Identify the business question or problem.
Define objectives, success metrics, and key performance indicators (KPIs).
Collaborate with stakeholders to align on goals and expectations.
Gather data from multiple sources (databases, APIs, CSV/Excel files, cloud platforms, third-party sources).
Ensure completeness, relevance, and reliability of data.
Data Quality
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My first experience was to use poewer query to Handle missing values, outliers, and duplicates.
Standardize formats, normalize data, and correct inconsistencies.
Prepare datasets for analysis using tools like Python (Pandas, NumPy), SQL, or Excel. Python was fast.
Perform exploratory data analysis (EDA) to understand patterns, trends, and anomalies.
Use descriptive statistics, visualizations, and correlation analysis.
Identify key variables that impact business outcomes
Apply statistical techniques or machine learning models (regression, classification, clustering) as needed.
Generate actionable insights and predictive outcomes.
Validate models and ensure accuracy using appropriate metrics
Present insights through dashboards, charts, and reports (Power BI, Tableau, Matplotlib, Seaborn).
Make findings understandable for both technical and non-technical stakeholders.
Highlight trends, patterns, and recommendations clearly.
Deploy models or insights into production systems for real-time or automated decision-making.
Monitor performance, update models, and refine insights as new data arrives
Gather feedback from stakeholders.
Refine analyses, visualizations, or models based on business needs.
Ensure continuous improvement and alignment with evolving goals.
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