AI for Data Analyticsin Edinburgh

Module 1: Introduction to AI for Data Analytics

• Fundamentals of data analytics and artificial intelligence
• Role of AI in modern data-driven organisations
• Traditional analytics versus AI-powered analytics
• Overview of AI analytics tools and applications

Module 2: Data Collection and Preparation

• Data sources and acquisition methods
  • Structured and unstructured data
  • Files, spreadsheets, databases, and APIs
• Data cleaning and preprocessing
  • Handling missing values and duplicate records
  • Identifying inconsistent data formats

Module 3: Exploratory Data Analysis (EDA)

• Understanding dataset structure and characteristics
• Descriptive statistics and summary analysis
• Identifying trends, distributions, and outliers
• Using AI tools to assist exploratory analysis

Module 4: Python Fundamentals for Data Analytics

• Introduction to Python for analytics workflows
• Working with essential Python libraries
  • Pandas for data manipulation
  • NumPy for numerical operations
• Loading, filtering, transforming, and summarising datasets

Module 5: AI-Powered Data Analysis Tools

• Introduction to AI-assisted analytics platforms
• Using AI tools to understand datasets
• Generating analytical summaries and explanations
• Verifying AI-generated calculations and recommendations

Module 6: Statistical Analysis and Interpretation

• Measures of central tendency and variability
• Correlation and relationship analysis
• Basic probability and statistical reasoning
• Interpreting statistical findings in business contexts

Module 7: Data Visualisation and Reporting

• Principles of effective data visualisation
• Creating charts, graphs, and dashboards
  • Bar charts, line charts, and scatter plots
  • Histograms and distribution visualisations
• Using AI to support report generation and insight summaries

Module 8: SQL and Database Analytics

• Fundamentals of relational databases
• Retrieving data using SQL queries
  • SELECT, WHERE, GROUP BY, and ORDER BY
  • Aggregate functions and basic joins
• Combining database queries with AI-assisted analysis

Module 9: Predictive Analytics and Machine Learning Basics

• Introduction to predictive analytics
• Understanding classification and regression
• Exploring training and testing datasets
• Interpreting predictions and model evaluation results

Module 10: Trend Analysis and Forecasting

• Identifying historical patterns and trends
• Introduction to time-series analysis
• Understanding basic forecasting techniques
• Evaluating forecast limitations and uncertainty

Module 11: Natural Language Analytics

• Asking data-related questions using natural language
• Converting analytical questions into structured tasks
• Generating summaries and explanations from datasets
• Validating AI-generated insights against source data

Module 12: Business Intelligence and Decision Support

• Translating business questions into analytical objectives
• Identifying key performance indicators (KPIs)
• Interpreting dashboards and business reports
• Presenting evidence-based recommendations

Module 13: Data Quality, Privacy, and Responsible AI

• Assessing data completeness, consistency, and accuracy
• Protecting confidential and personally identifiable information
• Recognising AI-generated errors and misleading conclusions
• Applying human review and responsible data-handling practices

Module 14: AI Analytics Workflow Automation

• Identifying repetitive analytical tasks
• Exploring AI-assisted data transformation and reporting
• Designing repeatable analytics workflows
• Reviewing outputs for accuracy and consistency

Module 15: Capstone Project – AI-Powered Data Analytics

• Define a practical business or analytical problem
• Collect, prepare, and explore a suitable dataset
• Apply AI-assisted analysis and visualisation techniques
• Interpret findings and develop actionable recommendations
• Present a final analytical report with documented limitations