Curriculum

• Programming techniques for parallel and distributed computing 
  • MapReduce
  • Functional programming
  • Service-Priented software architecture 
• Ingest
  • Processing CSV and JSON data formats
  • Connecting to APIs and database systems
  • Web scraping 
• Transform
  • Data frames
  • Dates and times
  • Data profiling and cleaning
• Explore 
  • Statistics: T-Tests, histograms and distributions
  • Data visualization

Students who complete the course will be able to:

• Ingest data from files, APIs, database systems, and webpages.
• Process data in CSV and JSON format.
• Convert dates and times and analyze time series data.
• Assess data quality.
• Profile and clean data.
• Explore and visualize data using histograms and other graphics.

• Introduction to business intelligence
• Data Warehousing
  • Dimensional modeling
  • Warehouse aggregates
  • Data quality
    
  • Extract, transform, and load (ETL) process
    
  • Physical design   
    
  • Data warehousing lifecycle
    
• Reporting and data analysis
  • Online analytical processing (OLAP)
  • Commercial query and reporting tools
    
• Data mining
  • Data mining methodology
  • Statistical methods
    
  • Decision trees
    
  • Association rules
    
  • Clustering
    
  • Neural networks
    
  • Data preparation

Students who complete this course will be able to:

• Design and develop a Star schema and describe best practices for dimensional modeling.
  
• Design and develop a basic ETL process and explain the challenges of the ETL process.
  
• Identify and develop valuable aggregates for a given problem.
  
• Design and develop different types of reports and reporting requirements.
  
• Describe the limitations of SQL with respect to analytical reports.
  
• Describe common data mining tasks.
  
• Describe data mining techniques and implement at least one technique.
  
• Explain the value of transactional data with respect to business intelligence.
  
• Explain the importance of data quality and the challenges of producing high quality data.
  

Topics include:

• Cloud fundamentals
• Serverless computing
• Data ingestion and transformation
• Purpose-built, cloud-native NoSQL database services

Students who successfully complete this class will have:

• Explained cloud computing concepts and principles
• Explained tradeoffs of key-value and document databases
• Designed and developed a cloud-based, web application with a single key-value table storing multiple entities with an indexing schema to support predefined access paths
• Designed, developed, and queried a graph database
• Hands-on experience with common data engineering services
• Prepared for industry certifications including cloud practitioner and data engineer

Topics include:

• Introduction to distributed databases
  • Need for distributed databases
  • Types of distributed architectures
  • Challenges with distributed computing architectures
• Comparison of various distributed DBMS architectures
• Scalability and reliability of distributed architectures
• Performance tuning on distributed databases.

Students who successfully complete this class will have:

• Describe and apply general principles and concepts of distributed computing and distributed computing networks.
• Design and implement distributed databases
• Compare and contrast consolidated and distributed query processing and concurrency control.
• Describe distributed database management reliability
• Describe noSQL solutions to voluminous semi-structured data.
• Describe and apply techniques to tune database management systems

1. Fundamentals of information security and privacy
   
   • Goals of security (confidentiality, integrity, availability, authentication, non-repudiation and
                accountability)
   • Vulnerabilities and exploits on DBMS and data sets (e.g., Programming flaws, SQL injection,
                  statistical inference attacks)
   • Threat modeling and security analysis
2. Information Security with data storage and management
   
   • Cryptography (symmetric key, asymmetric key, secure hashes and modes of operation)
   • Secure design principles (e.g., least privilege, complete mediation, separation of privilege, least common mechanism, defense in depths
   • Authentication
   • Access control
   • Access logs
   • Security mechanisms (e.g., perimeter security, host-based security)
   • Secure operations (backups, hardening distributed databases, disaster recovery, business continuity)
3. Privacy
   
   • Statistical inference attacks and controls
   • Legal issues (e.g. HIPAA, FERPA, ECPA)
4. Reliability
   
   • Failures
   • Fault tolerance
     

Students who complete this course will be able to:

1. Enumerate the main goals of security and privacy including confidentiality, integrity, availability, authentication, non-repudiation, and accountability.
   
2. Analyze and develop threat models for the security of database management systems,
   networks and distributed database infrastructures.  
   
