How To Be Organized When Handling Projects

Interview Tips For Data Analysts

👋 Hey there !!

Welcome to Career Compass! In this week’s newsletter, I’ll cover:

🎯 How to be Organized When Handling Projects

🎯 A System to Data Cleaning

🎯 Interview Tips

Let’s get started!

Table of Contents

Read time: 5 minutes

🎯Be Organized When Handling Projects

In the world of data, staying organized is more than keeping your desk clean. It's about managing projects and deadlines to improve efficiency and reduce stress.

⁉️ Why is this important?

Precision and accuracy are critical when working on data projects, as the cost of not meeting goals or compromising Data Integrity can be high.

👨‍🏭 How can we address this?

Here are some ways to be more organized:

👴 Overcome Parkinson's Law → As “work expands to fill the time available for completion,” step up to the challenge by clarifying your role and responsibility and focusing on doing deep work in small chunks of time.

Batch repetitive tasks → Schedule a dedicated time daily to do those tedious tasks. Constant context switching has proven to be one of the worst advice on productivity.

📖 Check your progress once a day → Reviewing all the good work you’ve done throughout the day when finishing up is crucial. A shutdown routine will help you see your progress and add, modify, or delete tasks.

This is how I structure my day to meet both professional and personal goals.

⚠️ CTA

Learn more about project management in this article by the Project Management Institute.

Do you have any more strategies to be organized? Reply and let me know!

🎯Data Cleaning Process

Cleaning data can seem like decoding an ancient script.
However, it is one of the most important tasks as it directly impacts the accuracy and reliability of your data analysis.

👨‍🏭 How can we address this?

Acronyms can work when facing Data Cleaning. Here's a system you can use:


⭕ Check for Nulls → Either drop observations, input missing values based on other observations or speak to the person in charge of generating that data.

🟰 Address Duplicates → Find and remove Duplicates. This is crucial to keep data integrity.

🆗 Review Spelling and Grammar → This is especially important when integrating data from multiple sources.

🔛 Ensure Formatting → Standardize your data's appearance for consistency.

 Survey your data  Engage with the data to understand the context of your dataset by asking, “Does this make sense”?

⚠️ CTA

Read this article in DataSpace Academy to learn more about the importance of Data Cleaning.

🎯Interview Tips

My biggest advice is to prepare and over-prepare. It’s the best way I know to increase your chances of becoming the ideal candidate.

Prepare and over-prepare

➡️ To-do list

  1. Research Deeply → Understand the company’s core values, industry, and culture. Search on LinkedIn for people who work for the company and try to reach out to them to make a connection.

  2. Match Skills to Role → Align your unique experiences with the job’s specific demands. Here are 10 project ideas.

  3. Prepare Examples → Use your portfolio to demonstrate your expertise. Don’t worry, I’ve got your back. Check out my Ultimate Portfolio, a one-stop shop for all your data projects.

  4. Ask Insightful Questions → Asking thoughtful questions shows you’re interested in the role and allows you to learn more about the company. I explain why this is so important in this post.

Always remember

  1. The hiring manager is trying to determine whether you’re the ideal person for the job. And I said ‘ideal’ here and not ‘best’ because the ideal candidate is often not actually the best candidate.

  2. The interview is a two-way interview.

  3. The hiring manager or organization should decide whether you’d be a great fit for the role, but you should also determine whether the role or company would be a good fit for you.

⚠️ CTA

If you want to get the best Resume for Data Analytics, check out my Ultimate Resume Template to know exactly what to aim for.

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👋 That’s it! Thank you!

See you next week.

Mo Chen

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