Data Analytics: Tools and techniques for Acquiring Insights from Big Data
Data analytics provides a set of both qualitative and quantitative techniques to analyze data in order to convert information into useful knowledge.
Course specific requirements:
- One half year (30 ECTS) of university education.
Course specific requirements come in addition to the general requirements, and must be completed before the application-deadline 1 February. It is recommended to have completed one full year of university studies (60 ECTS) before the program starts.
It is also recommended that students have basic knowledge of statistics and programming.
The knowledge obtained can be used to understand better our world (e.g., following the scientific approach). For example, natural environments (the sciences), social interactions, and engineering systems are often complex, and the underlying causal models are difficult to understand. Data-driven modeling and hypothesis generation is essential for understanding system behavior and interactions.
The last decade or so has seen sizable decreases on costs to gather, store, and process data, creating a fertile environment for the use of empirical (data analytics) approaches to problem solving based on big-data. The range of real world problems that can be solved is wide.
This course is aimed at teaching students about a set of tools and techniques that are state-of-the-art and commonly used for data analytics in the industry. Examples and practical exercises are geared towards demonstrating real-world use cases and make students proficient in using these tools as well as understanding the theory behind them.
The main goals for this course are:
- Familiarising the students with the potential of data analytics for solving different real-life problems.
- Provide the students with knowledge and skills to allow them to critically draw conclusions from different sources of data.
- Introduce the students to modern analytics (methods and tools) for real-world applications to enable them to build a data application.
The course will be offered both with 5 ECTS and 10 ECTS – the assignments differ between the two.
Full course descriptions: