Course Catalog Description
This course will provide an overview of issues and trends in data quality, data storage, data scrubbing, and data flows. Topics will include data abstractions and integration, enterprise level data issues, data management issues with collection, warehousing, preprocessing and querying, similarity and distances, clustering methods, classification methods, text mining, and time series. Case studies will be presented in support of the theoretical concepts. Furthermore, the Hadoop-based programming framework for big data issues will be introduced along with any governance and policy issues. These concepts will be applied to areas such as digital marketing and computational advertising, energy and healthcare analytics, social media and social networks, and capital markets financial data. A one credit Hanlon lab course, FE-513: Practical Aspects of Database Design is co-requisite to this course in order to facilitate learning of the practical side of data management.
This course is the first course for the certificate in Financial Services Analytics. Financial services analytics is the science and technology of creating data-driven decision-making analytics for the financial services industry. This can lead to more effective business operations, enhanced customer services and product offerings, and improved risk analysis and risk management. This course is the key building block in this certificate as good data and the understanding of data is critical to the creation of robust financial services analytics. The financial services analytics certificate has four key areas making up its knowledge base:
- Foundations of Financial Data Science (FE-582)
- Introduction to Knowledge Engineering (FE-590)
- Financial Systems Technology (FE-595)
- Data Visualization Applications (FE-550)
Co-Requisite: FE 513 – Practical Aspects of Database Design
After taking this course, the students will be able to:
- Have a working knowledge of the issues of data quality, data storage, data scrubbing, data flows, and data encryption and their potential solutions.
- Understand and design various schemas needed for the representation of financial data.
- Tackle problems dealing with data management issues such as collection, warehousing, preprocessing and querying.
- Will get a primer on database management as well as advantages and disadvantages from the attached lab course FE 513.
- Understand how to write applications using the map-reduce feature of Hadoop clusters.
- Have a working understanding of all the databases available for them through the Hanlon lab.
- Apply the newly acquired data management and database skills to financial data from the capital markets, social media, and the financial services sector.
No single textbook covers all the topics. Several references will be used and supplementary notes will be provided whenever appropriate.
- Charu C. Aggarwal, Data Classification: Algorithms and Applications. CRC Press, 2015. (ISBN: 978-1-4665-8674-1)
- Charu C. Aggarwal, Data Mining. Springer, 2015. (ISBN: 978-3-319-14141-8)
- Deborah Nolan and Duncan T. Lang, Data Science in R: A Case Studies Approach to Computational Reasoning and Problem Solving, CRC Press, 2015. (ISBN: 978-1-4822-3481-7)
- Norman Matloff, The Art of R Programming, No Starch Press, 2011. (ISBN: 978-1-59327-384-2)
- Cathy O’Neil and Rachel Schutt, Data Science, O’Reilly, 2014. (ISBN: 978-1-449-35865-5)
||Introduction to Financial Data Science
Data Science Process
Sample Data Processing
The Basic Data Types
The Major Building Blocks: A Bird’s Eye View
Introduction to R
Case Study: Exploratory Data Analysis (NYC Real Estate)
| Week 2
||Financial Data Quality Issues and Data Scrubbing.
Feature Extraction and Portability
Data Reduction and Transformation
Handling Missing Entries
Handling Incorrect and Inconsistent Entries
Sampling for Static Data and Data Streams
Dimensionality Reduction Intro
| Week 3
||Case Study: Data and Web Technologies
Web page retrieval, scrapping, regular expression extraction, basic statistical techniques to identify wrong data entries
Piecewise linear model
| Week 4
||Similarity and Distances
Impact of High Dimensionality
Lp-norm. Generalized Minkovski Distance. Contrast
Impact of Locally Irrelevant Features. Impact of Different Lp-Norms
Match-Based Similarity Computation
Impact of Data Distribution. ISOMAP
Impact of Local Data Distribution. Similarity on Categorical Data
Similarity on Mixed Quantitative and Categorical Data
Text Similarity Measures. Time Series Similarity Measures
| Week 5
Case Study: Clustering (NYC Real Estate)
Financial Data Simulation
| Week 6
Linear Discriminant Analysis
Quadratic Discriminant Analysis, K-NN
| Week 7
||Mining Text Data
Document Preparation and Similarity Computation
Specialized Clustering Methods for Text
Specialized Classification Methods for Text
Case Study: MangoDB Application
| Week 8
||No Class (Spring Break)
| Week 9
||Case Study: Using Statistics to Identify Spam
| Week 10
||Tree-Based Methods. Regression Trees. Tree Pruning.
| Week 11
||Financial Time Series
Using Decision Tree to Trade Stock
Building a Trading Strategy
Handling Time-Dependent Data in R
The Prediction Models
| Week 12
| Week 13
||Hadoop. HDFS. MapReduce. Hive. Pig
| Week 14
||Final Project Presentations