Kofi Baryeh

ONE LESSON I KEEP LEARNING IS THIS: Never stop learning. There was a time I doubted whether I could transition into data analytics because I did not come from a strong computer background. No formal tech foundation. No advanced programming experience. Just curiosity, determination, and the willingness to start from where I was. At the beginning, many things felt unfamiliar: • SQL queries • Databases • Data cleaning • Dashboards • Statistical analysis There were moments of confusion and frustration. But I chose not to let that stop me. Step by step, I kept learning. One concept at a time. One challenge at a time. One project at a time. Looking back, I can clearly see how far I have come and I’m grateful to God for helping me through every stage of the journey. What once looked impossible is gradually becoming achievable. This experience has taught me that your starting point does not determine your destination. You may feel behind today. You may feel unqualified today. You may feel inexperienced today. But if you keep learning, keep improving, and stay consistent, growth will come. I’m still learning. Still building. Still trusting God for what is ahead. 💭 What skill are you currently developing that once felt difficult? #LearningJourney #DataAnalytics #CareerGrowth #GrowthMindset #FaithAndWork #SQL #HealthDataAnalytics

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Week 2 of my Data Learning Journey🎉 This week taught me something important, data analysis doesn’t start with dashboards or charts. It starts with clean data. We focused on data cleaning, identifying and fixing errors, inconsistencies, and inaccuracies in datasets. Some of the practical steps we explored included: 🔹 Removing duplicate records 🔹 Fixing formatting issues using Text to Columns 🔹 Identifying missing values using filters and Find & Select 🔹 Correcting typos with Find and Replace My key takeaway: A large portion of data analysis happens during the data cleaning stage. Messy data leads to misleading insights, and that can affect decision-making. Clean data truly is the foundation of reliable analysis. #DataAnalytics #LearningJourney TechSkill Lab Africa Deborah Afolayan

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Unique REMOTE Actuarial Analytics & Data Management role requires big-picture thinking and blended actuarial/technical expertise to modernize data solutions & steer portfolio! https://lnkd.in/edKndybR #Actuary #Actuarial #DataManagement #DataBricks #Remote #EzraPenland #109867

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Without a guiding question, data is just noise. This phrase was discussed in one of the brainstorming session I had yesterday (22/10/2025) with liked-minded analysts and I concluded with this piece: As data analysts, we often get excited by dashboards, trends, and huge datasets. But data alone doesn’t create insight, purpose does. Every meaningful analysis starts with a question: What problem are we trying to solve? What decision needs to be made? What story does this data need to tell? Without a clear direction, even the most sophisticated tools can produce beautiful but meaningless visuals. But when guided by the right question, data transforms become insightful, actionable, and valuable. In analytics, clarity is more powerful than complexity. So before diving into the data, pause and ask: What am I really trying to understand? That’s where true data storytelling begins. Thanks to my cousin Kevwe Anidi and other liked-minded analysts. What a wonderful platform Analytica Haven. #AnalyticaHavenConsistency02  #StayConsistentWithAnalytica #DataAnalyst #Brainstorming

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🚩 Dashboards aren’t supposed to look like Christmas trees 🎄 Have you ever open a dashboard and just sit there like, “Wait… what am I even looking at?” Charts everywhere. Colors shouting at you. 3 minutes in, and still no clue what’s important. That’s not insight. That’s noise. 📍 Dashboards aren’t built to impress. They’re built to guide decisions. → More visuals ≠ more value → One clear story > ten half-stories → If people feel lost, the dashboard failed 📍 Your dashboard isn’t for you. It’s for the person who needs to act on it. → If it looks like a Christmas tree, you built it for yourself. →If it looks like a compass, you built it for the business. 📌Drop one dashboard tip or advice so others can learn #dataliving #favouribude

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Reflecting on the intersection of AI and data analytics reveals a dual narrative: the fear of replacement versus the opportunity for evolution. AI can generate SQL queries or create visualizations, but this is only possible after a human comprehends the business context, the intent behind the request, and the insights hidden within the data. The real work often begins before utilizing the tool. Even when AI produces a query or chart, it may lack optimization, relevance, or alignment with the intended purpose. The crucial layer of judgment, interpretation, and decision-making remains our responsibility. However, it is important to acknowledge that AI will reduce the number of people required for certain tasks. If previously ten individuals executed a job, now perhaps five can accomplish it with AI's assistance. Those at risk are those who operate like AI, merely following commands without proactive thinking. Value lies with those who ask the right questions, propose better approaches, understand business impact, and take end-to-end ownership. As analysts, we have the opportunity to leverage AI to our advantage by: - Speeding up scripting and data exploration - Improving documentation and communication - Enhancing quality checks and reducing repetitive tasks - Boosting productivity and efficiency in addressing business challenges AI is not here to replace us; it challenges us to elevate our skills. The more initiative and ownership we demonstrate, the more irreplaceable we become. I am also eager to learn from the community: apart from scripting or visualization enhancements, how are you integrating AI into your daily analytics work? #dataanalytics #AI #chatgpt #openai #copilot #analyst #visualisation #aitrends2025

