Mowaninuola Umarudeen
@DataWithMowaExperienced in SQL, Excel, Python Libraries and Data Visualization Tools. On a mission to continuously grow and share knowledge in the data space.
Language Breakdown
Lines of code distribution across 10 owned repositories
I-Shaped Developer
I-shapedSpecialist — deep expertise in Jupyter Notebook
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Repos
10
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0
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+18%
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These are my Labs and projects from the Cisco Networking Academy Data Analytics Essentials course. Demonstrates my foundational skills in data cleaning (Excel), query execution (SQL), and visualization (Tableau). It focuses on the core data analysis lifecycle.
Sureway Couriers Service Charges to Sigma Industries Accuracy Analysis Project using Python Programming Language. Check the README file for more information.
In this course, I learnt how to analyze data using descriptive statistics, Excel formulas, and conditional formatting to uncover key insights. Through real-world case studies, I developed skills in customer analysis, performance analysis, and data storytelling — presenting findings clearly using Microsoft Excel and Google Sheets.
In this course, I learnt how to master in-demand technical skills with the Meta Marketing Analytics Professional Certificate. I learnt to collect, clean, and visualize data using SQL, Python (Pandas), and Tableau. Gain expertise in A/B testing, audience segmentation, sales funnel optimization, and running ad experiments with Meta Ads Manager.
Tracking my journey through the Google Digital Customer Engagement Specialization. A 6-course series focused on growing customer loyalty, building personas, and optimizing marketing funnels. Covering social media and email campaigns, brand authority development, e-commerce checkout optimization, and designing retention-driven loyalty programs.
This is an Eight‑course program by Aptly and Meta that builds marketing analytics skills. It covers marketing principles, OSEMN framework, SQL, Python, Tableau, and Meta Ads Manager. I learnt to collect, analyze, and visualize data, segment audiences, evaluate campaigns, and apply insights in real projects.
This project analyzes TPH WhatsApp group chats using Python to understand member participation, sentiment analysis, response patterns, sender engagement, and message activity.
This project is a simple machine learning API built using Flask. It uses a trained model on the Iris dataset to predict flower species based on input features like sepal length, sepal width, petal length, and petal width.
In this project, I looked closely at how well a website is working. I checked: Traffic Sources and Demographics, Session Analysis, User Engagement and Behaviour, Channel Performance, and Website Traffic Forecasting (forecasted the website for the next 24 hours)
This is my profile README.
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