This course is an applied statistics course with an emphasis on data analysis. In this course we will study several statistical modeling techniques and discuss real-life problems over which we’ll have a chance to apply statistical tools to learn from data. We will be covering some of the fundamental statistical methods like linear regression, principal component analysis, cross-validation and p-values. The lectures are designed to help the participants apply these techniques on large sets of data using a statistical programming language such as R.
Applied Statistics (DA 503)
CONTENT
PROGRAMME OUTCOMES
1. Develop the ability to use critical, analytical, and reflective thinking and reasoning 5
2. Reflect on social and ethical responsibilities in his/her professional life. 2
3. Gain experience and confidence in the dissemination of project/research outputs 4
4. Work responsibly and creatively as an individual or as a member or leader of a team and in multidisciplinary environments. 2
5. Communicate effectively by oral, written, graphical and technological means and have competency in English. 4
6. Independently reach and acquire information, and develop appreciation of the need for continuously learning and updating. 5
1. Design and model engineering systems and processes and solve engineering problems with an innovative approach. 4
2. Establish experimental setups, conduct experiments and/or simulations. 4
3. Analytically acquire and interpret data. 5
1. Comprehend the conceptual foundations of analytical methods and techniques within the scope of business analytics, 5
2. Acquire theoretical and practical knowledge on applied information systems by developing fundamental programming skills, 4
3. Improve decision making by turning high-volume data into useful information and integrating data analysis tools 4
4. Turn high-volume data into useful information by using quantitative models and understanding and managing data analysis techniques, communicate and visualize the results for business use 5
5. Understand the data quality, data integrity and data accuracy concepts, and occupational ethics regarding data privacy and intellectual property 3