PraiseGem🦀

What I studied? Computer science. What I do now? Data analysis, Data science,Data engineer How I got here? @Theroothub Picked me and gave be the grounds, Self taught (data science and data engineering) Was it easy? Damn, No fam! Burned nights and brain cells, crashed 3 laptops, started using glasses Was it worth it? Heck Yeah! The joy seeing the systems i built solve problems around the world There's no limit to what you can become, just start the journey already

@emmannuel_codes @RoseMarvelous4 There are people with speed leverage sha.... Don't 100% bank on this

@RoseMarvelous4 First build the idea.. then half way through start talking about it 🤧😅 before anyone can steal your idea boomm you've launched

How do you people protect your idea from being stolen when you’re trying to build in public? How do you ensure you don’t overshare? I’m waiting for answers ooo 👀🌚

@Napes_0fficial Send the right message at the right time

Messaging that moves the needle. 📈 Sending the right message at the wrong time is the same as sending no message at all. Master your customer lifecycle with Customer.io. Create automated workflows that react to user behavior in milliseconds. More engagement. Less churn. Better ROI. See why top-tier product teams are making the switch. 👇 Link: share.customer.io/7ghGgj

@witty_cruz Sorry chief....Thumbs up 🌚

Went to play football today and came back with broken fingers. 🤕🤕 Will be out of the pitch for 2-4 weeks. Out of the pitch, not out of web3. We keep shipping Solana and Solflare 💛👑

A retail company sought to better understand its sales and profitability performance across products, regions, and shipping methods to improve decisionmaking and maximize revenue. The company lacked clear visibility into which areas were driving profit and which were underperforming, limiting its ability to optimize pricing, logistics, and regional strategies. The dashboard revealed an average profit of approximately 28.61 and average sales around 246.64, indicating moderate profitability. Profit contribution was heavily skewed toward the Technology category (~59%), followed by Furniture (~25%) and Office Supplies (~16%), showing a strong dependency on a single category. Shipping costs were highest for Standard Class (~0.6M) compared to other modes, suggesting inefficiencies in logistics. Regionally, discount distribution was highest in Central (~1.54K) and South (~0.99K), indicating aggressive pricing strategies in these areas. Over time, both sales and profit showed a consistent upward trend, peaking around 2014, followed by a noticeable drop afterward, signaling potential market or operational challenges. The findings suggest the need to diversify revenue streams beyond Technology, optimize shipping costs, and reassess discount strategies in highdiscount regions. Addressing these areas can improve profitability and ensure more balanced business growth. Analyzed and visualized: September,2024

In a Nigerian fintech company offering data analytics services to consumer businesses, this Power BI sales dashboard tracks performance for a client selling food and beverage products (categories: Delish, Juices, Yummies) across international markets. The dashboard shows operations in 6 locations (Australia, Canada, India, Nigeria, UK, USA), 22 distinct products sold, 14K total activities, and average sales of 532. Geo-map highlights activity concentration in regions like North America and Asia (tallest bars). Sales trend line starts at a peak of ~82K, then drops sharply and flattens to ~4K–10K levels in recent periods, indicating early momentum that faded significantly. These numbers expose declining sales velocity despite multi-country reach and product variety. Focusing resources on high-activity regions (strongest geo-bars) and top-performing categories could reverse the drop from 82K peak to low single-digit thousands, lifting average sales beyond 532 and expanding beyond current 6 locations to capture more of the 14K activity potential. Analyzed and visualized: October,2025

