- Level Foundation
- Duration 14 hours
- Course by University of California, Irvine
-
Offered by
About
Prepare yourself for interviewing and landing a job in the DS/AI field. In this course, we will discuss what needs to be done before, during, and after the interview process. We will also provide tips and tricks on how to practice for a major component of data science interviews: the technical interview. Finally, this course will cover best practices for accepting or declining a job offer, salary negotiations, and how to create a career development plan. By the end of this course, students will be able to: - Recall what actions need to be done before, during, and after an interview. - Discuss a technical interview preparation plan. - Identify job offer acceptance or refusal best practices. - Create a career development plan.Modules
Develop and Refine Your Interviewing Skills
1
Assignment
- Module 1 Knowledge Check
1
Discussions
- A Better Interview Next Time
2
Readings
- Required Readings & Resources
- Supplemental Resources
Preparing for Technical Interviews
1
Assignment
- Module 2 Knowledge Check
1
Discussions
- Strengthening Your Interview Answers
2
Readings
- Required Readings & Resources
- Supplemental Resources
Negotiating a Job Offer
1
Assignment
- Module 3 Knowledge Check
1
Discussions
- Job Offer Negotiations
2
Readings
- Required Readings & Resources
- Supplemental Resources
Career Planning for DS/AI Professionals
1
Assignment
- Module 4 Knowledge Check
1
Peer Review
- Data Science/Artificial Intelligence Career Development Plan
1
Discussions
- Putting It Into Practice
2
Readings
- Required Readings & Resources
- Supplemental Resources
Auto Summary
Enhance your career in Data Science & AI with expert guidance on interviewing, job offer negotiations, and career planning. Led by Coursera, this foundational course covers pre-, during, and post-interview strategies, technical interview preparation, and salary negotiation techniques. Complete the course in 840 minutes and choose from Starter, Professional, or Paid subscriptions. Ideal for aspiring data scientists aiming to excel in their careers.

Camille Funk