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Data Mining and Management Strategies | Syllabus

Lecture # Subject Title
Lecture Faculty: Cheri Speier-Pero, PhD
Week 1: Enterprise Database and Data Models
1 Course Introduction
2 Enterprise Data
3 Types of Enterprise Databases
4 Database Management Systems and Relational Database Design
5 Enterprise Data and Multidimensional Databases
6 Creating Multidimensional Data
7 Sourcing the Data in Data Cubes
8 The Role of ETL in Analytics
9 More on ETL
Lecture Faculty: Cheri Speier-Pero, PhD
Week 2: Extracting Data from a Database
10 Conceptual Database Model
11 Cardinality
12 Extracting Data from a Database
13 Basic Queries
14 Advanced Queries - Part 1
15 Advanced Queries - Part 2
16 Structured Query Language (SQL)
17 SQL Functionality for ETL and SQL Server
Lecture Faculty: Arend Hintze, PhD
Week 3: Large Scale Implementation of Hadoop® MR
18 Single vs. Parallel Approach
19 Hadoop® One Framework for Multiple Problems
20 Hadoop® Architecture and File System
21 Hadoop® Distributed File System (HDFS)
22 The MapReduce Paradigm
23 MapReduce Examples
24 MapReduce Streaming
25 Hadoop® Zoo
Lecture Faculty: Arend Hintze, PhD
Week 4: Getting Data: Social Networks and Geolocalization
26 How the Web Works
27 Anatomy of an HTML Page
28 Parsing
29 Web Crawlers
30 Web Spiders
31 API
32 Text Analysis
33 Ethics
Lecture Faculty: Arend Hintze, PhD
Week 5: Unstructured Data, Graphs and Networks
34 Nodes and Edges
35 Degree Distributions and Hubness
36 Small World Property - Degrees of Separation 
37 Centrality, Betweenness, and Closeness
38 Clustering and Coefficient
39 Network Motifs
40 Modularity
41 Data Formats
Lecture Faculty: Arend Hintze, PhD
Week 6: Clustering: Understanding the Relationship of Things
42 The Idea Behind Clustering
43 Types of Clusters
44 Distances Between Points
45 K-Means Clustering
46 Not Every Cluster Is a Good Cluster
47 How Good Are My Clusters?
48 Hierarchical Clustering
49 Min, Max, and Mean
Lecture Faculty: Arend Hintze, PhD
Week 7: Classifications: Putting Things Where They Belong
50 The Idea Behind Classification
51 Reading and Interpreting a Classification Tree
52 Making a Decision Tree
53 Alternative Impurity Measures
54 Expansion to 2D
55 How Good Is My Classifier?
56 But I Only Have Training Data
57 A Brief Look at Association Rule Mining
Lecture Faculty: Arend Hintze, PhD
Week 8: Classifications: Advanced Methods
58 Rule-Based Classifier
59 Extracting Rules
60 Nearest Neighbors
61 Classifiers – Defined Boundaries
62 Artificial Neural Networks
63 Limits, Boundary Conditions and Choosing the Right Classifier
64 Clustering vs. Classification
65 Outlier and Anomaly Detection