Module 1
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Introduction to Data Science
Introduction to Data Science, Machine Learning, Artificial Intelligence relationships. Python basics, Python IDEs, Jupyter Notebook installation and usage for data exploration.
Comprehensive Data Science curriculum with Python, Machine Learning, Statistics, and SQL: from data exploration to predictive analytics and deep learning fundamentals.
Introduction to Data Science, Machine Learning, Artificial Intelligence relationships. Python basics, Python IDEs, Jupyter Notebook installation and usage for data exploration.
Pandas fundamentals: Series, DataFrames, reading/writing data, grouping, merging, joining. NumPy arrays, indexing, manipulation and operations for numerical computing.
Matplotlib installation and usage, basic plotting commands, multiple plots and legends. Object-oriented plotting methods, styling visualizations for insights.
Introduction to Machine Learning with scikit-learn, problem setting, loading datasets, learning, predicting and ML conventions for building robust models.
Introduction to Statistics: measures (mean, median, mode), probability theory, standard deviation, variance, bias-variance tradeoff, underfitting and overfitting concepts.
Distance metrics (Euclidean, Manhattan). Outlier detection using IQR, box plots, scatter plots and Cook's Distance for data quality assessment.
Handling missing values: NA identification, central imputation, KNN imputation, dummification. Data cleansing and preprocessing for quality modeling.
Understanding correlation: positive, negative, zero correlation. Pearson correlation coefficient calculation, interpretation and identifying feature relationships.
Linear regression fundamentals: linear equations, slope, intercept, R-squared values. Building regression models with scikit-learn and evaluating performance.
Logistic regression, ODDS ratio, probability of success/failure. ROC curves, bias-variance tradeoff and binary classification evaluation metrics.
K-Means clustering, K-Means++ algorithm, hierarchical clustering approaches. Unsupervised learning for customer segmentation and pattern discovery.
Support vectors, hyperplanes, linear hyperplanes. SVM kernels: linear, radial, polynomial for classification and regression tasks with non-linear data.
SQL introduction, installation, DDL/DML/DQL statements, operators, data types. INSERT, UPDATE, DELETE operations and COMMIT/ROLLBACK transaction control.
SELECT statement capabilities, filtering with WHERE, sorting with ORDER BY. Pre-defined functions, IF-THEN-ELSE in SELECT and basic SQL reporting.
Aggregate functions (SUM, COUNT, AVG), GROUP BY clause, HAVING clause for filtered aggregations. Complex aggregation queries for analytics.
Subquery types, SET operators, multiple queries combination. Correlated subqueries, EXISTS/NOT EXISTS operators, WITH clause and recursive CTEs.
Date/Time tracking, time zones, CURRENT_DATE/TIMESTAMP functions. EXTRACT, TZ_OFFSET, FROM_TZ, TO_TIMESTAMP, INTERVAL data types for temporal analysis.
System vs object privileges differentiation, user creation, grant/revoke privileges, role management, password management and access control.
End-to-end Data Science project: data collection, preprocessing, exploratory analysis, feature engineering, model building, evaluation and presentation of insights.
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