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?

您最近是否經常聽到新的行業流行語-Data Analytics( 早于AI-ML) ？ 聽起來復雜但足夠簡單嗎？ 了解模型背后的邏輯，但不知道如何編碼？ 擔心在投入潮流之前花太多時間學習編碼嗎？

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.

不用擔心，有一些很棒的工具可供非編碼器免費使用，這些工??具可以立即幫助開發復雜的模型。 這些工具完全免費供個人使用，非常簡單直觀，可以幫助一種實踐，而無需學習如何編寫代碼。

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.

我是一個業余編碼員，但是非常喜歡機器學習。 我可以編寫代碼，但我會盡量避免(感謝上帝，感謝Excel中的那個Recording Macro選項)，直到無法避免為止。

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.

我當時正在開發一種用于預測道路交通量的模型，當我開始尋找非編碼器資源并發現這些寶石時，不得不嘗試很多事情。 我正在討論我發現的最好的三個。 同樣，這些是面向個人用戶的開源軟件，但是具有商業用途的定價版本。

這些工具不能做什么 (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.

請注意，盡管這些工具消除了對編碼的需求，但是您對模型的理解，數據準備的基礎知識和統計信息應該高于最低要求。 原因是在編寫代碼時，您確切地知道正在執行的操作以及如何執行操作，而在大多數這些工具中，默認參數是預加載的，有時代碼對用戶不可見。 因此，如果用戶沒有進行全面的質量檢查，很容易引起模型錯誤的注意。

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，這些工具不會告訴您使用哪種數據清除技術，要構建哪種模型或要比較哪種統計量，這些工具將使您輕松地完成上述所有任務，并給您更多的時間進行思考和分析數據。

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

現在您已經閱讀了所有警告，讓我們直接潛入。

1. Knime Analytics (1. Knime Analytics)

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

到目前為止，這是開源領域中最好的工具。

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是一個非常直觀的平臺，可在工作流環境中使用拖放節點幫助創建模型。 它基于python構建，具有用于數據輸入，數據清理，建模(回歸，聚類，分類，神經網絡等)，統計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.

它有一個臺式機版本( 我喜歡它 )和一個服務器版本，供希望在網絡上開發和部署這些模型工作流的人們使用。 在您的計算機上安裝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.

建立神經網絡所需的每個動作都有節點。 導入數據，對其進行分區，將零件饋給學習者，預測變量(測試集)，然后饋給評分員以檢查模型的準確性。 可以在使用連接器相互連接的節點中設置參數，并且可以依次執行。

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是開源的機器學習，數據可視化和分析工具。 Orange還可以處理按工作流程模式排列的小部件，并具有一些用于特定任務(時間序列，生物信息學等)的專用庫。

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更加流暢，但其節點列表不如Knime詳盡。 它具有多種可視化選項，可以進行體面的數據分析。 它基于python構建，可以幫助創建和評估模型以進行回歸，分類，NN，聚類，時間序列等。

3.藍天統計 (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，可用于數據建模和可視化。 它是開源的，可用于臺式機。 它具有豐富的GUI，它可以幫助R新手簡化學習過程，因為R代碼可見的每個功能。

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缺乏工作流樣式的體系結構和節點功能。 相反，它具有類似于MS Office功能區選項卡的選項卡下列出的功能。 BlueSky的優點在于它基于R，R是一種用于統計數據分析的功能強大的語言。 它具有命令編輯器，并且由于代碼對用戶完全可見，因此用戶可以輕松地隨意修改代碼。 它確保R的普通用戶可以使用此應用程序節省大量時間。

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.

市場上有許多數據分析工具，但是其中大多數不是開源的。 這使得仍處于數據科學探索階段的個人用戶很難。

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.

這三個工具是我最喜歡的小數據分析問題。 對于那些可能對學習編碼的想法望而卻步的新手來說，它們可以節省大量時間。

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!

該列表基于2019年末可用的工具。如果我發現更多類似的工具，我將對其進行更新。 我希望您發現這個故事對您開始數據分析之旅有所幫助！