Learning Results
In this curricular unit it is intended that the student acquires the fundamental techniques of descriptive and predictiveData Analytics for Logistics and Supply Chain. Specifically, in this context, it is intended that the student is able to:
1) Identify opportunities for process improvement using Data Analytics;
2) Outline the data analysis process;
3) Detect and solve data quality issues;
4) Apply descriptive analytics techniques to explore, prepare and extract information and knowledge from the data;
5) Use, following best practices, the most adequate predictive analytics methods to obtain goal forecasts.
Program
P1 – The role of Data Analytics
P2 – Data quality
P3 – Descriptive analytics
P4 – Predictive analytics
P5 – Logistics and Supply Chain applications
Internship(s)
NAO
Bibliography
[1] Albright, S.C., Winston, W.L. (2019). Business Analytics: Data Analysis and Decision Making, 7th Edition. CengageLearning.
[2] Dasu, T., Johnson, T. (2003). Exploratory Data Mining and Data Cleaning. Wiley.
[3] Hyndman, R.J., Athanasopoulos, G. (2021). Forecasting: Principles and Practice, 3rd Edition. OTexts.
[4] Moreira, J., Carvalho, A., Horvath, T. (2018). A General Introduction to Data Analytics. Wiley.
[5] Tipi, N. (2021). Supply Chain Analytics and Modelling: Quantitative Tools and Applications. Kogan Page.
[6] Vandeput, N. (2021). Data Science for Supply Chain Forecasting, 2nd Edition. De Gruyter.