Programming is an integral a part of data science. Among different things, it's considered that a mind which understands programming logic, loops, functions has higher chances of becoming a successful data scientist. So, what about people that never studied programming subject in their school or college?
Are they doomed to have an unsuccessful career in data science?
There are the people that want to enter data science domain but don’t understand coding very well. In fact, many people are from the non-programming league. Therefore, it feels terrible once something you have never learned haunts you at each step now.
Good news is, There is a way! There are Many ways using which you'll be able to ignite your appetite to learn data science without doing coding. These tools usually obviate the programming aspect and provide easy Graphical User Interface so anyone with minimal data of algorithms can simply use them to build predictive models. Many firms (especially startups) have recently launched Graphical User Interface driven data science tools. Few Data analytics consulting or data science consulting companies are a2c, action labs, Argo, beyond the arc, calibre.
Here is a list of 8 essential data science tools:
1- The R Project for statistical Computing:
R is a perfect alternative to statistical packages like SPSS, SAS, and Stata. It is compatible with Windows, Macintosh, UNIX, and linux platforms and offers extensible, open source language and computing environment. The R environment provides with code facilities from data manipulation, the calculation to the graphical display.
A user can define new functions and manipulate R objects with the help of C code. As of now, there are eight packages that a user can use to implement statistical techniques. In any case, a good range of modern statistics can be implemented with the assistance of cran family of internet websites.
There are no license restrictions and anyone can offer code enhancements or give with a bug report.
Matplotlib is a python 2d plotting library with which statistical data illustration is easy to intercept. It produces a quality illustration of data in the type of histograms, graph, bar charts, power spectra etc. It can be used in python scripts, ipython shell, python and web application servers. It provides an interface almost like MATLAB for easy plotting. For advanced plotting features, the user has full control over font properties, line designs with object oriented interface and various set of functions which people familiar with MATLAB will find easy to get started with. Keep a track of latest developments through the source code or in what’s new page section. You can mail the professionals concerning some bug, patches on GitHub tracker.
3- Logical Blue:
“Data Science is not Rocket Science” is what this company believes in. It provides with user friendly software platform that lets businesses use data to automate the decision making and improve revenue generation. irrespective of what business goals you have for predictive analysis the professionals employees is well skilled to provide help related to machine learning and statistics.
RapidMiner’s open source data science platform provides help of rich data science to make calculative decisions. an all in one data science platform, that has pre-defined data preparation tools and machine learning algorithms to support all of data science wants. The visual data science workflow designer provided by RapidMiner has built-in templates and repeatable workflows.
5- Barcode Generator:
Barcode scanning apps have great potential to search out pertinent data from the massive information cache. particularly for industries that belong to logistics or shipping. With an assigned price to a barcode with respective to products, you can find specific product without any hassle of manually searching through the database. For data scientists who got to manage and keep track of inventory, barcode generator is the perfect remedy to find and find a product with quick scanning.
The above highlighted tools can be helpful to get hold of pertinent points in a data. These tools can help the data analytics consulting or data science consulting companies to get done with their work without much difficulty and reap productive results.