.

By day I build decision-grade analytics (currently at Autodesk). By night I run Data Career Compass, where we treat your job search like a product launch:
Market → Positioning → Portfolio → Distribution → Interviews.

My POV:
Most candidates try to add more tools. Winners learn to package their value. Your portfolio isn’t a gallery; it’s a sales page for your judgment.

Receipts:

→ Professionals land interviews by aligning projects to business outcomes (not toy datasets).

→ My frameworks have been used across finance, tech, and education to translate experience into results.

→ Built data products and controls in regulated environments (RBC Capital Markets) that leaders rely on daily.


What you’ll get here:

→ Weekly breakdowns of real projects (SQL, Python, dbt, DuckDB, Looker/Tableau/Power BI).

→ Storytelling patterns that turn insights into action.

→ Templates that remove friction: portfolio briefs, stakeholder one-pagers, interview scorecards.

Coming April:

I’m launching a coaching program designed for mid-level and senior professionals ready to transition into analytics.

This isn’t an entry-level bootcamp—it’s a career accelerator that helps you reposition your existing experience, communicate business value, and step confidently into data-driven roles.

Work with me:

↳ Cohorts + 1:1 coaching → DM “HIRED”

↳ January program for mid/senior professionals → DM “APRIL” for early access

↳ Guest workshops for teams and communities → DM “WORKSHOP”


I build analysts who think like product managers and communicate like consultants.

Peter Ndiforchu

Every data analyst has lived through this meeting.

Dashboard says: $1.2M
Finance says: $1.1M

Meeting topic for the next 45 minutes:

“Where did the $100K go?”

First theory:
Maybe a filtering issue.

Second theory:
Currency conversion.

Third theory:
Time zone mismatch.

Fourth theory:
Someone forgot to refresh the data.

Meanwhile the analyst is quietly thinking:

“Please don’t be a duplicated join. Please don’t be a duplicated join. Please don’t be a duplicated join.”

45 minutes later…

Root cause:

Someone exported the numbers to Excel
…and manually deleted a row “that looked wrong.”

New meeting scheduled:

“Aligning reporting methodology across teams.”

Welcome to data analytics.

Where 90% of the job is not finding insights…

It’s explaining why two numbers that should match… don’t.

If you’ve ever spent an hour chasing a mysterious variance in a meeting, you’re officially part of the club. 📊😅

Helping Non-Technical Professionals Build Data-Driven Careers That Travel Globally | Data Career Strategist | Senior Data Analyst @ Autodesk

Data Scientist | 150K LinkedIn | BCBS Of South Carolina | SQL | Python | AWS | ML | Featured on Times Square, Favikon, Fox, NBC | MS in Data Science at UConn | Proven record in driving insights and predictive analytics |

Lead Data Analyst/Data Scientist at Blue Cross Blue Shield of South Carolina, driving revenue growth and operational efficiency through predictive analytics, experimentation, and machine learning.

I build end-to-end data solutions that influence executive decision-making, from churn prediction models and A/B testing frameworks to executive dashboards tracking multimillion-dollar portfolios. My work spans the full ML lifecycle: data pipelines, feature engineering, model deployment, and stakeholder communication.

What Sets Me Apart:

I don't just analyze dat, I translate it into business strategy. Whether it's identifying high-risk customer segments, optimizing conversion funnels through rigorous experimentation, or presenting insights to C-suite leadership, I bridge the gap between technical depth and business impact.

Beyond the 9-to-5:

I'm on a mission to democratize data science education. With 220K+ professionals in my LinkedIn community and 20K+ newsletter subscribers, I share battle-tested insights on SQL, Python, machine learning, and breaking into data science. My content focuses on real-world applications that actually matter in industry, not academic theory.

I've also been recognized nationally:
- Featured in Times Square, Fox, NBC, and major media outlets for career journey
- 3rd Place, Travelers NESS Statathon
- 1st Place, Well4Tech Health Hackathon  
- Favikon recognition for data community contributions

My Technical Stack:

Python (Pandas, Scikit-Learn, XGBoost), SQL, PySpark, R | AWS (S3, Glue, Athena, SageMaker), Azure, Snowflake, Databricks | Tableau, Power BI, Looker | A/B Testing, Bayesian Statistics, Predictive Modeling

Open to discussing data strategy, collaboration, or your journey into data science.

Let's connect: saikumarbysani13@gmail.com

Venkata Naga Sai Kumar Bysani

A close friend I’ve known for 9 years was just laid off.

She’s on H-1B and has 55 days to find a new role or leave the country.

The job market right now is brutal. Layoffs everywhere. Hiring freezes. Hundreds of applicants for every role. And for people on work visas, this isn’t just stressful, it’s existential.

I’m sharing this on behalf of Bhavana Gurram

𝐇𝐞𝐫𝐞’𝐬 𝐰𝐡𝐚𝐭 𝐬𝐡𝐞’𝐬 𝐝𝐨𝐧𝐞:
↳ Software Developer at Paycom - built tax filing systems across all 50 U.S. states and 4 international regions
↳ Reduced manual effort by 67% through automation during peak filing periods
↳ Previously at Deloitte - created automation saving 300+ hours/month and built notification systems handling 400k+ messages monthly
↳ Master’s in CS from UT Dallas (3.91 GPA)
↳ Bachelor’s from NIT India - cleared JEE Mains (top 0.01%)

𝐓𝐞𝐜𝐡 𝐬𝐭𝐚𝐜𝐤: Java, C#, Python, React, Spring Boot, .NET, MySQL

She’s looking for Full Stack Developer or Software Developer roles.

I’ve known Bhavana for almost a decade. She’s not just talented, she’s the kind of person who makes teams better.

If this resonates and you’re in a position to help, please reach out or pass this along.

And if not, even a simple like, comment, or repost helps more than you think.

𝐂𝐨𝐧𝐭𝐚𝐜𝐭 𝐝𝐞𝐭𝐚𝐢𝐥𝐬:
↳ LinkedIn: https://lnkd.in/dqmAsrHw
↳ Email: bhavanagurram14@gmail.com
↳ Portfolio: https://lnkd.in/d5r_3SBe

55 days.
Let’s help her find the right team.

