Peter Ndiforchu's Linkedin Analytics

7 Powerful Reasons to Start as a Data Analyst Data analytics could be your perfect entry into a thriving and impactful field. Here’s why starting as a Data Analyst is a great move: 1. Impactful Work ↳ Data-driven decisions directly influence business strategy, helping you shape the success of the company from day one. 2. Gateway to Specialization ↳ Starting as a Data Analyst opens doors to advanced roles like Data Scientist, Business Analyst, and more. 3. Problem Solving ↳ Tackle complex business challenges with data, developing critical problem-solving skills that make you indispensable. 4. Barrier to Entry ↳ Data analytics doesn’t require a PhD. With the right training, you can enter this field quickly and start making an impact. 5. Transferable Skills ↳ Skills like data analysis, deriving insights, and decision-making are in high demand across industries—from tech to healthcare and finance. 6. Quick Wins ↳ You’ll have an immediate impact as you create dashboards, generate reports, and provide insights that help drive business performance. 7. Continuous Learning ↳ Data analytics is always evolving. You’ll continually learn new tools, techniques, and trends, ensuring growth throughout your career. Ready for a career change? Start your journey as a Data Analyst today and unlock endless career possibilities. --- p.s.: I am hosting a webinar on the MVP Mindset of Data Analytics and Data Science with Douceur Tengu. RSVP in comments

SQL is more diverse than you might think - it's not just about writing queries. When I first started working with SQL, I thought it was all about SELECT statements and JOINs. But here's the reality of SQL operations: → DDL commands (CREATE, DROP, ALTER, TRUNCATE, COMMENT) are the database engineers toolkit for structuring databases. → DML commands (SELECT, INSERT, UPDATE, DELETE) are what data analysts and scientists use daily to work with data. → DCL commands (GRANT, REVOKE) give database admins control over user permissions. → TCL commands (COMMIT, ROLLBACK) help manage database transactions. The fascinating part is that these operations often overlap between roles: ↳ Database administrators handle security ↳ Data engineers manage data pipelines ↳ Analytics engineers transform data ↳ Data analysts query and analyze ↳ Data scientists explore patterns Before you limit yourself to just one aspect of SQL, remember that different roles require different SQL skills. Your SQL journey might take you across multiple domains. Embrace the diversity of what SQL can offer in your data career. P.S. Which SQL commands do you use most in your role? Share your experience.

Most aspiring data analysts obsess over tools. SQL. Python. Tableau. But here’s the hard truth: → Skills don’t get you hired. Value does. In a recent live session with Kwankah Taka, we pulled back the curtain on what actually moves the needle in data careers: ↳ Why your portfolio belongs in your resume—not just as a link, but as a story of impact ↳ How to transition careers by leveraging your existing experience (you’re more prepared than you think) ↳ Why curiosity beats credentials—because the best analysts ask better questions, not just write better code The 3 pillars for landing a role: → SQL + Storytelling + Stakeholder Savvy We also shared real-life career paths, tackled imposter syndrome, and addressed the question no one asks: → When should I start applying? (Hint: After your second project, not your second certification.) Here’s what we’ve learned mentoring 100+ analysts: ↳ You don’t need to be perfect. You need to be visible. ↳ You don’t need 100 applications. You need alignment. ↳ You absolutely need a community rockstars to support you. We’re opening spots for our next mentorship cohort. It includes hands-on projects, resume + LinkedIn optimization, mock interviews, and a personal growth tracker. Want in? Drop a “DATA” in the comments or shoot me a DM. Let’s build your data career the right way.

