Methods of Data Analysis and Business Forecasting

Educational Aim

• To introduce MSc students to various data analysis concepts and technologies, their strengths and limitations with ascent on application a range of advanced statistical techniques and using software tools to implement it in process of planning and taking business decisions.
  To aspire MSc students need to know how to collect, manipulate and analyze data sets to make informed decisions and to provide a foundation for data-driven decisions.
  

Annotation

• This course introduces the core data analysis (DA) techniques, algorithms, research issues and practical skills for applying DA techniques to solve real-world problems. Students will study the major data analysis problems as different types of computational tasks (prediction, classification, clustering, etc.) and the algorithms appropriate for addressing these tasks. Topics include data understanding and visual data exploration, data preprocessing and transforming, data classification and clustering, forecasting techniques etc. Students will understand principles and concepts; learn how to choose algorithms for different analysis tasks and get insight into DA techniques and algorithms. Students . Students are expected to do independent reading of research papers and to do critical review.
  

Obligatory Reading List

• 1. Washington S., Karlaftis M. G., Mannering F., Anastasopoulos P. (2020) Statistical and Econometric Methods for Transportation Data Analysis.3rd Edition. Chapman and Hall/CRC, 496 https://doi.org/10.1201/9780429244018
  2. Nisbet R., Elder J., Miner, G. 2009. Handbook of Statistical Analysis & Data Mining Applications. Elsevier Inc. https://ebookcentral.proquest.com/lib/tsilv/detail.action?docID=452830
  3. Giudici, P., Figini, S. 2009. Applied Data Mining for Business and Industry, Second edition. Wiley & Sons Ltd.
  4. Berry, Michael J.A., Linoff, Gordon S. 2004. Data Mining Techniques For Marketing, Sales and Customer Relationship Management. Second Edition. Wiley Publishing Inc.
  

Additional Reading List

• 1. Data analysis in the transport and logistics sector: still plenty of potential to unlock, (2020)
  2. https://www.mazars.com/Home/Industries/Transport-logistics/Data-analysis-in-the-transport-logistics-sector
  3. Dalgleish, Michael, Hoose, Neil. 2008. Highway Traffic Monitoring and Data Quality. Artech House. 233 p.
  4. Washington, Simon P., Karlaftis, Matthew G., Mannering, Fred L. 2003. Statistical and Econometric Methods for Transportation Data Analysis. Chapman&HALL/CRC. 425 p.
  5. Vercellis, C. 2009. Business Intelligence Data Mining and Optimization for Decision Making. Wiley & Sons Ltd.
  

Learning Outcomes

• LO1: Able to design, analyse, improve and manage the transport and logistics systems and technologies of the future
  LO2: Able to apply the data analysis methods using real data: to data analysis task setting, to determine the appropriate techniques and to implement in their future research and business activities; to evaluate and explain the results of different algorithms of data analysis
  LO3: Able to identify promising business and research applications of data analysis and forecasting methods and to communicate in terms of the conventions of the discipline
  LO4: Able to provide specialized up to-date knowledge of transport engineering in technology, develop specialized problem solving skills required for research, and acquire leadership and innovation skills applicable to solving technology problems.
  

Assesment Strategy


• Elements	Element weighting (%)	Description
  Examination	50	written examination
  Practical Tasks	40	A series of practical assignments (labs)
  Quizzes	5	Multiple choice quiz for 4 topics
  Home-work	5	Glossary