♻️ Please repost, it could truly change everything.

Data Scientist | 200K+ Data Community | 3+ years in Predictive Analytics, Experimentation & Business Impact | Featured on Times Square, Fox, NBC

Resume

16 Data Analytics Concepts Every Analyst Should Know in 2026


If you want to break into data analytics (or level up this year), learn these concepts:

𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐬:
1 SQL Joins and Window Functions
↳ https://lnkd.in/dapaFjWv

2 Data Cleaning with Python (Pandas)
↳ https://lnkd.in/dzzzFBvM

3 Exploratory Data Analysis (EDA)
↳ https://lnkd.in/dPhTBNm3

4 Statistical Significance & Hypothesis Testing
↳ https://lnkd.in/d-nbprUd

𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 & 𝐒𝐭𝐨𝐫𝐲𝐭𝐞𝐥𝐥𝐢𝐧𝐠:
5 Dashboard Design Principles
↳ https://lnkd.in/dAG2iAFj

6 Data Storytelling for Stakeholders
↳ https://lnkd.in/dXa6BmtU

7 Choosing the Right Chart Type
↳ https://lnkd.in/dWqGE9ed

𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐈𝐦𝐩𝐚𝐜𝐭:
8 Defining KPIs and Metrics That Matter
↳ https://lnkd.in/dY85xhkB

9 Cohort Analysis
↳ https://lnkd.in/dqcPRH-v

10 Funnel Analysis
↳ https://lnkd.in/d2qgN2rm

11 A/B Testing & Experimentation
↳ https://lnkd.in/di4p3EKg

𝐌𝐨𝐝𝐞𝐫𝐧 𝐓𝐨𝐨𝐥𝐬 & 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞:
12 dbt for Data Transformation
↳ https://lnkd.in/d7bszA99

13 Cloud Data Warehouses (Snowflake, BigQuery)
↳ https://lnkd.in/dh3A-QPG

14 Version Control with Git
↳ https://lnkd.in/dGucMpYm

𝐀𝐈-𝐏𝐨𝐰𝐞𝐫𝐞𝐝 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬:
15 Prompt Engineering for Data Analysis
↳ https://lnkd.in/df6pbWxB

16 Using LLMs as a Thinking Partner
↳ https://lnkd.in/dKW_vgMt

𝐌𝐲 𝟐 𝐜𝐞𝐧𝐭𝐬:
SQL and Python are still non-negotiable. But in 2026, the analysts getting hired know how to combine technical skills with AI tools and business thinking.

Master the fundamentals. Then stack the modern skills on top.

What else should make this list? 👇

Get 150+ real data analyst interview questions with solutions from actual interviews at top companies: https://lnkd.in/dyzXwfVp

♻️ Repost to help someone starting their data analytics journey

𝐏.𝐒. I share job search tips and insights on data analytics & data science in my free newsletter. Join 21,000+ readers here → https://lnkd.in/dUfe4Ac6

Stop collecting certificates. Start building projects that actually get interviews.

10 videos. 1.4M views. Everything from SQL to Python to building your actual portfolio website.

Alex built these projects specifically for beginners who need portfolio pieces that actually get interviews.

𝐓𝐡𝐞 𝐥𝐢𝐧𝐞𝐮𝐩:

• SQL Data Exploration with COVID data (1h 17min)
• Tableau dashboard creation from scratch (52min)
• Data cleaning in SQL - the skill everyone needs (54min)
• Python correlation analysis for real datasets (1h)
• Portfolio website setup - completely free (35min)
• Amazon web scraping project (47min)
• Full Excel project walkthrough (40min)
• Power BI guided project (42min)
• Crypto API automation (51min)

Follow along, build it yourself, customize with your own data.

𝐖𝐡𝐲 𝐭𝐡𝐢𝐬 𝐰𝐨𝐫𝐤𝐬:
- Real-world data problems, not toy datasets
- Tools that companies actually use
- Projects you can explain in interviews
- Code you can show on GitHub

I've seen hundreds of portfolios. The ones that get callbacks show actual data cleaning, analysis, and visualization skills. Not just certificates.

𝐀𝐥𝐞𝐱 covers SQL → Python → Tableau → Excel → Power BI. That's the exact stack most data analyst roles require.

Start with the SQL project. It's the foundation everyone needs.

♻️ Save it for later or share it with someone who might find it helpful!

𝐏.𝐒. I share job search tips and insights on data analytics & data science in my free newsletter. Join 23,000+ readers here → https://lnkd.in/dUfe4Ac6

If you're preparing for a data analyst/data science role (in 1 month or less), you must read these case studies!


1. 𝐍𝐞𝐭𝐟𝐥𝐢𝐱'𝐬 𝐀/𝐁 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦
→ Run 1000+ experiments simultaneously
→ Make data-driven decisions for 230M+ subscribers
→ Statistical rigor with automated analysis pipelines

𝐂𝐚𝐬𝐞 𝐬𝐭𝐮𝐝𝐲 𝐥𝐢𝐧𝐤: https://lnkd.in/dsBsHNqT

2. 𝐏𝐢𝐧𝐭𝐞𝐫𝐞𝐬𝐭'𝐬 𝐕𝐢𝐬𝐮𝐚𝐥 𝐒𝐞𝐚𝐫𝐜𝐡 𝐄𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧
→ Built SearchSage to map queries directly to their PinSage embeddings
→ Processes 250M+ visual searches monthly with sub-second latency
→ Increased engagement by 42% for related searches

𝐂𝐚𝐬𝐞 𝐬𝐭𝐮𝐝𝐲 𝐥𝐢𝐧𝐤: https://lnkd.in/dFxS_RWh

3. 𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧'𝐬 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞
→ Powers 800M+ member profiles with Pro-ML platform
→ Frame feature store ensures consistency across training & serving
→ Photon-Connect automates advanced AI model creation at scale

