oludolapoanalyst,Oludolapo Somoye,github

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Name: Oludolapo Somoye

Type: User

Bio: I am a Data Analyst. I have skills in Data cleaning, Data Processing, Data Analysis, Data Visualization, and Presentation of Insights.

👋 Hi, I’m @OludolapoAnalyst
👀 I’m interested in Data Analyst jobs. I am open to freelancing, personal project, full time as well as part-time jobs. I am available for remote positions as well...
👀 Check out more of more works here: https://mavenanalytics.io/profile/Oludolapo-Somoye/184048376
💞️ I’m looking to collaborate on a diverse range of projects. Let's talk about your offerings. ..
📫 How to reach me ...https://www.linkedin.com/in/oludolapo-somoye/

Oludolapo Somoye's Projects

airbnb

Here is the data and description for the ArBnB project using SQL and PowerBI. The dataset has weekday and weekend files for different cities in Europe. Firstly, with SQL, csv files were loaded and edited. Both weekend and weekday files were merged, then connected with PowerBI for analysis and visualization.

indian_flight_data

The project analyzes a .csv dataset of flight itineraries of 12 airlines on domestic Indian flights. The average price of tickets is Rs.9,048. The dataset contains basic information about each flight (such as date, time, departure airport, arrival airport, price, departure time, arrival time, route, and total stops).

northwind-project

This project involves creating SQL tables and loading the tables with data

oludolapoanalyst

Config files for my GitHub profile.

sleep-and-lifestyle-dataset

The Sleep Health and Lifestyle Dataset comprises 400 rows and 13 columns, covering a wide range of variables related to sleep and daily habits. It includes details such as gender, age, occupation, sleep duration, quality of sleep, physical activity level, stress levels, BMI category, blood pressure, heart rate, daily steps, and sleep disorders.

supermarket_data

The growth of supermarkets in most populated cities are increasing and market competitions are also high. The dataset is one of the historical sales of supermarket company which has recorded in 3 different branches for 3 months data. Predictive data analytics methods are easy to apply with this dataset.

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