Atieno Grace
Data Analyst and Business Intelligence Specialist
About Me
Hello, I’m Atieno Grace, a Data Analyst based in Phoenix, Arizona, with over three years of experience in data processing, machine learning and business intelligence. My expertise includes SQL, Python, Power BI, and ETL processes and I’m passionate about transforming data into actionable insights that drive strategic decisions.
Currently, as a Data Analyst Intern at Quote Kong, I developed scalable data pipelines that reduced processing time by 30%, while integrating data from multiple sources to improve cross-team accessibility. My work in designing interactive dashboards boosted decision-making speed by 20% and led to a 12% increase in revenue optimization strategies. I also implemented cloud-based solutions on AWS, Google Cloud and Azure, ensuring both scalability and data security.
Previously, I served as Team Leader at AIESEC, where I managed global volunteer programs and increased engagement by 25%. I used data analytics tools like Power BI to optimize volunteer retention strategies, resulting in a 40% improvement. As an Opportunity Manager at AIESEC in JKUAT, I facilitated international internships and optimized volunteer matching, reducing onboarding time by 20% and improving educational access for over 500 children.
I thrive in collaborative, remote environments and have a proven track record of automating processes, improving data accessibility and contributing to cross-functional teams. Fluent in English and Swahili, I’m eager to leverage my technical skills, strategic thinking and problem-solving abilities to drive data-driven decision-making and business success in any environment.
COMPETENCIES
Data Analysis
I excel in transforming raw data into actionable insights that drive strategic decisions. For example, at Quote Kong, I identified trends that boosted revenue optimization by 12%. My experience includes conducting statistical analysis and utilizing advanced tools like SQL and Python to generate data-driven insights that support business growth and efficiency.
Project Management
I manage cross-functional teams to successfully complete projects on time. At AIESEC, I coordinated global volunteer programs, increasing engagement by 25% and improving retention by 40%. My ability to apply data analysis tools like Power BI ensures project goals are met efficiently, aligning with both short-term and long-term business objectives.
Data Governance and Data Management
I ensure data security and accuracy, which are critical for decision-making. I have managed data governance across platforms like AWS and Google Cloud, reducing data processing time by 30%. My expertise in data integration and management ensures teams have reliable, accessible data to drive business success.
Data Visualization and Reporting
I specialize in creating clear, engaging visual reports that make complex data easy to understand. Using Power BI and Tableau, I designed dashboards that sped up decision-making by 20%. My reporting has consistently helped stakeholders quickly interpret key data and take informed actions, improving business outcomes like a 18% increase in user engagement.
Machine Learning and Predictive Analytics
I leverage machine learning to build models that predict trends and optimize business processes. My work at Quote Kong led to a 12% revenue increase through predictive insights. I use algorithms to identify patterns, helping businesses anticipate changes and make data-driven decisions that enhance overall performance.
Business Intelligence and Dashboard Development
I design intuitive dashboards using Power BI and Tableau that support fast, data-driven decisions. My work in developing interactive dashboards at Quote Kong reduced decision-making time by 20%. By automating reports, I ensure stakeholders receive timely, actionable insights that improve strategic planning and operational efficiency.
Case Study: Tackling Employee Churn to Streamline Workforce Efficiency
Introduction:
As a Data Analyst Intern at Quote Kong, I was tasked with addressing a critical business challenge: a 27% surge in employee churn within the company's India division. The high churn rate posed a significant risk to operational efficiency, creating a short-staffed workforce that hindered overall productivity. Recognizing the urgency of the situation, I leveraged my expertise in data analysis, predictive modeling, and machine learning to devise a data-driven solution aimed at reducing churn and improving employee retention.
Business Challenge:
The primary business challenge was to curb the increasing churn rate, which was affecting the company’s ability to maintain a consistent and effective workforce. With churn rates outpacing new hires, departments were left understaffed, leading to decreased morale and productivity. The goal was to understand the root causes of churn and implement strategies to enhance retention, thereby stabilizing the workforce and boosting business performance.
Strategic Approach:
To address this issue, I employed a comprehensive approach, using advanced machine learning, statistical analysis, and data visualization techniques to identify the underlying causes of churn and forecast future trends. My approach consisted of the following key steps:
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Data Analysis: I thoroughly analyzed employee data, segmenting it by factors such as job satisfaction, performance metrics, and department-specific trends. This granular analysis provided insights into the patterns driving the churn rate.
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Identification of Key Drivers: Through advanced statistical analysis, I pinpointed several critical factors contributing to the churn surge, including workplace culture, compensation disparities, and lack of career growth opportunities. Understanding these factors allowed for targeted intervention.
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Machine Learning for Churn Prediction: I developed a machine learning model using Python to predict future churn rates based on historical data and employee behavior. The model’s predictive accuracy helped management anticipate at-risk employees and address potential issues before they resulted in resignation.
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Data Visualization with Power BI: I designed an interactive Power BI dashboard that displayed churn statistics, predicted trends, and key employee metrics. This dashboard allowed HR and management teams to monitor churn in real-time, enabling them to take proactive actions to improve retention.