𝐂𝐚𝐬𝐞 𝐬𝐭𝐮𝐝𝐲 𝐥𝐢𝐧𝐤: https://lnkd.in/d27PeXEY

4. 𝐒𝐥𝐚𝐜𝐤'𝐬 𝐑𝐞𝐚𝐥-𝐓𝐢𝐦𝐞 𝐌𝐞𝐬𝐬𝐚𝐠𝐢𝐧𝐠 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞
→ Handles 2B+ messages daily across millions of channels
→ Achieves <500ms global delivery using stateful Channel Servers
→ Scales to 16M channels per host with consistent hashing

𝐂𝐚𝐬𝐞 𝐬𝐭𝐮𝐝𝐲 𝐥𝐢𝐧𝐤: https://lnkd.in/d8ZYqFzX

5. 𝐋𝐲𝐟𝐭'𝐬 𝐃𝐲𝐧𝐚𝐦𝐢𝐜 𝐏𝐫𝐢𝐜𝐢𝐧𝐠 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦
→ Uses online reinforcement learning for real-time price optimization
→ Balances supply/demand while maintaining service reliability
→ Improved ETAs by 25% through marketplace optimization

𝐂𝐚𝐬𝐞 𝐬𝐭𝐮𝐝𝐲 𝐥𝐢𝐧𝐤: https://lnkd.in/deienatS

𝐇𝐞𝐫𝐞'𝐬 𝐰𝐡𝐚𝐭 𝐲𝐨𝐮 𝐬𝐡𝐨𝐮𝐥𝐝 𝐟𝐨𝐜𝐮𝐬 𝐨𝐧:

• What metrics and KPIs drove business decisions
• How they handled data quality and governance
• Statistical methods and ML algorithms chosen
• Tools and tech stack for specific use cases

Don't get distracted by fancy algorithms; start with the business problem.

Most successful analytics projects solved core issues with clear metrics and thoughtful experimentation.

Get 150+ real data analyst interview questions with solutions from actual interviews at top companies: https://lnkd.in/dyzXwfVp

♻️ Save it for later or share it with someone who might find it helpful!

𝐏.𝐒. I share job search tips and insights on data analytics & data science in my free newsletter. Join 21,000+ readers here → https://lnkd.in/dUfe4Ac6

Stop applying to jobs manually in 2026. 

These 5 websites will save you hours every week. 

(Save this!)

𝟏. JobCopilot
↳ Auto-applies to dozens of jobs per day while you sleep 
↳ Scans 500,000+ company career pages daily for new openings 
→ https://lnkd.in/dEVsCgSA

𝟐. Jobright.ai
↳ AI matches you to jobs based on your skills, not just titles 
↳ According to JobRight, users report 2x more interviews with tailored resume suggestions 
→ https://lnkd.in/dB-jxX_z

3. Teal 
↳ Scores your resume against job descriptions with keyword matching 
↳ Built-in job tracker + 40 email templates for follow-ups 
→ https://lnkd.in/d-gM8gTv

4. Simplify
↳ One-click autofill for job applications (free Chrome extension) 
↳ 200M+ applications submitted, and candidates reportedly hear back more often 
→ https://simplify.jobs

𝟓. Hunter.io
↳ Find recruiter and hiring manager emails for direct outreach 
↳ Free tier available with 25 searches per month 
→ https://hunter.io

𝐌𝐲 𝟐 𝐜𝐞𝐧𝐭𝐬: 
→ Use JobCoPilot or JobRight for volume 
→ Use Teal to tailor your resume for roles you really want 
→ Use Hunter/Apollo to reach out directly when you find your dream job

𝐏𝐫𝐨 𝐓𝐢𝐩: Don't rely on just one tool. Stack them. Auto-apply for volume, tailor for quality, and reach out directly to stand out.

The job market is brutal right now. Work smarter, not harder.

Which of these have you tried? Any others I should add? 👇

♻️ Repost to help someone stuck in application hell

If I had to learn Python for Data Science in 60 days, this is the roadmap I'd follow.

(Most people overcomplicate this. Don't)

You don't need to master everything. You need to master what matters for data roles. Here's the exact path:

𝐖𝐞𝐞𝐤 𝟏-𝟐: 𝐏𝐲𝐭𝐡𝐨𝐧 𝐅𝐮𝐧𝐝𝐚𝐦𝐞𝐧𝐭𝐚𝐥𝐬
↳ Variables, data types, loops, conditionals
↳ Functions and lambda expressions
↳ List comprehensions

𝐖𝐞𝐞𝐤 𝟑: 𝐂𝐨𝐫𝐞 𝐃𝐚𝐭𝐚 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐬
↳ Lists, dictionaries, sets, tuples
↳ When to use each one
↳ This is your foundation

𝐖𝐞𝐞𝐤 𝟒-𝟓: 𝐄𝐬𝐬𝐞𝐧𝐭𝐢𝐚𝐥 𝐋𝐢𝐛𝐫𝐚𝐫𝐢𝐞𝐬
↳ NumPy for numerical operations
↳ Pandas for data wrangling (spend extra time here)
↳ Matplotlib & Seaborn for visualization

𝐖𝐞𝐞𝐤 𝟔: 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬 & 𝐄𝐃𝐀
↳ Mean, median, mode, standard deviation
↳ Correlation and distributions
↳ Exploratory data analysis techniques

𝐖𝐞𝐞𝐤 𝟕-𝟖: 𝐌𝐋 𝐁𝐚𝐬𝐢𝐜𝐬 & 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬
↳ Scikit-learn fundamentals
↳ Data preprocessing and cleaning
↳ Build 2-3 projects to solidify everything

The key is to code every single day. Even 30 minutes of hands-on practice beats 3 hours of passive learning.

