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Do you ever wonder what is the process and methodology behind revolutionary technologies such as Artificial Intelligence and Machine Learning? The answer is Data Science. With the availability of various Data Science tools in the market, implementing AI has become easier and more scalable. In this article, we’ll discuss the best tools for Data Science in the market.
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Data Science is the art of drawing useful insights from data. To be more specific, it is the process of collecting, analyzing and modeling data to solve real-world problems.
You can read more about Data Science by going through the following blogs:
Its applications vary from fraud detection and disease detection to recommendation engines and thus growing businesses. These wide ranges of applications and increased demand have led to the development of Data Science tools.
In the below section we’ll be discussing in-depth about the best Data Science tools in the market. But before we go there it is important that you understand that this blog is focused on the different Data Science tools and not the programming languages that can be used to implement Data Science. So, don’t expect there to be a war between which is better for Data Science, Python or R.
With that being said let’s dive straight into the Data Science tools.
The main feature of these tools is that you don’t have to use programming languages in order to implement Data Science. They come with pre-defined functions, algorithms, and a very user-friendly GUI. Hence, they can be used to build convoluted Machine Learning models without the use of a programming language.
Several start-ups and tech giants have been working on developing such user-friendly Data Science tools. However, since Data Science is a very vast process, it is often never enough to use one tool for the entire workflow.
Therefore, we’ll be looking at Data Science tools used for the different stages in a Data Science process, i.e.:
Apache Hadoop is a free, open-source framework that can manage and store tons and tons of data. It provides distributed computing of massive data sets over a cluster of 1000s of computers. It is used for high-level computations and data processing.
Here’s a list of features of Apache Hadoop:
Azure HDInsight is a cloud platform provided by Microsoft for the purpose of data storage, processing, and analytics. Enterprises such as Adobe, Jet, and Milliman use Azure HD Insights to process and manage massive amounts of data.
Here’s a list of features of Microsoft HD Insights:
Data Science Tools for Exploratory Data Analysis
The buzz around Informatica is explained by the fact that their revenue has rounded off to around $1.05 billion. Informatica has many products focused on data integration. However, Informatica PowerCenter stands out due its data integration capabilities.
Here’s a list of features of Informatica PowerCenter:
There is no surprise that RapidMiner is one of the most popular tools for implementing Data Science. RapidMiner ranked at number 1 in the Gartner Magic Quadrant for Data Science Platforms 2017, and in the Forrester Wave for predictive analytics and Machine Learning, and one of the top performers in the G2 Crowd predictive analytics grid.
Here’s are some of its features:
Data Science Tools for Data Modelling
H2O.ai is the company behind open-source Machine Learning (ML) products like H2O, aimed to make ML easier for all.
With around 130,000 data scientists and approximately 14,000 organizations, the H20.ai community is growing at a strong pace. H20.ai is an open-source Data Science tool that is aimed at making data modeling easier.
Here are some of its features:
DataRobot is an AI-driven automation platform, that aids in developing accurate predictive models. DataRobot makes it easy to implement a wide range of Machine Learning algorithms, including clustering, classification, regression models.
Here are some of its features:
Data Science Tools for Data Visualization
Tableau is the most popular data visualization tool used in the market. It allows you to break down raw, unformatted data into a processable and understandable format. Visualizations created by using Tableau can easily help you understand the dependencies between the predictor variables.
Here are a few features of Tableau:
QlikView is another data visualization tool that is used by more than 24,000 organizations worldwide. It is one of the most effective visualization platforms for visually analyzing data to derive useful business insights.
Here are a few features of QlikView:
Now that you know the top Data Science Tools, I’m sure you’re curious to learn more. Here are a few blogs that will help you get started with Data Science:
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