Ifeoma Augusta Adigwe

• Led comprehensive data analysis projects focusing on property market trends, leveraging SQL, Python, and Excel to process and interpret large datasets.
• Developed predictive models to forecast property prices, facilitating informed investment decisions and resulting in a 19% increase in profitable acquisitions.
• Pioneered the development and automation of monthly sales and financial reports through Microsoft Power BI, leading to enhanced accuracy and efficiency in generating management and departmental

reports, resulting in a remarkable 21% reduction in report time.

• Conducted exploratory data analysis (EDA) on housing market patterns, identifying key factors influencing property demand and pricing fluctuations.
• Collaborated with the sales and marketing teams to optimize property listings by analyzing customer preferences, leading to a 17% increase in lead conversion rates.
• Designed and implemented visually compelling dashboards using Tableau, enabling stakeholders to gain actionable insights into market dynamics and investment opportunities.
• Collaborated with Data Science team to build predictive models, resulting in a 10% increase in forecast accuracy for client.
• Utilized geographic information systems (GIS) to map property data, providing valuable spatial insights for targeted marketing and expansion strategies.
• Contributed to the development of automated reporting systems, reducing manual effort by 35% and enhancing the efficiency of regular data-driven decision-making processes.
• Conducted regular performance assessments of real estate portfolios, providing strategic recommendations to improve asset management and maximize ROI(Return on Investment).

• Worked with PostgreSQL to extract and manage data from Company’s database analyzing trends in customer behavior, market, sales, inventory management, competitor performance and other business operations to identify areas for improvement.

• Developed customer segmentation model that provided a comprehensive understanding of the customer database and enabled identification of specific customer groups based on their behavior, demographics, and preferences, which resulted in increased engagement and sales from the customer base.

• Created and maintained reports and interactive dashboards using business Intelligence tools such as Tableau and Microsoft Power BI to provide valuable insights to business stakeholders that enabled them to make data-driven decisions which as a result optimized operations, improved customer experiences, identified areas for cost savings, and ultimately drove profitability up to 38%.

• Performed exploratory data analysis on large datasets using Python and statistical software, identifying trends, anomalies and reducing data processing time by 12%.

• Predicted products price with 27% accuracy to enable the company make informed investment.

• Collaborated with stakeholders from different departments to identify and align key performance indicators (KPIs) and develop metrics to track and improve business objectives and performance.

• Developed machine learning models for customer churn prediction, resulting in a 15% reduction in customer attrition rate.

• Managed projects using Agile methodologies, JIRA, Slack and Kanban boards to ensure timely delivery of results.

• Assisted in collecting and organizing property-related data using Excel spreadsheets and CRM tools, ensuring data accuracy and integrity for analysis.
• Conducted preliminary data analysis on housing market trends, identifying correlations between property features and selling prices to support pricing strategies.
• Designed SQL queries to manage and retrieve data, improving data accessibility by 19%.
• Supported the senior data analyst in creating and maintaining visual dashboards using Power BI, enabling team members to visualize market trends and make data-driven decisions effectively.
• Collaborated with cross-functional teams to gather requirements and assist in the development of data models, contributing to the enhancement of predictive analytics for property valuation.
• Assisted in generating reports on property listings' performance metrics, providing insights that contributed to refining marketing strategies and optimizing property listings for higher visibility.
• Contributed to the development and maintenance of data documentation and standard operating procedures (SOPs), ensuring consistency in data handling processes across the organization.
• Conducted quality checks on data sets, identifying and rectifying discrepancies, which improved data accuracy by 39%.
• Actively participated in team meetings, discussions, and training sessions to deepen knowledge of real estate market dynamics and data analysis techniques.