Data Science Project Package
1. Data Analysis & Insights
- Exploratory Data Analysis (EDA): Identifying trends, patterns, and anomalies in your data.
- Data Visualization: Creating compelling visualizations (e.g., dashboards) using tools like Python (Matplotlib, Seaborn), Tableau, or Power BI.
- Statistical Analysis: Hypothesis testing, A/B testing, and providing actionable insights.
2. Predictive Modeling
- Building machine learning models to forecast outcomes, such as:
- Customer behaviour (e.g., churn prediction).
- Sales or demand forecasting.
- Risk assessment models.
- Deliverables include:
- Model deployment for real-time or batch predictions.
- Code documentation and performance metrics.
3. Data Engineering
- Data collection and cleaning:
- Handling missing values.
- Parsing and integrating data from various sources (CSV, SQL, APIs).
- Data pipeline development:
- Automating ETL (Extract, Transform, Load) processes.
- Optimizing data workflows.
4. Custom Machine Learning Solutions