Data Science ToolsData science tools are used for diving into raw and complicated data into valuable insights.

Data Science is a vast stream and involves handling data in various ways. As an engineer, you should be aware of the best Data Science tools available in the market to complete your work efficiently. Knowing how to use Data Science tools can help you build a bright and promising career in Data Science.

The top 10 Data Science tools available in the market are:

1. Data Science tools SAS

SAS (Statistical Analysis System) is one of the oldest Data Science tools in the market. One can perform granular analysis of textual data and can generate insightful reports via SAS. Many data scientists prefer the visually appealing reports generated by SAS.

Besides data analysis, SAS is also used to access/retrieve data from various sources. It is widely used for multiple Data Science activities like data mining, time series analysis, econometrics, business intelligence, etc. SAS is platform-independent and is also used for remote computing. One can’t ignore the role of SAS in quality improvement and application development.

2. Data Science tools APACHE HADOOP

Apache Hadoop is an open-source software widely used for the parallel processing of data. Any large file is distributed/split into chunks and then handed over to various nodes. The clusters of nodes are then used for parallel processing by Hadoop. Hadoop consists of a distributed file system responsible for dividing the data into chunks and distributing it to various nodes.

Besides the Hadoop File Distribution System, many other Hadoop components are used to parallelly process data, such as Hadoop YARN, Hadoop MapReduce, and Hadoop Common.

3. Data Science tools TABLEAU

Tableau is a data visualization tool that assists in decision-making and data analysis. You can represent data visually in less time by Tableau so that everyone can understand it. Advanced data analytics problems can be solved in less time using Tableau. You don’t have to worry about setting up the data while using Tableau and can stay focused on rich insights.

Founded in 2003, Tableau has transformed the way data scientists used to approach Data Science problems. One can make the most of their dataset using Tableau and can generate insightful reports.

4. Data Science tools TensorFlow

TensorFlow is widely used with various new-age technologies like Data Science, Machine Learning, Artificial Intelligence, etc. TensorFlow is a Python library that you can use for building and training Data Science models. You can take data visualization to the next level with the aid of TensorFlow.

TensorFlow is easy to use as it is written in Python and is widely used for differential programming. One can deploy Data Science models across various devices using TensorFlow. TensorFlow uses an N-dimensional array as its data type, which is also called a tensor.

5. Data Science tools BIGML

BigML is used for building datasets and then sharing them easily with other systems. Initially developed for Machine Learning (ML), BigML is widely used for creating practical Data Science algorithms. You can easily classify data and find the anomalies/outliers in the data set using BigML.

The interactive data visualization process of BigML makes it easy for data scientists to make decisions. The Scalable BigML platform is also used for time series forecasting, topic modeling, association discovery tasks, and much more. You can operate on large sets of data using BigML.

6. Data Science tools KNIME

Knime is one of the widely used Data Science tools for data reporting, mining, and analysis. Its ability to perform data extraction and transformation makes it one of the essential tools used in Data Science. The Knime platform is open-source and free to use in various parts of the world.

It uses ‘Lego of Analytics,’ a data pipelining concept for integrating various components of Data Science. The easy-to-use GUI (Graphical User Interface) of Knime helps perform data science tasks with minimum programming expertise. The visual data pipelines of Knime are used to create interactive views for the given dataset.

7. Data Science tools RAPIDMINER

RapidMiner is a widely used Data Science software tool due to its capacity to provide a suitable environment for data preparation. Any Data Science/ML model can be prepared from scratch using RapidMiner. Data scientists can track data in real-time using RapidMiner and can perform high-end analytics.

RapidMiner can perform various other Data Science chores like text mining, predictive analysis, model validation, comprehensive data reporting, etc. The high scalability and security features offered by RapidMiner are also remarkable. Commercial Data Science applications can be developed from scratch using RapidMiner.

8. Data Science tools EXCEL

Part of Microsoft’s Office tools, Excel is one of the best tools for Data Science freshers. It also helps in understanding the basics of Data Science before moving into high-end analytics. It is one of the essential tools used by data scientists for data visualization. Excel represents the data in a simple way using rows and columns to be understood even by non-technical users.

Excel also offers various formulas for Data Science calculations like concatenation, find average data, summation, etc. Its ability to process large data sets makes it one of the critical tools used for Data Science.

9. Data Science tools R

R provides a scalable software environment for statistical analysis and is one of the many popular programming languages used in the Data Science sector. Data clustering and classification can be performed in less time using R. Various statistical models can be created using R, supporting both linear and nonlinear modeling.

You can perform data cleaning and visualization efficiently via R. R represents the data visually in simple ways so that everyone can understand it. R offers various add-ons for Data Science like DBI, RMySQL, dplyr, ggmap, xtable, etc.

10. Data Science tools QLIKVIEW

QlikView is one of the widely used Data Science tools and is also concerned with business intelligence. QlikView helps data scientists to derive relationships between unstructured data and perform data analysis. You can also demonstrate a visual representation of data relationships via QlikView. One can perform data aggregation and compression via QlikView in less time.

You do not have to spend time determining the relationships between data entities as QlikView does it for you automatically. Its in-memory data processing provides faster results as compared to other Data Science tools in the market.