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No-Code Analytics – The Best Introduction to Data Science


November 21, 2021

No-code analytics

No-code analytics has been one of the best additions in the data science space.


Although reading books and watching lectures is a great way to learn analytics – it is best to start doing. However, it can be quite tricky to start doing when it comes to languages such as Python and R if someone does not have a coding background. Not only do you need to know what you are doing in terms of analytical procedures, but you also need to understand the nuances of programming languages which adds onto the list of things to learn to just get started. Therefore, the best middle ground between knowledge acquisition (books, videos, etc.) and conducting advanced analytics (Python, R, etc.) is by using open-source analytics software. These types of software are great for both knowledge acquisition and actually doing analysis as documentation is built into the software and you can start doing relatively complex tasks with only mouse clicks. Even if you know how to code, the same analysis is usually conducted faster using these types of software.

The term “data analytics” has become synonymous with programming languages such as Python and R. Although these powerful languages are necessary for conducting advanced analytics with the latest and greatest algorithms, they are not necessary to start analyzing complex datasets! Data analytics software can either be open-source (Orange) or have a free version associated with it (RapidMiner). These tools are great for beginners as the time it takes to learn the nuances of coding languages can instead be spent on the data analytics process and statistical theory which is important for Python and R users as well. Think about it, if you woke up one day and knew everything about Python and R, would you still be able to conduct thorough and accurate analysis? Even if your code works and an output is given, the output may be wrong due to lack of knowledge within the data analytics domain. We live in a beautiful world where very smart people create completely free software so the public can use them without a price barrier. A great website that illustrates the trend of open-source software is In this website, you can type in any paid commercial software, and it will recommend open-source alternatives that serve as a substitute for the commercial software. The purpose of this article is to provide the ideal introduction to data analytics for anyone who is interested in this fascinating subject. The software we will be covering can do analytical tasks such as regression, classification, clustering, dimensionality reduction, association rules mining, deep learning/neural networks, ensemble methods, text mining, genetic algorithms, network analysis, image analytics, time series, bioinformatics, and spectroscopy. Some of the software listed can also be connected to a SQL database. In this article, we will go over the various no-code software that is either completely open-source or has a powerful free/academic version associated with.



RapidMiner was founded in 2007 and is still used today. RapidMiner is used by over 40,000 organizations and has been doing well according to the Gartner Magic Quadrant. The types of analyses that can be done are quite broad ranging from simple regression to genetic algorithms and deep learning. It is a point-and-click interface where “widgets” are placed and connected to one another in order to perform analytics. These are essentially pre-written blocks of code that conduct certain operations. Hyperparameters can be tuned on the side after clicking the widget. One thing that makes RapidMiner unique is its automated machine learning functionality. With just a couple of clicks, various algorithms will run and output the performance metrics where you can compare the results and choose the best model. RapidMiner believes in no black boxes, so it is possible to see how the algorithm works after implementing the automated machine learning. Other capabilities can also be done such as text mining and big data (e.g., Radoop) through the various extensions that it provides. In my opinion, the strongest part of RapidMiner is how rapidly (pun intended) one can learn the theory and underlying mechanisms of how the model works. The documentation is built into the software so you can right-click on each functionality/algorithm and gain a description of each. Each description covers a synopsis, a brief description of the overall algorithm, a description of each hyperparameter, as well as a tutorial on how to use it. The tutorial is extremely useful as you can use it as a template for your own dataset. In tutorials, widgets are formed with the use of sample data so you are given a usable example of how you can use it. Just, plug in your own data and make certain changes and you are good to go! RapidMiner also incorporates a “wisdom of crowds” functionality where statistics are given on hyperparameter tuning and widget creation. For instance, are you trying to determine the number of trees of your random forest? Well, RapidMiner will state something like “50% chose a value between 100 and 149” along with a bar graph that shows what percentage or RapidMiner users chose what. This streamlines the learning process to see what the professionals are choosing. Overall, I highly recommend RapidMiner for learning analytics and should be one of the first tools someone uses when starting to learn.


