If you're in: 1. Data 📊, 2. Finance 💰, 3. Tech 💻… Let’s connect 🤝 I’m currently learning data analysis, exploring SQL 🧠, data cleaning 🧹, and how data drives decisions 📈 Would love to connect with people building, learning, and growing in this space #DataAnalysis #TechCommunity #Finance #LearningInPublic #30DaysOfData

Day 3 of my Data Analysis Challenge 📊 Let’s make metadata real using your phone 👀 When you take a picture, the image is the data… But your phone also stores extra details — that’s metadata 👇 - Date & time the photo was taken - Location (GPS) - Camera type - File size - Resolution You don’t always see it, but it’s there — giving context to the photo. Without this metadata, it’s just a random image. With it, you know when, where, and how it was created. That’s the power of metadata. #DataAnalysis #LearningInPublic #30DaysOfData

Day 2/30 of my Data Analysis Challenge 📊 Where does your data really come from? 👀 Most people jump straight into analysis… but forget the most important question: “Can I trust this data?” Data doesn’t just appear — it comes from somewhere: Spreadsheets, databases, surveys, apps, even manual entries. And here’s the truth 👇 Your insights are only as good as your data source. Clean charts don’t fix bad data. Fancy dashboards don’t correct wrong inputs. A great analyst doesn’t just analyze — they investigate: ✔ Who collected this data? ✔ How was it recorded? ✔ Is anything missing or biased? Because one wrong source can lead to: ❌ Wrong decisions ❌ Lost money ❌ Misleading conclusions So next time you open a dataset, pause and ask: 👉 “Do I understand where this came from?” That question alone will set you apart as a data analyst. #DataAnalysis #DataAnalytics #LearningInPublic #30DaysOfData #DataSources

The results are in!!🥳 We just wrapped our #30DaysOfData Challenge 2026 and our scholars absolutely delivered! 🔥 Congratulations to our TOP 3 🏆 🥇 Samuel Nzebor — 261,320 XP 🥈 Ibukun Egwuogu — 206,236 XP 🥉 Goodness Okoro — 195,596 XP 30 days. No excuses. Just growth. 💪🏽

1/2 #30DaysofData with @DataFestAfrica || Day 14 In continuation of the dataset visualization exploration using Seaborn. @DataCampDonates #DataCommunityAfrica #DCA #DCDonates #DataJourney #DataFestAfrica #DataScience

1/2 #30DaysofData with @DataFestAfrica || Day 13 In today’s session, I explored data visualization using Seaborn. Although I didn’t cover as much as I did on previous days, ...... @DataCampDonates #DataCommunityAfrica #DCA #DCDonates #DataJourney #DataFestAfrica #DataScience

Things I’ve learned on Excel this week: Working with data Creating formulas Working with tables Managing and filtering data Working with structured references which are easier to filter and understand. #DataAnalysis #30daysofdata

🧵 Day 1 of #30DaysOfData #BuildInPublic Journey Today marks the start of my data analysis consistency challenge! 🚀 ✅ Loaded my dataset — Global Energy Data (OWID) #DataScience #DataAnalysis #Python #JupyterNotebook #Pandas #Streamlit #Plotly #LearningInPublic #TechTwitter

Day 14 of #30DaysOfData Raw data isn’t always model-ready. 🔹 Transform: make it usable (log, encode, scale) 🔹 Normalize: keep values on the same scale (Min–Max, Z-score) These steps keep models fair and insights reliable. #DataScience #MachineLearning #Analytics

Day 13 of #30DaysOfData Outliers aren’t always errors sometimes they’re the insights that stand out for a reason. 🎯 Detect: Z-score, IQR, Boxplot, Isolation Forest ⚙️ Handle: Investigate, Transform, or Remove Great data scientists don’t rush to delete. They ask why. #DS

Day 12 of #30DaysOfData Missing values are inevitable, how you handle them shapes your model’s accuracy. ✅ Techniques: Deletion (if few) Imputation (Mean, Median, Mode, KNN) Advanced: Multiple or Model-based methods Handle them smartly. Data integrity drives good science. #DS

Day 10 of #30DaysOfData Probability is the backbone of Data Science, it helps us handle uncertainty and make better predictions. 🔹 Quantifies uncertainty 🔹 Powers ML models 🔹 Guides smarter decisions Nothing in data is ever certain, probability helps us make sense of it. #Ds

Day 9 of #30DaysOfData Statistics is the backbone of data science - it helps us find meaning in uncertainty. 🔹 Mean – Central value 🔹 Median – Middle point, resists outliers 🔹 Variance – Measures data spread 🔹 Std. Deviation – Shows how far values deviate #DataScience

Day 8 of #30DaysOfData Master variable types to analyze smarter. 🔹Qualitative Data – non-numeric, descriptive (e.g., opinions, colors, categories) 🔹Quantitative Data – numeric, measurable (e.g., age, income, scores) Know your variables → better insights & models. #datascience

Day 7 of #30DaysOfData Not all data are the same. Knowing the type guides how you clean, analyze & model it. 🔹 Structured: Tables (SQL, Excel) 🔹 Unstructured: Text, images, audio 🔹 Semi-structured: JSON, XML Know your data. Choose the right method. #DataScience

Day 6 of #30DaysOfData Data tells a story but without visualization, it stays hidden. Visualization reveals patterns, outliers & insights, making data clear for everyone. Tools: Matplotlib, Seaborn, Power BI, Tableau. A single chart can speak louder than rows of numbers. #DS

Day 5 of #30DaysOfData Exploratory Data Analysis (EDA) is how we make sense of data before modeling: 🔹 Spot patterns 🔹 Detect anomalies 🔹 Test hypotheses 🔹 Guide feature selection EDA turns data into understanding - without it, models risk being blind guesses. #DataScience

Day 4 of #30DaysOfData Great models can’t fix bad data. That’s why Data Cleaning is critical in Data Science: ✅Handle missing values ✅Remove duplicates ✅Correct errors ✅Standardize formats It may not be glamorous, but clean data is the foundation of trustworthy insights. #DS

Day 3 of #30DaysOfData The Data Science Lifecycle turns raw data into real impact: 1️⃣ Define the Problem 2️⃣ Collect Data 3️⃣ Clean & Prepare 4️⃣ Analyze 5️⃣ Model 6️⃣ Evaluate 7️⃣ Deploy & Monitor Miss a step, and the project risks failure.Master them, and data drives transformation.

Day 2 of #30DaysOfData Data alone is not enough. Data is raw and unprocessed. Information organizes it into context. Insight extracts meaning that drives decisions. Only when data becomes insight does it create real value. That’s the essence of Data Science. #DataScience