Top 12 Data Science Tools – 2020
As everyone knows Artificial Intelligence and Machine Learning are sky-rocketing in this modern digital era – Do you have any idea about what technology is driving beyond them? The answer is well-known i.e. Data Science – Collecting, Observing, and manipulating the data is one of the key factors in technology advancement. Today, in this article we will be discussing a List of Data Science tools available in the market, and their role in Learn Data Science.
Before we’re going to discuss the Data Science tools, let’s explore the latest statistics related to Data Science.
“Data Science stands for extracting useful insights from raw data and using this data for predictive analysis to enrich business insights.”
Data is the key component in modern technology because every aspect related to data directly or indirectly.
Nearly – 2.5 Quintillion Bytes of Data produced daily
In the near future – approximately 1.7 Mb of data will be created by every person for every second on the planet. Just imagine how many analysts/data scientists are required to process this data? Don’t have words to explain – It has a massive range of exploration in modern technology. Data Science careers has the hottest demand in the upcoming years.
Let’s deep dive into the List of Data Science Tools
- Apache Spark
- Apache Hadoop
- IBM Watson Studio
SAS is referred to as a statistical Analysis System and specially designed for statistical operations. SAS is demonstrated as a Closed Source Proprietary Software that is used by large enterprises to process & analyze data. It provides a vast no. of tools & libraries for data scientists to prepare effective data modeling structures. This will help to process and organize the enterprise’s data in a very effective manner. SAS has strong community support with respect to the company as well as 3rd parties. It was highly reliable in statistical analysis. SAS is highly expensive compared to other Data Science Tools.
Features of SAS
- SAS is highly expensive in terms of effective usage. The base plan has so many limitations that if you want to upgrade you need to spend a lot of money on advanced features.
- SAS is highly reliable for statistical operations
Apache Spark in the open-source & unified analytics platform. For convenient in-memory data processing that enables real-time, advanced and batch analytics for machine learning and AI applications.
Apache Spark is one of the leading cluster-computing frameworks in the world. It can be deployed in different ways like Streaming data, graph processing and Machine learning.
Apache Spark supports various languages which include Python, Java, R and Scala.
Features of Apache Spark
- Apache is beneficial to enhance business insights in E-Commerce, Healthcare and Entertainment domains.
- It is lightning fast & easy to use.
Tableau is one of the leading Business Intelligence in the world. It used to create data visualizations to understand enterprises data in an effective way. Tableau BI is very comfortable to connect any database.
Interested to Learn – Adobe Analytics Interview Questions
Tableau makes it easier for everyone to create data visualizations according to their needs. It plays a vital role in understanding the data, and this leads to enhanced business insights. Get Tableau Certification to advance your career potential in Data Visualization & Business Intelligence.
Features of Tableau Data Visualization
- It is easy for beginners because it has a drag and drop method for creating complex dashboards.
- Good community support.
- Numerous Data Sources available in Tableau
- Robust Security
- Trend lines and predictive analysis
Matplotlib is a dedicated library for developing static, interactive and animated visualizations in Python language.
Matplotlib is hosted on GitHub.
- Semantic way to produce subplot and complex grids
- It works very fast in an effective manner
- Matplotlib contains high-quality plots and graphics to print a range of graphs as bar charts, scatter plots, histograms and heat maps for Data analysis.
- It has broad community support and is easy to learn for beginners.
Scikit-learn is an open-source machine learning library for Python Programming Language. It provides supervised learning algorithms for data processing batches.
Scikit-learn developed using well-known technologies viz. NumPy, Matplotlib, and Pandas.
Features of Scikit-Learn
- Reusable in various contexts and easily accessible to everybody.
- Scikit-Learn is open-source software, and also commercially usable.
- For predictive data analysis, it is a very efficient tool.
TensorFlow is an end-to-end open-source platform for machine learning. It possesses a flexible, comprehensive ecosystem of libraries, community resources and tools that lets scientists deploy ML applications very efficiently and effectively.
Features of Tensor Flow
- Easy Model Building
- Robust Machine Learning Applications
- It was originally developed by Google and had broad community support.
- The extensive scalability of computation across data sets and machines.