3. Analyze and develop threat models on the privacy of data (such as inference attacks).
   
4. Perform security analysis on centralized and distributed database installations using techniques such as the Open Source Security Testing Methodology (OSSTMM).
   
5. Describe and apply cryptographic algorithms, and mechanisms including secure hashes, secret key and public key cryptography, and their modes of operation to secure both stored data and data in transit across networks.
   
6. Describe and apply standard secure design principles including least privilege, complete mediation, least common mechanism, economy of mechanism, defense in depth, reluctance to trust, and privacy to the different database installations.
   
7. Describe and deploy authentication, fine-grained access control and accountability mechanisms (such as access logs) on database management systems and distributed and centralized database installations.
   
8. Describe and deploy mechanisms that provide security such as intrusion detection systems and privacy such as those that protect against statistical inference attacks on databases.
   
9. Perform secure operations including backup, recovery and secure updates.
   
10. Administer security by enumerating the steps of risk management and developing security policies and plans such as acceptable usage policies, and business continuity and disaster recovery plan.
    
11. Enumerate and identify privacy issues of data taking into account the federal and state laws that govern privacy such as HIPAA, FERPA, and the Electronic Communication and Privacy Act.
    
12. Describe reliability mechanisms to achieve fault tolerance in distributed databases.
    

1. Information architecture
2. Information governance
3. Master data management
4. Information quality
5. Data integration
6. Metadata management
   

Students who complete this course will be able to:

1. Explain the importance of data governance.
2. Develop policies for protecting and securing data and information assets.
3. Design and develop a system to protect and secure data and information.
4. Develop a program that integrates data from multiple sources.
5. Profile data elements.
6. Analyze the quality of an individual data element.
7. Analyze the overall quality of a data source.
8. Design and develop a system to capture and manage metadata.
   

1. Analysis of algorithms a. Time and space b. Amortized analysis c. I/O bottlenecks
2. Memory hierarchy a. Caching b. External memory organization (disk organization)
3. Sorting and searching algorithms (counting sort)
4. External memory and cache-oblivious data structures and algorithms (e.g., types of B-trees)
5. Hashing
6. Algorithms that exploit temporal and spatial locality
7. Succinct data structures (rank, tries, suffix arrays) to store data compactly.
8. Advanced topics, such as
   
   • Data compression
   • Pattern matching
   • Search engine indexing
   • NP completeness

Students who complete this course will be able to:

1. Compare and contrast temporal and spatial efficiency of distributed algorithms and data structures used to store, query and process medium to large datasets.
2. Describe and analyze the performance issues of the different memory organizations used to store large data sets.
3. Describe and apply data structures and distributed algorithms that achieve efficiencies in query and processing times of medium to large datasets.
4. Describe and apply data structures and algorithms that store data compactly.
5. Describe current distributed algorithms and data structures used to store, query and analyze medium to large datasets.

1. Databases and their evolution
2. Big data technology, no-SQL
3. AI techniques
4. Logic Rule, Uncertainty
5. Bayes rule, Naïve Bayes, Bayesian Network
6. Sentiment analysis
7. Association rule mining
8. Learning latent model, Machine learning
9. Cluster, classification
10. Linear and logistic regression
11. Least square, optimization
12. Non-linear model, Neural Network
13. Dimensionality reduction
14. Anomaly detection
15. Recommend system
16. Parallel computing, Map Reduce
17. Analytics tools
    

Students who complete this course will be able to:

1. Categorize data into groups based on attributes.
2. Classify information based on existing data.
3. Identify the relationship between elements of a decision.
4. Understand optimization, maximizing certain outcomes while minimizing others.
5. Develop decision logic or rules that will produce the desired action.
6. Predict an event in the future effectively based on certain model
7. Seek out subtle data patterns to answer questions about customer performance, such as fraud detection models.
8. Understand requirements in using big data analytics.
9. Simulate human behavior or reaction to given stimuli or scenarios.
   

Each section of ITEC 695 will have one instructor. Each student will have a project advisor. Each student will design, implement, and test a substantial component of an information system. Multiple students may work together to develop an information system involving multiple components.