Are you looking to get a discount on popular programming courses? Then click here. View offers

Data Science

Disclosure: is supported by its audience. When you purchase through links on our site, we may earn an affiliate commission.

Data Science Tools

Posted in Data Science

Data Science has proven to be a boom to both the IT and the business industry. The technology includes obtaining value from data, understanding the data and its patterns and then predicting or generating outcomes from it. Data science is much in demand by companies to analyze their massive volume of data sets and generate optimized business insights from them, thereby increasing profits for the company. 

Data scientists play an essential role in this since they are responsible for organizing, evaluating, and studying data and its patterns. These professionals perform analysis by identifying relevant questions, collecting data from relevant sources, data organization, transforming data to the solution, and communicating the findings for better business decisions. Apart from having appropriate qualifications and education, an aspiring data scientist must be skilled at a certain set of tools. He must be fluent in at least one of the tools from the lifecycle of a data science project, namely: data acquisition or capture, data cleaning, data warehousing, data exploration or analyzing, and finally, data visualization.

Data science tools are generally segregated into two types:

  • One that is for programmers and others that are for business users or
  • Those who do not have programming experience.

Let us now study the tools in detail. 

Data Science Tools

1. Data Acquisition and Data Cleansing Tools 

Collecting data from its raw format into sensible and useful data for business users and organizations is a big challenge for data-driven companies working on massive volumes of data. ETL tools solve this issue of gathering data from different sources and converting the data into an understandable format for further analysis. ETL tools start the process by extracting the data from underlying sources, followed by transforming the data to a data model and finally loading the data to the target destination. Some of the most popular ETL tools are: 

1. Talend 

Developed in 2005, Talend is an open-source data integration tool. The tool is known to yield software solutions for data preparation, integration, and application integration. Real-time statistics, easy scalability, efficient management, early cleansing, faster designing, better collaboration, and native code are the advantages of this tool. 

Some of the significant features of this tool are listed below: 

  • Efficient development and deployment of the tasks: The tool automates as well as maintains the task. 
  • Affordable: Tools are open-source.
  • Advance Tool: It won’t become outdated soon as it is designed, keeping in mind both present and future development requirements.
  • Unified Platform
  • Huge Community

2. Go Spot Check

A powerful application for field teams to collect and share data in real-time. It is an analytics and BI platform that allows the user to gather and collect real-time details and perform a quick analysis to make smart business decisions. 

The tool performs three simple steps: create, gather, and analyze to achieve data analysis. Users can analyze data in real-time and can also access dashboards to utilize for monitoring work progress and performance. 

The tool has the following features: 

  • Customizable form builder
  • Task distribution
  • Configurable reporting

3. IBM Datacamp 

The tool is responsible for acquiring documents, extracting useful information, and feeding the documents into other business processes downstream. It can perform these tasks with a high degree of automation, flexibility, and accuracy. 

The functionality of the Datacamp is broadly defined into three areas: 

  • Acquisition of the documents. 
  • Processing of documents to pull out useful information
  • Delivering content and data to back end systems

This tool supports multiple-channel capture by processing paper documents on various devices such as scanners, mobile, multifunction peripherals, and fax. The tool uses natural language processing, text analytics, and machine learning technologies to automatically identify and extract and classify content from unstructured or variable documents. The software and reduce labor and paper costs, deliver meaningful information, and support faster decision making. 

The tool offers the following features: 

  • Enriched Mobility: Enables the users to capture and submit documents on their demand using their smartphone or tablet. It offers improved mobility with iOS and Android apps and provides SDK, enabling customers to embed Datacap mobile functionality into their iOS and Android apps.  
  • Robotic Process Automation: Insert advanced document recommendation into IBM Robotic Process Automation(RPA) with Automation Anywhere. Use the Datacap MetaBot for native and easy integration. 
  • Intelligent Capture: Automatically classifies and extracts content from unstructured, complex, or highly variable documents. It uses machine learning to capture information and analyze the content to understand the information and the context and then determine the next course of action. 
  • Data protection with role-based content management: Provides features to help protect sensitive data. Enables content collaborations to censor high-level information. It allows the user to control confidential data, and mask and restrict content for users to deliver only necessary content. 

