The field of data science, big data, machine learning, and AI is inspiring and complicated at the same time. Data science is also rapidly growing with new tools, technologies, algorithms, datasets, and use cases. For a business user, it can be fairly daunting to choose the best data science tools. This is where this guide helps.
In this guide, we will explore the best Data Science tools which most of the leading organizations are using for implementing Data Science.
What is the difference between Big Data Analytics and Data Science?
Big data analytics deals with a vast collection of data from different sources and turns it into valuable and actionable insights that help companies develop agility and gain a competitive advantage over others.
Data science uses mathematics and statistics extensively along with programming skills to chart trends and anomalies. If analysts report the who, what, when, and other Ws of today’s data, scientists use it to forecast what will happen tomorrow.
Top Data Science Tools
Following is a mini-collection of Data Science tools.
1. Grid Dynamics
Grid Dynamics is one of the well-known Data Science solutions. All the companies including both small and large can use Grid Dynamics in their projects. Even if you are new to the field of Data Science you can start with learning Grid Dynamics as their features are designed to be both easy-to-use and powerful.
Grid Dynamics provides a wide range of functions and other tools that can be used to extract insights from the data as well as to analyze it. Grid Dynamics will help you –
- Enhance the customer experience. Using machine learning and advanced analytics, organizations can automatically optimize billions of micro-decisions related to customer experience, customization, message targeting, and timing.
- Monitor streams of metrics and detect issues in real-time. Anomaly detection is designed to monitor a very large number of metrics in real-time and detect unusual patterns.
- Identify new promotion opportunities and optimize promotion calendars. A price and revenue management tool helps you improve performance and enhance the competitive edge.
Along with all the positive moments listed above, it is also used for transforming the raw data in such a format that it becomes easy to understand and use.
2. Tableau
Tableau is the fastest-growing data visualization and data analytics tool that aims to help people see and understand data. One interesting aspect of Tableau that has been a key player in making it everyone’s favorite Data Science tool, is its easy drag-and-drop functionality. Any type of graphs, plots, and charts can be made easily in it without the need for any programming. As a result, people from different backgrounds – from ecommerce to research to industry – are using it.
Moreover, Tableau also comes with real-time data analytics capabilities and cloud support.
3. RapidMiner
RapidMiner is a leading data science platform that unites data prep, machine learning & predictive model deployment.
The tool comes with a user-friendly interface that is inspired by the block-diagram approach. You just have to arrange the blocks in the correct manner and you can execute a large number of algorithms without writing any code. The program allows the user to enter raw data, including databases and text, which is then automatically and intelligently analyzed on a large scale.
Final Thought
Data Science uses a combination of various skills and several tools that are available to simplify the process. Beyond that, most of these solutions do not require much experience in using them. Thus everyone who knows how to apply Data Science can use them. You can run very complex algorithms with just one or two lines of code.
These tools help companies to handle very large datasets and find solutions to complex problems for more smart results.
