ALGORITHMS FOR DATA SCIENCE

Course Description

This course covers the algorithmic techniques and approaches required to handle various types of structured, semi-structured and unstructured data. The goal of the course is to teach algorithmic methods that serve as the cornerstones for handling and analyzing large datasets in a variety of formats. The course specifically covers how to pre-process big datasets, store big datasets effectively, design quick algorithms for big datasets, and evaluate the performance of designed algorithms. Algorithms for sorting, searching and matching as well as graph and streaming algorithms will be introduced. Upon completion of this course, students will have a broad knowledge of different algorithms for pre-processing, organizing, manipulating and storing different data types. Students will also be able to carry out performance analysis of each algorithm.

ALGORITHMS FOR DATA SCIENCE

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Course outcomes

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Course Syllabus

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Learning Outcomes

1. L01 Explain algorithm and analyze algorithm complexity SOLO 4
2. L02 Process binary search trees and heaps SOLO 3
3. L03 Perform hashing techniques SOLO 4
4. L04 Compare different sorting algorithms and perform the most efficient sorting technique SOLO 4
5. L05 Compare different searching algorithms and perform the most efficient searching technique SOLO 4
6. L06 Analyze graphs and implement breadth-first search algorithm SOLO 4
7. L07 Perform dynamic programming techniques SOLO 4

Program Outcomes

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