4. Mozenda

Mozenda is an enterprise cloud-based web-scraping platform. It helps companies collect and organize web data most efficiently and cost-effectively possible. The tool has a point-to-click interface and user-friendly UI. The tool has two parts- an application to build the data extraction project and Web Console to run agents, organize results, and export data. It is easy to integrate and allows users to publish results in CSV, TSV, XML, or JSON format. The tool also provides API access to fetch data and has inbuilt storage integrations like FTP, Amazon S3, Dropbox, and more. 

5. Octoparse

Octoparse is client-side web scraping software for Windows. It is a web-scraping template that turns unstructured or semi-structured data from websites into a structured data set without coding. It is useful for people who are not well versed in programming. A web scraping template is a simple yet powerful feature. Its purpose is to input the target website/keywords in the parameters on the pre-formatted tasks, so the user doesn’t have to configure any scraping rules nor writing code. 

6. OnBase

OnBase is a tool developed by Hyland, is a single enterprise information platform that is designed to manage user’s content, processes, and cases. The tool mainly centralizes user’s business content in a secure location and then delivers relevant information to the user when they need it. OnBase allows the organization to become more agile, efficient, and capable, thereby increasing productivity, delivering excellent customer service, and reduce risk across their enterprise. 

Some features of the tool as under: 

  • One Platform: Tool provides a single platform for building content-based applications while complementing other core business systems.
  • Low-code configuration: Reduce cost and development time by rapidly creating content-enabled solutions with low-code application development platform. 
  • Anywhere, any way 

OnBase can be deployed in the cloud, extended to mobile devices, and integrated with existing applications. 

2. Data Warehousing Tools 

The idea behind the data warehousing technique is to collect and manage data from varied sources to provide meaningful business insights to the user. A large amount of information is stored electronically by a business that, instead of transaction processing, is designed for query and analysis. Data warehousing is a process of transforming data into useful information and making it available to users for analysis. 

Let us see some data warehousing tools: 

1. Amazon Redshift

Amazon Redshift is a petabyte-scale, fully managed cloud data warehouse service. The warehouse allows enterprises to scale from a few hundreds of gigabytes of data or more. The tool enables the user to use data to acquire insights for the business and customers. Redshift compromises of nodes called Amazon Redshift clusters. After provisioning the clusters, the user can upload datasets to the data warehouse. The customer can then perform analysis and queries on the data. 

Features of Amazon Redshift: 

  • Supports VPC: Redshift can be launched within VPC, and through a virtual networking environment, the user can have access to control the cluster.  
  • Encryption: Data stored can be encrypted and configured while creating tables. 
  • SSL: Connections between Redshift and the client is encrypted using SSL
  • Scalable: The number of nodes can be easily scaled with a few simple clicks in the Redshift data warehouse. 
  • Cost-Effective: The tool has no up-front costs; neither does it have any long term commitments and on-demand pricing structure. 

2. Google BigQuery 

BigQuery is a highly scalable and serverless data warehouse tool that is designed for productive analysis of data with unmatched price performance. Since there is no infrastructure to manage, the user can focus on uncovering meaningful insights using SQL without access to a database administrator. Data is analyzed by creating a logical data warehouse over columnar storage and also the data from object storage and spreadsheets. The tool creates blazing-fast dashboards and reports with the in-memory BI engine. Machine learning solutions are used to carry out geospatial analysis with the help of SQL. 

BigQuery allows the user to securely share insights within the organization and beyond as datasets, queries, spreadsheets, and reports. 

