Course curriculum

    1. About the instructor

    2. What’s covered in this course?

    1. Types of data roles covered?

    2. The current landscape and why it's tough

    3. Challenges you'll face applying for jobs

    4. What to expect with a career in data

    5. What to look for in a data role

    6. How and when to evaluate factors for a role

    1. Personal branding

    2. Tips for maintaining your brand

    3. Positioning your skills

    1. Great resume examples

    2. Anatomy of a strong resume

    3. Summarizing your achievements

    4. Plain vs designed (fancy) resume templates

    5. Resume design tips

    6. Github profile examples and tips

    7. Personal website and blog examples

    1. Finding roles

    2. Warm vs cold applications

    1. Understanding the interview process

    2. Interview prep

    3. Technical tests

    4. AI tests

About this course

  • Free
  • 28 lessons
  • 2.5 hours of video content

Course Overview

While demand for data analysts, scientists, engineers, and AI practitioners remains strong, today’s candidates often need to play the 'numbers game' when applying for roles, sometimes needing to submit 50–100 applications before landing a job offer. Strong technical skills alone are no longer enough. Employers are looking for professionals who know how to position themselves, communicate their value, and stand out in a crowded market.

This course is a practical, step-by-step guide to building your personal brand, positioning your data skills and navigating the modern data hiring process with confidence.

Course curriculum

    1. About the instructor

    2. What’s covered in this course?

    1. Types of data roles covered?

    2. The current landscape and why it's tough

    3. Challenges you'll face applying for jobs

    4. What to expect with a career in data

    5. What to look for in a data role

    6. How and when to evaluate factors for a role

    1. Personal branding

    2. Tips for maintaining your brand

    3. Positioning your skills

    1. Great resume examples

    2. Anatomy of a strong resume

    3. Summarizing your achievements

    4. Plain vs designed (fancy) resume templates

    5. Resume design tips

    6. Github profile examples and tips

    7. Personal website and blog examples

    1. Finding roles

    2. Warm vs cold applications

    1. Understanding the interview process

    2. Interview prep

    3. Technical tests

    4. AI tests

About this course

  • Free
  • 28 lessons
  • 2.5 hours of video content
  • 70+ downloadable resources
  • 12 month access

What's included?

  • Lifetime Access

    Your purchase includes lifetime access to the course and resources.

  • On-Demand Video

    Access hours of on-demand video and learn at your own pace.

  • Live Coaching Discounts

    Get 15% off the purchase of live coaching sessions with the instructor.

Trusted By

Students enrolled in my courses work at some of the world's biggest brands

What you'll learn

A comprehensive curriculum that will teach you everything you need to know about multi-modal market research, from interviews to ethnography to surveys.

  • Understand the modern data hiring landscape

  • Learn how to positioning yourself for success

  • How to build a resume that gets interviews

  • Create a portfolio that proves your value

  • How to find data roles and opportunities that are a fit

  • Learn how to master the interview process

  • Post interview do's and don'ts

Meet Your Instructor

Stephen Tracy

Data Scientist, Educator and Entrepreneur

With over a decade of experience in advertising, market research, and data, Stephen has worked across the globe—from Canada to Singapore—holding senior positions at some of the world's leading ad agencies and data providers, including IPG, Publicis, and YouGov. A successful entrepreneur, he co-founded and scaled Milieu Insight, a data analytics startup. In his spare time, he shares insights on all things data through his blog, Analythical.com. Stephen holds a B.A. (Honours) degree and a Master's degree (MI) in information and data science from the University of Toronto.