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The Fastest Way Into Data? Start With What You Already Know. Breaking into data doesn’t mean starting from scratch. You can build on what you already know. The truth is, it’s much easier to learn tools and skills when you already have a solid foundation in your field. Why? Because you understand the language, challenges, and opportunities of that industry. Here’s how it looks in practice: • If you’re in logistics → Become a Supply Chain Analyst. • If you’re in healthcare → Become a Healthcare Analyst. • If you’re in graphic design or the arts → Become a Data Visualization Analyst. • If you’re in banking/finance → Become a Financial Analyst. • If you’re in marketing → Become a Marketing Analyst. And the list goes on. Because the world of data is vast. You can specialize as a: • Customer Insights Analyst • HR/People Analyst • Risk Analyst • Retail Analyst • Sports Analyst • Policy Analyst • Education Analyst • Business Intelligence Analyst • Product Analyst • Operations Analyst • Cybersecurity/Data Privacy Analyst And many more... Every industry needs data. And every professional already has a head start in the field they know best. So if you want to break into data fast, don’t reinvent the wheel. Start with what you know, and build data skills around it. That’s how you go from zero to valuable, faster than you think. Agree?

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🚀 🌟 INSERT INTO My Brain! 😄 So I was working with SQL and honestly… inserting values into a table feels like teaching a database how to remember people! Just like: “Hey MySQL, meet Holly, Mike, and Dustin. Be nice to them. Store their info safely.” 😌 In the image below 👇, you’ll see me adding new records into my users table using the classic: INSERT INTO table_name (columns) VALUES (...) And yup — the moment that query ran successfully… chef’s kiss. The feeling of seeing clean, structured data appear in the result grid is unmatched for any aspiring data analyst. 🔥 💡 Why I’m hyped about this? Because small SQL operations like this are BIG STEPS toward: ✔ understanding data pipelines ✔ cleaning & managing datasets ✔ building dashboards that actually make sense Every query makes me better. Every table brings me closer to becoming a stronger Data Analyst. 📊✨ Let’s keep leveling up! #SQL #MySQL #DataAnalytics #LearningJourney #AspiringDataAnalyst #TechSkills #InsertIntoLife

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Is it actually hard to break into a Data Career? 📉 I analyzed survey data from 630 professionals to find out. According to the data in my new Power BI dashboard, only 6.98% of professionals found it "Very Difficult" to enter the field. The majority found the barrier to entry manageable. Other quick stats: Top Earner: Data Scientist ($94k avg). Top Tool: Python. Average Age: ~30 years old. Check out the full visualization below to see the breakdown by country and job title. Need a dashboard like this for your business? I specialize in Power BI and SQL solutions. Let's chat. #PowerBI #DataVis #Freelancer #Python #DataAnalysis

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One thing I’ve learned in healthcare analytics: Clean data is everything. I’m currently expanding my expertise in data cleaning and data quality processes to strengthen the foundation behind every report and insight I deliver. In healthcare ��� especially when working with EHR, authorization, and payer data — even small inconsistencies can impact operational decisions and patient outcomes. I’m excited to sharpen my skills in: • Detecting anomalies • Standardizing data formats • Managing missing or duplicate records • Strengthening overall data integrity Better data → better decisions → better patient outcomes. #HealthcareData #DataQuality #HealthcareAnalytics #ProfessionalGrowth

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📘 “Eliminate distractions” – A principle from Storytelling with Data As a Data Analyst, I’m always inspired by how data visualization combines clarity and storytelling. While reading Storytelling with Data, one concept that stood out to me was: "You know you’ve achieved perfection not when you have nothing more to add, but when you have nothing more to take away." — Antoine de Saint-Exupéry This idea reminded me of my approach when designing this Power BI Sales Performance Dashboard — focusing on clarity by highlighting what matters and removing what doesn’t. In this dashboard, I aimed to: — Highlight key insights like profit, total sales, and top-performing   categories — Eliminate unnecessary visuals to enhance focus and readability — Design a clean and modern layout that makes data easier to interpret I’m currently reading Storytelling with Data and applying its lessons to improve my dashboards step by step. My goal is to keep learning and continuously refine my skills to make data tell a clearer story. 💬 What do you think about this dashboard? I’d love to hear your thoughts and feedback! #StorytellingWithData #PowerBI #DataVisualization #DataAnalytics #DashboardDesign #BusinessIntelligence #Clarity #MicrosoftPowerBI #LearningJourney #DataDriven