Rising living costs were hitting households hard, with transportation and logistics driving up food prices. The team needed a unified view to estimate current prices by location and quantify how fuel price changes ripple through staples like garri. Problem statement Fuel price fluctuations directly inflate commodity costs due to higher transport expenses for farmers, traders, and distributors. Without clear, location-specific visibility, businesses and policymakers struggled to predict or mitigate affordability issues for essentials. Analysis The dashboard reveals strong positive correlation: as fuel prices rise from ~720 NGN/litre to ~860 NGN/litre, the average garri price increases linearly from ~120K to ~130K+ (the trend line slopes upward across the x-axis fuel values). With garri's national average at 128,010 NGN (likely per bag or large unit, aligning with recent wholesale trends where 50kg bags range ~₦35,000–₦50,000 and smaller units reflect per-kg averages around ₦800–₦1,200), fuel emerges as a key driver. The map allows state-level drilling (e.g., selecting Benin or other marked locations), and tabs switch between commodities, enabling comparison of fuel's outsized effect on transport-heavy items like garri versus others. Effect of the analysis Quantifying this link (e.g., ~10–15% garri price rise over the displayed fuel range) enables proactive measures: fintechs can adjust lending models for small traders, alert users to price trends via apps, or inform policy advocacy for fuel subsidies/stabilization. Lower fuel volatility could cap commodity inflation, easing household budgets and reducing food insecurity in a market where staples like garri remain core (recent data shows per-kg garri ~₦820 nationally, down from peaks but still sensitive to energy costs). The tool empowers data-driven decisions to buffer economic shocks. Analyzed and visualized: March, 2025

In a mid-sized fintech company in Nigeria, leadership observed rising employee exits disrupting product delivery and increasing hiring costs. The Power BI dashboard quantified the attrition drivers. Introduction The company had been growing fast, but voluntary departures climbed noticeably over the past 18 months, particularly among software engineers, data analysts, and operations staff. Problem statement High attrition was eroding team stability. Key question: which measurable factors were pushing employees to leave? Analysis The data pointed to clear patterns: Work-life balance had the strongest link employees rating it "Good" accounted for the largest group of stayers (highest bar at ~19–20K count), while "Poor" ratings showed the smallest stayed group and much higher leavers. Remote work was a major differentiator: average attrition value reached 0.80 for employees without remote options versus 0.40 for those with remote access — exactly double the rate when flexibility was absent. Salary showed imbalance: stayed employees were more concentrated in higher pay brackets (50–60% in upper bands), while leavers skewed toward lower salary ranges. Innovation access was severely limited 82.86% of employees (20,600+) reported "No" opportunities, with only 17.04% (≈5,000) saying "Yes." Company reputation correlated directly with exits: average attrition value rose steadily from ~0.58 at "Excellent" reputation down to ~0.42 at "Poor" (a ~38% increase in attrition as perception worsened). Effect of the analysis These numbers translated to real impact. Offering remote work could potentially halve attrition for that segment (from 0.80 to 0.40). Improving work-life balance for the "Fair" and "Poor" groups (currently the second- and third-largest leaver bars) would retain a significant portion of the ~10–20K employees in lower-rated categories. Addressing the 83% lack of innovation would engage the majority workforce frustrated by limited growth paths. Fixing salary compression in lower bands and halting reputation decline (which amplifies attrition by ~38% at the low end) would reduce overall turnover. Combined, these targeted changes could cut replacement costs (frequently 1–2× annual salary per role) and stabilize delivery velocity in a competitive Nigerian talent market. Analyzed and visualized: July,2024

A healthcare analytics initiative was launched to evaluate the overall health performance of citizens across three cities. The goal was to understand health patterns and identify areas needing improvement. Despite access to healthcare services, there was limited visibility into how health outcomes varied across locations, genders, and over time, making it difficult to drive targeted interventions. The dataset covered 1,611 citizens across 3 cities, with an overall average health score of approximately 2.58/4, indicating moderate health conditions. Gender analysis showed that females had the highest health score (~3.2) compared to others (~2.6) and males (~2.3), highlighting a noticeable disparity. Citylevel performance was relatively balanced, with each city contributing roughly 31–33% of overall health outcomes, suggesting no single city was significantly outperforming the others. Over time, health performance fluctuated slightly, peaking around 2.58 in 2021 and dropping to about 2.47 in 2022, before stabilizing again near 2.58 in 2023 and declining slightly afterward. These trends indicate that while health outcomes are relatively stable, there are underlying inconsistencies across demographics and time. The insights highlight the need for targeted health interventions, particularly focusing on lowerperforming demographics such as males, while also ensuring consistent healthcare delivery across all cities. Monitoring yearly fluctuations can help policymakers respond proactively to declines in health performance. Analyzed and visualized: November, 2025