Outcomes:
The outcomes of this project were both measurable and impactful:
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Reduction in Churn: By utilizing the machine learning model, we were able to predict future churn trends accurately. This proactive approach led to a 15% reduction in churn in the first quarter, directly improving workforce stability.
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Enhanced Retention Strategies: The identification of key churn drivers led to the implementation of targeted retention strategies, including career development programs and improved compensation packages. Employee satisfaction increased, further stabilizing the workforce.
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Operational Efficiency: The Power BI dashboard provided real-time data on churn and retention, allowing HR to streamline processes and make data-backed decisions. This facilitated quicker responses to staffing issues and optimized resource allocation.
Insights Gained:
This case reinforced the power of predictive analytics and machine learning in workforce management. It demonstrated that a data-driven approach can not only identify problems but also provide actionable insights that drive business improvement. The project also highlighted the importance of data visualization in making complex insights accessible to stakeholders for timely decision-making.
Conclusion:
Through the successful reduction of employee churn, I was able to showcase my ability to apply advanced data analytics to solve critical business challenges. This project highlighted my skills in data analysis, predictive modeling, and strategic decision-making, all of which contributed to improving retention and enhancing operational efficiency. The case study underscores my readiness to deliver impactful results and bring valuable expertise to any organization, particularly in remote data analytics roles.
Technical Proficiencies Utilized:
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SQL: For querying large datasets and identifying trends in employee data.
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Python: To build machine learning models that predict churn and analyze employee behavior.
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Power BI: For creating interactive dashboards that visualized churn statistics and predictive models.
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Excel: Utilized for preliminary data cleaning, transformation, and statistical analysis.
Core Competencies Displayed:
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Data Storytelling
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Statistical Analysis
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Data Modeling
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Machine Learning
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Predictive Analytics
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Data Visualization
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Dashboard Development
Case Study: Optimizing Airport Traffic Data for Improved Flight Operations
Introduction:
As a Data Analyst Intern at Quote Kong, I tackled a significant challenge involving unstructured airport traffic data. The data was disorganized, making it difficult to extract insights into flight delays and cancellations, which directly impacted operational efficiency and customer satisfaction. Taking the initiative, I reorganized the data into structured components to enable clear analysis and informed decision-making.
Business Challenge:
The unstructured data prevented effective tracking of delays and cancellations. With 40.54% of flights delayed and 1.47% cancelled, it was vital to provide stakeholders with actionable insights to improve airport operations and resource allocation. Without clear data, airport managers lacked the means to optimize traffic flow and mitigate inefficiencies.
Strategic Approach:
I approached the challenge using my skills in SQL, Python, and Power BI:
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Data Structuring & Transformation: I used SQL and Python to clean and restructure the data, identifying key trends in delays and cancellations.
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Data Integration & Analysis: I integrated data from multiple sources and conducted statistical analysis, revealing that 40.54% of flights were delayed and 1.47% were cancelled, providing actionable insights for operational improvements.
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Dashboard Creation: Using Power BI, I developed an interactive dashboard that visually presented key metrics, making it easy for stakeholders to understand delays and cancellations and take data-driven action.
Outcomes:
The project led to measurable improvements in airport operations:
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Faster Decision-Making: The dashboard enabled airport managers to identify problem areas quickly and prioritize corrective actions, leading to more efficient operations.
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Improved Operational Efficiency: By targeting key delays and cancellations, the team was able to optimize flight schedules and traffic management.
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Enhanced Stakeholder Collaboration: The dashboard provided clear insights that aligned teams and empowered stakeholders to make informed decisions, improving overall airport performance.
Insights Gained:
This project highlighted the importance of data structuring, integration, and visualization in transforming unorganized data into business insights. It reinforced my ability to collaborate with stakeholders and present actionable findings in an accessible way.
Conclusion:
The case study showcases my proficiency in data cleaning, integration, and visualization, demonstrating my ability to solve complex operational issues through data analysis. This project positions me as a strong candidate for remote data analytics roles, where actionable insights and cross-functional collaboration are key.
Technical Proficiencies Utilized:
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SQL: Used for querying and cleaning the flight data, ensuring it was structured and ready for analysis.
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Python: Applied for data transformation and statistical analysis, identifying key trends like delays and cancellations.
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Power BI: Utilized for creating interactive dashboards that presented data insights in a visually compelling and easy-to-understand format.
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ETL Processes: Integrated data from multiple sources, ensuring data consistency and accuracy for analysis.
Core Competencies Displayed:
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Data Cleaning and Transformation
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Data Visualization and Dashboard Development
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Statistical Analysis and Insights Extraction
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Data Integration and ETL Processes
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Cross-Functional Collaboration
Case Study: Enhancing Hospital Performance Visibility through Data Analysis
Introduction:
As a Data Analyst Intern at Quote Kong, I collaborated with Massachusetts General Hospital to create a high-level KPI report from a subset of patient records. This report aimed to answer critical questions about hospital performance, including patient admissions, average length of stay, cost per visit, and insurance coverage for procedures.
Business Challenge:
The hospital needed clear, actionable insights to help executives make informed decisions on resource allocation and cost management. The challenge was to provide reliable data that would allow hospital leaders to assess operations and optimize patient care effectively.
