Meme
Meme @Ahi_Meme ·
#DataAnalyticsLockedIn
Meme Meme @Ahi_Meme ·
Day 19/120 📊. Dove into advanced Power Query today - splitting & merging columns, handling errors, cleaning messy data. Still learning, still messy but I can already feel my ETL skills leveling up.
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Deborah E.A
Deborah E.A @debaka15 ·
Still early days, and I’m trying not to get overwhelmed by all the new concepts, but I can already tell it’s a different way of thinking compared to what I’ve used before. I guess this is where we set the ball rolling. #DataanalyticsLockedIn
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Kwesi_Kona✞
Kwesi_Kona✞ @o_tafiri_gya_ ·
Day 43 of 120 Data Analysis Expressions popular known as DAX was the topic for today i learnt about what dax is and the 3 pillars of dax which is calculated columns,calculated tables and measures @Rita_tyna #DataAnalyticsLockedIn
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Adeojo Omolola
Adeojo Omolola @LolaAdeojo ·
Day 38/120: More pizzas, more prep time… but not always. Found a clear trend, larger orders take longer, but not perfectly. Some small orders took longer than expected, likely due to other factors. This part is more about interpreting the problem. #DataAnalyticsLockedIn
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Chris Vian
Chris Vian @Data_vian ·
Day 43 Instead of writing massive aggregations on day one, I isolated 8 random customers to trace their upgrade journeys. You can't calculate accurate macro metrics if you don't understand the micro logic first. Full post here: tinyurl.com/2m3phfub #DataAnalyticsLockedIn
#dataanalyticslockedin #businessintelligence | Victor Anoke

New dataset. New business. Completely different problem. Day 43 of 120. I moved from a small restaurant to a subscription-based food streaming service: Foodie-Fi. The question isn't about who's...

From linkedin.com
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Adeojo Omolola
Adeojo Omolola @LolaAdeojo ·
Day 37/120: Working through the Pizza Runner SQL case study. Focused on pizza metrics—orders, deliveries, changes, and time patterns. Simple questions, but the logic got tricky fast. Next: customer experience and runner insights. #DataAnalyticsLockedIn
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NenyeKez
NenyeKez @JoyKezie ·
Nexora Technologies thought pricing caused revenue swings. Data showed the real issue: demand volatility, product concentration & regional imbalance. Built analytics to uncover it. Better visibility → Better decisions. #DataAnalytics #Excel #DataAnalyticsLockedIn
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LOLU
LOLU @Lolu_rayo ·
Replying to @Lolu_rayo
I expected online to dominate, but over 70% of revenue still came from physical shopping experiences. Maybe it’s not just about convenience; people still value the experience : seeing, touching, trying, and instantly owning what they buy. #DataAnalyticsLockedIn #BuildingInPublic
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