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 Υπάρχουν πολλά ακόμη πεδία στο data analytics που μπορούν να εξερευνηθούν μετά τις βασικές έννοιες που έχουμε ήδη καλύψει. Παρακάτω παρατίθεται μια οργανωμένη λίστα με θεματικές ενότητες

 1. Data Preparation & Cleaning

• Missing values handling (imputation methods)
• Data normalization & standardization
• Feature encoding (one-hot, label encoding)
• Data transformation (log, box-cox, binning)
• Dealing with imbalanced data

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 2. Exploratory Data Analysis (EDA) – Προχωρημένα

• Multivariate analysis
• Dimensionality reduction (PCA, t-SNE)
• Feature selection techniques
• Time series exploration (seasonality, trend, decomposition)

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 3. Statistical Methods

• Hypothesis testing (t-test, χ², ANOVA)
• Probability distributions
• Confidence intervals
• Statistical significance & p-values

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 4. Machine Learning Basics (εάν θες να πας πιο πέρα)

• Supervised learning (regression, classification models)
• Unsupervised learning (clustering, anomaly detection)
• Model evaluation metrics (accuracy, precision, recall, ROC-AUC)
• Cross-validation
• Overfitting / underfitting

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 5. Time Series Analysis

• ARIMA / SARIMA
• Forecasting principles
• Smoothing methods (moving averages, exponential smoothing)

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 6. Data Visualization & Reporting

• Principles of effective visualization
• Dashboards (Power BI / Tableau)
• Chart selection best practices
• Storytelling with data

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 7. Big Data Concepts (προαιρετικά)

• Hadoop / Spark basics
• ETL pipelines
• Distributed computing
• Data lakes vs data warehouses

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 8. SQL & Databases

• JOINs (inner, outer, cross)
• Window functions (ROW_NUMBER, RANK, PARTITION BY)
• Subqueries & CTEs
• Indexing & query optimization

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 9. Python for Data Analytics

• Pandas (groupby, merges, pivot tables)
• NumPy (vectorization, broadcasting)
• Visualization libraries (Matplotlib, Seaborn, Plotly)

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 10. Data Ethics & Governance

• Data privacy (GDPR basics)
• Bias in datasets
• Responsible data use