The Apache Hadoop is an open-source framework which comprises a java based software library that is used to distribute large data sets & predictive modelling across various computing clusters, breaking them into smaller distributions run simultaneously in a parallel way.
It is one of the widely used java based software libraries for Big Data. In this technology era, every enterprise needs an effective way of useful data on an ongoing basis.
It is fully dedicated to store and analyze large sets of unstructured data.
Features of Hadoop:
- Apache Hadoop provides an opportunity to work with multiple concurrent tasks to run from one to thousands of computer servers without any resistance.
- It is open-source – Freely available to everyone
- It has broad community support.
- Apache Hadoop is capable of processing effectively even when the time of node fails.
- Hadoop is easy to use & highly scalable with faster data processing
The DataRobot is an end-to-end AI platform for enterprises, and it automates the data science process for building, deploying and monitoring. It is powered by open-source algorithms and available in the on-premise, as a managed AI service and in the cloud.
DataRobot’s platform allows them to prepare their data, build and validate ML models including predictive and time series models, and deploy in a single solution. DataRobot is used by Data Scientists, executives, IT professionals and Software Engineers.
- DataRobot comprises Python SDK and APIs.
- DataRobot also supports parallel processing for predictive modeling.
- DataRobot furnishes you the potential of AI to stimulate better business outcomes.
Official Website : https://www.datarobot.com/platform/
IBM Watson Studio
IBM Watson is a comprehensive tool to help data analysts and data scientists prepare data and develop predefined models at scale across any cloud.
IBM Watson Studio consists of an elegant drag-and-drop interface that enables non-programmers to quickly build and deploy data building processes. It has flexible, open multi-cloud architecture, and it provides a capability to strive for enterprises to simplify their AI and Data Science.
Official Website: https://www.ibm.com/in-en/cloud/watson-studio
Features of IBM Watson
- With its AUTOAI, IBM Watson automates AI lifecycle management.
- IBM Watson Visual Recognition is an advanced feature that enables to develop a process using images. For text, it has IBM Watson Natural Language Classifier.
- Develop, Deploy and run data models with one-click integration using IBM Watson Studio.
RapidMiner is a fully transparent, complete life-cycle, and end-to-end Data Science Platform. It consists of some major functionalities viz. model building, validation, data preparation, and deployment.
RapidMiner contains a highly effective Graphic User Interface (GUI) to connect existing or predefined blocks for data prediction.
- RapidMiner is used for Data Visualization, Data Preparation, and Statistical modeling.
- RapidMiner is used to create Design models with Visual work-flow designer or automated modeling.
- It is seamlessly optimized & integrated for building Machine Learning models.
RapidMiner has a fully advanced version called RapidMiner Turbo Prep. It is especially used to analyze and create effective predictive modeling.
Official Website: https://rapidminer.com/
Xplenty is a leading ETL, ELT, and Data Integration Platform that can combine all your data sources.
Xplenty is a complete toolkit for developing data pipelines with various data sources. This platform enables you to integrate, process and prepare data for analytics on the cloud. It explores solutions for sales, developers, marketing, and customer support.
Features of Xplenty
- Easy to create ETL pipelines without consideration of technical experience because of low-code or no-code options.
- It provides an API component for better customization and flexibility.
- A wide range of customer support
- An elastic and scalable cloud platform
- A complete toolkit for building data pipelines
WEKA stands for – Waikato Environment for Knowledge Analysis developed by University of Waikato, New Zealand. It is open-source software licensed under GNU.
Weka is a collection of machine learning algorithms for data analysis. It contains tools for classification, visualization, data pre-processing, clustering, regression and association rules.
Features of WAKA
- It is written in Java Programming Language
- Platform independent – accessible to all suitable platforms.
- Highly interactive Graphical User Interface (GUI)
- Consists of various Data Prepossessing Tools
- Different Machine Learning Algorithms for Data Mining
Website : https://www.cs.waikato.ac.nz/ml/weka/
Data plays an important role in today’s world. Having a clear understanding of the latest data science tools available in the market will help you to grab exciting career opportunities in this domain. We hope the above-covered tools will help you with that.