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Day 43 of my data skills update learning from Zach Wilson and and DataExpert.io was the one where I turned my capstone project in! After 6 weeks of learning the analytics engineering tools and the data engineering process and the hard work that I put into coding and building I was able to turn in my project. Time to wait on grading, but I'm proud of myself for this. Here are a few things I learned: - The Google Analytics API does not have a user_id or session_id to allow for easy joining. - The Google Analytics API also doesn't give you latitude and longitude for mapping. So I had to put the map I wanted to make on the backburner for the DE capstone. - When setting up a connection to Snowflake in Airflow, you will get an "access" error if you also have the Snowflake connection url as part of your task in your Airflow DAG. - The free version of Tableau doesn't have a Snowflake connection. After the grading has been done and I pass, I will share my dashboard for everyone to see.

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"I just got my offer letter." "$78,000 as a Business Intelligence Analyst." "Four months ago, I was working night shifts at Target for $15/hour." That voicemail came in last Friday. From Maria in Phoenix — one of my mentorship students. She was crying. Happy tears. Because months earlier, she believed everything was collapsing. Her situation: Maria arrived in the US from Pakistan on a student visa. Invested $105,000 in a Master's in Information Systems. Graduated in May 2024. By September: → 240 job applications → 8 phone screens → 2 interviews → 0 offers OPT time shrinking. Pressure rising. She chose what many students feel pushed toward — a placement agency. They charged $11,500 promising quick results. Instead they: → Added fake Power BI experience → Listed unverifiable projects → Coached interview deception → Placed her into a $72K role Reality surfaced quickly. Week 1 — Asked to optimize a dashboard. No foundation in performance concepts. Week 3 — Couldn’t explain the data model during review. Week 5 — Put on monitoring. Week 7 — Employment terminated. With it came immigration risk. 60 days to recover. She contacted me late at night feeling defeated. Savings remaining: $3,200. I explained the path forward clearly: We focus on capability, not shortcuts. Weeks 1–2 ✅ Resume rebuilt removing fabricated claims ✅ Academic work reframed into junior-level narratives ✅ One defendable Power BI solution created Weeks 3–4 ✅ Structured SQL practice grounded in business use ✅ Retail analytics portfolio using her experience ✅ Interview response coaching Weeks 5–6 ✅ Frequent mock interviews ✅ Honest exit-explanation preparation ✅ Targeted applications Weeks 7–8 ✅ Multiple interview invitations ✅ Final rounds completed ✅ Two offers secured ✅ Accepted $78K BI Analyst role in Phoenix No exaggeration. No risk exposure. Just disciplined preparation. Maria later shared something important: She wished she invested in capability before seeking shortcuts. My one-to-one mentorship focuses on sustainable growth through: 📊 Power BI depth 💾 SQL query design 📈 Portfolio credibility 🎯 Interview readiness 💼 Resume positioning 🧠 Analytical thinking Delivered through live collaboration, direct feedback, and tailored progression. If this resonates, begin the conversation. Comment 💼 or message TRANSFORM for details. Maria moved from overnight shifts to analytical delivery responsibility. Capability changed her trajectory. Keep building forward. #DataAnalytics #PowerBI #CareerGrowth #InternationalStudents #SQL #BusinessIntelligence

