學習excel數據分析

它是視覺的，它是透明的，并且使事情變得簡單。 (It’s visual, it’s transparent, and it keeps things simple.)

The more I advance into analytics, the more I come back to Excel as a teaching and prototyping cool. Yes, of course, Excel has its weaknesses — but as a medium for learning, it’s unmatched.

我越深入分析，就越能重用Excel作為教學和原型制作的工具。 是的，當然，Excel有其弱點-但作為學習的媒介，它是無與倫比的。

Here’s why:

原因如下：

它減少了認知開銷 (It reduces cognitive overhead)

Cognitive overhead is described as “how many logical connections or jumps your brain has to make in order to understand or contextualize the thing you’re looking at.”

認知開銷被描述為“為了理解或上下文化您正在看的東西，您的大腦必須進行多少邏輯連接或跳躍。”

Often an analytics learning journey looks like this:

通常，分析學習之旅如下所示：

Learn a brand-new statistical technique.

學習一種全新的統計技術。

Learn how to implement the brand-new technique using brand-new coding techniques

了解如何使用全新的編碼技術來實施全新技術

Progress to more advanced statistical and coding techniques, without ever having felt really comfortable with the basics.

從未接觸過基本知識即可獲得更先進的統計和編碼技術。

It’s hard enough to learn the statistical foundations of analytics. To learn this while also learning how to code invites sky-high cognitive overhead.

學習分析的統計基礎非常困難。 在學習這一點的同時還學習如何編碼會帶來巨大的認知負擔。

Now, I do believe there is great virtue to practice analytics via coding. But it’s better to isolate these skill sets while mastering them.

現在，我確實相信通過編碼實踐分析具有很大的優(yōu)勢 。 但是最好是在掌握這些技能時將其隔離。

Excel provides the opportunity to learn statistical techniques without the need to learn a new programming language at the same time. This greatly reduces cognitive overhead.

Excel提供了學習統計技術的機會，而無需同時學習一種新的編程語言。 這大大減少了認知開銷。

Credit: Andrew Neel/Unsplash圖片來源：Andrew Neel / Unsplash

這是一個視覺計算器 (It’s a visual calculator)

The first mass-market offering of a spreadsheet was called VisiCalc — literally, a visual calculator. I think of this often as one of the spreadsheet’s biggest selling points.

電子表格的第一個大眾市場產品稱為VisiCalc-實際上是一個視覺計算器。 我經常將其視為電子表格的最大賣點之一。

Especially to beginners, programming languages can resemble a “black box” — type the magic words, hit “play” and presto, the results. Chances are the program got it right, but it can be hard for a newbie to pop open the hood and see why.

特別是初學者，編程語言可以像一個“黑匣子” -鍵入魔法的話，點擊“播放”和急 ，結果。 該程序可能正確無誤，但是對于新手來說，很難打開引擎蓋并弄清原因 。

By contrast, Excel lets you watch an analysis take shape each step of the way. It lets you calculate and re-calculate, visually.

相比之下，Excel使您可以觀察分析過程中每個步驟的形成。 它使您可以直觀地進行計算和重新計算。

Seeing is believing, right?

眼見為實吧？

你不能走捷徑 (You can’t take shortcuts)

Open-source tools like R and Python give you access to a wide variety of packages, which usually means you don’t have to “start from scratch” with basic functions.

諸如R和Python之類的開放源代碼工具使您可以訪問各種軟件包，這通常意味著您不必使用基本功能“從頭開始”。

While Excel add-ins for analytics are available, many of them cost. But that’s OK! In fact, left with the bare building-blocks of Excel, there’s more opportunity to get face-to-face with what’s being built.

盡管可以使用Excel加載項進行分析，但其中很多都是需要花費的。 但是沒關系！ 實際上，僅留下了裸露的Excel構建塊，就有更多的機會與所構建的內容進行面對面的交流。

In Excel, we can’t always rely on an external package to conduct our analysis for us. We’ve got to get there by our own devices.

在Excel中，我們不能總是依靠外部程序包來為我們進行分析。 我們必須通過自己的設備到達那里。

它迫使你變得敏捷 (It forces you to be agile)

A temptation in data analysis is to build the most complicated possible model at first, and then work backward to find something that works. It’s better to go in reverse: start with a minimum viable product, and iterate from there.

數據分析的一種誘惑是，首先要構建最復雜的模型，然后再進行反向工作以找到可行的方法。 最好相反：從最低限度可行的產品開始，然后從那里進行迭代。

It’s a lot harder to build a complicated model in Excel than in Python — which is a limitation, when we need complicated models — but as a prototyping tool, this is great, because it forces us to start small.

在Excel中比在Python中構建復雜的模型要困難得多-這是局限性，當我們需要復雜的模型時-但作為原型工具，這很好，因為它迫使我們從小做起。

我們不在這里制作生產模型 (We’re not making production models here)

I just highlighted some of the many benefits of learning analytics in Excel. Can you think of others? Or maybe you’re not convinced?

我只是強調了在Excel中學習分析的許多好處。 你能想到別人嗎？ 或者，也許您不相信？

One of the biggest objections to doing analytics in Excel is that it can be error-prone and hard to reproduce.

在Excel中進行分析的最大反對意見之一是它容易出錯并且難以復制。

That is absolutely true, but we’re just learning here. We’re not making production models.

絕對正確， 但是我們只是在這里學習。 我們不制作生產模型。

Don’t discard Excel’s aptitude as a teaching tool for its shortcomings as a fast, reproducible analytics workflow.

不要因為快速，可重復的分析工作流程而將Excel的缺點作為教學工具而放棄。

在Excel中學習分析：下一步是什么？ (Learning analytics in Excel: What next?)

I’ve learned more about statistics and analytics by experimenting in Excel than any other tool, and I hope this approach can work for you too.

通過在Excel中進行實驗，我比其他任何工具都學到了更多有關統計和分析的知識，我希望這種方法也能為您服務。

If you’d like to see what Excel can do for your learning path, check out my presentation at the MS Excel Toronto free online meetup, where I’ll be demonstrating important statistical concepts in Excel. Learn more here.

如果您想了解Excel可以為您的學習道路做些什么，請在MS Excel Toronto免費在線聚會上查看我的演示文稿，我將在此演示Excel中的重要統計概念。 在這里了解更多 。

For a master class in demonstrating analytics in Excel, check out John Foreman’s book, Data Smart: Using Data Science to Transform Information into Insight. This book uses the humble spreadsheet to introduce algorithms that many would only imagine to do by coding.

有關在Excel中演示分析的大師班，請查看John Foreman的書《 數據智能：使用數據科學將信息轉換為Insight》 。 本書使用不起眼的電子表格介紹了許多人只能通過編碼才能想到的算法。

How do you prefer to learn statistics and analytics? Do you see other pros or cons of using Excel? Let’s talk in the comments.

您更喜歡學習統計和分析嗎？ 您看到使用Excel的其他優(yōu)點或缺點嗎？ 讓我們在評論中談談。

Originally published at https://georgejmount.com on August 2, 2020.

最初于 2020年8月2日 發(fā)布在 https://georgejmount.com 上。

翻譯自: https://towardsdatascience.com/why-excel-is-the-best-way-to-learn-data-analytics-e3cd7018012b

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