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$5 USD / hour
Flag of NIGERIA
ubima, nigeria
$5 USD / hour
It's currently 12:08 AM here
Joined October 23, 2021
0 Recommendations

Godswill A.

@amadigodswill33

 4.7 (4 reviews)
3.5
3.5
100%
100%
$5 USD / hour
Flag of NIGERIA
ubima, nigeria
$5 USD / hour
100%
Jobs Completed
100%
On Budget
71%
On Time
14%
Repeat Hire Rate

Data Analyst | SQL Expert | Virtual Assistant

Greetings, I bring to the table a wealth of experience and expertise in unlocking the full potential of data. With a meticulous approach to data analysis and an adept command of advanced SQL techniques, I specialize in transforming raw data into meaningful insights. Excel serves as a powerful ally in my toolkit, ensuring not only accurate data analysis but also presenting findings in a visually compelling manner. Utilizing Tableau and Power BI, I craft engaging dashboards and reports that effectively tell a visual story, facilitating informed decision-making. Beyond data analytics, I extend virtual assistant services tailored to your specific needs. Whether it involves streamlining tasks, managing calendars, or organizing data, my organizational and multitasking skills are dedicated to enhancing your productivity. This holistic approach ensures that your business processes benefit not only from optimized data analytics but also from seamless operational support. Proficient in Handling the Following Tasks: ➤ Data Analysis: Deriving meaningful insights from complex datasets. ➤ BI Dashboard Development: Utilizing Tableau and Power BI to create centralized dashboards from multiple data sources. ➤ Data Cleaning: Expertise in SQL, Excel, and Python for thorough data cleansing. ➤ Python Automation: Leveraging libraries such as Pandas, Selenium, Re (for regex), and others for automation and data manipulation. ➤ Web and Email Scraping: Proficient in gathering information from web and email sources. ➤ VBA Code Writing: Automating tasks, including form generation, within Excel workbooks. ➤ Statistical Analysis: Conducting statistical analysis using R Studio. ➤ Reporting: Creating daily production reports and weekly summary dashboards. I Offer: ? Proven Analytical Skills ? Efficiency and Accuracy ? Excellent Communication ? Flexibility ? Availability Embark on a journey with me to optimize your data processes and receive unparalleled operational support. My commitment to analytical precision, coupled with a client-centric approach, ensures the delivery of solutions that exceed expectations. Let's elevate your data strategies and operational efficiency together.
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Portfolio

