原创译文|12种免费思维导图工具助你成为结构化思维专家（上）

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简介

我们从一个简单的练习开始吧 “如果我们任命你为店长，去管理经营状况最糟糕的一个店面。那么，在经营这家店时，你会考虑哪些方面？你要做什么去改变当前糟糕的局面呢？”

花几分钟好好考虑一下这个问题吧。在纸上写下一些你考虑的事情，然后我们继续往下看。

你觉得这个练习怎么样？难不难？你确定你已经写下了所有可能的方面了吗？

如果你不确定你已经想得很全面，或者你的列表看上去只是一个待办清单，没有任何框架或者结构，那么这篇文章将在很大程度上给你提供帮助。

结构化思维的重要性

每个人都有能力在同一时间思考多方面的问题。但是，数据科学家的特别之处在于他们不仅能思考，还能将思考转化成一种结构化的设想。

结构化思维是在无结构问题上加一个框架。有了思维结构不仅能够帮助分析师从宏观层面了解这个问题，还能帮助他们发现需要进一步了解的细节之处。结构化思维让我们把想法组织成结构化模式，然后我们就可以找到哪些地方需要我们重点投入精力。

遗憾的是人们并没有意识到结构化思考的重要性，在遇到非结构化问题时，他们也没有去追溯他们的非结构化本能。

本文我将列举一些对数据科学家来说很实用的思维导图工具。这些工具为新想法的诞生提供了绝佳的途径。这些工具会拓宽你的思维宽度，提高理解能力和记忆能力。我相信你看完了这篇文章以后，你一定可以在你的工作中使用它们。

什么是思维导图工具

当我第一次听到这些工具的时候，我也有这个疑问。其实这些工具的存在都是源于“思维导图”的概念。思维导图并非新概念，到今天它已经存在数个世纪了。基本上说，它就是一种创造性思维。

思维导图是创造思维的地图，它是以视觉形态表达想法和概念。其实思维导图就是以一种结构化的方式展示你的思想。有时候，你觉得说服别人相信你的观点是非常困难的，你遇到过这种情况么？你要说服的人可能是管理者、老板或投资人。当你的想法不能在对方的脑海中形成视觉影响力的时候，这种情况就会发生。这个时候，思维导图工具就可以替你解围。这些工具通过缤纷的色彩、树状结构、图片和动画形式来保证你的想法可以用最好理解的方式呈现出来。

