Adrian Julius Aluoch Data Analyst
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I help students understand complex concepts by breaking them down into clear, structured, and practical explanations. My approach focuses on problem-solving, step-by-step guidance, and real-world examples so you not only complete your assignments but also genuinely understand the material behind them. I believe learning should feel manageable, well-organized, and empowering.

I specialize in data analysis, statistics, research methods and analytical problem-solving. Whether you need help working through spreadsheet tasks, coding assignments, regression models, research interpretation, or general coursework, I provide explanations that are easy to follow and tailored to your specific needs.

My goal is to make complex ideas simple. I prioritize clarity, accuracy, and structured reasoning in every task. I focus on helping students develop confidence with analytical tools and methods, while ensuring that the work meets high academic standards. I’m patient, detail-oriented, and committed to delivering support that feels both helpful and reliable.

If you need dependable help with coursework, labs, quantitative analysis, or any academic task that requires careful thinking and strong communication, I’m here to support you. I enjoy helping learners overcome challenges, improve their understanding, and achieve better results through clear guidance and practical, example-driven explanations.

Subjects

  • Data Analysis using SQL, Excel & Power BI Beginner-Expert

  • Data Analysis with R & Python Beginner-Expert

  • Statistical Analysis with R & Python Beginner-Expert

  • Data Pipelines & Workflow Automation Beginner-Expert

  • Data Visualization & Storytelling Beginner-Expert

Experience

No experience mentioned.

Education

  • Bachelor of Science in Computer Science (Aug, 2021–Aug, 2025) from Moi University main campus, Eldoret

Fee details

    KSh1,000–1,500/hour (US$7.73–11.59/hour)

    My rates are designed to support consistent, long-term learning and depend on session frequency, depth of analysis, and urgency.

    This covers:
    1. Undergraduate statistics (probability, hypothesis testing, regression)
    2. Guided problem-solving and exam preparation
    3. Applied data analysis using R, Python, SQL, Excel, Power BI and Tableau
    4. Statistical modeling, time series analysis, and anomaly detection
    5. Data pipelines & workflow automation
    - Extracting data from APIs, files, or databases
    - Building ETL/ELT pipelines
    - Automating data flows and preparing data for analysis
    6. Interpretation, visualization & data storytelling
    - Turning analysis into clear insights
    - Designing effective charts and dashboards
    - Communicating results for technical and non-technical audiences

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