Databases with SQL, ethics and machine learning

How SQL databases can be used to manage data (AC 1.1):

• Fundamental concepts of relational databases.
• Through ERDs and understanding how tables relate to each other, learners gain insight into the structure and organisation of data.
• Key concepts include tables, rows, columns, and primary and foreign keys. Overview of database structures: How data is stored, organised, and retrieved efficiently.
• Real-World Context: Databases include Customer Relationship Management (CRM), inventory management, and library systems.
• Use case: How businesses rely on relational databases to manage large volumes of structured data.
• Business Applications: customer relationship management (CRM), inventory management systems, e-commerce platforms, financial transaction processing, reporting and analytics, data warehousing

SQL statements for data manipulation and aggregation (AC 1.2):

What is SQL (Structured Query Language), and why is it essential for databases? 

Executing SQL queries to retrieve and manipulate data (AC 1.3):

• Explanation of key SQL components: SELECT, FROM, WHERE, and ORDER BY.
• Demonstrates basic SQL commands on an online SQL platform
• Understand the purpose and syntax of basic SQL commands. Be able to write and execute SQL queries to retrieve, filter, and sort data from relational databases.
• Debug SQL queries effectively.

Role of database relationships (AC 1.4):

• Relationship Types: one-to-one relationships (user profiles), one-to-many relationships (customers-orders), many-to-many relationships (students-courses), self-referencing relationships (employee-manager)
• Foreign Keys: referential integrity enforcement, cascade operations, relationship constraints, preventing orphaned records, maintaining consistency
• Entity-Relationship Concepts: entities as tables, attributes as columns, relationships as foreign keys, ER diagram basics

Classify different join types, including Left, Right, Inner & Outer Join (AC 1.5):

• Joins in SQL are essential for combining data from two or more tables based on related columns. They enable us to retrieve meaningful insights from relational databases by merging data points that share a common relationship.
• SQL Joins combine data from multiple tables based on related columns.
• Joins merge and retrieve related data for analysis.
• Use case: Joins enable integrated analysis across tables.

Explain the syntax, structure, and common data types used in SQL to support effective interaction with relational databases (AC 1.6):

• SQL queries are made up of statements and clauses. The SELECT, FROM, and WHERE clauses are fundamental building blocks of SQL queries.
• Use case: specify what data you want, where to find it, and how to narrow it down to what's relevant.
• Data types define the kind of data that can be stored in a column, ensuring accurate storage and efficient querying.
• Understanding data types ensures accuracy, optimises performance, and supports scalability by storing data efficiently and preventing errors.
• SQL structure, syntax, and data types give the ability to handle and analyse data effectively.
• Use case: solve real-world problems quickly and efficiently, making SQL an essential skill for data professionals.

Machine learning concepts, algorithms, and applications (AC 2.1):

• Overview of supervised learning models and classification techniques.
• Decision Trees and Random Forest algorithms, and their role in decision-making processes.
• Strengths and weaknesses of classification methods in predictive analytics.
• Model evaluation metrics (accuracy, precision, recall, and F1-score) and how they impact decision-making.
• Ethical concerns in classification, including bias in datasets and challenges to interpretability.
• Business Applications: customer churn prediction, sales forecasting, fraud detection, recommendation systems, sentiment analysis, predictive maintenance

Implementing and evaluating ML models using Python (AC 2.2):

• Scikit-learn Basics: model import and instantiation, fit and predict methods, split to train and test, normalisation, pipeline creation
• Overview of unsupervised learning models and clustering techniques.
• Understanding K-Nearest Neighbours (KNN) and K-Means Clustering for Pattern Recognition. Techniques for determining the optimal number of clusters using the elbow method and silhouette analysis. Comparing hierarchical clustering and K-Means for different business scenarios.
• Practical industry applications of clustering, including customer segmentation and anomaly detection.
• Ethical challenges in clustering include the risks of biased grouping and misinterpreting clustering results.
• Evaluation Metrics:

1. Classification Metrics: accuracy score, confusion matrix, precision and recall, F1 score,
2. Regression Metrics: mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), R-squared score

• Learners will engage in practical exercises to build and refine classification and clustering models using real-world datasets.
• They will analyse model outputs and interpret results to understand predictive accuracy and generalisability.
• Special emphasis will be placed on understanding bias within datasets and its impact on AI-driven predictions.
• Understand both technical and ethical considerations.