Step-by-Step Learning Stages
SQL Mastery & Data Wrangling in Python
Master SQL aggregations, window functions (`RANK`, `DENSE_RANK`, `LAG`), CTEs, joins, and Python Pandas/NumPy dataframe manipulation.
Statistics, Probability & A/B Testing
Understand Central Limit Theorem, p-values, t-tests, chi-square tests, confidence intervals, sample size estimation, and experimental A/B testing design.
Exploratory Data Analysis & Data Visualization
Build interactive charts and business dashboards with Seaborn, Plotly, Tableau, and Power BI. Tell compelling data stories to executive stakeholders.
Machine Learning & Time Series Forecasting
Apply predictive modeling (Regression, Logistic Classification, Random Forests, XGBoost), customer clustering (K-Means, RFM), and time-series forecasting (ARIMA, Prophet).
Recommended Portfolio Projects
3 practical projects to prove your analytical & predictive data science skills.
Executive Sales & Revenue Insights Dashboard
Clean complex retail transaction data with SQL, extract YoY sales growth, customer acquisition costs, and build an interactive Tableau/PowerBI dashboard.
E-Commerce A/B Test & Conversion Rate Study
Analyze clickstream experiment data, verify sample ratio mismatch (SRM), compute two-tailed t-tests and p-values, and present business recommendations.
Customer Segmentation & Lifetime Value Engine
Combine Recency-Frequency-Monetary (RFM) scoring with K-Means clustering to identify high-value customer segments and predict 12-month Customer Lifetime Value (CLV).