Autumn 2020
Prof. Amit Sethi and Prof. Manjesh K. Hanawal
Author: Ayush Sarraf
Pre-requisite courses: CS101
Pre-requisite skills: It helps if one has prior knowledge of Python, but it’s not a pre-requisite. Coding tutorials of python are provided. No pre-requisite in terms of knowledge of particular subject is required.
Course Content: Programming Basics (Python programming, R, Data Structures), Visualization/Plotting, Data Science Libraries (Pandas, PyPlot, matplotlib) Databases, GPUs/CUDA programming, Parallel/distributed computing for data science (Map/Reduce, Spark/Hadoop), working on the cloud (Amazon Web services, Google Cloud Platform, Azure, etc). The course will be programming heavy, with i n-class and take-home programming exercises A project can be optionally included.
Motivation to take up the course: My interest in the sector of data analytics had grown tremendously after having taken the AE 102 course in my second semester. The fact that we could not complete the AE 102 course due to covid added to the curiosity.
Information about Projects/Assignments: The assignments are weekly and based on the aspect of data analysis covered in that particular week. They are quite open - ended in general and requires a lot of exploration. The project was done in teams of 2. It was based on any data-set the team wanted to explore and emphasis was laid on exploratory data analysis and visualization rather than ML models.
Quizzes/Midsem/Endsem papers Difficulty: 0/5
Overall Course Difficulty: 3/5
Evaluation Structure: No quizzes, midsems and endsems! The course has only assignments and a course project. 10 coding assignments - 70% 1 course project - 30%
Lecture Style and Delivery: The lectures were well delivered. Emphasis was laid on the exploratory data analysis part and other aspects of data science were just touched upon slightly to simulate interests to take up further courses in the minor.
Attendance Policy: No DX grade given.
General funda: The course focuses on a lot of hands-on experience and exploration with data. So, do not get lost into it and building good googling skills is a help.
Should you do this course?: Anyone who is interested in data analytics and wants to get their hands dirty at data visualization and exploration.
References: Python tutorial - https://docs.python.org/3/tutorial/ Rest the lecture material is sufficient.