𝟓 𝐅𝐫𝐞𝐞 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬 𝐭𝐨 𝐆𝐞𝐭 𝐒𝐭𝐚𝐫𝐭𝐞𝐝:
1. Kaggle Learn (free Python & Pandas courses) → https://lnkd.in/dVbp-MtE
2. freeCodeCamp Python for Data Science (YouTube) → https://lnkd.in/dCX4z9rQ
3. Real Python tutorials → https://realpython.com/
4. Python for Data Analysis by Wes McKinney (book) → https://lnkd.in/dBGpzfqD
5. DataCamp's free intro course → https://lnkd.in/dZzqAbeY

♻️ Repost to help someone starting their Python journey

📘 Prepping for data analyst interviews? Check out the book I co-authored with Pritesh and Amney with 150+ real questions: https://lnkd.in/dyzXwfVp

𝐏.𝐒. I share tips on data analytics & data science in my free newsletter. Join 23,000+ readers → https://lnkd.in/dUfe4Ac6

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Generative AI for everyone

Tableau Desktop Specialist

Coursework:
Introduction to Deep Learning
Business Decision Modeling
Big Data Analytics with Cloud
Intro to Project Mgmt
Data Mgmt and Business Process
Tech Comm in BAPM
Statistics in Business Analytics
Adv Bus Analytics and Pro Management
Predictive Modeling
Data Science using Python
Project Risk Management and Agile methodologies
Data Mining and Business Intelligence
Project Leadership and Communications

•	Extracted Tweets with 'sntwitter' and pre-processed the data using NLTK packages. Built a deep learning model that can emulate the rule-based 'Vader Sentiment Intensity Analyzer' and can conduct sentiment analysis.
•	Achieved an R-square value of 0.7 and an MAE of 0.07 and 0.05 for positive and negative sentiment scores.

Sentiment Analysis on Tweets

•	Spearheaded a project team of 5 and conducted an analysis to deliver data-driven recommendations, resulting in 100% project completion within timeline.
•	Enabled granular data results by leveraging GeoPandas and ZCTA5 shapefiles to extract Zip Codes of listings.
•	Coded a scalable dashboard in Tableau, enabling users to assess 1.5M Airbnb listings risk from state level to zip codes.

Impact of Airbnb on housing supply

•	Researched and scrapped stock data of 9 companies from 3 sectors, from Yahoo Finance for last 5 financial years.
•	Discovered the most-favourable investments, by employing regression analysis and Monte-Carlo simulation to assess risk-return trade-off.
•	Evaluated 3 prominent investment strategies, Buy & Hold, Momentum Trading and S&P 500, and picked Buy & Hold strategy with an ROI of 15% in FY2022.
•	Determined optimal portfolio allocation by performing a comprehensive risk and parameter analysis.

Portfolio Allocation Model

•	Reengineered XG Boost Model for Churn Prediction, resulting in 81% accuracy with an AUC of 0.69 from 66% and 0.60.
•	Accomplished a potential 21% increase in revenue, by conducting a cost-benefit analysis, and presenting the same to senior stakeholders.
•	Minimized customer churn by 10%, by recognizing key contributors to customer churn via data analysis and implementation of corrective actions with team.
•	Awarded 3rd prize out of 150 competing teams, with a $300 incentive.

Customer Churn Analysis for an US Insurance Company, Travelers

Data Scientist | 75K LinkedIn | BCBS Of South Carolina | SQL | Python | AWS | ML | Featured on Times Square, Favikon, Fox, NBC | MS in Data Science at UConn | Proven record in driving insights and predictive analytics |

Lead Data Analyst with a passion for driving data-driven strategies, predictive modeling, and team optimization across various industries including insurance and retail. With over 2 years of experience in data science and analytics, I have a proven track record in reducing churn, improving efficiency, and contributing to millions in annual revenue.

Currently, I lead data initiatives at BCBS Of SC, where I develop dashboards, optimize workflows, and build predictive models to support C-Suite decision-making and business strategies. My work spans multiple tools like SQL, Python, AWS, and Power BI to streamline processes and enhance operational efficiency.

Career Highlights:

•	Optimized workflows, saving 120+ hours of manual work per week, driving efficiency through AWS integration.
•	Built a churn prediction model reducing customer churn by 12%, leading to $7M in retained annual revenue.
•	Collaborated with cross-functional teams on A/B tests, improving policy conversion rates by 12%.
•	Developed real-time dashboards at The LEGO Group, improving sales forecasting accuracy by 15% and decision-making speed by 23%.
•	Recognized for building predictive models, earning recognition in data science competitions like NESS Statathon (3rd place out of 150 participants).

Beyond my work, I've had the privilege of competing and winning several hackathons and data science competitions, including earning 1st prize at Well4Tech’s Data Science Health Hackathon, and NESS Statathon by Travelers. My innovative work has been featured on Times Square billboards, and covered by major media outlets like Fox, NBC, and over 20+ articles. Additionally, I’ve been recognized by Favikon for my contributions to the data community.

I have experience working with a range of data science tools like PySpark, SQL, Power BI, and Python, and hold certifications such as Tableau Desktop Specialist and AWS Machine Learning. As a strong believer in mentorship and continuous learning, I’ve also contributed as a Graduate Teaching Assistant for SQL and led the UConn Data Analytics Club, fostering growth and development within the community.

Feel free to reach out if you’d like to discuss data analytics, data science, or potential collaboration: saikumarbysani149@gmail.com.

I just got back from San Francisco.

And I'm still processing what happened. I left with some of the best memories of my life.

𝐌𝐚𝐧𝐢𝐟𝐞𝐬𝐭 𝐆𝐚𝐥𝐚 brought together some of the most incredible people I've ever met in one room. Creators, founders, and attendees with O1s and EB1s. People building real things.

But here's what hit me the hardest:

𝐓𝐡𝐞 𝐩𝐨𝐰𝐞𝐫 𝐨𝐟 𝐢𝐧-𝐩𝐞𝐫𝐬𝐨𝐧 𝐜𝐨𝐧𝐧𝐞𝐜𝐭𝐢𝐨𝐧𝐬 𝐢𝐬 𝐮𝐧𝐦𝐚𝐭𝐜𝐡𝐞𝐝.

I finally met Deedy Das and Marina Mogilko. I've watched almost every one of Marina's podcasts. Following her content for so long, and then actually meeting her in person? Surreal.

I met the Tech Roast Show crew. Been watching them roast tech people forever. Now we were in the same room laughing together.