Below are the features of the tool: 

  • Blazing-Fast: Data warehouse can be set in a few seconds, and the user can start querying the data immediately. This data warehousing tool eliminates the time-consuming work of resources and also reduces the downtime time with serverless infrastructure, which is responsible for handling the maintenance. 
  • Scale Seamlessly: By leveraging Google’s serverless infrastructure that uses automatic scaling and high-performance streaming ingestion to load data, the tool enables us to meet the challenges of real-time analytics. 
  • Robust Analysis: The tool integrates well with ETL tools like Talend to enrich the user’s data with DTS. It also supports BI tools such as Tableau, MicroStrategy, Looker, and Data Studio so that anyone can create stunning reports and dashboards. 
  • Protection Against Data and Investments: BigQuery eliminates data operation burdens with automatic data replication for disaster recovery and high availability of processing for no additional charge. 
  • Control Costs: The tool only requires to pay for storage and compute resources that the user uses. Separation of storage and computing makes it easy to scale independently and endlessly on demand, resulting in low-cost, economical storage. 

3. Microsoft Azure

Microsoft Azure is an ever-expanding set of cloud services to help an organization meet its business challenges. It offers freedom to build, manage, and deploy applications on a massive global network using tools and frameworks. There are various product types that Azure offers. Some of them are data storage, analytics, hybrid integration, artificial intelligence and machine learning, databases, and development.  

The features that Azure offers are: 

  • DR and Back-up: Some organizations use Azure for backup and disaster recovery(DR), while some use it as an alternative to the data center. 
  • Pricing and cost: The software works on the pay-as-you-go model, which charges based on the usage. Moreover, Azure-native tools like Azure Cost Management can help to monitor, visualize, and optimize cloud spend. 
  • Competition: Azure is one of the major cloud service providers on a global scale, competing with Google Cloud Platform (GCP), Amazon Web Services(AWS), and IBM. 

4. MySQL

MySQL is an open-source Relational Database Management System(RDBMS). It is one of the best RDBMS and uses SQL(Structured Query Language) to develop 

Various web-based software applications, especially in web servers. Although there are various ways to store data, databases are considered to be the most convenient method in data science as data is required to be stored in an easily accessible and analyzable way. 

We can collect, clean, and visualize data with MySQL. The following is an overview of how MySQL can be used to achieve this: 

  • Collecting Data: The system imports data into the database from sources such as XLS, CSV, XML, and more. Statements used are this purpose are: 
  • Clean the Tables: Incomplete or irrelevant parts of data are referred to as dirty data, which is removed in this step from the data that was collected in the earlier step. Following are the functions used to clean up the data: 
    • LIKE() – the simple pattern matching
    • TRIM() – Removing the leading and trailing spaces.
    • REPLACE() – To replace the specified string.
    • CASE WHEN field is empty THEN xxx ELSE field END – To evaluate conditions and return value when the first one is met.
  • Analyze and Visualize Data: Analyzing and visualization of data can be done using standard SQL queries to find relevant answers to specific questions. 

5. Snowflake 

Snowflake is a fully relational ANSI SQL data warehouse so that the user can leverage the tools and skills of their organization already uses. Updates, deletes, analytical functions, transactions, stored procedures, materialized views, and complex joins give the user the full capabilities that the user needs to make the most of their data. 

Features of Snowflake: 

  • Zero Management: The administration and management demands of traditional data warehouses and big data platforms are eliminated with Snowflake. There’s no infrastructure to manage; Snowflake automatically handles infrastructure, availability, optimization, data protection, and more so the users can focus on using their data instead of managing it.
  • Diverse Data: Snowflake supports all forms of business data, whether from traditional sources or newer machine-generated sources, without requiring cumbersome transformations and tradeoffs. Snowflake’s patented technology natively loads and optimizes both structured and semi-structured data such as JSON, Avro, or XML and makes it available via SQL without sacrificing performance or flexibility.
  • Compelling Performance: Snowflake processes queries and tasks in a fraction of the time conventional on-premises and cloud data warehouses require The tool’s columnar database engine uses advanced optimizations such as automatic clustering. Automatic clustering removes the hassle re-clustering data manually when loading raw data into the table. Users can get accurate performance whenever they need it as the tool can scale up and down. 
  • Any Scale of Data, Users, and Workload: Snowflake’s multi-cluster, shared data architecture separates storage and compute, thereby making it possible to scale up and down on-the-fly without downtime or disruption.
  • Failover and Business Continuity: Replicates data across cloud providers, across cloud regions, keeps apps and data, operate confidently with failover and business continuity.
  • Broad Ecosystem: Users can rapidly integrate Snowflake with custom and packaged tools and applications. The tool’s native and standards-based connectors, including ODBC, Javascript, JDBC, Node.js, R, Spark, and Python, unlock the power of cloud data warehousing for developers and tools that use a variety of languages and frameworks.
  • Pay Only for What You Use: Usage-based pricing for computing and storage. This means the user only pays for the amount of data they store and the amount of computer processing the use.
  • Share Data Seamlessly: Data warehouse is extended to the Data Sharehouse with Snowflake’s multi-tenant architecture along with secure and governed modern data sharing. User’s data is shared across the organization or with any of their business partners and customers within minutes, and without having to move data. Customers can easily forge one-to-one, one-to-many, and many-to-many data sharing relationships with data sharing. 

