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Analytical Software and Frameworks (DLMBDSA02)

Course No.:

DLMBDSA02

Course Title:

Analytical Software and Frameworks

Hours Total:

150 h

Credit Points:

5 ECTS

Course Type: Wahlpflicht

Course Availability: WS, SS

Course Duration: Minimum 1 Semester

Admission Requirements:

None

Course Coordinator / Instructor:

See current list of tutors in the Learning Management System

References to Other Modules:

Please see module description

Course Description:

Analytical Software and Frameworks provides insight into contemporary software and platforms solutions for data analytics in business. The course particularly introduces The R Project for Statistical Computing, which provides software solutions for statistics and artificial neural networks. Commercial and open-source for cloud computing, distributed computing and machine learning, as well as a commercial development platform for in-memory database analytics are covered.  Additional software solutions may be covered by the lecturer as convenient.

Course Objectives and Outcome:

Upon successful completion of this course, students will be able to:

  • understand how cloud computing and distributed computing support the field of data analytics.
  • understand in-memory database technologies for real-time analytics.
  • apply advanced statistics and machine learning solutions to solve data analysis problems.
  • compare the capabilities and limitations of the presented software solutions.

Teaching Methods:

The learning materials include printed and online course books, vodcasts, online knowledge tests, podcasts, online tutorials, and case studies. This range of learning materials is offered to students so they can study at a time, place, and pace that best suits their circumstances and individual learning style.

Course Content:

  1. Introduction
    1. Software Systems
    2. Frameworks
    3. Distributed Computing
    4. Databases and Data Warehousing
  2. Statistical Modelling
    1. The R Project for Statistical Computing
    2. Artificial Neural Networks in R
    3. Other Statistics Solutions
  3. Machine Learning
    1. Apache Mahout
    2. Microsoft Azure ML
    3. Other Machine Learning Solutions
  4. Cloud Computing Platforms
    1. Google Cloud
    2. Oracle Cloud Services
    3. IBM
    4. Amazon AWS
    5. Microsoft Azure
    6. Other Cloud Computing Solutions
  5. Distributed Computing
    1. Apache Hadoop
    2. Apache Spark
    3. Other solutions
  6. Database Technologies
    1. In-Memory Analytics
    2. NoSQL
    3. SAP HANA
    4. Other Solutions

 

Literature:

  • Chambers, B., & Zaharia, M. (2018). Spark: The Definitive Guide: Big Data Processing Made Simple. Newton, MA: O'Reilly Media.
  • Elmasri, R., & Navathe, S.B. (2015). Fundamentals of Database Systems (7th ed.). New York: Pearson.
  • Lander, J. P. (2017). R for Everyone: Advanced Analytics and Graphics (2nd ed.). Boston, MA: Addison-Wesley Professional.
  • Lyubimov, D., & Palumbo, A. (2016). Apache Mahout: Beyond MapReduce. North Charleston, SC: CreateSpace Independent Publishing.
  • Modi, R. (2017). Azure for Architects: Implementing cloud design, DevOps, IoT, and serverless solutions on your public cloud. Birmingham: Packt Publishing.
  • Valliappa Lakshmanan, V. (2018). Data Science on the Google Cloud Platform: Implementing End-to-End Real-Time Data Pipelines: From Ingest to Machine Learning. Newton, MA: O'Reilly Media.
  • Walkowiak, S. (2016). Big Data Analytics with R: Utilize R to uncover hidden patterns in your Big Data. Birmingham: Packt Publishing.
  • White, T. (2015). Hadoop: The Definitive Guide: Storage and Analysis at Internet Scale (4th ed.). Newton, MA: O'Reilly Media.
  • Wittig, A., & Wittig, M. (2018). Amazon Web Services in Action (2nd ed.). Shelter Island, NY: Manning Publications.

An actual list with course-specific mandatory reading as well as references to further literature is available in the Learning Management System.

Prerequisites to Qualify for Assessment:

  • Depending on the course: Completion of online knowledge tests (approx. 15 minutes per unit, pass / not pass)
  • Course evaluation

Assessment:

  • Written Assessment: Written Assignment

Student Workload (in hours): 150

Self-study: 110 h
Self-examination: 20 h
Tutorials: 20 h