Podcast
Questions and Answers
How is the course grading split up?
How is the course grading split up?
- 50% individual course project, 25% module quizzes, 25% research paper presentation
- 45% three module team projects, 30% individual course project, 10% module quizzes, 10% research paper presentation, 5% cool tools presentation (correct)
- 40% individual projects, 30% quizzes, 20% team projects, 10% research paper presentation
- All of the above
What is the duration of 'Cool Tools' presentations?
What is the duration of 'Cool Tools' presentations?
- Under 3 minutes
- Around 10 minutes
- Exactly 5 minutes (correct)
- No specific duration
How often are the lectures/demos that students need to watch on their own each week?
How often are the lectures/demos that students need to watch on their own each week?
- Twice a week
- Not more than 2 hours per week (correct)
- Once every two weeks
- At least 3 times a week
What is the purpose of 'In the News' segment at the beginning of the class?
What is the purpose of 'In the News' segment at the beginning of the class?
What is the required software for the course?
What is the required software for the course?
What is the weightage of the individual course project in the grading scheme?
What is the weightage of the individual course project in the grading scheme?
Based on the syllabus, how many weekly demo videos are required for the individual course project?
Based on the syllabus, how many weekly demo videos are required for the individual course project?
What is the main purpose of 'Cool Tools' presentations in the course?
What is the main purpose of 'Cool Tools' presentations in the course?
How long must 'Cool Tools' presentations be, according to the rubric?
How long must 'Cool Tools' presentations be, according to the rubric?
What is the frequency of 'Cool Tools' presentations in the course?
What is the frequency of 'Cool Tools' presentations in the course?
What is a key expectation at the end of the course?
What is a key expectation at the end of the course?
What is an essential ability for a successful data scientist/ML engineer according to the text?
What is an essential ability for a successful data scientist/ML engineer according to the text?
What is an important aspect of following best practices in the field according to the text?
What is an important aspect of following best practices in the field according to the text?
What is a critical skill needed by a successful data scientist/ML engineer according to the text?
What is a critical skill needed by a successful data scientist/ML engineer according to the text?
What is a key ability required for a successful data scientist/ML engineer?
What is a key ability required for a successful data scientist/ML engineer?
What is expected of students at the end of the course?
What is expected of students at the end of the course?
What is emphasized as essential in each module of the course?
What is emphasized as essential in each module of the course?
Which skill is NOT mentioned as important for a data scientist/ML engineer?
Which skill is NOT mentioned as important for a data scientist/ML engineer?
Flashcards are hidden until you start studying
Study Notes
Course Structure
- The course grading is split up (exact weightage not specified)
- 'Cool Tools' presentations are 5-7 minutes long
- Lectures/demos need to be watched on their own by students each week, with a frequency of 1-2 per week
- 'In the News' segment is at the beginning of each class, purpose is not specified
Course Requirements
- Required software for the course is not specified
- Individual course project weights 30% of the grading scheme
- 6 weekly demo videos are required for the individual course project
'Cool Tools' Presentations
- Main purpose is to share interesting data science/ML tools and techniques
- Presentations are 5-7 minutes long, frequency is 1-2 per month
- Each student is required to give at least one presentation
Course Expectations
- Key expectation at the end of the course: ability to design and deploy a data science/ML project
- Essential ability for a successful data scientist/ML engineer: ability to communicate insights and results effectively
- Important aspect of following best practices: reproducibility
- Critical skill needed: ability to extract insights from data
- Key ability required: ability to work with large datasets
- Expected of students at the end of the course: ability to design and deploy a data science/ML project
- Emphasized as essential in each module: practical skills in data science/ML tools and techniques
- Not mentioned as important for a data scientist/ML engineer: knowledge of a specific programming language
Studying That Suits You
Use AI to generate personalized quizzes and flashcards to suit your learning preferences.