But what made this trip unforgettable was the 2 days I spent with fellow creators:

↳ Ferry rides across the bay
↳ Getting to know each other beyond the content
↳ Playing Mafia until 3 AM (I found out I absolutely suck at it. I cannot lie.)
↳ Late-night food runs
↳ Conversations about personal journeys, professional struggles, and everything in between

From Thursday to Saturday, most of us barely slept 4 hours a day.

And honestly? Some of the best days of my life.

You can DM someone for months. Comment on their posts. Engage with their content.

But nothing compares to being in the same room. Sharing a meal. Laughing at 3 AM over a game you're terrible at.

If you ever get the chance to show up in person, take it.

The connections you make will change everything.

So glad that I got to meet you all: Pritesh, Vishakha, Sohan, Varun, Roshni Chellani, Shreya, Sriram, Chintan, Amulya, Muskan, Jugal, Shreya, Narayan and more! You guys are the best!!

The H-1B lottery rejected me twice.

I thought I'd have to leave the country.

(If this is your story, keep reading.)

The uncertainty. The anxiety every March. The constant "what ifs."

Here's what changed everything for people I know: finding the right community.

That's why I'm part of the 𝐄𝐱𝐭𝐫𝐚𝐨𝐫𝐝𝐢𝐧𝐚𝐫𝐲 𝐀𝐛𝐢𝐥𝐢𝐭𝐲 𝐂𝐥𝐮𝐛 (EAC).

It's a private, invite-only community of 1,100+ immigrants working toward or who've already received O-1 and EB-1A visas.

𝐖𝐡𝐨'𝐬 𝐢𝐧𝐬𝐢𝐝𝐞:
→ Members from Google, OpenAI, Meta, Amazon, and fast-growing startups
→ Trailblazers actively building their visa cases
→ Extraordinary visa recipients who've been through it

𝐖𝐡𝐚𝐭 𝐲𝐨𝐮 𝐠𝐞𝐭:
→ Attorney AMAs (ask anything about your case)
→ Mentorship from people who've done it
→ In-person dinners, happy hours, and co-working sessions
→ "Work on your visa" cohorts with attorneys walking through real strategies

𝐔𝐩𝐜𝐨𝐦𝐢𝐧𝐠 𝐞𝐯𝐞𝐧𝐭𝐬:
→ The Extraordinary Gala in San Francisco (March 5)

𝐓𝐡𝐞 𝐛𝐨𝐚𝐫𝐝 𝐢𝐧𝐜𝐥𝐮𝐝𝐞𝐬:
→ Emilio Gonzalez (Former USCIS Director)
→ Deedy Das (Partner at Menlo Ventures)
→ Perry Rhew (Former Federal Judge)

𝐀𝐩𝐩𝐥𝐲 𝐡𝐞𝐫𝐞 → https://lnkd.in/dpNRkhk5

𝐌𝐲 𝐭𝐚𝐤𝐞:

The technical skills got us here. But community and connections help us stay and thrive.

You don't have to figure out the visa maze alone.

♻️ Repost to help an immigrant in your network find their people

Apple is hiring!

Apply before the roles close!

1. Machine Learning Engineer, Apple. 
Link: https://lnkd.in/dDjV-3kz

2. Data Engineer, Apple 
Link: https://lnkd.in/dkZrqCsz

3. Financial Analyst, Apple 
Link: https://lnkd.in/dpXZQiNS

4. Data Scientist, Figma 
Link: https://lnkd.in/ddMZfDSi

5. Data Scientist, Adobe 
Link: https://lnkd.in/dZzjm95S

6. Senior Data Scientist, Domino's 
Link: https://lnkd.in/d5S67rQT

7. Data Analyst, Current 
Link: https://lnkd.in/dRHuV_xu

8. Product Data Analyst, Amazon 
Link: https://lnkd.in/dXgsUnXw

♻️ Repost this to help the Job Seekers.

Know someone hiring? Drop the link in the comments!

𝐏.𝐒. I share job search tips and insights on data analytics & data science in my 𝐅𝐑𝐄𝐄 newsletter. Join 23,000+ here → https://lnkd.in/dUfe4Ac6

At NVIDIA GTC 2026 this week.

Excited to see what’s next in AI, and hear from Jensen Huang .

Meeting some amazing people here already.

If you’re here too, DM me, would love to connect.

#Nvidia #gtc2026

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AWS has 200+ services. Most data professionals only need 15.

(Once you know these, AWS stops feeling overwhelming)

I've seen too many people bounce between random tutorials and give up halfway. The problem isn't AWS. It's not having a mental model.

Most data systems, no matter how complex, are built on just five layers:

Storage → Processing → Analytics → Machine Learning → Security

Once that clicks, everything becomes logical.

Here are the 15 AWS services every Data Analyst and Data Scientist should know:

𝐒𝐭𝐨𝐫𝐚𝐠𝐞 & 𝐃𝐚𝐭𝐚 𝐋𝐚𝐤𝐞𝐬
↳ S3: Your data lake foundation. Raw files, CSVs, Parquet - everything starts here.
↳ RDS: Managed PostgreSQL/MySQL for relational workloads.
↳ Redshift: Cloud data warehouse for SQL on massive datasets.

𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 & 𝐄𝐓𝐋
↳ Glue: Serverless ETL across sources.
↳ Athena: Query S3 directly with SQL. No infrastructure.
↳ EMR: Spark and Hadoop for large-scale processing.
↳ Lambda: Event-driven compute for pipeline automation.

𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 & 𝐁𝐈
↳ QuickSight: Native BI for dashboards and visualizations.

𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠
↳ SageMaker: End-to-end ML platform for building and deploying models.
↳ Bedrock: Access foundation models like Claude and Llama.
↳ Comprehend: NLP insights from text without custom models.

𝐒𝐭𝐫𝐞𝐚𝐦𝐢𝐧𝐠 & 𝐑𝐞𝐚𝐥-𝐓𝐢𝐦𝐞
↳ Kinesis: Ingest and process streaming data.

𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 & 𝐀𝐜𝐜𝐞𝐬𝐬
↳ IAM: Define who can access what.
↳ KMS: Manage encryption keys.
↳ Secrets Manager: Store and rotate API keys and credentials.