11668024
CLIENTS GOAL The client aimed to enhance the quality and usability of the Nashville Housing dataset by addressing inconsistencies and standardizing key data elements. The primary objectives were to ensure accurate analysis and reporting, as well as to improve overall data integrity. MY CONTRIBUTION: In this comprehensive data cleaning and standardization project, my role encompassed various critical tasks aimed at refining the dataset: 1. Standardizing Sales Date: a) Added a new column, StandardSaleDate, to establish a consistent date format ('YYYY-MM-DD'). b) Executed an update to populate this column, ensuring uniformity across the dataset. 2. Populating Property Address: a) Identified and filled missing values in the PropertyAddress field. b) Leveraged data from related records to populate missing addresses, ensuring completeness. 3. Breaking out Addresses into Individual Columns: a) For both PropertyAddress and OwnerAddress, split into individual columns (Address, City, State)
Data Cleaning and Standardization Housing Dataset | SQL
Data Cleaning and Standardization Housing Dataset | SQL
CLIENTS GOAL The client aimed to enhance the quality and usability of the Nashville Housing dataset by addressing inconsistencies and standardizing key data elements. The primary objectives were to ensure accurate analysis and reporting, as well as to improve overall data integrity. MY CONTRIBUTION: In this comprehensive data cleaning and standardization project, my role encompassed various critical tasks aimed at refining the dataset: 1. Standardizing Sales Date: a) Added a new column, StandardSaleDate, to establish a consistent date format ('YYYY-MM-DD'). b) Executed an update to populate this column, ensuring uniformity across the dataset. 2. Populating Property Address: a) Identified and filled missing values in the PropertyAddress field. b) Leveraged data from related records to populate missing addresses, ensuring completeness. 3. Breaking out Addresses into Individual Columns: a) For both PropertyAddress and OwnerAddress, split into individual columns (Address, City, State)
Data Cleaning and Standardization Housing Dataset | SQL
11667986
Web Scraping of Largest Companies in the US | Python
1. Client's Goal: The client, a market research firm, aimed to compile a comprehensive list of the largest companies in the United States by revenue. They needed up-to-date and accurate data to analyze market trends and make informed business decisions. 2. My Contribution to the Project: I undertook the task of web scraping the relevant information from the Wikipedia page titled "List of largest companies in the United States by revenue." Using Python, I employed the requests library to fetch the HTML content and utilized BeautifulSoup for parsing. The main objective was to extract the table containing the data of the largest companies and store it in a structured format. a) I identified the target table within the HTML using BeautifulSoup, specifically the second table with the class 'wikitable sortable'. b) Extracted column headers and data for each company from the table. c) Utilized Pandas to create a DataFrame, matching the extracted data with the corresponding column
Web Scraping of Largest Companies in the US | Python
11667958
1. Client's Goal The client goals are highlighted below: a) Uncover data trends to adapt marketing strategies to dynamic conditions. b) Identify the highest-value customer segment through RFM analysis. c) Propose a tailored marketing and growth strategy for Sprocket Central. d) Recommend external datasets for deeper insights into customer preferences. e) Specify target customers from the new 1000-list and broader market segment. 2. Details about my Contribution to the Project: Led the data quality assessment phase, evaluating three datasets based on seven dimensions. Utilized Excel to clean, standardize, and transform data for optimal preparation. Conducted an extensive exploratory data analysis in Excel, focusing on uncovering trends, determining the highest-value customer segment through RFM analysis, and profiling customers across age groups. Employed Tableau for comprehensive data analytics, providing visualizations and insights into total customers, revenue generation, top brands
Data-Driven Growth: Sprocket Central Business Growth | Excel
1. Client's Goal The client goals are highlighted below: a) Uncover data trends to adapt marketing strategies to dynamic conditions. b) Identify the highest-value customer segment through RFM analysis. c) Propose a tailored marketing and growth strategy for Sprocket Central. d) Recommend external datasets for deeper insights into customer preferences. e) Specify target customers from the new 1000-list and broader market segment. 2. Details about my Contribution to the Project: Led the data quality assessment phase, evaluating three datasets based on seven dimensions. Utilized Excel to clean, standardize, and transform data for optimal preparation. Conducted an extensive exploratory data analysis in Excel, focusing on uncovering trends, determining the highest-value customer segment through RFM analysis, and profiling customers across age groups. Employed Tableau for comprehensive data analytics, providing visualizations and insights into total customers, revenue generation, top brands
Data-Driven Growth: Sprocket Central Business Growth | Excel
11667941
1. Client's Goal for the Project: The client's primary goal for this project was to enhance the organization and structure of their product data. Specifically, they wanted the item codes separated from the product names to facilitate better data management and analysis. 2. My Contribution to the Project: I played a crucial role in achieving the client's goal by implementing the following key steps: a) Item Code Separation: I developed a solution to separate the item codes from the product name field, ensuring that each item code was accurately extracted and placed into a designated field. b) Price Lookup: Utilizing an Excel sheet containing the item codes and corresponding prices, I implemented a lookup mechanism to fetch and integrate the prices for each item code into the dataset. This step aimed to enrich the product data with pricing information. c) Data Standardization: To ensure consistency and ease of use, I formatted the Master Sheet according to standard conventions. This ste
Item Code Separation and Price Lookup | Microsoft Excel
