Data Science is an interdisciplinary field that uses techniques from statistics, computer science and mathematics in conjunction with domain specific knowledge to extract knowledge and insights from structured and unstructured data to make informed decisions. Data Science can be applied in a wide range of fields, including business, computational sciences, social sciences, humanities, and political science. Data Science is relevant today because we live in a data driven world where data is ubiquitous. More notably, in the past decade this explosion of data, coupled with availability of affordable general-purpose tools and algorithms, has opened up opportunities for the development of specialised data analytics software and services across several domains.
The undergraduate programme in Data Science is intended to be a rigorous programme with an interdisciplinary flavour. The programme builds on the mathematical foundations of computing, complemented by hands-on experience through project work mentored by faculty to address some of the key problems in the field of artificial intelligence (AI), computational and data sciences with applications in computational sciences, computer vision, digital agriculture, econometrics, e-commerce, environmental studies, finance, medical imaging, supply chain management, etc.
The Data Science programme at Krea is intended to be a rigorous program with an interdisciplinary flavour. The core courses have been designed to impart the necessary programming, computational, mathematical, and data-analytical skills to the students. The elective courses confer the necessary domain knowledge and enables students to apply the theoretical and practical knowledge learned through the required courses in a diverse set of domains. Additionally, the undergraduate programme in Data Science incorporates cutting edge tools and techniques employed by the industry. The courses are primarily project-based and provide students with opportunities to gain hands-on experience by analysing real-world data sets using various sophisticated tools for modeling, optimisation, data mining, and visualisation. Lastly, students are trained to conceive data through multiple branches of sciences and humanities that would play a vital role in approaching various interdisciplinary problems.
One of the distinguishing aspects of our interdisciplinary programme is the opportunity for our students to work with faculty and domain experts to build on solid analytical and conceptual foundations of computer science, statistics, and mathematics subjects to solve some real-world problems relevant to society. This is primarily driven by a research-based Capstone project in their final year, where students apply various artificial intelligence tools and techniques to solve some of the vital problems in computational sciences, business, social sciences and humanities.
Major in Data Science
To earn a Major or a Double Major in Data Science (DATA), a student must complete the following:
|Credit requirements for
|Credit requirements for Major
|48 credits (44 required + 4
DATA or cross-listed electives)
|60 credits (44 required + 16 DATA
or cross-listed electives)
|Capstone option: 64 credits
(44 required + 8 DATA or
cross-listed electives +12
capstone) Non-Capstone option:
64 credits (44 required +
20 DATA or cross-listed
|Capstone option: 80 credits
(44 required + 24 DATA or
cross-listed electives +
12 capstone) Non-Capstone
option: 80 credits (44 required +
36 DATA or cross-listed electives)
Note: that the Capstone option is available only with a four-year degree.
Minor in Data Science
A student wanting to minor in data science should gain 32 credits from pursuing Data Science courses and cross-listed electives. Out of the 32 credits, 28 credits should come from Data Science required courses (see below) and the remaining 4 credits should be gained from pursuing a DATA or cross-listed elective. For a 3-year exit option, a minor in data science requires 24 credits for graduation.
The following courses are required for a student to graduate with a Minor in Data Science:
|Python for Data Science, Mathematical Foundations
of Data Science, Data Structures and Algorithms, Applied
Statistics, Introduction to Statistical Learning,
and Machine Learning
|Python for Data Science, Mathematical Foundations of Data
Science, Introduction to Linear Algebra, Data Structures and
Algorithms, Applied Statistics, Introduction to Statistical Learning,
and Machine Learning
Concentration in Data Science
A student wanting to pursue a Concentration in Data Science should gain 16 credits from pursuing Data Science courses listed below:
Python for Data Science, Mathematical Foundations of Data Science, Data Structures and Algorithms, Applied Statistics