07 - Analytics

Introduction to Analytics and Data Mining

• Teacher: Ivika Jäger, Mittuniversitetet, November 2024

Types of Analytics

• Descriptive Analytics: Analyzes historical data to understand patterns and trends.

• Predictive Analytics: Uses historical data and statistical algorithms to anticipate future outcomes.

• Prescriptive Analytics: Provides recommendations for actions based on data analysis.

Predictive Analytics

• Purpose: To predict future trends and behaviors.

• Methods Used:

  • Regression analysis

  • Time series analysis

  • Machine learning algorithms

  • Classification models

  • Data mining

Examples of Analytics in Practice

• Retail Company: Uses descriptive analytics to evaluate past sales data and customer behavior to inform new product strategies.

• E-commerce Company: Applies predictive analytics to forecast product demand for holiday seasons based on historical sales data.

• Logistics Company: Utilizes prescriptive analytics to determine optimal delivery routes by analyzing historical and real-time data.

Data Mining Overview

• Definition: The process of extracting knowledge from large datasets.

• Key Techniques:

  • Prediction: Forecasting future events, for example, sales forecasting.

  • Classification: Assigning data to predefined categories.

  • Clustering: Grouping data points without predefined labels.

  • Association: Identifying relationships between variables, e.g., co-purchase behavior.

Data Mining vs. Statistics

• Statistics: Starts with a hypothesis, tests with sample data (e.g., observing ice cream sales based on weather).

• Data Mining: Explores all data to find patterns (e.g., identifying higher sales on weekends).

Common Myths about Data Mining

• Myth: Data mining provides immediate clear predictions.

  • Reality: Requires domain knowledge and time.

• Myth: An advanced degree is necessary.

  • Reality: There are accessible tools available for anyone.

• Myth: Only large firms can utilize data mining.

Data Mining Process

1. Business Understanding: Define objectives and problems.

2. Data Understanding: Evaluate data quality.

3. Data Preparation: Clean and preprocess data.

4. Model Building: Utilize algorithms to find patterns.

5. Testing and Evaluation: Check model performance.

6. Deployment: Implement insights within business dynamics.

Text Mining

• Purpose: Converts unstructured text into structured data.

• Challenges:

  • Correctly tagging words (nouns vs. verbs).

  • Language ambiguity.

• Solution: Modern AI tools like LLMs enhance contextual understanding.

Web Mining

• Purpose: Analyzes web content, structure, and usage.

• Components:

  • Web Content: Extracting information from web pages.

  • Web Structure: Understanding website links.

  • Web Usage: User behavior analysis.

• SEO: Strategies to increase website visibility through keywords, tags, backlinks.

Deep Learning vs. Machine Learning

• Machine Learning (ML): Requires feature definitions.

• Deep Learning (DL): Automatically learns features, reducing manual work.

• Comparison: DL handles complex data representations more efficiently than traditional ML.

Features in Deep Learning

• Definition: Features (columns in datasets) are explanatory variables.

• Role of Neurons: Process features, not observations.

Challenges in Deep Learning

• Requires advanced hardware (e.g., GPUs).

• Needs large, high-quality datasets.

• Manual data labeling is time and cost-intensive.

• Overcoming these challenges enhances predictive capabilities.

Deep Neural Networks

• Evolution: Early networks had few layers; modern networks can have millions of neurons.

• Input Data: Processes multidimensional inputs (e.g., image pixels).

Types of Neural Networks

• Multilayer Perceptron (MLP): Simple feedforward network for basic tasks.

• Recurrent Neural Network (RNN): Retains feedback and memory for contextual learning.

• Long Short-Term Memory (LSTM): Type of RNN for memory efficiency.

Iterative Optimization in Deep Learning

• Models fine-tune weights iteratively to reduce prediction errors.

• Requires repeated evaluations of input-output relations to improve model performance.

AI as a Service (AIaaS)

• Offers pre-configured AI solutions via cloud providers.

• Streamlines routine tasks, allowing businesses to focus on innovation.

ChatGPT Data Analysis Capabilities

• Ability to upload and analyze data files (e.g., CSV).

• Can interpret images, take real-time screenshots, and engage in interactive tasks.

Practical Analysis of Coffee Sales Data

• Dataset Overview: Comprises 2,341 entries; includes date, time, payment type, amount, and coffee type.

• Analysis Focus: Identifying trends in coffee purchases, payment methods, and customer preferences.

Conclusion

• The notes summarize various aspects of analytics, data mining, and the applications of AI in business decision-making and enhancing data interpretation.