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I built an agent that audited and cleaned 9 tables in 12 minutes. This is Chapter 6 of my LinkedIn Learning pilot series: 𝐁𝐮𝐢𝐥𝐝 𝐘𝐨𝐮𝐫 𝐅𝐢𝐫𝐬𝐭 𝐀𝐈 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 𝐰𝐢𝐭𝐡 𝐂𝐥𝐚𝐮𝐝𝐞 𝐂𝐨𝐝𝐞. This is what a subagent does. A specialist who focuses on one job and does it well. I created a Data Quality Agent by describing what I wanted: ↳ Check every table for missing values and duplicates ↳ Find outliers and inconsistent formatting ↳ Flag referential integrity issues ↳ Fix what you can, log every change Then I ran it with one prompt: "Check my e-commerce data for quality issues, clean it, and generate a report." I didn't tell it which agent to use. Claude Code read the agent descriptions and picked the right one automatically. The output: a detailed quality report with table-by-table breakdown, specific numbers for every issue found, and 9 cleaned CSV files ready for analysis. Work that would take a human analyst one to two weeks. Done in 12 minutes. 𝗧𝗿𝗮𝗱𝗲𝗼𝗳𝗳𝘀 𝘁𝗼 𝗸𝗻𝗼𝘄: ↳ Subagents work best with clear, narrow scope ↳ You still need to review the cleaning decisions it makes For most data quality workflows, the speed is worth the review time. What data quality task would you automate first? 👇 ♻️ Repost if someone in your network is building with Claude Code. #AIDataAnalyst #ClaudeCode #DataQuality

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I have learned that no amount of analytics can fix poor data quality. It’s easy to get caught up improving your model, your visualization, or use more advanced tools- but if the data foundation is weak, everything built on top of it starts to wobble. Data quality isn’t about just accuracy. It’s about trust – trust from leadership who rely on your insights, trust from co-workers who rely on your work to do their jobs more effectively, and trust from stakeholders who rely on your expertise. Investing time and resources in cleaning, validating, and cleaning again isn’t glamorous and not always fun, but it’s what separates data noise from data intelligence. What is a data quality issue you’ve run into that changed how you approach your work? #DataQuality #DataGovernance #Analytics #DataScience 

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Ever wonder what the landscape of the data industry actually looks like? I recently analyzed survey data from 630 data professionals to uncover trends in salaries, tool preferences, and overall job satisfaction. Here is a breakdown of my latest portfolio project: 1. The Context/Business Question What is the current state of the data profession? I wanted to understand the demographic makeup of data experts, the most valuable skills to learn, and whether professionals are actually satisfied with their compensation and work-life balance. 2. Tools Used • Data Cleaning: Power Query • Visualization & Dashboarding: Power BI 3. The Process I extracted and cleaned the raw survey data of 630 respondents. This involved standardizing diverse job titles, grouping geographic locations, and structuring the data model to allow for interactive filtering by job role and country. 4. Key Findings • Python is King: Across all roles (Analysts, Engineers, Scientists), Python massively dominates as the favorite programming language, with R and C/C++ trailing far behind. • The Salary Hierarchy: Data Scientists and Data Engineers command the highest average salaries, while Data Analysts sit comfortably in the middle tier. • Satisfaction is Lacking: Despite the hype around tech jobs, respondents reported a low happiness score regarding their salaries (4.27/10) and only a moderate score for work/life balance (5.74/10). • Entry is Tough: Nearly 67% of professionals surveyed found breaking into the data field to be "Difficult" or "Neither easy nor difficult," disproving the myth that entry-level roles are easy to grab. 5. Actionable Recommendations • For Aspiring Professionals: Stop getting distracted by shiny new tools and focus heavily on mastering Python. Additionally, expect a challenging job hunt—persistence is key. • For Employers & Recruiters: To attract and retain top data talent, companies need to urgently address the low salary satisfaction (4.27/10) and improve work/life balance initiatives. Check out the dashboard below! What surprises you the most about these industry trends? Let me know your thoughts. 👇 #DataAnalysis #DataScience #PowerBI #PortfolioProject #DataAnalytics

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𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗜𝘀 𝗛𝗮𝗿𝗱𝗲𝗿 𝗧𝗵𝗮𝗻 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴 Hot take. Most people get excited about regression models and predictive analytics. But in healthcare? Data wrangling determines whether your model even makes sense. You can build the most beautiful model… But if: • ICD codes were grouped incorrectly • Null values weren’t handled • Cohorts weren’t defined properly Your prediction is noise. In my experience, 60–70% of the work is data cleaning and validation. Modeling is the final layer — not the foundation. Agree or disagree? 〰️Kosi Clean data. Clear decisions. Better healthcare.

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One thing I’m becoming more intentional about in data work is writing things down. Not notes for memorization. Notes for thinking. Sometimes I don’t open a tool at all. I just write: What is the question? What do I already know? What would a useful answer look like? That clarity often matters more than jumping straight into queries or charts. Data work isn’t always about doing more. Sometimes it’s about slowing down enough to think clearly.

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PSA for aspiring Data Scientists, Data Analysts, and Data Engineers: You can love SQL. Or you can hate SQL. But you can’t get away with avoiding SQL!

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