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Sem 5 Prep
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Flashcards · BI & Data Science
Chapter
Every chapter
What business intelligence and data science are
Charts, maps and dashboards
Linear programming: turning a business problem into a model
Integer, binary and mixed-integer programming
Non-linear programming and the evolutionary solver
What machine learning is
Clustering: finding groups without labels
k-Nearest Neighbours
Section B only: decision trees, entropy and information gain
Logistic regression, and how to judge a classifier
Bayes' theorem and Bayesian networks
Recommender systems and collaborative filtering
R: objects, vectors and data frames
R: cleaning, aggregating and plotting
Market basket analysis and association rules
Principal component analysis
Multiple linear regression and reading summary(lm)
Classification in R, end to end
Monte Carlo simulation and forecasting new products
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The DIKW ladder
Flip (Space)
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