My Journey Into Data Modeling and Analyst Techniques

我在数据建模与分析技术领域的探索之旅

When I started learning about data analysis, I thought it was all about numbers and charts. But the deeper I go, the more I realize it’s about how you shape and prepare data to tell a story. Recently, I’ve been exploring data modeling and common analyst techniques, and I want to share what that journey has felt like.

当我刚开始学习数据分析时，我以为这仅仅是关于数字和图表。但随着研究的深入，我愈发意识到，其核心在于如何塑造和准备数据，从而讲述一个完整的故事。最近，我一直在探索数据建模和常用的分析技术，我想分享一下这段旅程的感悟。

One of the first lessons was about making data reusable. At first, I didn’t understand why this mattered, why not just work with data once and move on? But then I realized: reusable data saves time, reduces errors, and makes collaboration easier. It’s like cooking a big meal and storing portions for later instead of starting from scratch every time.

最初的课程之一是关于如何实现数据的可重用性。起初，我不明白这为什么重要，为什么不能处理完数据就直接结束呢？但后来我意识到：可重用的数据可以节省时间、减少错误，并使协作变得更加容易。这就像做了一顿大餐后将部分食物储存起来，而不是每次都从零开始烹饪。

Next, I learned how to model data using queries. This was eye-opening. Queries aren’t just commands; they’re like conversations with your data. You ask questions, and the data responds. I remember the first time I wrote a query that combined multiple tables, it felt like unlocking a hidden dialogue.

接下来，我学习了如何使用查询（Queries）进行数据建模。这让我大开眼界。查询不仅仅是指令，它们更像是与数据之间的对话。你提出问题，数据给予回应。我记得第一次编写合并多个表的查询时，那种感觉就像是开启了一段隐藏的对话。

Then came Power Query, and honestly, it felt like magic. Instead of writing everything manually, I could visually shape and transform data. It was like sculpting clay, pulling, merging, and reshaping until the dataset told the story I needed.

随后我接触到了 Power Query，老实说，这感觉就像魔法一样。我不再需要手动编写所有代码，而是可以通过可视化方式塑造和转换数据。这就像是在雕塑黏土，通过拉伸、合并和重塑，直到数据集呈现出我想要讲述的故事。

I also practiced techniques every analyst uses: Converting data in Power Query to make formats consistent. Finding and removing duplicates (because nothing ruins analysis faster than duplicate records). Changing case and replacing values to clean messy text. Combining data with merge columns to create richer insights. Creating logical functions to add intelligence to datasets. Building aggregate datasets to summarize and simplify.

我还练习了每位分析师都会用到的技术：在 Power Query 中转换数据以确保格式一致；查找并删除重复项（因为没有什么比重复记录更能破坏分析结果了）；更改大小写并替换值以清理杂乱的文本；通过合并列来组合数据以获得更丰富的洞察；创建逻辑函数为数据集增加智能；构建聚合数据集以进行汇总和简化。

Each of these felt like learning new tools in a toolbox. At first, they seemed small, but together they make analysis smoother, faster, and more reliable. What I’ve learned is that data modeling and cleaning aren’t chores, they’re the foundation of analysis. Without them, insights crumble. With them, you can trust your results and tell stories that matter.

这些练习就像是在工具箱里学习使用新工具。起初它们看起来微不足道，但组合在一起后，它们让分析过程变得更顺畅、更快捷、更可靠。我学到的是，数据建模和清理并非苦差事，它们是分析的基石。没有它们，洞察力就会崩塌；有了它们，你才能信任分析结果，并讲述有意义的故事。

I used to think analysts just “ran numbers.” Now I see that analysts are storytellers, shaping raw data into something meaningful. ✨

我曾经认为分析师只是在“跑数字”。现在我明白，分析师其实是讲故事的人，他们将原始数据塑造成有意义的内容。✨

Takeaway: If you’re starting out, don’t skip the fundamentals. Queries, Power Query, and cleaning techniques may not sound glamorous, but they’re the skills that make you a real analyst.

总结：如果你刚入门，千万不要跳过基础知识。查询、Power Query 和清理技术听起来可能不够“高大上”，但它们才是让你成为一名真正分析师的核心技能。