1BM110 Data Analytics for Business Intelligence (1BM110)
Summary
Extended Summary: Data Analytics for Business Intelligence (1BM110)
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Course
1BM110 Data Analytics for Business Intelligence (1BM110)
Institution
Technische Universiteit Eindhoven (TUE)
This is an extended summary of all lectures for the course Data Analytics for Business Intelligence (1BM110). This 50-page document (with a clickable table of contents for easier navigation) summarizes the essence of all topics covered in the course (as far as I could imagine when writing it). It i...
1BM110 Data Analytics for Business Intelligence (1BM110)
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1BM110 - course summary
Table of contents
Lecture 1: introduction
Big data
Business decisions
Business analytics
Data mining
Cross Industry Standard Process for Data Mining (CRISP-DM) framework
Lecture 1: data visualization & preprocessing
Data understanding
Categorical data
Numerical data
Non-numerical data
Misleading visualizations
Data preparation
Data integration
Data cleaning
Data reduction
Data transformation
Lecture 2: supervised learning 1
Introduction to supervised learning
Classification models
K-nearest-neighbour classifier (KNN)
Naïve Bayes classifier
Decision trees
Classification performance measurement
Binary classification
Receiver Operating Characteristic (ROC) curve
Kappa coefficient
Regression models
Linear regression
Regression vs classification
Experimental setup
Lecture 3: supervised learning 2
Support Vector Machines (SVMs)
Non-linear SVMs
Bias-variance trade-off
1BM110 - course summary 1
, Ensemble methods
Bagging
Boosting
Unsupervised learning (clustering)
Clustering
K-means clustering
Hierarchical clustering
Applying clustering algorithms
Lecture 4: temporal data
Grouping sequences & mapping
Mapping methods
Dynamic Time Warping (DTW)
Response features
Markov chains
Maximum likelihood estimation
Association analysis
Lecture 5: neural networks & Deep Learning (DL)
Perceptron & sigmoid neuron
Multi-layer perceptron (multi-layer neural network)
Training neural networks
Gradient descent
Momentum
Regularization
Lectures 6 & 7: Natural Language processing (NLP)
Domain & corpus
Corpus
Pre-processing
Linguistic processing
Knowledge resources
Text representation
Bag-of-Words (BoW) model
n-grams
Linguistic features model vs BoW model
Distributional Semantic Models (DSM)
Supervised NLP tasks
Unsupervised NLP tasks
Lecture 8: eXplainable Artificial Intelligence (XAI)
Interpretability vs explanations
Transparency
White boxes (intrinsically interpretable models)
Model-agnostic explanation methods
Model-specific explanation methods (for DNN)
Evaluation & measures
Lecture 1: introduction
Big data
Volume: quantity of generated and stored data
Variety: type and nature of the data
1BM110 - course summary 2
, Velocity: speed at which the data is generated
and processed
Business decisions
Decision Support System (DSS): computerized program used to support determinations, judgments,
and courses of action in an organization or a business.
Convential decision support: emphasis on deduction.
Business Intelligence (BI): data-driven DSS; methods that facilitate decision-making by integrating
information and processes through tools that transform data into useful and actionable information.
Business intelligence: emphasis on induction.
Business analytics
Descriptive analytics: using data to understand past and current business performance.
Data mining
Data mining: identifying patterns in data.
Examples of data mining.
Real-world data mining:
Too much data → data might be polluted
Unclear which data attributes are important
Results do not make sense
Cross Industry Standard Process for Data Mining (CRISP-DM) framework
Steps in the CRISP-DM framework:
1. Business understanding
2. Data understanding
3. Data preparation
4. Modeling
5. Evaluation
6. Deployment
The CRISP-DM framework.
1BM110 - course summary 4
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