Data Analysis & Insights
- Collect, clean, and structure datasets for accuracy and usability.
- Perform exploratory data analysis (EDA) to uncover trends, correlations, and anomalies.
- Translate complex datasets into clear business insights that guide smarter decisions.
2. Predictive Modeling & Machine Learning
- Build statistical and machine learning models to forecast outcomes (e.g., customer behavior, system performance).
- Apply regression, classification, and clustering techniques to solve real-world problems.
- Optimize models for accuracy and scalability.
3. Data Visualization & Storytelling
- Create dashboards and visual reports using tools like Tableau, Power BI, or Python libraries.
- Present findings in a way that non-technical stakeholders can easily understand.
- Turn numbers into compelling stories that drive action.
4. Technical Writing for Data Science
- Document processes, models, and workflows with clarity and precision.
- Produce user manuals, SOPs, and reports that bridge technical and business audiences.
- Ensure compliance and reproducibility in data projects.