我们为什么需要思维导图工具

在分析师中，思维导图工具对于那些特别喜欢用基于树形算法的人来说最有吸引力，因为他们喜欢用类似决策树、随机森林和boosting的算法。

思维导图是对人的想法的树形结构视觉展示。我第一次做思维导图时，我感觉自己好像是在把我的想法构建成树节点和枝节末端的树叶。

这些工具能够在以下几个方面给你们提供帮助：

1，它能够让你在日常工作中运用结构化思维

2，你将更容易地向你的顾客、经理和老板解释你的想法、策略

3，不用再使用千篇一律的PPT展示，而是用这种创造性的传达信息的方式

4，因为我们的大脑接受图片信息比文字信息快得多，思维导图可以让人们更快的知道你想说什么

5，树的枝干结构可以帮你快速分析观察复杂问题

6，你可以一次性重新设计或者修改整个思维链

7，长期使用思维导图工具能提高你的解决问题能力和记忆力

简单来说，运用这种创造性的便捷工具，数据科学家能够在更短时间内完成更多的工作。

如何建立思维导图？——练习案例

建立思维导图不难。你甚至用纸笔就能画出来。一般方法如下：在纸张中心写下主要思想。从中心向四周画出分支，把每个分支都相互连起来，所有分支最终都指向末端。

我们根据一个数据科学问题来建立一张思维导图。我们假定我们的任务是预测产品销量。

我根据假设生成来建立思维导图。假设生成可以让我们更细致的了解这个问题，用集体讨论的方式找出可能影响结果的因素。这就要求我们在看数据之前完全读懂这个问题的描述。

下面是一张简单的思维导图，我在这张图中展示了可能影响产品销量的因素。

原创译文|12种免费思维导图工具助你成为结构化思维专家（上）

我们看看这张图，我从四个层面考虑了可能的影响因素：店铺层面、产品层面、客户层面和宏观层面。下面我快速的解释一下每一个层面。

店铺层面假设

城市类型 ：大城市和一线城市的店铺销量应该更高，因为这些城市居民收入更高；

人口密度 ：人口密度高的地方店铺销量应该更高，因为需求更多；

储存容量 ：面积大的店铺销量应该更高，因为他们可以提供一站式消费，而且人们喜欢在一个地方就买齐所有东西；

竞争对手 ：附近有相似的店铺的店销量更低因为面临竞争更大；

市场营销 ：市场划分更明确的店铺销量应该更高，因为他们可以准确的为客户提供服务并进行宣传、以吸引更多客户；

店铺地点 ：受欢迎的集贸市场附近的店铺销量应该更高，因为这些市场客流量大；

店铺氛围 ：装修精良、管理得当、员工礼貌的店铺会吸引更多消费者，因此销量会更高；

产品层面假设

名牌：名牌产品销量应该更高，因为消费者更信任品牌；

包装：包装精美的产品可以吸引消费者，销量更好；

用途：和特殊用途商品相比，日常使用的产品销量更好；

展示区域 ：在店铺里更大柜台展示的产品可以最先抓住消费者的注意力，卖的更好；

店铺中的可见性 ：产品在店铺中的摆放位置会影响销量。和摆在后面的产品相比，摆在入口处的产品可以最先吸引消费者的注意力；

宣传：在大部分情况下，店铺中宣传更多的产品销量更高；

促销活动 ：大减价或者有折扣的产品销量更高；

客户层面假设

客户行为 ：店铺产品与当地消费者需求匹配，就可以获得更高销量；

工作性质 ：领导层工作的消费者比其他层面工作的消费者购买力更高；

家庭规模 ：家庭成员越多，在购买产品上的开支越大；

年收入 ：消费者的年收入越高，购买昂贵产品的可能性越大；

购买历史 ：有个这方面信息，我们就可以看到这个用户的购买频率；

宏观层面假设

自然环境 ：如果政府宣布自然环境是安全的，消费者在购买产品时就不会担心这个产品是否是环境友好的；

经济增速 ：如果当下的经济形势显示会持续上涨，人均收入会增加，那么消费者的购买力会提升；

提醒一下，这并不是一个详细清单，仅仅是我列出的21个基本的假设，你可以进一步思考，做一个你自己的列表。然后，我会依据数据开始数据分析，然后建模。

我相信在这个练习之后，你能明白思维导图能更好的展示你的想法，更能理解思维导图的重要性。

在《12种免费思维导图工具助你成为构化思维专家（下）》中，我们会一一介绍12种免费的思维导图工具，希望大家持续关注！

英文原文

Introduction

Let us start this with a simple exercise, the kind of which every data scientist faces regularly:

You have been appointed as a store manager for our worst performing store. What are the possible factors / changes you would make in your store?

Take a few minutes to think over this. Once you have written at least a few factors, let’s move ahead.

homer

So, how was the exercise? Was it easy or difficult? How sure are you that you have written all possible factors as part of this thought capture?

If you are not sure that you have captured most of the factors or your list looks like a simple to do list without any framework or structure, this article should help you tremendously.

The art of Structured thinking

Everyone has the ability to think simultaneously in all directions. But, the ability to think and ideate in a structured manner is what makes a data scientist special.

Structured thinking is a process of putting a framework to an unstructured problem. Having a structure not only helps an analyst understand the problem at a macro level, it also helps by identifying areas which require deeper understanding. Structured Thinking allows us to map our ideas in structured fashion, thereby enabling us to identify which areas need the most attention.

Sadly, people don’t realize the importance of structured thinking or go back to their unstructured instincts as soon as they face an unstructured problem. When I give the problem mentioned above to people, more than 70% of them don’t put a structure to it.

In this article, I’ve listed down some amazing mind mapping tools useful for a data scientists. These tools provide an excellent way of generating ideas in a creative way. These tools will enhance your breadth of thinking and your ability to comprehend and retain more information. Once you have gone through this article, you can bring these tools in your work flow process.

Note: Majority of the tools listed below can be availed for FREE. Links for access / download are provided. This article is not meant to promote any tool commercially.

13 Free Mind Mapping Tools For a Data Scientist To Generate More Ideas

What are Mind Mapping Tools ?

I too had this question when I first heard about these tools.

So, the existence of these tools derive from the concept of ‘mind-mapping’. Mind Mapping isn’t a new concept. It’s been used for centuries now. Basically, it’s a form of creative thinking.

Mind mapping is a process of creating mind maps used to convey ideas and concepts in a visual form. Mind maps are nothing but a visual representation of your thinking in a ‘structured manner’. Sometimes, you might find yourself in a situation where you find it immensely difficult to convince people with your idea. Be it your manger, boss or an investor. Has it happened with you?