This job rejection taught me a valuable lesson in career growth. I’ve faced my fair share of rejection in job applications. A few years ago, I applied for a dream role in a top tech company. I was confident, over-prepared, and hopeful. Then came the rejection. Here’s what I’ve learned from going through countless rejections: 1. Rejections are not failures; they’re learning opportunities. ↳ Every rejection helps refine your approach. Make sure you take the feedback and get better each time. 2. The average recruiter spends only a few seconds on your application. ↳ What stands out? The stories you tell, how you differentiate yourself. Humanize your application to make it memorable. 3. Your application isn’t just about your past experiences, it’s about positioning yourself for the next role. ↳ Focus on how your unique skills align with the job you’re applying for. Customize for the role, but most importantly, for the company. 4. Tailor your application to the company’s needs. ↳ Don’t just match your resume to the job description. Research the company, its culture, and its goals to show you’re the perfect fit. 5. Use everything you’ve got. ↳ Side projects, freelancing, personal initiatives—everything counts. Your value is not just in your past job titles but in your entire journey. After that rejection, I didn’t blend in—I stood out. And today, I’ve landed roles that truly align with my vision. Don't let rejections define you—use them to fuel your next big opportunity.

Recruiter: N-dih-for-chuh! Sorry for butchering your last name Me: Let me help, it's dih-for-chuh Recruiter: [Tries to pronounce but butchers my name again] Me: Haha, good try! It's dih-for-chuh (Silent "N"). Recruiter: So, dih-for-chuh? Me: Incredible! You said it perfectly. Recruiter: I did? What a great name. Okay, let's get started. It's really that simple. People don't need to be screened out because of their names. And we don't need to get upset when people don't pronounce our names correctly. Recruiters – have the patience to learn how to pronounce it correctly. Candidates – have the patience to teach someone how to say it right. It's called being human. That little bit of empathy can go a long way.

AI isn't just a trendy tool anymore—it's the new standard. When Shopify's CEO made AI mandatory, it sent ripples through the business world. But this isn't about replacing jobs. It's about evolving them. The reality is clear: those who embrace AI will outperform those who don't. Here's what's actually happening in the workplace: → Innovation is accelerating at unprecedented rates → Tasks are being completed faster than ever → Decision-making is becoming data-drivenAnd here's why this matters for every professional: The gap between AI adopters and non-adopters is widening daily. Think about it: → Insights are deeper and more accurate → Manual processes are being automated → Analysis that took days now takes hoursIt all comes down to adaptation and growth. The transformation isn't about replacing human intelligence—it's about augmenting it. But here's what most people miss: ↳ AI is becoming as fundamental as email was 20 years ago. The choice isn't whether to use AI, but how to use it effectively. And here's the wake-up call for professionals: ↳ The future belongs to those who leverage AI, not fear it. Every industry is being transformed. Every role is being enhanced. The question isn't if AI will impact your work, but how you'll use it to stay ahead. Make a small investment in learning AI today.

Simplicity is key 🔑

There are so many courses out there with very little value. I call them time wasters because you don't get the skills; all you get is a meaningless certificate. Skills are the new currency, and finding the right courses is the best bargain you can make. Here are 8 data analytics courses worth your time and money: 1/ Data Analytics Professional Certification by DeepLearning.AI (NEW!): Learn foundational data analytics skills from the creators of the renowned "Machine Learning Specialization," including Andrew Ng 2/ Intermediate SQL for Data Analytics (NEW!) Advance your SQL skills with insights from former MrBeast Analyst and popular YouTuber Luke Barousse 3/ Google Data Analytics Professional Certificate Join over 2.6 million learners gaining practical skills from top Google practitioners. 4/ IBM Data Analyst Professional Certificate: Learn directly from top IBM technologists, already trusted by over 367,000 students. 5/ Microsoft Certified: Power BI Data Analyst Associate: Master in-demand business intelligence skills with Power BI and prepare effectively for the PL-300 exam. 6/ Microsoft Business Analyst Professional Certificate: Boost your business analysis skills and readiness for the PL-900: Microsoft Power Platform Fundamentals exam. 7/ Meta Data Analyst Professional Certificate: Develop critical marketing analytics capabilities, including the latest in data management techniques. 8/ Tableau Business Intelligence Analyst Professional Certificate: Acquire advanced BI skills from Tableau experts and prep for the Tableau Data Analyst Certification. Your time is precious—invest wisely! Which of these have you tried, or do you have others to recommend? Drop your thoughts below! 👇