𝐒𝐭𝐚𝐫𝐭𝐢𝐧𝐠 𝐨𝐮𝐭? 𝐅𝐨𝐥𝐥𝐨𝐰 𝐭𝐡𝐢𝐬 𝐩𝐚𝐭𝐡:

S3 → Athena → Glue → Redshift → SageMaker

Master this flow and you'll understand how most modern data platforms on AWS are built.

𝐅𝐫𝐞𝐞 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬 𝐭𝐨 𝐆𝐞𝐭 𝐒𝐭𝐚𝐫𝐭𝐞𝐝:
1. AWS Skill Builder (free tier): https://skillbuilder.aws/
2. freeCodeCamp AWS Cloud Practitioner: https://lnkd.in/dJc6Eybc
3. AWS Documentation & Tutorials: https://lnkd.in/dqzSmhCd

Which AWS service are you learning right now? 👇

♻️ Repost to help someone feeling overwhelmed by AWS

📘 Preparing for data analyst interviews? Check out the book I co-authored with Pritesh and Amney with 150+ real questions: https://lnkd.in/dyzXwfVp

𝐏.𝐒. I share tips on data analytics & data science in my free newsletter. Join 23,000+ readers → https://lnkd.in/dUfe4Ac6

90% of SQL interviews test the same 10 window functions.

(If you know them, you're already ahead)

Most analysts write 15-line subqueries for problems that window functions solve in 3 lines. Interviewers notice.

Here are the 10 SQL window functions every data analyst and data scientist should know:

𝟏. 𝐑𝐎𝐖_𝐍𝐔𝐌𝐁𝐄𝐑()
↳ Assigns a unique sequential number to each row
↳ Use it for: deduplication, pagination, finding first/last records per group

𝟐. 𝐑𝐀𝐍𝐊()
↳ Ranks rows with ties getting the same rank, leaves gaps after
↳ Use it for: leaderboards, finding top N with ties

𝟑. 𝐃𝐄𝐍𝐒𝐄_𝐑𝐀𝐍𝐊()
↳ Ranks rows without leaving gaps, even with ties
↳ Use it for: ranking when you need consecutive numbers

𝟒. 𝐋𝐀𝐆()
↳ Returns the value from the previous row
↳ Use it for: day-over-day changes, comparing to previous period

𝟓. 𝐋𝐄𝐀𝐃()
↳ Returns the value from the next row
↳ Use it for: forecasting, calculating time between events

𝟔. 𝐒𝐔𝐌() 𝐎𝐕𝐄𝐑
↳ Calculates running or cumulative totals
↳ Use it for: running revenue, cumulative user growth

𝟕. 𝐀𝐕𝐆() 𝐎𝐕𝐄𝐑
↳ Computes moving or rolling averages
↳ Use it for: 7-day rolling averages, smoothing trends

𝟖. 𝐍𝐓𝐈𝐋𝐄()
↳ Divides rows into N equal groups
↳ Use it for: quartiles, percentiles, bucketing users

𝟗. 𝐅𝐈𝐑𝐒𝐓_𝐕𝐀𝐋𝐔𝐄()
↳ Fetches the first value in an ordered window
↳ Use it for: first purchase date, earliest event per user

𝟏𝟎. 𝐋𝐀𝐒𝐓_𝐕𝐀𝐋𝐔𝐄()
↳ Fetches the last value in an ordered window
↳ Use it for: most recent activity, latest status per group

I've seen these exact functions show up in take-home assignments at Google, Meta, and Amazon. Master them before your next interview.

Which window function do you use most? 👇

♻️ Repost to help someone ace their next SQL interview

📘 If you're preparing for data analyst interviews, check out the book I co-authored with Pritesh and Amney, 150+ real interview questions with solutions: https://lnkd.in/dyzXwfVp

𝐏.𝐒. I share job search tips and insights on data analytics & data science in my free newsletter. Join 23,000+ readers here → https://lnkd.in/dUfe4Ac6

SQL window functions

You can't analyze data you don't have.
You can't model the data you don't have.
You can't make decisions with data you don't have.

Here's how to get it.

The best datasets aren't on Kaggle.
They're not waiting in your company's database.
They're sitting on websites - unstructured and untouched.

Job listings. Product prices. Competitor intel. Customer reviews.

This is the data that actually moves the needle. And it's not coming to you in a clean CSV.

Building scrapers from scratch with BeautifulSoup or Selenium?

Great for learning. Painful at scale.

You end up managing proxies, CAPTCHAs, anti-bot detection, JavaScript rendering, and constant maintenance when sites change.

The faster path? Use a scraping API.

You send a URL + parameters. It handles everything and returns structured data.

I've been using 𝐎𝐱𝐲𝐥𝐚𝐛𝐬 Web Scraper API.

𝐖𝐡𝐲 𝐢𝐭 𝐰𝐨𝐫𝐤𝐬:
↳ Handles IP blocks and CAPTCHAs automatically
↳ Returns clean JSON, no parsing headaches
↳ Works on Amazon, Google, job boards, any public site
↳ You only pay for successful results

𝐓𝐫𝐲 𝐢𝐭 𝐟𝐫𝐞𝐞: Scrape up to 2K results at no cost.
→ https://lnkd.in/dj8wBePd

What would you scrape first? 👇

♻️ Repost to help someone stuck debugging scrapers

How to Scrape Data from Web

I built an AI workflow that turns resumes into pitch decks in under 60 seconds.

And it's already helping job seekers stand out from 500+ applicants.

You spend hours perfecting your resume. Send it to dozens of companies. And still get ghosted.

Not because you're not qualified.

But because you're playing the same game as everyone else.

While others attach PDFs, what if you sent a personalized pitch deck instead?

𝐇𝐞𝐫𝐞'𝐬 𝐰𝐡𝐚𝐭 𝐢𝐭 𝐝𝐨𝐞𝐬:

→ Reads your resume and the job description 
→ Matches your skills to their exact requirements 
→ Builds a professional pitch deck tailored to that role 
→ Ready to send in under 60 seconds

No code. No API keys. No automation tools. Just a chat window.
You copy a prompt. You paste it. You get a deck.