3. Data Analysis Tools

Data Analysis is the process of cleaning, modeling, and transforming data to discover useful information or patterns for business decision-making. The data analytics consists of various operations on the data sets or tables available in databases. The operations include data extraction, data profiling, data cleansing and data deduping, and more. There are several methods and techniques for data analysis based on business and technology. The major types of data analysis are: 

  • Text Analysis: It is the method to discover patterns in large data sets using databases or data mining tools. The process converts raw into useful business information. 
  • Statistical Analysis: This analysis includes past data for analysis. It is of two types: 
    • Descriptive Analysis: shows mean and deviation for continuous data, whereas percentage and frequency for categorical data.
    • Inferential Analysis: In this user can find different conclusions from the same data by selecting different samples. 
  • Diagnostic Analysis: Diagnostic analysis finds the cause of the insights found in Statistical Analysis. 
  • Predictive Analysis: It predicts future insights based on previous data. 
  • Prescriptive Analysis: Data-driven companies most use this type of analysis technique. The analysis combines the insights from all previous analyses to decide on the current problem or decision.

Evaluation of data involves various analytical and statistical tools. Some of the popular tools are listed below: 

1. Alteryx 

Why is Alteryx a useful tool for analysis?

  • Accelerates Analysis: Searching relevant information to be analyzed can be time-consuming and unproductive, resulting in recreating assets that already exist within the organization since they can be challenging to find. Alteryx overcomes this problem. Alteryx allows the user to quickly and easily find, manage, and understand all the analytical information that resides inside the organization. The tool accelerates the end-to-end analytic process and dramatically improve analytic productivity and information governance, generating better business decisions for all.
  • Combines data from multiple sources: The tool allows the user to connect to data resources like Hadoop and Excel, bringing them into Alteryx workflow and joining them together. Regardless of data being structured or unstructured, the tool allows creating the right data set for analysis or visualization by using data quality, integration, and transformation tools. 
  • Performs Predictive, Statistical, and Spatial Analysis: There are approximately more than 60 built-in tools for spatial and R-based predictive analytics within Alteryx that are as varied as drive times, regression, or clustering. Tools can also be customized, and even new tools can be created using Python or R.
  • Shares with Decision Makers: Analytical insights can be shared by various means: either by delivering reports in popular reporting formats or by packaging workflow as an analytic application that anyone with the correct permissions can use to run their analytics. The reports can also be exported to visualization formats like Qlik, Tableau, and Microsoft Power BI. 

2. Domino Data Lab 

Domino automates DevOps for data science so that the user can spend more time doing research and test more ideas faster. Automatic tracking of work enables reproducibility, reusability, and collaboration. The workbench enables the user to the following: 

  • Use their favorite tools on the infrastructure of their choice. 
  • Track experiments, reproduce and compare results. 
  • Find, discuss, re-use work in one place.


The users can deploy their work on Kubernetes compute grid to deliver business impact faster.

  • Deploy models as low-latency and high-availability APIs
  • Monitor data drift and performance drift.
  • One-click publishing of interactive Apps such as Shiny, Flask, or Dash
  • Schedule jobs for model training, ETL, or reporting

Manage Data

Domino is the only data science platform that gives visibility into computing utilization, projects, and data science products, to help manage the team as it grows.