In 2024, a fintech company in Nigeria began experiencing a steady rise in employee turnover despite maintaining stable business performance. While growth continued externally, internally the company was gradually losing talent. The company needed to understand the drivers of attrition, as increasing employee exits were leading to higher hiring costs, reduced team stability, and potential productivity loss. Analysis The data showed that overall job satisfaction (2.74/4) and environmental satisfaction (2.71/4) were only moderate, indicating a workforce that was not fully engaged. A key difference appeared in worklife balance, where employees who left had lower scores (2.59) compared to those who stayed (2.79), suggesting workload pressure as a major factor. Attrition was also higher among newer employees, with those who left averaging ~3.5 years in tenure compared to ~7.5 years for those who stayed. Promotion patterns further revealed that employees who exited had shorter promotion timelines (~1.4 years) versus ~2.2 years for retained employees, pointing to unmet expectations around career growth. Additionally, certain roles such as HR showed higher attrition, while relationship satisfaction remained nearly equal (about 50% split), indicating that structural factors, not interpersonal issues, were driving exits. Effect of the Analysis The insights highlighted the need to improve worklife balance, manage career expectations, and strengthen early employee engagement. Addressing these areas enables the company to reduce attrition, retain talent, and improve overall workforce stability. Analyzed and visualized: August,2024.

@uzair_dev_ WinRAR, i unzip all my files for free

Name a tool you've used for years and never paid for. I'll go first: GitHub.

@debar_fx I'm based in Solana

Imagine earning over 2x of Base weekly revenue on a bounty 🤯

@CHIEFIKENGA @Alkebulantribe This is gooodddddd

In 2022 I launched an NFT project called @Alkebulantribe . The collection put African kings, gods, myths and stories on the blockchain. 4 Years later that project has birth a multi award winning animation series called legends of Alkebulan . I’ve been building for Africa to the world and I won’t be stopping anytime soon. New episode coming out soon , here’s a clip , btw no AI was involved in the creation of this masterpiece .

Shipped a form builder this week. Drag and drop editor, live preview, embeddable on any site. Started as a file sharing tool and it keeps growing into something bigger. Screenshots of what it looks like in action.

This is one of the cutest video. The boy's parents picking him up from school dressed as Superman and Supergirl.

Happy Women's Day💙🩵

@Decentral_Diva @SuperteamNG Co ask😭💔

@SuperteamNG when Uyo?

Co-working Friday is BACK! Starting this Friday, builders, creatives, founders, and innovators will come together to: • Grind on personal projects • Brainstorm ideas • Network and collaborate • Sip great coffee while Shipping products No distractions, just focused energy and good vibes.

@soniaezema @SuperteamNG @blockhubafrica @solana @vancube_2 @NzubeEzudo @grok list all the locations for the coworker stuff

Attended the @SuperteamNG Enugu community’s Co-Working Fridays session held at @blockhubafrica. Also had the privilege of hosting fellow builders as we dove into meaningful conversations around @solana and the opportunities within its ecosystem. We also onboarded a few new builders into the space. Thanks to @vancube_2 for the opportunity. Big shoutout to @Harri_obi, @NzubeEzudo and the entire @SuperteamNG for consistently creating ways to keep the community engaged while building. Expect more solution-driven products from Enugu. It’s ICM season!

The world is not ready for thisss🔥🔥🔥 soon..... @Clapmihq will literally be a virtual wrestling ring to settle all banters You say you fit beat me for PS? oya enter clapmi ring You say I be trash fir solana? It's ok, let's banter on clap mi ring.... Come and settle your grudges 😂 I'll be around to sell pop corn to viewers sha😂

Soon everyone will get it. Hot takes on X, drags and bants are fun but can't be monetized. That's where clapmi comes in. You can literally monetized same activity and make it fun and profitable for both the banters, banger boys and their supporters. ClapMi is inevitable