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This is a first course in mathematics with emphasis on helping a learner with a bunch of tools and techniques required for studying several aspects of data sciences. We will illustrate the ideas through questions and puzzles. We would like to replace theorems with intuition and give pointers to inspired students to look up material for further study. By the end of the course, the student will be confident on the basics of linear algebra and probability. The course will end with a couple of exemplary ideas in data sciences and the importance of the math thus studied.
An important aspect of any course that is heavy on math, is to develop confidence to read up abstract material. The symbolc manipulation and logical reasoning are toolkits to infer unambiguously. It helps to stay confident and continuously interact with peers/TAs/Professor. Expect the material to be hard on your minds to begin with, but one will surely feel comfortable with time. An important tip is to take a relook at a mathematical concept multiple times, asking what is the question for which the concept is an answer. It helps to solve questions that challenge your conceptual understanding than solve multiple problems of similar type.
There will be performance points assigned to every student using the following rule:
| Attendance in classes/labs | 2 per hour |
| Late for the class/lab (irrespective of the urgency) | -10 per class/lab |
| Plagiarism/Cheating/Misconduct | PP will drop to 0 |
| Challenge Assignment | X |
- If a student misses a lab or class, his/her
$$PP$$ will be uniformly distributed to the rest of the class who were present. For example in a class of 30, if 10 people are absent for a lab session, 40$$PP$$ will be distributed to the remaining 20 students. Each student will get$$\frac{40}{20}=2$$ . - A student coming late for the class, loses 10
$PP$ and this will be uniformly distributed to all those present in the class. - X denotes that it is discretionary. The instructor can provide challenge assignments from time to time which will add in extra PP s to your credit.
- Plagiarism/Cheating/Misconduct will result in the reset of the performance points. It will roll back to 0. The only way we will figure out plagiarism is through viva voce.
- The deficit $$ PP $$s will be assigned by the instructor based on his discretion, evaluated based on sincerity and overall conduct.
- This will be in practice starting from September 1st 2022.
Final grades will be calculated based on the following rule:
Where PP stands for Performance Points and Total is the marks secured from theory, lab and project components.
Evaluation
| Type | Marks | |
|---|---|---|
| Theory Internals | Involves theory related grading: quiz and written assignments | 10 |
| Theory Externals | Final Theory Exam | 35 |
| Lab Internals | Grading based on lab performance and viva voce | 10 |
| Lab Externals | Final Lab Exam | 35 |
| Final Project | 10 |
Theory assignments will involve writeups that are mostly straight forward and will be indicative of the difficulty in the mid term and final exams. Questions for the final exam will be consequential concepts from the assignments. It would help if assignments are taken seriously for the student to secure good marks in the exams.
Lab assignments will involve you to work on programming based questions. These can be take home tests or in lab tests. The difficulty level is indicative of the final lab exam questions. The evaluation of lab assignments will involve a strong viva voce component.
The student will be asked to pick a project from a list of topics. The projects will mostly be explorative in nature. Every project will be executed by a team of at most 2 people. Evaluation of the project will be based on the report and presentation.