Finding and preparing for the right job in the DS/AI field can be tricky. In this course, we will explore how the job market has vastly different descriptions for the same job title, how to identify what a company is really looking for, and how to search the “hidden” job market. We will also overview major skills areas experts recommend revisiting before applying for jobs in this field, how to tailor your resume to catch the eye of a DS/AI hiring manager, and how to create a stellar portfolio. Finally, we will discuss the importance of marketing yourself and tips and tricks on how to do it well.
Finding and Preparing for the Right Job
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Finding and Preparing for the Right Job
This course is part of Interviewing for DS/AI Roles Specialization
Instructor: Camille Funk
Included with 
Gain insight into a topic and learn the fundamentals.
Beginner level
Recommended experience
10 hours to complete
3 weeks at 3 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Identify which skills to refresh in preparation for DS/AI applicaitons and interviews.
Make your portfolio and resume stand out by applying tips specific to the field.
Recognize how to market yourself and how career fairs, connecting with recruiters, and networking can help.
Skills you'll gain
Details to know
Shareable certificate
Add to your LinkedIn profile
Assessments
4 assignments
Taught in English
See how employees at top companies are mastering in-demand skills
Build your subject-matter expertise
This course is part of the Interviewing for DS/AI Roles Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate
Earn a career certificate
Add this credential to your LinkedIn profile, resume, or CV
Share it on social media and in your performance review
There are 4 modules in this course
Welcome to Module 1, The Job Search. In this module, we will overview DS/AI-specific job-search strategies including how to tell what a company is really looking for no matter the job title. We will also practice how to conduct a targeted job search and identify the “hidden” job market. Finally, we will compare two job postings with the same title and identify the differences in skill expectations and descriptions.
What's included
2 readings1 assignment1 discussion prompt2 plugins
Welcome to Module 2, Skills to Brush Up On. In this module, we will identify the major skills areas experts recommend revisiting before applying for data science jobs. We will also list what specific skills should be studied for each of the four major data science job categories. In our group discussion, we will identify and share what skills we personally plan to brush up on and why.
What's included
2 readings1 assignment1 discussion prompt
Welcome to Module 3, Portfolios, Resumes, and Cover Letters. In this module, we will discuss the importance of portfolios and resumes as well as the surprising lack of importance of cover letters for this field. We will also identify specific tips and tricks to improve your resume and overview what should be included in a portfolio to make you stand out. Finally, we will apply the module’s core concepts to update our own resumes, portfolios, and cover letters.
What's included
2 readings1 assignment1 discussion prompt1 plugin
Welcome to Module 4, Marketing Yourself. In this module, we will explain why marketing yourself is an important key to landing interviews in your desired field. We will learn how to market yourself, including how to connect with recruiters, how to build your online presence, and why and how to attend career fairs. In our group discussion, we will describe what kind of networking is beneficial in this field and one new strategy we plan to implement to better market ourselves.
What's included
2 readings1 assignment1 discussion prompt1 plugin
Instructor
5 Courses12,840 learners
Offered by
Recommended if you're interested in Data Analysis
Coursera Project Network
Queen Mary University of London
The State University of New York
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