Data Analytics

 

Data Analytics 

Overview

With a BSc degree in Mathematics and Statistics (certificate), I have a solid foundation in data science principles, which I leverage in my work as a self-taught software engineer. My academic background, combined with hands-on experience working with small to medium datasets, has equipped me with strong problem-solving and analytical skills that I apply to software development. I approach complex programming challenges with a logical mindset, enabling me to deliver efficient and effective solutions.

Key Skills & Technologies

  • Programming: Python, SQL, R
  • Frameworks/Libraries: NumPy, Pandas, Matplotlib, BeautifulSoup, Request
  • Tools: Excel, MySQL Workbench, Power BI, Tableau
  • IDE: Anaconda, JupyterNotebook
  • Concepts: Data manipulation, statistical analysis, data visualization, data interpretation, web scraping

Notable Projects

  1. Real Estate Pricing Model
    • Predictive Market Analysis: Developed a machine learning model to analyze home prices and market trends.
    • Data-Driven Pricing Strategy: Provided buyers and sellers with actionable insights to optimize pricing strategies.
    • Informed Decision-Making: Enabled better decision-making based on current and projected market conditions.
  2. Data Scraping for Financial Analysis
    • Web Scraping Automation: Built an automated system to collect real-time financial data, eliminating the need for manual data collection.
    • Efficiency & Accuracy Improvement: Enhanced the accuracy and speed of financial analysis and reporting.
  3. Covid Spread Prediction Analysis
    • Trend Forecasting: Analyzed COVID-19 spread patterns to predict future infection trends.
    • Data Visualization & Reporting: Created visualizations and a comprehensive report for a final-year capstone project.
    • Impactful Insights: Provided valuable data-driven insights into the pandemic’s progression and potential future scenarios.

Achievements & Impact

  • Enhanced Decision-Making with Data-Driven Solutions: Developed machine learning models and predictive analytics tools to empower users in industries like real estate, finance, and public health, helping them make more informed and strategic decisions.
  • Boosted Operational Efficiency: Automated workflows, reducing manual tasks and enhancing data accuracy, resulting in faster, more effective decision-making.
  • Optimized Data Utilization: Leveraged advanced data analysis and modeling techniques to generate actionable insights from complex datasets, driving better market strategies, financial analysis, and public health responses.
  • Delivered Scalable and Sustainable Solutions: Created systems and models designed for scalability, ensuring long-term value and adaptability across different sectors.
  • Strengthened Data Security and Insights: Implemented robust data processing techniques that ensured the integrity and security of critical information while providing valuable, real-time insights.

Future Goals & Learning

Working on deepening my understanding of machine learning models, focusing on applying these techniques to predictive analytics and forecasting.


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