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Gain practical expertise in data analysis, visualization, and machine learning. Master Python, Pandas, NumPy, and Scikit-Learn through hands-on labs, expert-led live training, and real-world projects in San Jose.
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A brutal, practical overview of descriptive statistics, probability distributions, and inferential concepts (sampling, Central Limit Theorem). Focus on application, not academic proofs.
Master the core process of hypothesis formulation, test selection, and p-value interpretation. Hands-on implementation of T-tests and ANOVA in Python for comparing means and making valid conclusions.
Apply Chi-Squared tests for categorical data analysis. Understand when to use non-parametric tests and implement them using Python's Statsmodels, ensuring you never draw a statistically invalid conclusion from real-world data.
Master the assumptions and interpretation of Simple and Multiple Linear Regression. Learn model diagnostics, variable selection, and how to effectively communicate model coefficients to business leadership using Scikit-learn.
Dive deep into Logistic Regression for binary classification problems. Understand concepts like log-odds, ROC curves, AUC, and how to set appropriate threshold values for optimal business impact using Scikit-learn.
Implement powerful non-linear classification models. Master Decision Trees and Random Forests in Python, learning hyperparameter tuning and variable importance interpretation for robust, high-accuracy predictions.
Master K-Means and Hierarchical Clustering for identifying hidden customer segments or data anomalies. Learn to interpret cluster validity and size for actionable business strategy using Scikit-learn.
Implement the Apriori algorithm for Market Basket Analysis. Learn best practices for model object saving/loading using joblib or pickle for production deployment.
Master Matplotlib and Seaborn to create complex, informative, and visually compelling plots (scatter plots, box plots, heat maps) to clearly communicate model findings and data insights.
Master key performance metrics (Accuracy, Precision, Recall, F1-Score) and techniques like cross-validation to ensure your models are robust and perform reliably on unseen data.
A practical overview of connecting Python to relational databases (PostgreSQL/MySQL) using libraries like SQLAlchemy—a mandatory enterprise skill.
Learn to create dynamic, reproducible reports and dashboards using Jupyter Notebooks. Final project consolidation, code optimization, and best practices for creating REST APIs for model serving.
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