Supply Chain Analytics
This 56-hour diploma course provides a broad introduction to the fields of supply chain management and data analytics. Students, by the end of the course, will have a better understanding of the role of supply chain management as well as how data analytics can help in the decision-making process. Each of the topics and concepts will be introduced by using actual problems and emphasizing the application of different data analytics techniques to solve the problems.
Course Content:
- Understanding the Supply Chain
- Application of Data Science and Data Analytics to Decision Making
- Designing Distribution Networks
- Importance of Data Visualization
- Planning and Coordinating Demand and Supply in a Supply Chain
- Planning and Managing Inventories
- Designing and Planning Transportation Networks
Foundations of Data Science
This 40-hour diploma course provides an overview of the field of data science, the job roles available and the skills necessary for the roles. It covers an introductory level of the data science workflow such as data acquisition, local data management, data wrangling, basic data visualization for exploratory analysis and an introduction to data mining concepts. It is carefully designed for professionals and students from various STEM and ABM backgrounds who want to upskill towards a data science career. Each topic has hands-on exercises with varying degrees of difficulty to ensure that participants are able to absorb the concepts.
Course Content:
Module 1: Data collection
Module 2: Data wrangling
Module 3: Feature Engineering
Module 4: Basic Data Visualization
Module 5: Geospatial Data
Module 6: Data Ethics
Introduction to Machine Learning
Machine learning (ML) is the science of getting computers to act without being
explicitly programmed. In order to come up with computational models, ML algorithms deduce
the rules and predictions by looking at many examples from a dataset. This short course aims to
expose the participants to the design and implementation of different computational models so
that they could directly and effectively apply these to a given real-world problem.
Course coverage:
- Module 1: Linear and logistics regressions
- Module 2: Optimizing a Machine Learning Model
- Module 3: Neural networks
- Module 4: Support vector machines