| 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 |
