Built Inventory Management System with Python, Streamlit, SQLite

🚀 Automate to elevate. Over the last months, I’ve been developing tools focused on optimizing reporting processes and reducing operational time across different workflows. By combining Python, Excel, and BigQuery, I build automation solutions that integrate data, eliminate repetitive tasks, and improve accuracy — allowing teams to focus on strategic decisions instead of manual work. Every project I design has one purpose: transform effort into efficiency. If your business or team needs to automate processes, consolidate data, or generate dynamic reports, I can build a custom Python solution tailored to your goals. 💼 Check out my service here: [👉 https://lnkd.in/eRrspJ4Q 👈]

Most analysts still think “automation” means just recording macros. But the truth? Macros automate clicks. Python automates logic. 🧠 Here’s what actually happens behind the scenes when you replace VBA with Python 👇 🔹 Excel reads your dataset. 🔹 Python (via pandas or openpyxl) takes over the workflow cleaning, merging, forecasting in seconds. 🔹 Dashboards refresh automatically without opening Excel. 🔹 Reports are generated, formatted, and emailed — while you sip your coffee. ☕ The result? ✅ Hours of manual work reduced to minutes. ✅ Zero formula errors. ✅ Fully traceable, auditable logic. 💡 Real-world example: A retail client reduced 4 hours of daily report prep to 12 minutes using Python scripts integrated with Power BI refreshes. If your reports still depend on manual refreshes, you’re losing time and competitive edge. The analysts winning in 2025 don’t type formulas. They write functions. #Python #ExcelAutomation #DataAnalytics #DataEngineering #Productivity #Automation #BusinessIntelligence #PowerBI #DataScience #TechTrends

I analyzed a 42-million-event e-commerce dataset to find out why 89% of viewers weren't converting. It wasn't a checkout problem. My funnel analysis in Python and Power BI showed the biggest drop-off was at the "Add to Cart" step. Key Stats: Only 11.15% of viewers added an item to their cart. But 60.17% of those who did add to cart ended up buying. This shows a classic "browse problem," not a "checkout problem." The data clearly shows the business should focus on improving the "View-to-Cart" conversion. Check out the full analysis, Python notebook, and Power BI dashboard on my GitHub. Link: https://lnkd.in/emG2-3ad #DataAnalysis #Python #PowerBI #ProductManagement #ECommerce

𝗦𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝗟𝗶𝗻𝗸 𝗟𝗮𝗯𝘀: 𝗚𝗲𝘁𝘁𝗶𝗻𝗴 𝗔𝘄𝗲𝘀𝗼𝗺𝗲 𝗪𝗼𝗿𝗸𝘀𝗽𝗮𝗰𝗲 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗶𝗻 𝗝𝘂𝘀𝘁 𝟯-𝟰 𝗟𝗶𝗻𝗲𝘀 𝗼𝗳 𝗖𝗼𝗱𝗲 🚀 I've been using Semantic Link Labs quite frequently over the past few years, and I'm continually impressed by how much visibility you can gain into your tenant with just a few lines of Python/Spark code. 💻 For administrators just getting started, one of the most practical questions is straightforward: 𝗛𝗼𝘄 𝗺𝗮𝗻𝘆 𝘄𝗼𝗿𝗸𝘀𝗽𝗮𝗰𝗲𝘀 𝗱𝗼 𝘄𝗲 𝗵𝗮𝘃𝗲, 𝗮𝗻𝗱 𝘄𝗵𝗶𝗰𝗵 𝗰𝗮𝗽𝗮𝗰𝗶𝘁𝘆 𝗶𝘀 𝗲𝗮𝗰𝗵 𝗼𝗻𝗲 𝗰𝗼𝗻𝗻𝗲𝗰𝘁𝗲𝗱 𝘁𝗼? From this simple query, you can instantly derive: ✅ Clear workspace-to-capacity mappings ❌ Workspaces showing "NULL" that aren't bound to any capacity 📈 Total workspace counts across your entire tenant 🌍 How capacities are distributed by SKU or region It's an incredibly 𝗽𝗼𝘄𝗲𝗿𝗳𝘂𝗹 𝘀𝘁𝗮𝗿𝘁𝗶𝗻𝗴 𝗽𝗼𝗶𝗻𝘁 for anyone looking to explore the Fabric and PowerBI ecosystem more deeply. 🔍 Shoutout to Michael Kovalsky & the developing team as always 🙌-Their work and insights around SLL have made the whole experience so much more !! accessible and genuinely fun to explore. I’ve written several articles about SLL usage before - feel free to check them out on bidiaries.com

