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$10 USD / hora
Bandera de NIGERIA
ikoyi, nigeria
$10 USD / hora
Aquí son las 9:16 p. m.
Se unió el noviembre 21, 2023
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Opeyemi O.

@opeyemisunkanmi

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$10 USD / hora
Bandera de NIGERIA
ikoyi, nigeria
$10 USD / hora
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Data Analyst / Visualization Expert with Power BI

I am a data analyst with experience in healthcare and business analytics. I am skillful in extracting key insights from data that can help to make good business decisions and solve major business problems both in healthcare and business industries. I am competent in the use of Microsoft Excel spreadsheet, MySQL, Microsoft Power BI, and Python packages for data ingestion, data cleaning and cleansing, data modeling, exploratory data analysis, data visualization, and machine learning modeling. I will design for you an excellent report and interactive dashboard with Microsoft excel spreadsheet and Microsoft Power BI. I can handle data from different sources including PDF files. Give me your data and you have all your answers. I can work effectively in a team with excellent communication and relationship skills. I am always available and more than ready for your projects.
Freelancer Data Processing Executives Nigeria
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Contacta Opeyemi O. sobre tu trabajo

Inicia sesión para comentar cualquier detalle por chat.

Portafolio

11702473
In this project, sales data was analyzed using different Python packages. The process involves data importation, cleaning, transformation, analysis, and summary. The whole process was done and documented in Jupyter Notebook using Python packages such as numpy , pandas, matplotlib, and seaborn. The Notebook could be assessed in my Git hub repository: https://github.com/opeyemiolasunkanmianalyst
Sales data analysis with python
11707455
This project involves the use of data analytics to explore and provide insights into the water crisis of MD Ndogo. The project also involved evaluating water-related crime in the nation. The data was extracted from the national database and all the tables were connected in Power BI using the tool's relationship and data modeling properties. Five interactive dashboards were designed to provide insights at different levels. Each stage of the project was communicated to the stakeholders with appropriate feedback
Exploring A National Water Crisis With Data Analytics
This project involves the use of data analytics to explore and provide insights into the water crisis of MD Ndogo. The project also involved evaluating water-related crime in the nation. The data was extracted from the national database and all the tables were connected in Power BI using the tool's relationship and data modeling properties. Five interactive dashboards were designed to provide insights at different levels. Each stage of the project was communicated to the stakeholders with appropriate feedback
Exploring A National Water Crisis With Data Analytics
This project involves the use of data analytics to explore and provide insights into the water crisis of MD Ndogo. The project also involved evaluating water-related crime in the nation. The data was extracted from the national database and all the tables were connected in Power BI using the tool's relationship and data modeling properties. Five interactive dashboards were designed to provide insights at different levels. Each stage of the project was communicated to the stakeholders with appropriate feedback
Exploring A National Water Crisis With Data Analytics
This project involves the use of data analytics to explore and provide insights into the water crisis of MD Ndogo. The project also involved evaluating water-related crime in the nation. The data was extracted from the national database and all the tables were connected in Power BI using the tool's relationship and data modeling properties. Five interactive dashboards were designed to provide insights at different levels. Each stage of the project was communicated to the stakeholders with appropriate feedback
Exploring A National Water Crisis With Data Analytics
This project involves the use of data analytics to explore and provide insights into the water crisis of MD Ndogo. The project also involved evaluating water-related crime in the nation. The data was extracted from the national database and all the tables were connected in Power BI using the tool's relationship and data modeling properties. Five interactive dashboards were designed to provide insights at different levels. Each stage of the project was communicated to the stakeholders with appropriate feedback
Exploring A National Water Crisis With Data Analytics
11702528