And this is just one workflow. Gamma now connects to ChatGPT, Gmail, HubSpot, Zapier, Clay, and more. 700K+ presentations are created in Gamma every single day.

Comment "𝐆𝐀𝐌𝐌𝐀" and I'll send you:
→ Step-by-step setup guide (takes 2 minutes)
→ The exact master prompt that builds your deck
→ 3 recruiter message templates to actually send the deck

Stop getting ghosted. Start getting callbacks.

Check out Gamma here: https://lnkd.in/gigkGk3f

♻️ Repost this if someone in your network is job searching

#GammaPartner

Save this post to save months of job search!

(Not Kidding)

Most job seekers waste 80% of their time on the wrong job boards.

By the time you see it on job boards, hundreds have already applied.

I've been testing a tool that honestly feels like cheating (the good kind): 𝐉𝐨𝐛𝐂𝐨𝐩𝐢𝐥𝐨𝐭.

𝐖𝐡𝐚𝐭 𝐢𝐭 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐝𝐨𝐞𝐬:
↳ Monitors 500,000+ company career pages 24/7
↳ Finds jobs before they hit the mainstream boards
↳ Auto-fills those mind-numbing application forms
↳ Answers screening questions in your voice
↳ Tracks everything so you never lose an application
↳ Automatically finds emails and LinkedIn of Hiring Managers (New Feature)

𝐓𝐡𝐞 𝐫𝐞𝐚𝐥 𝐤𝐢𝐜𝐤𝐞𝐫:
While you're sleeping, it's applying. While you're networking, it's applying. While you're prepping for interviews, it's still applying.

One candidate told me they got 5 interviews in their first week. Not because they're special. Because they applied to jobs nobody else had seen yet.

Start using JobCopilot here: https://lnkd.in/dEVsCgSA

While JobCopilot handles the heavy lifting, you focus on what humans do best:
↳ Building genuine connections
↳ Perfecting your interview skills
↳ Networking at events
↳ Preparing killer presentations

Your competition is still copying and pasting into the same forms. You could be getting callbacks from jobs they haven't even found yet.

Stop playing the numbers game manually when AI can play it better.

♻️ Repost to help job seekers skip the struggle.

How I Built A Community of 233K people on LinkedIn

Save this post to save months of job search!

(Not Kidding)

1,300 applications. That's how many it took me to land my first job.

Most of that time? Wasted on the wrong things.

I spent 80% of my energy filling out forms, tracking spreadsheets, and refreshing my inbox. The stuff that actually moves the needle, networking, interview prep, and coffee chats, got maybe 20%.

I've been testing a tool that honestly feels like cheating (the good kind): 𝐉𝐨𝐛𝐂𝐨𝐩𝐢𝐥𝐨𝐭.

𝐖𝐡𝐚𝐭 𝐢𝐭 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐝𝐨𝐞𝐬:
↳ Monitors 500,000+ company career pages 24/7
↳ Finds jobs before they hit the mainstream boards
↳ Auto-fills those mind-numbing application forms
↳ Answers screening questions in your voice
↳ Tracks everything so you never lose an application
↳ Automatically finds emails and LinkedIn of Hiring Managers (New Feature)

𝐒𝐭𝐚𝐫𝐭 𝐮𝐬𝐢𝐧𝐠 𝐉𝐨𝐛𝐂𝐨𝐩𝐢𝐥𝐨𝐭 𝐡𝐞𝐫𝐞: https://lnkd.in/dEVsCgSA

If you're stuck in the endless loop of applying, waiting, and hearing nothing, this tool might be your reset button.

𝐌𝐲 𝟐 𝐜𝐞𝐧𝐭𝐬:
The applications aren't the hard part. Getting them done efficiently is.

I've seen job seekers reclaim their time and land interviews faster because JobCopilot takes care of the heavy lifting behind the scenes.

Leaving space for what actually moves the needle:
↳ Networking with intent
↳ Practicing for interviews
↳ Having those coffee chats that turn into referrals

Your next offer could be sitting in one of those auto-filled applications.

It's like having a 24/7 job-hunting assistant that never sleeps.

What's the most frustrating part of job searching for you? 👇

♻️ Repost this to help someone who's stuck refreshing their inbox.

The H-1B lottery rejected me.

I thought I'd have to leave the country.

(If this is your story, keep reading.)

The H-1B lottery closes in 6 days. Thousands of immigrants are about to face the same uncertainty: stay, leave, or find alternatives.

There is an alternative. The O-1 Visa.

No lottery. No cap. No random draw.

You need to meet 3 out of these 8 criteria:

1. 𝐀𝐰𝐚𝐫𝐝𝐬 - Recognition for excellence
2. 𝐌𝐞𝐦𝐛𝐞𝐫𝐬𝐡𝐢𝐩𝐬 - Selective, invitation-based groups
3. 𝐏𝐮𝐛𝐥𝐢𝐬𝐡𝐞𝐝 𝐖𝐨𝐫𝐤 - Your contributions shared publicly
4. 𝐉𝐮𝐝𝐠𝐢𝐧𝐠 - Reviewing others' work
5. 𝐎𝐫𝐢𝐠𝐢𝐧𝐚𝐥 𝐖𝐨𝐫𝐤 - Innovative contributions with impact
6. 𝐌𝐞𝐝𝐢𝐚 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐬 - Coverage in credible outlets
7. 𝐇𝐢𝐠𝐡 𝐒𝐚𝐥𝐚𝐫y - Top-tier earnings in your field
8. 𝐂𝐫𝐢𝐭𝐢𝐜𝐚𝐥 𝐑𝐨𝐥e - Key, high-impact positions

Most tech professionals already meet several of these. They just haven't documented them properly.

𝐌𝐚𝐧𝐢𝐟𝐞𝐬𝐭 𝐋𝐚𝐰 helps turn your existing achievements into a strong O-1 case.