Stop waiting for job postings to apply. Let me tell you why this approach won’t get you anywhere fast. Traditional job searching is reactive. You sit and wait for a job posting, click "Apply," and cross your fingers. But here’s the thing — being reactive is a fast track to blending in with the crowd. Here’s what I did instead: I posted about a weekend project I worked on. A simple, straightforward post about a data analytics project I built. The result? 🚀 Over 25,000 impressions. 🚀 DMs from recruiters and Data Analytics Managers inquiring about my work. 🚀 People reaching out to discuss opportunities I wasn’t even aware of. That’s how you market yourself wisely. 📣 Be proactive. Rather than waiting for the job posting to appear, build a pipeline of interest. Post your work. Share your skills. Let the opportunities come to you. When the next role opens up, you want to be the first person they think of — not someone they're scrambling to find last minute. Take control. Stand out. This is how you make yourself unmissable.

I used to dread waking up. Not because I lacked ambition. Not because I didn’t have an education. But because my job made me feel like I couldn’t breathe. There were days I’d sit in the parking lot of the warehouse, staring at the building, hoping something would change. But nothing ever did—until I did. I knew deep down this wasn’t the life I envisioned for myself. I had an MBA. I had dreams. And if there was anyone who could rescue me from that situation, it had to be me. So I made a choice. To pivot. To build. To become the kind of person I knew I could be. 12 months later, I landed my dream role in Data Analytics. And along the way, I learned 5 powerful lessons that reshaped my life: 1. Pain is the trigger for transformation The discomfort of lifting boxes in a toxic warehouse was the spark I needed to reimagine what was possible. 2. Some people will water your dreams. Others will drown them. I stopped sharing my goals with people who didn't believe in them. One person literally told me it was impossible—because of the economy. Now I know: protect your vision until it’s strong enough to fly. 3. If you don’t invest in your growth, no one else will. Degrees are great. But they’re not enough. I took charge of my learning—and that changed everything. 4. Your personal brand is your life raft. I used to think branding was for celebrities. Until I started sharing my journey online—and realized opportunities find you when your story is visible. 5. No job is worth your health or your family. I stayed in a toxic job because I thought I didn’t have a choice. Turns out I did. And so do you. The world doesn’t need a more polished version of you. It needs the real you—fulfilled, bold, and lit up with purpose. So if you’re stuck, exhausted, or doubting your next move… Let my story be a sign: You’re one decision away from transformation.

You’re not burned out. You’re being called to something bigger. The worst alarm clock isn’t your phone buzzing at 6AM. It’s the sinking feeling you get when you realize you’re waking up for the wrong life. If you’ve ever dreaded another lifeless meeting… Looked five years into the future and hated the view… Felt drawn to a new field you barely knew existed… Or craved freedom your current career could never offer… It’s not burnout. It’s your future pulling you forward. But how do you actually pivot when everything feels overwhelming? The truth is: You don’t fall into a better career. You build your way into it — brick by brick. It starts with brutal honesty: → What do you really want in your next chapter? Move to investigation: → Talk to real people doing the work you dream about. → Ask uncomfortable questions. Peel back the LinkedIn highlight reels. Design your blueprint: → One career target. One job role. One deadline. Stack your skills with precision: → Learn what makes you undeniable in that space. → Prioritize action over analysis. Activate your network: → Share your vision early. → Watch support show up from unexpected places. Will it be easy? No. You’ll doubt yourself. You’ll question the timeline. You’ll face resistance. But pivoting isn’t about chasing comfort — it’s about choosing growth. Millions have done it. You can too. Start messy. Start scared. Start imperfectly. But start. Your future self is already clapping for you. One brave decision today could rewrite the next decade of your life. Will you make it? --- Want to pivot into Data Analytics without a technical degree? Join my free webinar on May 3rd at 2PM EST. See link in comments

