Folayan Ayodeji Opeyemi
Data Analyst
Phone:
+1(289) 716-4109
Email:
Location:
Ontario, Canada
A Bit About Me
I am a data analyst who is looking to work with data to discover underlying trends and patterns and exploit insights to drive decision making. One thing that stands me out is my ability to learn by doing, and this helps me achieve my goals. Over the years, I have learned and utilized data analysis skills and tools which have led to improved food production by over 30% and In my current role, I have completely reduced the time taken to infer insights and make recommendations by over 50%.
Highlights of Skills
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Microsoft Certified Power BI Data Analyst (PL-300)
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SQL and NO SQL. Exprerienced in writing complex queries/stored procedures using CTEs, Window and analytical functions
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Azure: Cloud Technologies [Azure Data Factory, Azure SQL Database, Azure Synapse analytics, Azure Data lake]
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Power BI and Tableau
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SSRS, SSIS and SSAS
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Advanced Microsoft Excel
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Statistics and Mathematics
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Python programming
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Web Scraping
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Able to handle large dataset, including cleaning, handling null values/outliers and analysis.
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Excellent communication skills (written and verbal) for effective presentation of significant amount of information, paying close attention to details and make relevant recommendations
Work Experience
March 2018 - August 2021
Federal Ministry of Agriculture, Nigeria
November 2020 - April 2021
UTIVA, Nigeria
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Researched, identified, extracted, compiled, transformed, aggregated and analyzed data from multiple data sources for analysis, report/dashboard creation and making recommendations which led to 25% increase in food production
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Used SQL and Excel pivot tables/ vlookup to generate monthly reports, highlighting actionable insights and communicating complex reports to senior management leading to strategy development that increased profit by over 60%.
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Managed master data using Microsoft Excel ensuring data confidentiality and carried out updates, maintenance, validating errors, data quality assurance to ensure reliability of data
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Collaborated with colleagues to research the various factors affecting the storage of some agricultural produce using statistical analysis such as correlation, regression which reduced storage losses by over 20%
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Data visualization: Extracted data from databases using SQL queries, performed data filter/clean and loaded the data sets to Power BI for analysis (ETL process). Measures were created through complex DAX formulas such as CALCULATE, FILTER, RELATED, ALL etc. Actionable insights were presented through a BI dashboard/report showing trends, time-series charts, forecasts, KPIs and cards which improved food imports and exports by more than 50%
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Customer segmentation: Utilized RFM analysis, and machine learning techniques such as K-Means, Hierarchical clustering and Principal Component analysis to perform customer segmentation on a customer dataset using python and libraries such as Pandas, Sklearn, NumPy, Seaborn and Matplotlib
Certification
November, 2021
October, 2021
October 2021
Education
March 2018 - August 2021
November 2020 - April 2021
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University of Ilorin, Nigeria.
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M.Eng Mechanical Engineering, 2018
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Federal University of Technology, Akure, Nigeria.
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B.Eng Agricultural Engineering, 2014
Project objective: An analysis of the viability of a merger between two companies using data from a database and Excel file.
Tools used: SQL and Power BI
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SQL queries was used to extract the data from the database and loaded into Power BI for analysis
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Data preprocessing techniques such as filtering, sorting, promoting headers, changing data types, removing null values, identifying outliers and text formatting was carried out to make the data useful
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Data modelling was carried out, the tables were connected majorly using one-to-many relationships using their primary keys and foreign keys
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The analysis based on business questions and KPIs were carried out using various DAX formulas and a dashboard containing card, area charts, maps and slicers was used to present insights.
Project objective: : An analysis of a dataset containing over a million rows recorded by a brewery for a total of 3 years, to understand the brand and to maximize profit
Tools used: Microsoft SQL server, Microsoft Excel and Python
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Data preprocessing techniques such as filtering, sorting, ensuring the right data type, removing null values and text formatting was carried out in order to make the data useful
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Other pre-processing analysis carried out include identifying outliers and descriptive analysis was carried out.
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The analysis was carried out using pivot tables, and a report containing slicers, timelines and charts was created to present insights.
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Exploratory data analysis was also carried out using python libraries such as Numpy, Pandas, Seaborn and Matplotlib.
Project objective: : An analysis of FAO dataset on food exports to determine the major causes of food exported to the EU
Tools used: Microsoft Power BI
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Data preprocessing techniques such as filtering, sorting, ensuring the right data type, removing null values and text formatting was carried out in order to make the data useful
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Data modelling to connect related tables together using primary key and foreign key relationships
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A report/dashboard showing various insights and causes of food rejection was created