  • Monitor and control compute spent
  • Oversee projects and find opportunities to help
  • View the models and model-backed products the organization’s data science team has made, and understand how they are used

3. KNIME Analytics Platform 

KNIME makes understanding the data and designing data science workflows and reusable components accessible to everyone by being intuitive, open, and continuously integrating new developments. 

Features of KNIME

  • Builds Workflows: Create visual workflows with an intuitive drag and drop GUI, without the need to code. KNIME allows the user to choose from 2000 nodes to build workflow, model each step of the analysis, control the flow of data, and ensures the work is updated. The software also blends tools from different domains with KNIME native nodes within a single workflow, including scripting in machine learning, Python or R, or connectors to Apache Spark.
  • Blends Data:
    • KNIME Combines simple text formats such as CSV, PDF, XLS, JSON, XML, unstructured data types, or time-series data.
    • Connects to a host of databases and data warehouses to integrate data from Oracle, Microsoft SQL, Apache Hive, and more. 
    • Accesses and retrieve data from sources such as Twitter, AWS S3, Google Sheets, and Azure.
  • Shapes Data: 
    • Derives statistics, including quantiles, mean, and standard deviation, or even applies statistical tests to validate a hypothesis. It also integrates correlation analysis, dimension reduction, and more into workflows.
    • Aggregates, sorts, filters, and joins data either on the local machine, in-database, or in distributed big data environments.
    • Cleans data through normalization, data type conversion, and handles missing values. KNIME detects out of range values using anomaly detection algorithms and outliers. 
    • Extracts and selects features or constructs new ones to prepare the dataset for machine learning with random search, genetic algorithms, or backward- and forward feature elimination. It also manipulates text, applies formulas on numerical data, and apply rules to filter out or mark samples.
  • Machine Learning and Artificial Intelligence:
    • Builds machine learning models for classification, regression, dimension reduction, or clustering, using advanced algorithms including deep learning, tree-based methods, and logistic regression.
    • Optimizes model performance with hyperparameter optimization, boosting, bagging, stacking, or building complex ensembles.
    • Validates models by applying performance metrics, including Accuracy, R2, AUC, and ROC. Performs cross-validation to guarantee model stability.
    • Explains machine learning models with LIME, Shapley values. The tool also understands model predictions with the interactive partial dependence/ICE plot.
    • Makes predictions using validated models directly, or with industry-leading PMML, including on Apache Spark.
  • Discover and Share Insights: 
    • Visualizes data with classic (bar charts, scatter plot) as well as advanced charts (parallel coordinates, sunburst, network graph, heat map) and customize them to user’s needs.
    • Displays summary statistics about columns in a KNIME table and filter out anything irrelevant.
    • The tool exports the reports as PDF, PowerPoint, or other formats for presenting results to stakeholders.
    • Stores processed data or analytics results in many standard file formats or databases. 
  • Scale Execution with Demands:
    • Builds workflow prototypes to explore various analysis approaches. Inspect and save intermediate results to ensure fast feedback and efficient discovery of new, creative solutions.
    • Scales workflow performance through in-memory streaming and multi-threaded data processing.
    • Exercises the power of in-database processing or distributed computing on Apache Spark to increase computation performance further.

4. Rapid Miner

Rapid Miner is a data science platform developed mainly for non-programmers and researchers for quick analysis of data. The user has an idea in their mind, and easily creates processes, import data into them, run them over and throw a prediction model. The tool supports importing ML models as well to web apps like flask or nodeJS, android, iOS, and more, thereby unifying the entire spectrum of the Big Data Analytics Lifecycle. 

The strength of the tool is that it simplifies the scattered tasks of data mining and analysis. The tool loads data from various frameworks like Hadoop, Cloud, RDBMS, NoSQL, pdf, and many more. Then it pre-processes and prepares data using standard industrial methods by grouping items by categories or spawning new child tables or join tables or interpolating missing data. Further, it trains AI models as well as optimal deep learning models such as Random Forests, XGBoots, Gradient Boost, and more, or clustering or pruning outliers, even visualizing outputs. Finally, the models are deployed on the cloud or in the production environment. The software only requires to create user interfaces for the collection of real-time data and execute it on a real model to serve a task. 

