Have you been hearing the new industry buzzword — Data Analytics(it was AI-ML earlier) a lot lately? Does it sound complicated and yet simple enough? Understand the logic behind models but don't know how to code? Apprehensive of spending too much time learning to code before jumping on the bandwagon?

您最近是否经常听到新的行业流行语-Data Analytics( 早于AI-ML) ？ 听起来复杂但足够简单吗？ 了解模型背后的逻辑，但不知道如何编码？ 担心在投入潮流之前花太多时间学习编码吗？

Worry not, there are some awesome tools available for free for non-coders that can help develop complicated models in no time. These tools are completely free for personal use, extremely easy and intuitive and can help one practice without the hassle of learning how to code.

不用担心，有一些很棒的工具可供非编码器免费使用，这些工​​具可以立即帮助开发复杂的模型。 这些工具完全免费供个人使用，非常简单直观，可以帮助一种实践，而无需学习如何编写代码。

I am an amateurish coder but a big machine learning enthusiast. I can code but I avoid it as much as I can (Thank God for that Recording Macro option in Excel), till the point I cannot avoid it.

我是一个业余编码员，但是非常喜欢机器学习。 我可以编写代码，但我会尽量避免(感谢上帝，感谢Excel中的那个Recording Macro选项)，直到无法避免为止。

I was working on developing a model for forecasting traffic on a road and had to try a lot of things when I started looking for non-coder resources and found these gems. I am discussing the best three I found. Again, these are open source for individual users but have priced versions for commercial uses.

我当时正在开发一种用于预测道路交通量的模型，当我开始寻找非编码器资源并发现这些宝石时，不得不尝试很多事情。 我正在讨论我发现的最好的三个。 同样，这些是面向个人用户的开源软件，但是具有商业用途的定价版本。

这些工具不能做什么 (What These Tools Cannot Do)

Please be aware, although these tools remove the need for coding, your understanding of models, basics of data preparation, and statistics should be above the bare minimum. The reason is that when you code, you exactly know what is being done and how, while in most of these tools, default parameters are preloaded, and sometimes the code is not visible to the user. Thus it is easy for model errors to go unnoticed in case the user does not do a thorough QA.

请注意，尽管这些工具消除了对编码的需求，但是您对模型的理解，数据准备的基础知识和统计信息应该高于最低要求。 原因是在编写代码时，您确切地知道正在执行的操作以及如何执行操作，而在大多数这些工具中，默认参数是预加载的，有时代码对用户不可见。 因此，如果用户没有进行全面的质量检查，很容易引起模型错误的注意。

In addition to this, these tools will not tell you which data cleaning technique to use, which model to build, or which statistic to compare instead, the tools will let you do all the above tasks easily and give you more time to think and analyze data.

除此之外，这些工具不会告诉您使用哪种数据清除技术，要构建哪种模型或要比较哪种统计量，这些工具将使您轻松地完成上述所有任务，并给您更多的时间进行思考和分析数据。

Now that you have read all the warnings let us directly dive in.

现在您已经阅读了所有警告，让我们直接潜入。

1. Knime Analytics (1. Knime Analytics)

This is by far, the best tool in the open source domain.

到目前为止，这是开源领域中最好的工具。

Knime is a very intuitive platform that helps create models using drag and drop nodes in a workflow kind of environment. It is built on python, has widgets for data input, data cleaning, modeling (regression, clustering, classification, Neural Networks, etc), statistics, and majorly used representations.

Knime是一个非常直观的平台，可在工作流环境中使用拖放节点帮助创建模型。 它基于python构建，具有用于数据输入，数据清理，建模(回归，聚类，分类，神经网络等)，统计信息和主要使用的表示形式的小部件。

It is has a desktop version (I love it) and a Server version for people who want to develop and deploy these model workflows on the web. Installing Knime on your machine is fairly easy, and using it is even more. Below is an example of an NN Model.

它有一个台式机版本( 我喜欢它 )和一个服务器版本，供希望在网络上开发和部署这些模型工作流的人们使用。 在您的计算机上安装Knime非常容易，使用它甚至更多。 以下是NN模型的示例。

There are nodes for every action needed to build a Neural Network. Importing the data, partitioning it, feeding a part to a learner, a predictor (test set), and then a scorer for checking the accuracy of the model. Parameters can be set in nodes that are connected to each other using connectors and can be executed in sequence.

建立神经网络所需的每个动作都有节点。 导入数据，对其进行分区，将零件馈给学习者，预测变量(测试集)，然后馈给评分员以检查模型的准确性。 可以在使用连接器相互连接的节点中设置参数，并且可以依次执行。

2.橙色 (2. Orange)

Orange is an open source machine learning, data visualization, and analysis tool. Orange also works on widgets arranged in a workflow pattern and has some specialized libraries for specific tasks (time series, bioinformatics, etc).

Orange是开源的机器学习，数据可视化和分析工具。 Orange还可以处理按工作流程模式排列的小部件，并具有一些用于特定任务(时间序列，生物信息学等)的专用库。

Orange’s UI is more fluid but its node list is less exhaustive than Knime. It has numerous visualization options and can produce decent data analytics. It is built on python and can help create and evaluate models for regression, classification, NN, clustering, time series among other things.

Orange的UI更加流畅，但其节点列表不如Knime详尽。 它具有多种可视化选项，可以进行体面的数据分析。 它基于python构建，可以帮助创建和评估模型以进行回归，分类，NN，聚类，时间序列等。

3.蓝天统计 (3. BlueSky Statistics)

Bluesky is an R based tool that can be used for data modeling and visualizations. It is open source and available for desktops. It has a rich GUI and it can help ease the learning curve for R newbies as for each function the R code is visible.

Bluesky是基于R的工具，可用于数据建模和可视化。 它是开源的，可用于台式机。 它具有丰富的GUI，它可以帮助R新手简化学习过程，因为R代码可见的每个功能。

BlueSky lacks workflow style architecture & node functionality. Instead, it has functions listed under tabs similar to MS Office ribbon tabs. The beauty of BlueSky is that it is built on R which is an incredibly powerful language for statistical data analysis. It has command editor and as the code is completely visible to the user, it is extremely easy for users to modify the code as they like it. It ensures that regular users of R can save a considerable amount of time using this application.

BlueSky缺乏工作流样式的体系结构和节点功能。 相反，它具有类似于MS Office功能区选项卡的选项卡下列出的功能。 BlueSky的优点在于它基于R，R是一种用于统计数据分析的功能强大的语言。 它具有命令编辑器，并且由于代码对用户完全可见，因此用户可以轻松地随意修改代码。 它确保R的普通用户可以使用此应用程序节省大量时间。

There are numerous data analytics tools available in the market but most of them are not open source. This makes it difficult for individual users who are still in the exploratory phases of data science.

市场上有许多数据分析工具，但是其中大多数不是开源的。 这使得仍处于数据科学探索阶段的个人用户很难。

These three tools are my top favorite to dabble with small Data Analytics problems. They can save an immense amount of time for newbies who might be daunted with the idea of learning to code.

这三个工具是我最喜欢的小数据分析问题。 对于那些可能对学习编码的想法望而却步的新手来说，它们可以节省大量时间。

This list is based on tools available in late 2019. I will update this if I find any more similar tools. I hope you find this story helpful in beginning your journey into Data Analytics!

该列表基于2019年末可用的工具。如果我发现更多类似的工具，我将对其进行更新。 我希望您发现这个故事对您开始数据分析之旅有所帮助！