Data science is the knowledge of converting raw information into useful data. Various skills like mathematics, statistics, programming skills, and ML (Machine Learning) algorithms are used to derive valuable information. These data are used for making important decisions for the business of an organization.
Other than using programming languages to derive useful data, data analysts use tools to maximize the value of the derived data. Data Science is one of the most in-demand skills in the 21st century. Industries like travel, engineering, automobile, finance, and many others have various applications associated with data science. These data science toolkits maximize the value of the derived data to make informed decisions.
This blog gives you details about the list of tools that are used for the various stages of data science.
Data science tools:
Before we get into the list of data science tools, there are various lists of data science tools that are used by data scientists at different stages.
The various stages are listed:
- Data storage.
- Exploratory data analysis.
- Data Modelling
- Data Visualization.
List of tools used in Data Storage:
Apache Hadoop:
Hadoop is an open-source framework that can manage millions of data. Apache Hadoop is used for the computation of high-level data processing. Hadoop provides various data science tools that can be integrated.
Some of the lists of Apache Hadoop data science tools are:
- Apache Mahout – Used for clustering and recommendation systems.
- Apache Pig – High-level scripting language for data analysis.
- Apache Spark – Open source computing system for general-purpose data.
- Apache Sqoop – Tool designed to connect Hadoop relational databases
KNIME:
KNIME stands for Konstanz Information Miner which helps users to integrate data across various platforms. The KNIME provides accurate data for the learner using over 300+ data sources. This tool improves the functionality that every data scientist looks for.
- KNIME helps to access data from any data resource.
- Data analysts can explore the data with interactive charts and visualizations.
- Data analysts can compile data segments and reuse the same data for later purposes.
- Able to integrate languages like Python, R, and JavaScript.
Microsoft HD insights:
Azure provides HD insights which is a cloud platform that stores data for processing and analytics effectively. Organizations like Milliman and Adobe use Insights to manage the data.
Other features of HD insights are:
- Data analysts can integrate with Apache Hadoop and Spark clusters to process data.
- The storage system for HD insights is Azure Blob.
List of tools used in exploratory data analysis:
Exploratory data analysis (EDA) is an approach that helps to know about raw data and gain insights before proceeding with further analysis. In EDA, people perform tasks such as data visualization, cleaning, and summarizing the data.
Below is the list of various EDA tools:
Rattle (R package):
Rattle R Package is a GUI (Graphical User Interface) based tool used in data mining tasks. It makes data exploration and preparation easy for data analysts who are beginners in writing codes.
Some data mining operations use the R package:
- Data processing.
- Clustering.
- Visualization
- Preprocessing
- Classification.
RapidMiner:
Many data scientists and analysts widely use RapidMiner as a data science and analysis tool to handle huge amounts of data. They extensively use this data analysis tool for predictive analysis and machine learning.
Predictive Analysis: Predictive analysis is the process of using the data to predict future outcomes.
RapidMiner reduces the time for learning coding to start the data analysis. It is more suitable for non-technical students who want to excel in data mining.
Tools used in Data Modelling:
Erwin Data Modeller:
Many people widely use Erwin, a data modeling tool designed to organize and conceptualize data. Erwin supports a wide range of databases to streamline the data.
Analysts can create attributes, and relationships, and manage data types, constraints, and data elements.
Data type: Data types are the different types of data that a computer system can use.
Data elements: An individual unit of data within the dataset or data structure is Data Elements.
Datarobot:
They use an AI model called Datarobot to develop accurate predictive models. Data robots can help implement machine learning algorithms, clustering, and regression models of data.
Some features of Datarobot are listed below:
- One can analyze parallel programming with the help of various servers.
- Build, test, and train Machine learning models very quickly.
- Helps to implement Machine Learning processes at a large scale.
Tools used for Data Visualization:
Data visualization is the representation of the data in visual formats like charts, graphs, and maps or in the form of graphical formats.
We list some of the Data Visualization tools used for data analysis:
Tableau:
Most of the students and learners who are in the field of data analysis know about Tableau. One of the most popular data visualization tools used in the data analysis market is Tableau. Tableau can help break down every unformatted data into processable and understandable formats.
Some of the features of Tableau are:
- Helps to find patterns and correlations, and visualize massive amounts of data into broken accurate data sets.
- Tableau desktop helps to create customized reports in real time.
- Tableau has cross-database join functionality that helps to solve complex data problems.
Python libraries:
Some of the widely used data visualization tools that provide accurate information are Matplotlib, Seaborn, and Plotly. These libraries provide a wide range of functions like customization, chart and graph interactions, and static visualization options.
In Conclusion:
In recent years, the area of data science and analysis has grown significantly. This is because there is more data available and people want to get useful information from it. To be successful in this vast landscape, you need to know much about the top data science tools that have become the basis of modern data analysis and machine learning.
Getting to know these top data science tools can help learners improve their ability to get actionable insights from data, make smart choices, and gain an advantage in a world that is increasingly driven by data. Because of how quickly data science is changing, there will always be new tools and technologies. If you want to become a data scientist, it’s important to stay up-to-date and adapt to new trends. Using these tools will not only improve a person’s technical skills but will also open up a world of opportunities for using data to solve problems in the real world.
