DSC03

Compulsory

1

On completing the course, the student will be able to:

• Understand the basic concepts of probability theory and statistics as they are applied in data science.
• Apply mathematical tools, models, and methods to data analysis tasks, such as data fitting, regression, sampling, hypothesis testing etc.
• Learn the fundamentals of statistical inference and its implementations.
• Use R to conduct for data analysis, processing and visualization.

• Search for, analysis and synthesis of data and information, with the use of the necessary technology
• Decision Making
• Working independently
• Production of free, creative, and inductive thinking

The course overviews basic statistical foundations of Data Science and presents the most commonly used statistical methods in the field. The students will gain the necessary conceptual understanding of statistical methods used to analyze and interpret massive data sets as well as extract meaningful conclusions out of them. In addition, they will be able to apply mathematical tools, models and methods to data analysis tasks, such as data fitting, regression, sampling, hypothesis testing etc. using R. The topics covered include:

• Descriptive Statistics.
• Probability Distributions.
• Sampling and Sampling Distributions.
• Interval Estimation.
• Hypothesis Testing.
• Statistical Inference.
• Linear Regression.
• Nonparametric Methods.