11667930
Project Overview: Conducted a thorough analysis of oil spill data in the Niger Delta region (2021-2023) using SQL Server and Tableau. Scrapped the dataset from an oil spill agency website using Python, performed meticulous data cleaning in SQL Server, and employed Tableau for exploratory data analysis. Advanced data analysis methods in SQL Server were applied for a comprehensive examination. --- Project Highlights: Uncovered insights such as the peak in oil spills in 2022 (89.18% unrecovered), a decline in 2023, and top causes identified as Corrosion, Operational Error, and Sabotage/Theft. Highlighted the impact on specific states (Rivers, Delta, and Bayelsa), effective recovery techniques like Containment booms, and a notable shift in incidents in October 2022, potentially linked to government policies. High recovery months (June 2022, January 2023, and June 2023) were also identified. --- Project Success: The dynamic Tableau dashboard visually conveyed insights, contributing to actio
Oil Spill Analysis: Niger Delta (2021-2023) | SQL | Tableau
Project Overview: Conducted a thorough analysis of oil spill data in the Niger Delta region (2021-2023) using SQL Server and Tableau. Scrapped the dataset from an oil spill agency website using Python, performed meticulous data cleaning in SQL Server, and employed Tableau for exploratory data analysis. Advanced data analysis methods in SQL Server were applied for a comprehensive examination. --- Project Highlights: Uncovered insights such as the peak in oil spills in 2022 (89.18% unrecovered), a decline in 2023, and top causes identified as Corrosion, Operational Error, and Sabotage/Theft. Highlighted the impact on specific states (Rivers, Delta, and Bayelsa), effective recovery techniques like Containment booms, and a notable shift in incidents in October 2022, potentially linked to government policies. High recovery months (June 2022, January 2023, and June 2023) were also identified. --- Project Success: The dynamic Tableau dashboard visually conveyed insights, contributing to actio
Oil Spill Analysis: Niger Delta (2021-2023) | SQL | Tableau
Project Overview: Conducted a thorough analysis of oil spill data in the Niger Delta region (2021-2023) using SQL Server and Tableau. Scrapped the dataset from an oil spill agency website using Python, performed meticulous data cleaning in SQL Server, and employed Tableau for exploratory data analysis. Advanced data analysis methods in SQL Server were applied for a comprehensive examination. --- Project Highlights: Uncovered insights such as the peak in oil spills in 2022 (89.18% unrecovered), a decline in 2023, and top causes identified as Corrosion, Operational Error, and Sabotage/Theft. Highlighted the impact on specific states (Rivers, Delta, and Bayelsa), effective recovery techniques like Containment booms, and a notable shift in incidents in October 2022, potentially linked to government policies. High recovery months (June 2022, January 2023, and June 2023) were also identified. --- Project Success: The dynamic Tableau dashboard visually conveyed insights, contributing to actio
Oil Spill Analysis: Niger Delta (2021-2023) | SQL | Tableau
11668024
CLIENTS GOAL The client aimed to enhance the quality and usability of the Nashville Housing dataset by addressing inconsistencies and standardizing key data elements. The primary objectives were to ensure accurate analysis and reporting, as well as to improve overall data integrity. MY CONTRIBUTION: In this comprehensive data cleaning and standardization project, my role encompassed various critical tasks aimed at refining the dataset: 1. Standardizing Sales Date: a) Added a new column, StandardSaleDate, to establish a consistent date format ('YYYY-MM-DD'). b) Executed an update to populate this column, ensuring uniformity across the dataset. 2. Populating Property Address: a) Identified and filled missing values in the PropertyAddress field. b) Leveraged data from related records to populate missing addresses, ensuring completeness. 3. Breaking out Addresses into Individual Columns: a) For both PropertyAddress and OwnerAddress, split into individual columns (Address, City, State)
Data Cleaning and Standardization Housing Dataset | SQL
Data Cleaning and Standardization Housing Dataset | SQL
CLIENTS GOAL The client aimed to enhance the quality and usability of the Nashville Housing dataset by addressing inconsistencies and standardizing key data elements. The primary objectives were to ensure accurate analysis and reporting, as well as to improve overall data integrity. MY CONTRIBUTION: In this comprehensive data cleaning and standardization project, my role encompassed various critical tasks aimed at refining the dataset: 1. Standardizing Sales Date: a) Added a new column, StandardSaleDate, to establish a consistent date format ('YYYY-MM-DD'). b) Executed an update to populate this column, ensuring uniformity across the dataset. 2. Populating Property Address: a) Identified and filled missing values in the PropertyAddress field. b) Leveraged data from related records to populate missing addresses, ensuring completeness. 3. Breaking out Addresses into Individual Columns: a) For both PropertyAddress and OwnerAddress, split into individual columns (Address, City, State)
Data Cleaning and Standardization Housing Dataset | SQL
11667986
Web Scraping of Largest Companies in the US | Python
1. Client's Goal: The client, a market research firm, aimed to compile a comprehensive list of the largest companies in the United States by revenue. They needed up-to-date and accurate data to analyze market trends and make informed business decisions. 2. My Contribution to the Project: I undertook the task of web scraping the relevant information from the Wikipedia page titled "List of largest companies in the United States by revenue." Using Python, I employed the requests library to fetch the HTML content and utilized BeautifulSoup for parsing. The main objective was to extract the table containing the data of the largest companies and store it in a structured format. a) I identified the target table within the HTML using BeautifulSoup, specifically the second table with the class 'wikitable sortable'. b) Extracted column headers and data for each company from the table. c) Utilized Pandas to create a DataFrame, matching the extracted data with the corresponding column