In this project, an exploratory data analysis with Excel was done to derive key insights from a hospital dataset that contains the patient visits for different units of the hospital for the year 2022. Summary tables and a dashboard were presented to the stakeholders in addition to a written executive summary. Executive Summary The total number of hospital visits was 5194. Enrollee from Hygeia HMO makes up more than 0ne-third of all visits with 44%. This is followed by patients from Axa Mansard HMO and private patients at 23.63% and 23.58% respectively. Hence, private patients in addition to enrollees from Hygeia and Axa Mansard contributed to 91% of all visits in 2022. However, every other HMO contributed less than 10% of all visits. 70% of the patient were adults followed by school-age children with 13.5%. Teenagers and under five children were below 10% each. Please check my GitHub repository for the full details. https://github.com/opeyemiolasunkanmianalyst
Hospital Visits Analysis
In this project, an exploratory data analysis with Excel was done to derive key insights from a hospital dataset that contains the patient visits for different units of the hospital for the year 2022. Summary tables and a dashboard were presented to the stakeholders in addition to a written executive summary. Executive Summary The total number of hospital visits was 5194. Enrollee from Hygeia HMO makes up more than 0ne-third of all visits with 44%. This is followed by patients from Axa Mansard HMO and private patients at 23.63% and 23.58% respectively. Hence, private patients in addition to enrollees from Hygeia and Axa Mansard contributed to 91% of all visits in 2022. However, every other HMO contributed less than 10% of all visits. 70% of the patient were adults followed by school-age children with 13.5%. Teenagers and under five children were below 10% each. Please check my GitHub repository for the full details. https://github.com/opeyemiolasunkanmianalyst
Hospital Visits Analysis
In this project, an exploratory data analysis with Excel was done to derive key insights from a hospital dataset that contains the patient visits for different units of the hospital for the year 2022. Summary tables and a dashboard were presented to the stakeholders in addition to a written executive summary. Executive Summary The total number of hospital visits was 5194. Enrollee from Hygeia HMO makes up more than 0ne-third of all visits with 44%. This is followed by patients from Axa Mansard HMO and private patients at 23.63% and 23.58% respectively. Hence, private patients in addition to enrollees from Hygeia and Axa Mansard contributed to 91% of all visits in 2022. However, every other HMO contributed less than 10% of all visits. 70% of the patient were adults followed by school-age children with 13.5%. Teenagers and under five children were below 10% each. Please check my GitHub repository for the full details. https://github.com/opeyemiolasunkanmianalyst
Hospital Visits Analysis
11702473
In this project, sales data was analyzed using different Python packages. The process involves data importation, cleaning, transformation, analysis, and summary. The whole process was done and documented in Jupyter Notebook using Python packages such as numpy , pandas, matplotlib, and seaborn. The Notebook could be assessed in my Git hub repository: https://github.com/opeyemiolasunkanmianalyst
Sales data analysis with python
11707455
This project involves the use of data analytics to explore and provide insights into the water crisis of MD Ndogo. The project also involved evaluating water-related crime in the nation. The data was extracted from the national database and all the tables were connected in Power BI using the tool's relationship and data modeling properties. Five interactive dashboards were designed to provide insights at different levels. Each stage of the project was communicated to the stakeholders with appropriate feedback
Exploring A National Water Crisis With Data Analytics
This project involves the use of data analytics to explore and provide insights into the water crisis of MD Ndogo. The project also involved evaluating water-related crime in the nation. The data was extracted from the national database and all the tables were connected in Power BI using the tool's relationship and data modeling properties. Five interactive dashboards were designed to provide insights at different levels. Each stage of the project was communicated to the stakeholders with appropriate feedback
Exploring A National Water Crisis With Data Analytics
This project involves the use of data analytics to explore and provide insights into the water crisis of MD Ndogo. The project also involved evaluating water-related crime in the nation. The data was extracted from the national database and all the tables were connected in Power BI using the tool's relationship and data modeling properties. Five interactive dashboards were designed to provide insights at different levels. Each stage of the project was communicated to the stakeholders with appropriate feedback
Exploring A National Water Crisis With Data Analytics
This project involves the use of data analytics to explore and provide insights into the water crisis of MD Ndogo. The project also involved evaluating water-related crime in the nation. The data was extracted from the national database and all the tables were connected in Power BI using the tool's relationship and data modeling properties. Five interactive dashboards were designed to provide insights at different levels. Each stage of the project was communicated to the stakeholders with appropriate feedback