99% of data analysts are just technical. But the top 1%? They solve problems that move the business forward — and get promoted faster. Here are 20 signs you're becoming a problem-solving data analyst (not just a dashboard builder): 1. You document every step of your work to create repeatable value. ↳ Clear notes = scalable impact. 2. You check data quality before diving in. ↳ Garbage in, garbage out. 3. You use version control to track code evolution. ↳ Because accountability matters. 4. You explore the data before explaining it. ↳ No assumptions. Just patterns. 5. You automate repetitive tasks to save time for deep thinking. ↳ Scripts > burnout. 6. You build a personal code library to move faster on future projects. ↳ Invest in yourself like a product. 7. You test assumptions with multiple validation techniques. ↳ Confidence is earned, not assumed. 8. You keep projects tidy with logical folder structures. ↳ Organization = faster onboarding + fewer bugs. 9. You ask for reviews before shipping work. ↳ Good analysts seek feedback. Great ones act on it. 10. You read industry blogs and whitepapers regularly. ↳ Continuous learners outperform. 11. You prioritize business impact over technical elegance. ↳ Results > perfection. 12. You explain findings using simple, powerful language. ↳ Make the complex feel obvious. 13. You write clean, commented code others can maintain. ↳ Collaboration isn’t optional. 14. You save backups before the disaster hits. ↳ Always plan for "what if." 15. You reflect on your analytical mistakes for better decisions. ↳ Growth lives in reflection. 16. You foster strong relationships across teams with data as your language. ↳ Influence = trust + clarity. 17. You break down massive projects into manageable weekly goals. ↳ Micro-wins lead to macro-results. 18. You handle sensitive data with care and compliance. ↳ Trust is the real currency. 19. You visualize data with intention and clarity. ↳ Make insights impossible to ignore. 20. You actively seek evidence that could disprove your work. ↳ Ego kills good analysis. Want to be seen as a strategic thinker, not just a data cruncher? Save this list. Audit your habits. Become the analyst everyone wants on their team.

My first manager after school did more than just lead me at work. He believed in my dreams before they even made sense on paper. When I told him I was applying to graduate school in a foreign country, he didn’t hesitate. He wrote me a glowing referral letter. One that made my application stand out in a sea of qualified candidates. When I got my visa, he was the first to celebrate with me. Not because he had to but because he genuinely cared. He believed that success should be shared. And when I landed in this new country, trying to find my feet, he offered to write me another referral—this time for my very first job. Even though I was miles away and no longer on his team. That’s the kind of leadership that leaves a lasting impact. He didn’t just manage tasks. He championed people. He gave me the confidence to walk through new doors and reminded me that real leaders don’t stop leading just because the org chart changes. If that’s not leadership—I don’t know what is.

7 Data Science Perfection Myths (and how to avoid it) Perfectionism can be a roadblock in data science: ❌️It leads to wasted time ❌️It causes missed opportunities ❌️It can be make you feel inadequate Let's debunk some common myths and reveal what you should be focusing on instead. Here’s how to avoid the trap of perfectionism in data science: 1️⃣ Myth #1 - More Data = Better Results: More data doesn't always mean better outcomes. Sometimes, too much data can overwhelm your models. Focus on quality over quantity for faster insights and better performance. 2️⃣ Myth #2 - The Perfect Model Exists: There's no such thing as a "perfect" model. The goal is to build a model that provides value, not one that's flawless. Use the MVP mindset to create quick wins and iterate. 3️⃣ Myth #3 - Cleaning Data Must Be Perfect: Data doesn't need to be perfect to produce useful insights. Focus on cleaning it enough to answer business questions — not to remove every last imperfection. 4️⃣ Myth #4 - Perfection Increases Credibility Waiting for perfect models or dashboards before presenting them only delays progress. Delivering early, workable solutions builds credibility much faster than perfection. 5️⃣ Myth #5 - You Need to Know Everything Before Starting Data science is all about learning on the go. Don’t wait until you “know it all.” Start with the basics and build as you learn. 6️⃣ Myth #6 - More Features = Better Model Performance: Adding extra features to your models can lead to overfitting. Keep your models simple and focused on what really drives performance and business value. 7️⃣ Myth #7 - You Have Time to Perfect: Everything In the fast-paced world of data science, time is a luxury. Deliver quickly, get feedback, and iterate. Waiting for perfection means missing out on the real value you could be providing. 🔑 Focus on delivering incremental value, impact and results rather than chasing perfection. Join me and Douceur Tengu to discuss more on the MVP mindset of data science on April 19th. RSVP in comments. Spots limited.

