When looking for a data analysis job, how to “package” your project experience?

Project experience is a vital part of your resume. However, many job seekers, especially newcomers, are often confused by the lack of project experience. This article will provide you with practical guidance to help you understand what project experience is, how to dig out and "package" your own project experience, and how to avoid common mistakes.

Project experience is the most weird part of a resume. Because many people, especially newcomers, have never done any serious projects, so the project experience column is often awkwardly left blank.

Until one day, under the influence of mysterious power, the project experience of all newcomers became uniform, just like the scene of the Miss Korea beauty pageant. As an interviewer, I couldn't help laughing and crying. In order to help everyone pass the interview smoothly, I must talk about it today.

Project experience usually appears in this position (as shown below):

1. What is project experience?

First of all, let’s get to the bottom of it: What is a project? Answer: A project is a work done within a specific time frame with specific resources to achieve a specific goal.

In a nutshell: a project is not a temporary job, it is a special matter.

Therefore, many people will be very entangled:

• Cousin at the grassroots: I usually just run numbers and update the daily report, what can I do?
• Data newcomer: I am only doing odd jobs and have never worked on any high-end projects. What should I do?
• Newbie changing careers: My last job was not data analysis, does that mean I don’t have any projects?

The key to solving the problem is to understand what the project is.

The project is: special matters are handled specially, so:

1. Any project that meets the special requirements is a project (as shown below):

2. Participation, responsibility, and independent responsibility are all projects (as shown below):

3. A project that achieves its goal is a good project (as shown below):

After understanding the above three points, do you look around and find that you have a lot of project experience to write about?

In fact, what most students lack is the eyes to discover the highlights of their own work.

Now that we have figured this out, let’s look at how to write it specifically.

2. How to write project experience

The project experience templates we encounter on recruitment websites are often very complicated (as shown below):

But in reality,

First, many students work on “small” projects, and the work is not complicated;

Secondly, I am not the project leader, so I may not be clear about the cause and effect;

Third, if you write completely according to the template, it will make the resume very long and affect the reading. Therefore, if you don’t use the template, it is recommended to use the simplest way of writing (as shown below):

If you are more targeted, you can focus on different types of data projects.

• Analytical projects focus on analyzing problem scenarios (users? products? sales? ...)
• Algorithmic projects focus on models and effects (using XX model improves the effect by X%)
• Data product projects focus on data volume/update speed/usage rate in a simple and clear manner, directly highlighting their own advantages.

3. Common Mistakes in Project Experience

The biggest problem with project experience is that it becomes a hit online, as shown in the following figure:

Internet celebrity hits have been popular since 2017. At the peak, Teacher Chen saw 30 Titanic in one morning. The mood was simply... So I immediately made a screening list for the HR girl, including Boston, Iris, Lagou.com data analyst and so on.

Surprisingly, from 2017 to 2025, the list has become longer and longer. Fortunately, our HR girl has grown into a big sister, and even she has mastered the experience that "as long as two resumes appear in the same project, it will be a hit."

Other than that are minor bugs, such as:

• Too wordy. Hundreds of words are densely packed together, and it's hard to tell what they mean.
• Unclear expression. No description of what you did, no tools/algorithms for the process, no numbers for the results.
• There are too many technical terms. Dahua Information Technology Co., Ltd. Happy Garden Project, who can understand what "Dahua" and "Happy Garden" mean? Wouldn't it be better to just write a project to increase the activity of APP users?

Everyone should pay attention when writing. You should practice your resume more, especially programmers, who usually write too few formal documents and have poor writing skills.