Semantic Link Labs is a next level shift into giving the developer more control and visibility over fabric. If you want to get started learning semantic link labs, I’d suggest our free video class on YouTube: https://lnkd.in/gdGm3zn3 James Bartlett and Michael Carlo show you how use labs using demos in real notebooks.

🚀 Introducing My Python Data Analysis Software! I’m excited to share one of my latest projects — a Python-powered Data Analysis Software built with Tkinter, Pandas, and Matplotlib 🎯 This tool was designed to make data analysis easy, visual, and interactive, even for beginners. 💡 Key Features Include: ✅ Load and preview CSV or Excel files ✅ Display dataset summary (rows, columns, missing values, etc.) ✅ Generate descriptive statistics instantly ✅ View correlation matrix to detect relationships between variables ✅ Visualize any column (histograms, bar charts, etc.) ✅ Clean and modern UI/UX In the screenshot below, you can see how the app displays the distribution of age and generates automatic statistical summaries — all with just a few clicks! 📊 Building this project helped me strengthen my Python, GUI design, and data visualization skills. 💬 I’d love to hear your thoughts — what feature would you like me to add next? #Python #DataAnalysis #Tkinter #Matplotlib #Pandas #DataScience #Project #Programming #FatoluPeter #Portfolio

Why I Built a Simple Average Calculator (and What I Learned About Data Clarity) A few weeks ago, I was working on a side project involving stock performance tracking. I needed to calculate different types of averages — mean, median, and weighted average — across datasets like daily closing prices, trade volumes, and returns. Of course, I could’ve used Excel or a quick Python script, but I wanted something lightweight, fast, and web-based that anyone could use without needing coding knowledge. That’s what led me to create a simple Average Calculator tool. https://lnkd.in/gaV4fGba

Proud to share my latest project: a Real-Time Sentiment Dashboard powered by Python, Tweepy, and Power BI! This interactive dashboard fetches live tweets, analyzes sentiments using TextBlob, and visualizes public perception and engagement for any brand or topic. From tweet extraction and data cleaning to powerful business insights, it’s a full pipeline for real-time social media analytics. Excited about the potential for market research, brand management, and customer engagement! Check out the details and code here: https://lnkd.in/gY_6tZ38 #Python #PowerBI #DataScience #SentimentAnalysis #SocialMediaAnalytics #PersonalProject

📊 5 Free Data Analysis and Graph Plotting Software for Thesis (2025 Edition) Creating impactful and publication-ready visuals is a must for every researcher. Dr. Somasundaram R from iLovePhD shares the top 5 free software tools to simplify your data analysis and graph plotting process: 1️⃣ OriginLab – Professional data analysis & plotting (trial + free viewer available) 2️⃣ Matplotlib (Python) – Open-source library for static, interactive, and animated plots 3️⃣ Gnuplot – Command-line tool for scientific graphing across platforms 4️⃣ LabPlot – Open-source, GUI-based data visualization and analysis tool 5️⃣ SciDAVis – User-friendly, scriptable tool for publication-quality plotting 💡 Tip: Choose software based on your comfort level — from code-based (Matplotlib) to GUI-based (SciDAVis, LabPlot). These free tools help you analyze, visualize, and communicate your research effectively — all without spending a penny! Read the full guide: 👉 5 Free Data Analysis and Graph Plotting Software for Thesis #ResearchTools #DataAnalysis #ThesisWriting #ScientificVisualization #PhD #Matplotlib #OriginLab #LabPlot #SciDAVis #iLovePhD