Web Scraping of Largest Companies in the US | Python
11667958
1. Client's Goal The client goals are highlighted below: a) Uncover data trends to adapt marketing strategies to dynamic conditions. b) Identify the highest-value customer segment through RFM analysis. c) Propose a tailored marketing and growth strategy for Sprocket Central. d) Recommend external datasets for deeper insights into customer preferences. e) Specify target customers from the new 1000-list and broader market segment. 2. Details about my Contribution to the Project: Led the data quality assessment phase, evaluating three datasets based on seven dimensions. Utilized Excel to clean, standardize, and transform data for optimal preparation. Conducted an extensive exploratory data analysis in Excel, focusing on uncovering trends, determining the highest-value customer segment through RFM analysis, and profiling customers across age groups. Employed Tableau for comprehensive data analytics, providing visualizations and insights into total customers, revenue generation, top brands
Data-Driven Growth: Sprocket Central Business Growth | Excel
1. Client's Goal The client goals are highlighted below: a) Uncover data trends to adapt marketing strategies to dynamic conditions. b) Identify the highest-value customer segment through RFM analysis. c) Propose a tailored marketing and growth strategy for Sprocket Central. d) Recommend external datasets for deeper insights into customer preferences. e) Specify target customers from the new 1000-list and broader market segment. 2. Details about my Contribution to the Project: Led the data quality assessment phase, evaluating three datasets based on seven dimensions. Utilized Excel to clean, standardize, and transform data for optimal preparation. Conducted an extensive exploratory data analysis in Excel, focusing on uncovering trends, determining the highest-value customer segment through RFM analysis, and profiling customers across age groups. Employed Tableau for comprehensive data analytics, providing visualizations and insights into total customers, revenue generation, top brands
Data-Driven Growth: Sprocket Central Business Growth | Excel
11667941
1. Client's Goal for the Project: The client's primary goal for this project was to enhance the organization and structure of their product data. Specifically, they wanted the item codes separated from the product names to facilitate better data management and analysis. 2. My Contribution to the Project: I played a crucial role in achieving the client's goal by implementing the following key steps: a) Item Code Separation: I developed a solution to separate the item codes from the product name field, ensuring that each item code was accurately extracted and placed into a designated field. b) Price Lookup: Utilizing an Excel sheet containing the item codes and corresponding prices, I implemented a lookup mechanism to fetch and integrate the prices for each item code into the dataset. This step aimed to enrich the product data with pricing information. c) Data Standardization: To ensure consistency and ease of use, I formatted the Master Sheet according to standard conventions. This ste
Item Code Separation and Price Lookup | Microsoft Excel
11667930
Project Overview: Conducted a thorough analysis of oil spill data in the Niger Delta region (2021-2023) using SQL Server and Tableau. Scrapped the dataset from an oil spill agency website using Python, performed meticulous data cleaning in SQL Server, and employed Tableau for exploratory data analysis. Advanced data analysis methods in SQL Server were applied for a comprehensive examination. --- Project Highlights: Uncovered insights such as the peak in oil spills in 2022 (89.18% unrecovered), a decline in 2023, and top causes identified as Corrosion, Operational Error, and Sabotage/Theft. Highlighted the impact on specific states (Rivers, Delta, and Bayelsa), effective recovery techniques like Containment booms, and a notable shift in incidents in October 2022, potentially linked to government policies. High recovery months (June 2022, January 2023, and June 2023) were also identified. --- Project Success: The dynamic Tableau dashboard visually conveyed insights, contributing to actio
Oil Spill Analysis: Niger Delta (2021-2023) | SQL | Tableau
Project Overview: Conducted a thorough analysis of oil spill data in the Niger Delta region (2021-2023) using SQL Server and Tableau. Scrapped the dataset from an oil spill agency website using Python, performed meticulous data cleaning in SQL Server, and employed Tableau for exploratory data analysis. Advanced data analysis methods in SQL Server were applied for a comprehensive examination. --- Project Highlights: Uncovered insights such as the peak in oil spills in 2022 (89.18% unrecovered), a decline in 2023, and top causes identified as Corrosion, Operational Error, and Sabotage/Theft. Highlighted the impact on specific states (Rivers, Delta, and Bayelsa), effective recovery techniques like Containment booms, and a notable shift in incidents in October 2022, potentially linked to government policies. High recovery months (June 2022, January 2023, and June 2023) were also identified. --- Project Success: The dynamic Tableau dashboard visually conveyed insights, contributing to actio
Oil Spill Analysis: Niger Delta (2021-2023) | SQL | Tableau
Project Overview: Conducted a thorough analysis of oil spill data in the Niger Delta region (2021-2023) using SQL Server and Tableau. Scrapped the dataset from an oil spill agency website using Python, performed meticulous data cleaning in SQL Server, and employed Tableau for exploratory data analysis. Advanced data analysis methods in SQL Server were applied for a comprehensive examination. --- Project Highlights: Uncovered insights such as the peak in oil spills in 2022 (89.18% unrecovered), a decline in 2023, and top causes identified as Corrosion, Operational Error, and Sabotage/Theft. Highlighted the impact on specific states (Rivers, Delta, and Bayelsa), effective recovery techniques like Containment booms, and a notable shift in incidents in October 2022, potentially linked to government policies. High recovery months (June 2022, January 2023, and June 2023) were also identified. --- Project Success: The dynamic Tableau dashboard visually conveyed insights, contributing to actio
Oil Spill Analysis: Niger Delta (2021-2023) | SQL | Tableau

