Course Description

Data Science is the study of the generalizable extraction of knowledge from data. Being a data scientist requires an integrated skill set spanning mathematics, statistics, machine learning, databases and other branches of computer science along with a good understanding of the craft of problem formulation to engineer effective solutions. This course will introduce students to this rapidly growing field and equip them with some of its basic principles and tools as well as its general mind-set. Students will learn concepts, techniques and tools they need to deal with various facets of data science practice, including data collection and integration, exploratory data analysis, predictive modelling, descriptive modelling, data product creation, evaluation, and effective communication. The focus in the treatment of these topics will be on breadth, rather than depth, and emphasis will be placed on integration and synthesis of concepts and their application to solving problems.

Learning Outcomes

–>Describe what Data Science is and the skill sets needed to be a data scientist.

–>Explain in basic terms what Statistical Inference means. Identify probability distributions commonly used as foundations for statistical modelling. Able to fit a model to data.

–>Use python to carry out basic data analytics and machine learning applications.

–>Introduction to AI and some Machine Learning algorithms.

–>Understanding about data visualization tool with special emphasis to Hadoop.

Target Audience

The course is suitable for upper-level undergraduate students in computer science, mathematics, or any science streams.


Students are expected to have basic knowledge in mathematics and programming logics. If you are interested in taking the course, but are not sure if you have the right background, talk to the instructors. You may still be allowed to take the course if you are willing to put in the extra effort to fill in any gaps.

Course work

The course consists of


–>Problem Solving

–>Hands on implementation

–>Assignment/Case Study submissions