No-code analytics


Orange is probably the most visually pleasing software on this list and has some of the best data visualizations. It also has the most features for a completely free open-source software. This means that you can take the knowledge learned into the corporate world as it is free and open-source for everyone! Interestingly, the software runs on Python so a lot of the visualization should be familiar. The creators of this software are biostatisticians and so more scientific packages are included in the software such as biostatistics and spectroscopy. Orange also uses widgets similar to RapidMiner and can be downloaded under the Anaconda environment or as stand-alone software.


No-code analytics


JASP (Jeffreys’s Amazing Statistics Program) is mostly used for traditional statistics in the social sciences but has machine learning functionalities as well. It is more of a substitute for SPSS and the user interface looks very similar to it. The interesting thing about JASP is that the R language works under the hood so the data visualizations should look similar to it. This is a great way to learn traditional statistics as you can load a workflow based on certain statistical techniques where an already conducted analysis will be downloaded along with explanations for why certain analyses are done. The software documentation is also built-in to the software so you can easily learn about the statistical techniques and how to use them in the right way along with already-loaded example datasets. Academic papers and books are also cited under each statistical technique for further resources; R packages are also listed for each technique as well. In JASP, it is possible to conduct t-tests, ANOVA, regression, factor analysis, Bayesian statistics, meta-analysis, network analysis, structural equation modeling, and other classical statistical techniques as well as machine learning.


No-code analytics

Voyant Tools

Voyant Tools specializes in corpus analytics which relates to text data. To get started with minimal effort, you can pre-load corpus data from Shakespeare plays and have a dataset ready for analysis. There is a great number of functionalities within the software and is unique compared to the other software in that it comes in the format of a dashboard where you can change each “tile” with another form of analysis. Most of the analytical techniques encompass unique ways in visualizing textual data. Statistical techniques such as topic clustering are also possible.


No-code analytics


This one is a little different from the others as it pertains to obtaining data opposed to analyzing data. Webscraping is a popular way to obtain data from webpages since there is more control in how the data is collected compared to the use of secondary data. There are plenty of free web scraping services, but my favorite is DataMiner. With the free version, you can scrape up to 500 pages a month (although some websites such as Glassdoor are restricted unless you pay a minimal monthly fee). However, it is very intuitive and comes with live customer support for help in your web scraping projects. This software works by clicking on certain parts of the screen where the html code will be sensed. Then, the software will detect similar areas on the website and gather each instance as a row and put them all in one column. This can be repeated for other areas where you will end up with a nice, personalized dataset.

No-code analytics


Concluding Remarks

We live in a fascinating world where talented people are creating software to help newcomers in certain fields which exponentially increases the collective knowledge of society. There are other great analytical tools that I didn’t mention such as KNIME, Weka, QGIS, and Jamovi since I’m not as familiar with those, so go out there and explore more! Five, ten, one hundred years from now, this list will be outdated, and new types of code-free software will enter the field, each with a core competency. For instance, I can see the future having specific software for each data type (image, audio, etc.) or each type of data mining technique. We also have access to free real-world datasets from websites such as Kaggle where you can easily start exploring datasets that intrigue you. Datasets can range from Pokémon statistics to healthcare, so the possibilities for analysis are endless!

So, if you are interested in analytics, download a dataset that fascinates you and immediately start conducting advanced analytics by just mouse clicks using the software above, and if it fascinates you, also add on a keyboard to your toolkit to use more advanced methods using Python and R. The latest and greatest methods of analysis (which can be found on GitHub) can only be done using one of these tools so that would be the next step. Then, you can try to replicate scientific papers from I hope this article serves as a good introduction to this field, welcome to the world of analytics!


Author Biography

Dennis Baloglu graduated from UNLV with Bachelor’s degrees in Finance and Marketing along with Master’s degrees in Hotel Administration and Management Information Systems. He taught under the UNLV William F. Harrah College of Hospitality for 2 years and is a current Enterprise Marketing Analyst at MGM Resorts International. He is passionate about the intersection between data analytics and the hospitality industry since he believes data-driven decision-making and algorithms are the keys to industry success.


Company Designation: MGM Resorts International

Location: Las Vegas, NV

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