College aantekeningen
Data Science for Business Class Notes
- Instelling
- Universiteit Van Amsterdam (UvA)
Class notes taken during the lectures for Data Science for Business. Best when complemented with the book chapter notes.
[Meer zien]Voorbeeld 2 van de 11 pagina's
Voorbeeld 2 van de 11 pagina's
In winkelwagengesponsord bericht van onze partner
Gekoppeld boek
Titel boek:
Auteur(s):
- Uitgave:
- ISBN:
- Druk:
Voorbeeld van de inhoud
WEEK 1Why DS? Challenges:
1. Inability to build bridges b/w business and IT
2. Existence of a lot of data
3. Organisations are increasingly complex due to information and changing environments
Data warehouse → selecting & cleaning → transformation (70% of the time) → data mining →
interpretation & evaluation → knowledge/understanding
Machine learning, statistics and data mining
● Statistics:
○ More theory- and model-based
○ More focused on testing hypotheses
● ML
○ More heuristic
○ Focused on improving the performance of a learning agent
○ Also looks at real-time learning and robotics - areas not part of data mining
● Data mining and knowledge discovery
○ Integrates theory and heuristics
○ Focus on the entire process of knowledge discovery, including data cleaning,
learning, and integration and visualization of results
● Fundamental difference b/w ML and statistics is that ML is a bottom up approach and
statistics a top down approach
○ Statistics is an explanatory model not optimized to extend data to make
predictions and a ML model is a predictive model that also helps predict the
future
Data warehousing/storage: Coalesce data from across an enterprise, often from multiple
transaction-processing systems
Querying/reporting: Very flexible interface to ask factual questions about data
● No modeling or sophisticated pattern finding
● E.g., SQP, QBE
OLAP (Online analytical Processing)
● Provides easy-to-use GUI to explore large data collections
● Exploration is manual; no modeling
● Dimensions of analysis pre-programmed into OLAP system
Types of ML
1. Supervised learning
a. Classification
b. Regression
2. Unsupervised learning
3. Reinforcement learning: Mix. Learn from a loop of learning
, Terminology:
● Columns → attributes or features
● Variable or target attribute: What you want to predict
● Dimensionality of a dataset is the sum of the dimensions of the features
○ So number of columns (attributes, variables or features)
○ The more dimensions the harder it is to analyse the data
Data → categorical or numerical
● Categorical: nominal (e.g., binomial) or ordinal (ranking in classes)
● Numerical: interval (data where the zero-point is not fixed, e.g., temperature) or ratio
(fixed zero-point, can be divided, e.g., salary, height)
DM extracts patterns from data
● Some tasks can be done by using either supervised or unsupervised methods (e.g.,
similarity matching, link prediction, data reduction) and algorithms (e.g., artificial neural
networks (ANN))
WEEK 2
Decision trees: Fundamental and important algorithm in data science
Classification goal: Classify new data in existing categories
Classification techniques examples: Statistical analysis, decision tree analysis, support vector
machines, case-based reasoning, neural networks, Bayesian classifiers, genetic algorithms,
rough sets
Classification: Linear regression
● w0 + w1x + w2y ≥ 0
● Regression computes wi from data to minimize squared error to 'fit' the data
● Does not really help categorize x, just how close the dot or x is to the line
Decision tree classification task:
● Training set —> induction —> tree induction algorithm (learn model) —> model (decision
tree —> apply model —> deduction -- > test set
● No loops
● Each child cannot have more than one parent
Creating decision trees:
● Employs the divide and conquer method
● Recursively divides a training set until each division consists of examples from one
class
1. Create a root node and assign all of the training data to it
2. Select the best splitting attribute
3. Add a branch to the root node for each value of the split. Split the data into mutually
exclusive subsets along the lines of the specific split
4. Repeat steps 2 and 3 for each and every leaf node until the stopping criteria is reached
Voordelen van het kopen van samenvattingen bij Stuvia op een rij:
Verzekerd van kwaliteit door reviews
Stuvia-klanten hebben meer dan 700.000 samenvattingen beoordeeld. Zo weet je zeker dat je de beste documenten koopt!
Snel en makkelijk kopen
Je betaalt supersnel en eenmalig met iDeal, creditcard of Stuvia-tegoed voor de samenvatting. Zonder lidmaatschap.
Focus op de essentie
Samenvattingen worden geschreven voor en door anderen. Daarom zijn de samenvattingen altijd betrouwbaar en actueel. Zo kom je snel tot de kern!
Veelgestelde vragen
Wat krijg ik als ik dit document koop?
Je krijgt een PDF, die direct beschikbaar is na je aankoop. Het gekochte document is altijd, overal en oneindig toegankelijk via je profiel.
Tevredenheidsgarantie: hoe werkt dat?
Onze tevredenheidsgarantie zorgt ervoor dat je altijd een studiedocument vindt dat goed bij je past. Je vult een formulier in en onze klantenservice regelt de rest.
Van wie koop ik deze samenvatting?
Stuvia is een marktplaats, je koop dit document dus niet van ons, maar van verkoper patycyl. Stuvia faciliteert de betaling aan de verkoper.
Zit ik meteen vast aan een abonnement?
Nee, je koopt alleen deze samenvatting voor €4,99. Je zit daarna nergens aan vast.
Is Stuvia te vertrouwen?
4,6 sterren op Google & Trustpilot (+1000 reviews)
Afgelopen 30 dagen zijn er 52510 samenvattingen verkocht
Opgericht in 2010, al 14 jaar dé plek om samenvattingen te kopen