Summary: Business Intelligence & Business Analytics

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  • 1.1.2 Introduction to Databases

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  • What is a database and what does is consist of?

    • Definition database: “A collection of related tables, designed, maintained and utilized by multiple users, with software to update & query the data”
      Database system consists of 
      • 1. Data (the database) 
      • 2. Software 
      • 3. Hardware 
      • 4. Users 
  • What is a database management system (DBMS) and how is it used?

    Database management system (DBMS) is the software than controls the data 
    • Oracle, DB2, MS Access, MS SQL Server (Azure) 
    • MySQL (open source)
    Manipulation of data using a query language 
    • E.g. SQL (Structured Query Language) 

  • What are the database terminology?

    • Database 
    • Table = structured list of data of a specific type, divided by columns and rows 
    • Record/Tuple 
    • Field/Attribute 
      • Domain
  • 1.1.3 Relational database

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  • What is a primary key and foreign key?

    PK:  Field(s) that uniquely identifies each record in a table 
      • Bno (Book), Rno (Reader), Bno + Rno + Loan date (Loan) 
      • Null value (=no data entry) not allowed for PK
    FK: Attribute whose values match the primary key values in the related (parent) table 
    • E.g.: Vendor sells Products 
      • Precisely 1 vendor per product 
      • Conversely, a vendor might sell multiple products 
      • FK = ‘Vendor_Code’ [Product table]
  • What are 4 trends in the database world?

      • Trend 1: From disk-based to In-memory databases (e.g. SAP HANA)
      • Trend 2: From on-premise db to cloud db (e.g. MS Azure, Google cloud sql) 
        • db use becomes an operating expense instead of capital expense
      • Trend 3: No (not only) SQL databases
        • Hypothesis: For analytics, relational database are dominant
        • NoSQL Databases: Key-value, Document, Graph, Wide-column
      • Trend 4: Alternative data representations
        • For storing document-oriented files with hierarchies; use XQuery

  • 1.2.3 Data warehouse architectures

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  • Which DW development approaches are there and which is the best?

    • Data mart approach (bottom-up)
      • DW = a collection of data marts
      • Dimensional modeling
        • Consistency achieved by conformed dimensions
      • E.g.: Independent data marts, Bus, ‘Canned data warehouse
    • Enterprise dw approach (top-down)
      • DW = one integrated database
      • Entity-relationship modeling
      • E.g. Hub & spoke: EDW + dep. data marts, Federated DW
    • Which approach is best?
      • There is no one-size-fits-all strategy to DW, depending on: management’s information needs, inf. interdependence between organizational units, …
  • 1.6.2 Naïve Bayes

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  • Why is laplace smoothing needed?

    From previous slides, the probability of α given class c:
    • P( Outlook=“Sunny” | PlayTennis=“Yes” ) = 0
    • Problem:
      • An attribute value doesn’t occur with every class
      • Probability of α given class c becomes 0
    • Having a probability zero is problematic, because it wipes out all information in other probabilities
  • What is laplace smoothing?

    • Laplace Smoothing, or Correction, or Estimator 
      • Incorporates a small-sample correction in every probability computation
      • Increase the numerator/denominator 
      • Thus, no probability will be zero
  • What are advantages and disadvantages for Naive Bayes?

    • Naive Bayes is Not So Naïve:
      • Its beauty is in its simplicity
      • Ability to handle categorical variables directly
      • Computational efficient
      • Good classification performance, especially when the number of predictors is very large
    • Negative aspects:
      • Requires a very large number of records to obtain good results
      • Independence assumption may not hold for some attributes
  • 1.7.1 Quizes

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  • Consider you were given 8 items, i.e., records, with numerical variables X1 & X2 along with a dependent variable y that corresponds to color (blue/red). Plot illustrates data in 2D space Task → use k-Nearest neighbors with Euclidean distance to classify item (1,1)Predict the class of new item (1,1) when using k-Nearest neighbors with Euclidean distance and K=3.1. Class of item (1,1) is red.2. Class of item (1,1) is blue


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