Such situations occur when you idea fails to establish a visual impact in the mind of people at other end. That’s where mind mapping tools comes to your rescue. These tools use different colors, tree structures, pictures, animations to ensure that an idea is presented in the most comprehensible form.

What’s in it for a data scientist ?

Good question!

Among all the analysts, the idea of using mind mapping tool would appeal the most to people who are an ardent user of tree based algorithms such as decision trees, random forest, boosting etc.

Actually, mind maps follows a tree based structured, visual representation of ideas. When I first created one, I felt like as if I am creating tree nodes and terminal leaves of my ideas.

These tools can help you in following ways:

It will allow you to implement structured thinking in your day to day tasks.

It will be easier for you to explain your ideas, strategies to the clients, managers or your boss.

You can move away from the cliche powerpoint presentations and use this creative form of information.

Since our brain processes images / pictures faster than text, your mind maps would be much faster to comprehend by people.

The tree and stem structure would help you analyze and gain insight on complex subjects quickly.

You can redesign, modify the entire chain of ideas in one go.

Prolonged use of this tool can certainly enhance your problem solving and memorization power.

In short, a data scientist will be accomplish more in less time using this creatively fast approach.

How to create a Mind Map ? – Practice Time!

Creating mindmaps is easy. You can draw it even using a pen and paper. The general methodology is as follows: Write the main idea in the center. Draw branches from the center such they are connected with one another with final outputs shown towards the end.

Let’s create a mind map based on a data science problem.

I’ve taken the problem from Big Mart Sales Prediction III. The task is to predict the sales of products.

We’ll create a mind map for hypothesis generation. Hypothesis generation helps us to understand the problem in detail by brainstorming possible factors which can impact the outcome. It is done by understanding the problem statement thoroughly and before looking at the data.

You can read the problem statement after login at the competition page. Below is a simple mind map I’ve created representing the possible factors which can affect product sales. I’ve used coggle tool (listed below).

mind maps to solve a data science problem

Let’s understand it.

So, I’ve thought of possible factors on four levels: Store Level, Product Level, Customer Level and Macro Level. Let me quickly explain each factor:

Store Level Hypotheses :

City type: Stores located in urban or Tier 1 cities should have higher sales because of the higher income levels of people there.

Population Density: Stores located in densely populated areas should have higher sales because of more demand.

Store Capacity: Stores which are very big in size should have higher sales as they act like one-stop-shops and people would prefer getting everything from one place.

Competitors: Stores having similar establishments nearby should have less sales because of more competition.

Marketing: Stores which have a good marketing division should have higher sales as it will be able to attract customers through the right offers and advertising.

Location: Stores located within popular marketplaces should have higher sales because of better access to customers.

Ambiance: Stores which are well-maintained and managed by polite and humble people are expected to have higher footfall and thus higher sales.

Product Level Hypotheses:

Brand: Branded products should have higher sales because of higher trust in the customer.

Packaging: Products with good packaging can attract customers and sell more.

Utility: Daily use products should have a higher tendency to sell as compared to the specific use products.

Display Area: Products which are given bigger shelves in the store are likely to catch attention first and sell more.

Visibility in Store: The location of product in a store will impact sales. Ones which are right at entrance will catch the eye of customer first rather than the ones in back.

Advertising: Better advertising of products in the store will should higher sales in most cases.

Promotional Offers: Products accompanied with attractive offers and discounts will sell more.

Customer Level Hypotheses

Customer Behavior: Stores keeping the right set of products to meet the local needs of customers will have higher sales.

Job Profile: Customer working at executive levels would have higher chances of purchasing high amount products as compared to customers working at entry or mid senior level.

Family Size: More the number of family members, more amount will be spent by a customer to buy products.

Annual Income: Higher the annual income of a customer, customer is more likely to buy high cost products.

Past Purchase History: Availablity of this information can help us to determine the frequency of a product being purchased by a user.

Macro Level Hypotheses

Environment: If the environment is declared safe by government, customer would be more likely to purchase products without worrying if it’s environment friendly or not.

Economic Growth: If the current economy shows a consistent growth, per capita income will rise, therefore buying power of customers will increase.

Mind you, this is not an exhaustive list. These are just some basic 21 hypothesis I have made, but you can think further and create some of your own. After this step, I’ll download the data and proceed with data analysis and predictive modeling stages.

I’m sure, after this activity you would have understood that the importance of a mind map lies in representing one’s thoughts in a better manner (as shown above).

翻译：灯塔大数据

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