5 Major Data Science Tools To Know

Data science is an effective method behind revolutionary technologies such as Artificial Intelligence and Machine Learning techniques. With the wide variety of data science tools available, executing AI techniques has turned quick and simple.

Herein we will know what Data Science is, and the top data science learning tools are:

Data Science

Data science deals with vast data using modern tools and techniques to find unnoticed patterns, gain meaningful information and make effective decisions. Data science uses machine learning algorithms to develop predictive models.

Data used for analysis comes from various sources and in different formats.

The Data science life cycle comprises five distinct stages.

1. Collect: Mainly includes a collection of both structured and unstructured data. Data Acquisition, Data Entry, Signal Reception, and Data Extraction are the key aspects.

• Maintain: Processes the raw data to retrieve helpful information. Data Warehousing, Data Cleansing, Data Staging, Data Processing, and Data Architecture are the various maintenance activities.

• Process: Data scientists use the prepared data to study various patterns, scope, and differences to determine its usefulness in predictive analysis. Data Mining, Data Modeling, Data Clustering, and Data Summarization are some of the crucial processes.

• Analyze: Analysis happens in various forms such as Qualitative Analysis, Text Mining, Predictive Analysis, Regression, etc.

• Communicate: Data Visualization, Data Reporting, Business Intelligence, and Decision Making are the key communication activities. Analysts prepare analysis in readable and understandable formats such as charts, reports, and graphs.

Data Science Learning  Tools:

Data science tools execute Data Science without any programming language. The GUI, algorithms, and functions are integrated. Hence, these tools support developing complex machine learning models without a programming language.

• Apache Hadoop:

It is a free open source network capable of storing huge volumes of data and is used for high-level computation and data processing.

A large file is distributed across various nodes, which are later used for the parallel processing of data. In addition, Hadoop Common, Hadoop MapReduce, and  Hadoop YARN are the other Hadoop components used to process the data parallelly.

• SAS

SAS (Statistical Analysis System) is one of the early data science tool. Valuable, insightful report generation happens via SAS through textual data analysis. Moreover, SAS is optimized to retrieve data from different sources. It is majorly used to perform business intelligence, data mining, econometrics, etc. Platform-independent tool SAS can be used for remote computing, quality improvement, and developing applications.

• Tableau

Tableau has revolutionized the way data scientists approach the problems of data science. Tableau helps generate insightful reports with the best use of the datasets.

Tableau is a data visualization tool used to perform data analysis and decision-making. Time taken to represent data visually is relatively less, and the data visuals are also very easy to understand. Primarily the data analytics problems find solutions in less time. Also, it helps gain focus on rich insights without the worry of setting up the data.

• Power BI

Power BI is a rich tool of Data Science built with business intelligence. Power BI can be combined with the other Microsoft Data Science tools to execute data visualization. Rich and insightful reports and data analytics dashboards are the significant output of Power BI. An added advantage is that even non-technical professionals can understand the visual reports generated by it.

• DataRobot:

DataRobot is one important tool required for Data Science. It allows the drag and drop of the dataset quickly on the DataRobot user interface. Also, its simple GUI enables data analysis for data scientists and freshers.

DataRobot enables the development and deployment of more than 100 data science models. Organizations use it to provide high-end automation to the customers and users. The predictive analysis offered by DataRobot can support you in making intelligent decisions.

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