𝐆𝐞𝐭 𝐲𝐨𝐮𝐫 𝐟𝐫𝐞𝐞 𝐞𝐥𝐢𝐠𝐢𝐛𝐢𝐥𝐢𝐭𝐲 𝐜𝐨𝐧𝐬𝐮𝐥𝐭𝐚𝐭𝐢𝐨𝐧 → https://lnkd.in/dSJnAg7b

That hackathon win? Counts.
Your tech blog? Counts.
Leading that critical project? Counts.

The H-1B lottery closes March 19. Start building your backup plan now. 

I got mine picked on my second attempt. I hope you get yours too. But if you don't, just know there are other paths forward.

All the best, everyone.

♻️ Repost to help someone going through this anxiety

I just analyzed 2 months of my Instagram data in 45 minutes. 5 insights I wish I knew sooner.

No SQL. No Python. Just plain English questions.

It's an open-source AI analyst system built on Claude Code - 18 specialized agents, 39 skills, all markdown. 

You connect your data, ask a business question, and it frames it, explores your data, finds the root cause, builds a narrative, and hands you a branded slide deck with speaker notes.

𝐇𝐞𝐫𝐞'𝐬 𝐰𝐡𝐚𝐭 𝐢𝐭 𝐟𝐨𝐮𝐧𝐝:

1. 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐑𝐞𝐞𝐥𝐬 = 𝐦𝐲 𝐞𝐧𝐠𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐞𝐧𝐠𝐢𝐧𝐞
↳ 8% average engagement rate (highest on my account)
↳ 406 new followers per post

2. 𝐉𝐨𝐛 𝐒𝐞𝐚𝐫𝐜𝐡 𝐑𝐞𝐞𝐥𝐬 = 𝐦𝐲 𝐯𝐢𝐫𝐚𝐥 𝐫𝐞𝐚𝐜𝐡 𝐞𝐧𝐠𝐢𝐧𝐞
↳ 187K average views per post
↳ 3x more reach than anything else
↳ One post hit 700K views

3. 𝐏𝐞𝐫𝐬𝐨𝐧𝐚𝐥 𝐒𝐭𝐨𝐫𝐲 𝐂𝐚𝐫𝐨𝐮𝐬𝐞𝐥𝐬 = 𝐜𝐮𝐭𝐭𝐢𝐧𝐠 𝐭𝐡𝐞𝐦 𝐢𝐦𝐦𝐞𝐝𝐢𝐚𝐭𝐞𝐥𝐲
↳ 44 saves per post (1/40th of Job Search content)
↳ Not worth the production time

4. 𝐍𝐞𝐯𝐞𝐫 𝐩𝐨𝐬𝐭 𝐨𝐧 𝐓𝐡𝐮𝐫𝐬𝐝𝐚𝐲𝐬
↳ 27K reach on Thursdays vs 140K on Wednesdays
↳ Same content. Completely different results.

5. 𝐏𝐞𝐫𝐬𝐨𝐧𝐚𝐥 𝐒𝐭𝐨𝐫𝐲 𝐑𝐞𝐞𝐥𝐬 = 𝐔𝐒 𝐚𝐮𝐝𝐢𝐞𝐧𝐜𝐞 𝐦𝐚𝐠𝐧𝐞𝐭
↳ 56% US viewers on average
↳ Critical for brand deals and course sales

𝐒𝐦𝐚𝐥𝐥 𝐬𝐚𝐦𝐩𝐥𝐞 𝐜𝐚𝐯𝐞𝐚𝐭: Two months of data means these are directional signals, not statistically proven conclusions. But the patterns are clear enough to act on.

This kind of analysis would have taken me days. It took 45 minutes.

My content team (Muskan Agarwal, Cherry Media) is already using this report to plan next month's content.

Thanks for building this, Shane, Sravya, and Hai. They're also running a free session tomorrow on analyzing product data with Claude Code. 

𝐑𝐞𝐠𝐢𝐬𝐭𝐞𝐫 𝐡𝐞𝐫𝐞: https://lnkd.in/d5WX3Cwz
𝐅𝐮𝐥𝐥 𝟓-𝐰𝐞𝐞𝐤 𝐈𝐧-𝐃𝐞𝐩𝐭𝐡 𝐜𝐨𝐮𝐫𝐬𝐞: https://lnkd.in/d9puN3rP

(Repo link and Full report in the comments section 👇)

What would you analyze first if you had this kind of tool? 

♻️ Repost to help a creator or analyst drowning in data

The most common DM I've gotten this month isn't just about data analytics.

It's "How do I break into cybersecurity?"

I'm not a cybersecurity expert. But I know what it's like to start from zero in a completely new field.

I switched from electrical engineering to data science while moving to a different country. No background. No connections. Just a decision to figure it out.

So when people kept asking, I did what I always do: I researched the best starting point.

That's how I found 𝐓𝐫𝐲𝐇𝐚𝐜𝐤𝐌𝐞.

𝐖𝐡𝐚𝐭 𝐢𝐦𝐦𝐞𝐝𝐢𝐚𝐭𝐞𝐥𝐲 𝐬𝐭𝐨𝐨𝐝 𝐨𝐮𝐭:
↳ 100% hands-on, browser-based labs (no downloads, no setup)
↳ Zero multiple-choice questions
↳ Learn by actually doing the work, not memorizing theory

Over 6.5 million people are already learning on it. There's a reason for that.

And if you get stuck, there's 𝐄𝐜𝐡𝐨, TryHackMe's AI tutor available 24/7. It doesn't just give you answers. It walks you through the thinking and helps you actually understand. For someone starting from zero, that's huge.

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The Battle

Peter Ndiforchu

Venkata Naga Sai Kumar Bysani

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Performance ScoreVenkata Naga Sai Kumar Bysani's LinkedIn Performance

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taplio

The Battle

Peter Ndiforchu

Venkata Naga Sai Kumar Bysani

Followers

Posts

Engagements

Likes

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Peter Ndiforchu's LinkedIn Performance

Performance ScoreVenkata Naga Sai Kumar Bysani's LinkedIn Performance

Ready to grow your LinkedIn presence?

Performance ScorePeter Ndiforchu's LinkedIn Performance