Exploring A National Water Crisis With Data Analytics
This project involves the use of data analytics to explore and provide insights into the water crisis of MD Ndogo. The project also involved evaluating water-related crime in the nation. The data was extracted from the national database and all the tables were connected in Power BI using the tool's relationship and data modeling properties. Five interactive dashboards were designed to provide insights at different levels. Each stage of the project was communicated to the stakeholders with appropriate feedback
Exploring A National Water Crisis With Data Analytics
11702528
In this project, an exploratory data analysis with Excel was done to derive key insights from a hospital dataset that contains the patient visits for different units of the hospital for the year 2022. Summary tables and a dashboard were presented to the stakeholders in addition to a written executive summary. Executive Summary The total number of hospital visits was 5194. Enrollee from Hygeia HMO makes up more than 0ne-third of all visits with 44%. This is followed by patients from Axa Mansard HMO and private patients at 23.63% and 23.58% respectively. Hence, private patients in addition to enrollees from Hygeia and Axa Mansard contributed to 91% of all visits in 2022. However, every other HMO contributed less than 10% of all visits. 70% of the patient were adults followed by school-age children with 13.5%. Teenagers and under five children were below 10% each. Please check my GitHub repository for the full details. https://github.com/opeyemiolasunkanmianalyst
Hospital Visits Analysis
In this project, an exploratory data analysis with Excel was done to derive key insights from a hospital dataset that contains the patient visits for different units of the hospital for the year 2022. Summary tables and a dashboard were presented to the stakeholders in addition to a written executive summary. Executive Summary The total number of hospital visits was 5194. Enrollee from Hygeia HMO makes up more than 0ne-third of all visits with 44%. This is followed by patients from Axa Mansard HMO and private patients at 23.63% and 23.58% respectively. Hence, private patients in addition to enrollees from Hygeia and Axa Mansard contributed to 91% of all visits in 2022. However, every other HMO contributed less than 10% of all visits. 70% of the patient were adults followed by school-age children with 13.5%. Teenagers and under five children were below 10% each. Please check my GitHub repository for the full details. https://github.com/opeyemiolasunkanmianalyst
Hospital Visits Analysis
In this project, an exploratory data analysis with Excel was done to derive key insights from a hospital dataset that contains the patient visits for different units of the hospital for the year 2022. Summary tables and a dashboard were presented to the stakeholders in addition to a written executive summary. Executive Summary The total number of hospital visits was 5194. Enrollee from Hygeia HMO makes up more than 0ne-third of all visits with 44%. This is followed by patients from Axa Mansard HMO and private patients at 23.63% and 23.58% respectively. Hence, private patients in addition to enrollees from Hygeia and Axa Mansard contributed to 91% of all visits in 2022. However, every other HMO contributed less than 10% of all visits. 70% of the patient were adults followed by school-age children with 13.5%. Teenagers and under five children were below 10% each. Please check my GitHub repository for the full details. https://github.com/opeyemiolasunkanmianalyst
Hospital Visits Analysis

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Experiencia

Data Analyst

Victory Life Specialist Hospital
ene 2022 - Presente
I use Python, MySQL, Power BI, and Microsoft Excel to organize, analyze, visualize, and report hospital data. This has helped the hospital to reduce costs by 20 percent in the last 11 months.

Educación

bachelor of medicine

Obafemi Awolowo University, Nigeria 2012 - 2018
(6 años)

Calificaciones

Google Advanced Data Analytics Certificate

Google
2023
Those who earn the Google Advanced Data Analytics Certificate have completed seven courses that include hands-on, practice-based assessments and are designed to prepare them for advanced roles in data analytics and entry-level roles in data science. They are competent in exploring large datasets, applying data analysis techniques, and building models to extract insights. They are also competent in machine learning, predictive modeling, and statistics.

Contacta Opeyemi O. sobre tu trabajo

Inicia sesión para comentar cualquier detalle por chat.

Verificaciones

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Principales habilidades

Data Processing
Research
Excel
Data Mining
Data Analytics

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