Strategic Approach:
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Data Exploration and Clarity: I began by thoroughly exploring the dataset, ensuring an understanding of how key variables interacted. This provided a solid foundation for analysis.
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Data Cleaning and Transformation: Using Python and SQL, I cleaned and transformed the data, removing inconsistencies and integrating data from multiple sources through ETL processes to ensure completeness.
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Statistical Analysis and Reporting: I conducted statistical analysis to calculate key metrics, such as: Average Length of Stay: 7.25 days, Readmission Rates, Cost per Visit and Insurance Coverage for Procedures.
These insights were presented in an interactive Power BI dashboard, allowing executives to access real-time data.
Outcomes:
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Informed Decision-Making: The KPI report provided stakeholders with the clarity needed for better decision-making, helping them focus on reducing readmissions and optimizing patient care.
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Efficiency Gains: Automation reduced report generation time by 30%, providing faster access to data.
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Revenue Optimization: The insights led to a 12% increase in revenue optimization strategies, directing resources to areas that would yield the most significant financial impact.
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Improved Communication: Interactive dashboards enhanced stakeholder engagement, providing real-time visibility into hospital performance metrics.
Insights Gained:
This project reinforced the importance of data-driven decision-making in healthcare. By automating reporting and using data visualization, the project highlighted how actionable insights can drive both operational improvements and financial optimization.
Conclusion:
The KPI report enabled Massachusetts General Hospital to make more informed decisions, optimize operations, and enhance revenue. This case study demonstrates my ability to analyze complex data, create automated reporting systems, and collaborate with stakeholders to deliver impactful results in a remote work setting.
Technical Proficiencies Utilized:
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SQL: Used for data extraction and integration, ensuring the data was accurate and accessible.
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Python: Applied for advanced statistical analysis and automation of reporting processes.
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Power BI: Created interactive dashboards for real-time decision-making and stakeholder engagement.
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ETL Processes: Streamlined data transformation and integration, enhancing data accessibility and reporting efficiency.
Core Competencies Displayed:
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Data Analysis
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Business Intelligence
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Data Cleaning & Transformation
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Reporting Automation
Case Study: Tackling the Surge in Heart Disease Mortality Through Data Analysis and Predictive Modeling
Introduction:
As a Data Analyst with a deep expertise in machine learning, business intelligence, and statistical analysis, I was presented with an urgent case from the World Health Organization (WHO). The situation involved a significant oversurge in heart disease, which was responsible for accounting for a third of the annual deaths in a given year. This alarming trend demanded a quick and efficient approach to identify the key factors contributing to this surge and develop actionable insights that could guide public health interventions.
Business Challenge:
The challenge was to analyze vast data on heart disease, identify key risk factors, and predict future trends accurately to guide public health decisions. The goal was to provide actionable insights that would help in managing the growing health crisis.
Strategic Approach:
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Data Exploration & Clarity: I began by thoroughly analyzing the data to understand its structure and identify key variables contributing to heart disease. This involved data cleaning and preparation to ensure accurate analysis.
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Machine Learning Implementation: Using Random Forest, I built a predictive model that achieved 95.08% accuracy in forecasting heart disease outcomes. This model identified critical factors such as lifestyle habits, medical history, and environmental conditions that were strongly linked to the increase in heart disease.
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Predictive Insights: The model helped pinpoint areas for intervention, focusing on factors like poor diet, inactivity, and lack of healthcare access. These insights were critical in shaping preventive health strategies.
Outcomes:
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High Predictive Accuracy: The 95.08% accuracy of the Random Forest model provided reliable forecasts, enabling data-driven decisions.
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Identified Key Contributors: Factors like diet and inactivity were flagged as significant contributors to the surge, guiding targeted health interventions.
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Informed Public Health Strategies: The insights led to strategic prioritization of resources, ensuring effective public health responses in high-risk areas.
Insights Gained:
This project underscored the power of data analytics and machine learning in addressing public health crises. By leveraging advanced analytical techniques, we were able to uncover hidden patterns in the data and make predictions that informed critical interventions. It also reinforced the importance of clean, structured data in deriving reliable insights.
Conclusion:
This case exemplifies my ability to apply advanced data analysis and machine learning techniques to solve pressing issues. The insights generated not only helped predict future trends but also provided actionable recommendations for public health interventions.
Technical Proficiencies Utilized:
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SQL & Python: For data extraction, cleaning, and transformation, ensuring accurate and structured data.
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Power BI: Used to create visualizations that communicated key findings effectively.
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Machine Learning (Random Forest): Implemented to accurately predict heart disease outcomes, achieving 95.08% accuracy.
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Cloud Platforms (AWS, Google Cloud, Azure): Enabled scalable solutions for handling large datasets securely.
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ETL Processes: Streamlined the process of integrating and processing data from multiple sources.
Core Competencies Displayed:
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Data Analysis & Predictive Modeling
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Machine Learning Implementation
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Statistical Analysis & Business Impact Reporting
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Strategic Decision-Making & Problem-Solving
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Cross-Functional Collaboration & Stakeholder Communication