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5.0
$395.00 USD
Project for Godswill A.
Excellent writer. Looking forward to work with him again!
Research
Technical Writing
Report Writing
Article Writing
Research Writing
+1 more
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Flag of Gunjan S.
@msg2gk
•
2 years ago
5.0
$20.00 USD
Project for Godswill A.
was good working with this freelancer
Research
Technical Writing
Report Writing
Article Writing
Research Writing
+1 more
User Avatar
Flag of Pragati S.
@prashushinde9
•
2 years ago
1.0
$20.00 USD
Write for me Literature Review
Not recommended at all. Zero professionalism!
Research
Technical Writing
Report Writing
Article Writing
Research Writing
User Avatar
Flag of Khaled A.
@loodi978
•
2 years ago
4.4
$200.00 CAD
University/Academics Research- Network Science and Computational Fluid Dynamics
Godswill was responsive and delivered the job well.
Data Entry
Research
Excel
Statistics
Data Mining
2 years ago

Experience

Intern

Port Harcourt Refining Company, PHRC
Jun 2018 - Dec 2019 (1 year, 6 months)
Worked as a control room junior engineer and assistant to the Chief auditor production department, PHRC.

supervisor and computer instructor

DessyBelly Computers
Jul 2012 - Jun 2017 (4 years, 11 months)
I was the supervisor/ computer instructor, I carried out daily content creation, editing, proofreading, and graphics designing jobs. I was also in charge of supervising fresh computer trainees.

Education

Bachelor of Engineering

Federal University of Technology Owerri, Nigeria 2014 - 2019
(5 years)

Qualifications

Business Analytics with Excel

Simplilean
2023
Business analytics covers Excel basics, advanced features, data manipulation, visualization, statistical analysis, and data preparation. With these skills, one can extract insights, solve real-world business problems, and make data-driven decisions. This certification enhances analytical capabilities, and Excel proficiency, and is valuable for professionals in roles like data analysts, business analysts, and financial analysts

ALX Virtual Assistant Course_ Honors

ALX - Africa
2022
This course is designed to develop and practice professional skills that are most needed to be a successful Virtual Assistant -- such as: -Time management, task organization, and prioritization. - Managing a client’s email inbox, client calendar, and scheduling. -Creating slides. -Data entry and expense tracking. -Use of the Google Workspace suite and video conferencing tools. -Handling sensitive data and maintaining confidentiality, among others.

Certificate of proficiency In content creation and graphics designing.

DessyBelly Computers
2017
A certification on my proficiency in my job as a content writer and graphics designer at DessyBelly Enterprise.

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Top Skills

Report Writing 3
Data Entry 1
Excel 1
Data Mining 1
Python

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