My brother died in his late 20s. His definition of success changed my life forever. ↳ He was light years ahead of his peers, but not in the way you might think. Most saw him and thought: • Too young for his achievements • Too accomplished for his age • Too focused while others wandered ↓ But here's what they missed: While others optimized their lives for: • Next promotion cycles • LinkedIn connections • Investment portfolios • Corner office views He optimized for something different: • Communities built from scratch • Lives touched through his work • Hours spent helping others • Hearts healed (literally) ↓ His mission was crystal clear: Fighting for people battling heart conditions, just like him. ↳ The metrics he tracked? • Not his follower count • Not his bank balance • Not his job title But: • Number of patients supported • Families given hope • Lives transformed ↓ The greatest lesson he left us: True success isn't about what you gain, but what you give. ↳ Most spend their lives climbing the ladder of achievement. He spent his making sure others had a ladder to climb. Remember: Your legacy isn't written in your bank account. It's written in the lives you touch. ↓ Ask yourself: • What's your definition of success? • What legacy are you building? • Who are you really serving? Your job title will be forgotten. Your impact won't. Do you agreee?

Writing SQL is easy. Finding insights that drive business impact? That’s the real challenge. Here are 21 SQL queries that bridge that gap: (Not just write SELECT * FROM table): 1. SELECT COUNT(*) ↳ Understand scale before diving deeper. 2. SELECT DISTINCT column_name ↳ Spot inconsistencies or categorical levels fast. 3. SELECT column, COUNT(*) GROUP BY column ↳ See frequency distributions clearly. 4. SELECT column, AVG(value) GROUP BY column ↳ Get average performance across categories. 5. SELECT column, MAX(value) GROUP BY column ↳ Identify peak performance segments. 6. SELECT column, MIN(value) GROUP BY column ↳ Spot where things are underperforming. 7. SELECT column1, column2, COUNT(*) GROUP BY 1, 2 ↳ Explore relationships between key variables. 8. SELECT * WHERE column IS NULL ↳ Catch missing data issues before stakeholders do. 9. SELECT column, COUNT(*) GROUP BY column ORDER BY COUNT(*) DESC ↳ Detect dominant patterns. 10. SELECT DATE_TRUNC('month', date_column), COUNT(*) GROUP BY 1 ↳ Trend analysis on a monthly level. 11. SELECT * FROM table WHERE value_column > threshold ↳ Isolate anomalies or outliers. 12. SELECT column, ROUND(AVG(value), 2) GROUP BY column ↳ Provide rounded, presentation-ready insights. 13. SELECT column, COUNT(DISTINCT user_id) ↳ Measure unique engagement or participation. 14. SELECT column FROM table GROUP BY column HAVING COUNT(*) > x ↳ Filter out noise and focus on significant data. 15. SELECT user_id, COUNT(*) FROM events GROUP BY user_id ↳ Evaluate user activity levels. 16. SELECT CASE WHEN condition THEN 'A' ELSE 'B' END ↳ Categorize data for richer insight. 17. SELECT *, ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY date) ↳ Track user journeys over time. 18. SELECT LAG(value) OVER (ORDER BY date) ↳ Compare with previous periods for trends. 19. SELECT column1 || ' - ' || column2 AS combo ↳ Combine fields for grouped analysis. 20. SELECT * FROM table SAMPLE 1000 ROWS ↳ Speed up exploration with a smaller slice. 21. WITH CTE AS (...) SELECT * FROM CTE WHERE ... ↳ Modularize logic for better readability and reuse. Data Analysts who uncover value are never just querying—they're thinking in patterns. SQL is their language of clarity. Agree?

