Think like a Data Analyst: Data Cleaning Framework

MENTORSHIP Applications are now open for the Spring 2025 cohort of my mentorship program! Group info sessions with me are starting in the next few weeks - apply at theanalyticsaccelerator.com. Excited to meet you soon! THIS VIDEO Data cleaning is more than just fixing errors—it's about applying a framework and mental model that can help you shine in job interviews and on the job. In this video, I’ll walk you through a high-level approach to cleaning datasets, including how to think through common challenges, prioritize your efforts, and document your process like a pro. Stick around to learn the best practices for how to deal with "unsolvable" issues, i.e. missing or nonsensical data - and be sure to apply these tips to your portfolio projects! ABOUT ME If we haven’t met yet - hi! I’m Christine, a former data director and hiring manager helping ambitious career transitioners and recent grads land their first job as a data analyst. I'll be sharing the insights and strategies that empowered over 70% of job-hunting students in the first cohort of my mentorship program landed their first data job within 6 months, in today's super-competitive job market. If you want to become a standout data analyst through applied business skills, soft skills, and strategies for every part of the job hunt process - so that you can have confidence, professional leverage, and bring real impact to work - connect below for more insights and future workshops! LINKS - Get more tips in my newsletter - https://bit.ly/3LodfYs - Apply to the next cohort - https://theanalyticsaccelerator.com/ - Follow on LinkedIn - https://linkedin.com/in/christine-jiang - Instagram - https://instagram.com/cjiaang (my photography, because why not) Timestamps: 0:00 - Introduction 0:36 - High Level Thought Process 1:00 - The Analytical Lifecycle 2:16 - Data Cleaning Framework 3:11 - Conceptualize the Dataset 5:49 - Locate Solvable Issues 7:46 - Evaluate Unsolvable Challenges 10:02 - Augmentation 10:58 - Note and Document 11:39 - Review Video URL: https://youtu.be/iYEw8L3Un4c