Advanced Data Analytics Certification | Analytics Training in Wroclaw

Advanced Data Analytics Certification​ Course Outline

Day 1: Advanced Statistical Analysis & Data Engineering
Module 1: Advanced Data Analytics Foundations

• Evolution of Data Analytics & Key Trends
• Data Science vs. Data Analytics vs. Business Intelligence
• Frameworks for Decision-Making with Data
• Understanding Data Pipelines for Analytics

Module 2: Advanced Statistical Analysis & Hypothesis Testing

• Probability Distributions (Normal, Binomial, Poisson)
• Central Limit Theorem & Bayesian Inference
• Hypothesis Testing (T-Test, ANOVA, Chi-Square)
• Bootstrapping & Confidence Intervals

Module 3: Multivariate Analysis & Dimensionality Reduction

• Principal Component Analysis (PCA)
• Factor Analysis for Feature Selection
• LDA for Classification Problems
• Manifold Learning Techniques (t-SNE, UMAP)

Module 4: Advanced SQL for Data Analytics

• SQL for Analytical Queries (Window Functions, CTEs)
• Query Optimization & Performance Tuning
• Time Series Data Manipulation in SQL
• Recursive Queries & Advanced Joins

Day 2: Data Processing & Predictive Analytics
Module 5: Data Cleaning, Transformation & Feature Engineering

• Handling Missing Data (Imputation & Outlier Detection)
• Encoding Categorical Data (One-Hot, Target Encoding)
• Feature Scaling & Normalization Techniques
• Creating Synthetic Features for Better Models

Module 6: Exploratory Data Analysis (EDA) with Python

• Data Profiling & Summary Statistics
• Identifying Patterns & Outliers with Visualization
• Correlation Analysis & Feature Relationships
• Advanced Data Transformation Methods

Module 7: Predictive Analytics & Machine Learning Models

• Regression Models: Linear, Ridge, Lasso
• Classification Models: Logistic Regression, Decision Trees
• Bias-Variance Tradeoff & Model Validation Strategies
• Model Selection & Performance Metrics

Module 8: Ensemble Learning & Model Optimization

• Bagging (Random Forest) & Boosting (XGBoost, LightGBM, CatBoost)
• Stacking & Blending Techniques
• Hyperparameter Tuning (Grid Search, Bayesian Optimization)
• Cross-Validation & Model Robustness Testing

Day 3: Unsupervised Learning & Time Series Forecasting
Module 9: Unsupervised Learning & Clustering Techniques

• K-Means, DBSCAN, and Hierarchical Clustering
• Anomaly Detection with Isolation Forests
• Association Rule Mining (Apriori, FP-Growth)
• Customer Segmentation for Business Insights

Module 10: Time Series Analysis & Forecasting

• Understanding Trends & Seasonality
• ARIMA, SARIMA, and Exponential Smoothing
• Machine Learning for Time Series (XGBoost & LSTMs)
• Anomaly Detection in Time Series Data

Module 11: Advanced Data Visualization Techniques

• Customizing Visualizations with Matplotlib & Seaborn
• Interactive Dashboards with Plotly
• Advanced Charting Techniques (Heatmaps, Sankey Diagrams)
• Best Practices for Data Storytelling

Module 12: Explainable AI & Model Interpretability

• Feature Importance & SHAP Values
• Partial Dependence Plots & LIME
• Fairness & Bias Mitigation in Machine Learning
• Ethical Considerations in AI Decision Making

Day 4: Business Intelligence & Real-World Applications
Module 13: Business Intelligence with Tableau

• Data Preparation & Importing Large Datasets
• Creating Advanced Charts & Visualizations
• Interactive Dashboards & Storytelling in Tableau
• Data Blending & Advanced Calculations

Module 14: Data-Driven Decision Making & Optimization Techniques

• A/B Testing & Experimentation
• Decision Trees for Strategic Decision-Making
• Optimizing Business Processes with Data
• Real-World Applications of Data Analytics

Module 15: Data Governance & Quality Management

• Principles of Data Quality & Data Lineage
• Data Cleaning Frameworks for Large-Scale Analytics
• Handling Data Privacy & Compliance Regulations (GDPR, HIPAA)
• Automating Data Validation & Monitoring