Your network is more powerful than any job application. You don't know the extend to which human connections can create unknown opportunities 2 years ago, my kid brother was in his final year as an engineering student at Oklahoma State University. He was working on a school project that required him to attend civil engineering conferences. On this one conference there was a Chief Engineer giving a presentation. He took some notes and wrote some questions for his project to ask the Chief Engineer. After the conference was over, he walked over to him and asked his questions and got some helpful answers. He was grateful for the answers provided and was asked to send an email to keep in touch. My brother wrote to him that evening and only got a response 1 month after where he was called to be interviewed for a job opportunity. They scheduled an interview with other engineers in different departments and another follow up interview 3 days after. My brother woke up to the best thanksgiving gift with the news of a job offer with one of the most prominent transportation institutions in Oklahoma State as a Civil Engineer. There are no shortage of stories like this where human connections lead to unexpected opportunities If you are not making these types of connections intentionally, you are missing out on a lot in your job search efforts Human-centered job search beats digital-centered job search Focus on creating those human connections and see how your job search efforts materialize.

I taught myself SQL and Python… …on a moving bus. Most people waste their commute. I built a career with mine. Back when I started learning data analytics, it felt like everything was against me. Setting up tools like SQL and Python was overwhelming. I almost gave up before I even began. Add to that a 2hr 30min daily commute and a draining full-time job. I barely had the energy to keep going after work. But then I made a simple decision: Use the commute as study time. Bus. Train. Every. Single. Day. I coded through motion sickness. I debugged scripts while people watched Netflix beside me. Sometimes I failed. Sometimes it clicked. But I kept going. And looking back, that decision changed my life. Because I didn’t have “more time.” I made time. Those small consistent efforts compounded into momentum I never imagined. You don’t need more time. You just need to use the time you already have—on purpose. 1 hour a day is enough to change your life in a year. Make the time. It will be worth it. --- 📌 I'm hosting a data analytics career webinar tomorrow. You will find it really helpful on your journey. RSVP here: https://lu.ma/llwly5dh

I never thought making six figures was possible. I worked odd jobs just to survive. Stocking shelves at Walmart. Packaging boxes on an assembly line. I didn’t think much of it until my health started to break down. In July 2020, I was rushed to the hospital for surgery. The doctor told me I wouldn’t be able to work for two months. That’s when I realized—I couldn’t keep doing the physical work. I wasn’t ready for this. So I turned to a friend for help. He was making six figures in data analytics. I asked, “What can I do to get to where you are?” He gave me a head start. I took it and ran with it. Five years later, I take my family on trips without a second thought. I prioritize my health, my family, and my faith. Adversity turned out to be a blessing in disguise. What if I hadn’t faced that challenge? What if I hadn’t been forced to change? You don’t need to wait for life to push you. Take control of your future now. The best time to invest in yourself is today.

You'll never value workplace culture until you've survived a toxic one. Companies showcase themselves with glossy advertisements and trendy buzzwords: diversity, inclusivity, friendly environment. But here's what really happens behind those polished promises: → Inappropriate relationships influence power dynamics → Gossip circles form when colleagues are absent → Office politics run deep and shape decisions → Hostile communication becomes the norm I witnessed this reality when my boss's relationship with her superior created an unfair advantage. The environment was so toxic that curse words replaced "good morning." After 6 months of enduring this negativity, I made a bold decision: I chose my mental health over a steady paycheck. Walking in that morning to quit, without a backup plan, was terrifying. But sometimes the best decisions come from our darkest moments. The results were unexpected: → Landed in a supportive workplace → Found a new job within 2 months → Got my dream Data Analyst role a year later. Here's the wake-up call for professionals: ↳ No salary compensates for mental health damage ↳ Toxic environments will drain your potential ↳ The right workplace culture exists. The decision to leave a toxic workplace might seem risky, but staying could cost you more than just your peace of mind. What's holding you back from prioritizing your well-being at work? --- ♻️Repost to your network if this resonates ➕️ Follow Peter Ndiforchu for more