4. Data Visualization Tools 

Data visualization refers to the representation of the data in a pictorial or graphical format. Its purpose is to provide decision-makers to check analytics visually to see patterns and grasp difficult concepts. Data visualization pulls data from various disciplines, including scientific visualization, information graphics, and statistical graphics. Various approaches can achieve data visualization, a popular one being the Information Presentation, which includes statistical graphics and thematic cartography. Data visualization tools display information in a sophisticated way such as infographics, dials and gauges, geographic maps, sparklines, heat maps, and full bar, pie, and fever charts. The visualization tool is essential in analytics, demonstrating data and making data-driven insights available to workers throughout an organization. Data visualization software plays a vital role in big data and advanced analytics projects, as well. As businesses accumulate massive troves of data during the early years of the big data trend, they need a way to quickly and easily get an overview of their data, and visualization tools prove to be a natural fit in this case. 

 It is essential to visualize the outputs to monitor results and ensure that models are performing as intended when writing advanced predictive analysis using machine learning algorithms because it is easier to interpret visualizations of complex algorithms than to interpret numerical outputs.

The primary visualization tools discussed below are : 

1. Google Fusion Tables 

Fusion Tables is a web service provided by Google for the management of data. The service is used for gathering, visualizing, and sharing data tables. Data stored in multiple tables can be viewed and downloaded by the users. 

The Fusion tables provide a means for visualizing data with bar charts, line plots, pie charts, timelines, scatterplots, and geographical maps. The export of data is in a comma-separated values file format.

Some strengths of Fusion tables are: 

  • Allows to visualize bigger data table online
    • Import own data.
    • Visualize instantly 
    • Publish visualization on other web properties.
  • Allows two tables 
    • Merges data with other’s data 
    • Always up to date
    • Shares only what users want to.
    • Build on a public data set
    • Keep track of who owns what
  • Makes a map in few minutes
    • Converts location tables into maps
    • Finds the story in the data
    • Shares that map. 
  • Host data online
    • Fusion tables are in an online format
    • Always distributes the correct version of the data
    • Attracts developers by offering an API of the data instantly

2. Microsoft Power BI 

Power BI is an analytics service that delivers insights to enable informed, fast, and accurate decisions. The tool transforms data into stunning visuals and shares them with others on any device. It visually explores and analyzes data on the device and in the cloud, all in one view. Power BI collaborates on and shares customized dashboards and interactive reports and scales through the organization with built-in governance and security. 

Features of Power BI are: 

  • Intelligence clouds: Creates and shares interactive data visualizations across global data centers, including public clouds, to meet user's compliance and regulation needs. 
  • Unify Enterprise Analytics and Self Service: Power BI provides both enterprise data analytics and self-service needs on a single platform. Power BI accesses powerful semantic models, an application lifecycle management (ALM) toolkit, an open connectivity framework, and fixed-layout, pixel-perfect paginated reports.
  • Accelerates Big Data Prep: Simplifies the way the user analyzes and shares massive volumes of data. This platform reduces the time it takes to get insights and increase collaboration between business analysts, data engineers, and data scientists by using no-limits Azure.
  • AI Helps in Fast Answers: Takes advantage of AI technology and helps non-data scientists prepare data, build machine learning models, and find insights quickly from both structured and unstructured data, including text and images.
  • Unparalleled Excel Integration: Anyone who's familiar with Office 365 can easily connect Excel queries, data models, and reports to Power BI Dashboards—helping to gather quickly, analyze, publish, and share Excel business data in new ways.
  • Stream in Real Time: The software gives access to real-time analytics from factory sensors to social media sources, so that the user can make timely decisions. 

3. Qlik 

Qlik sense is a visual analytics platform that supports a range of use cases such as centrally deployed guided analytics apps and dashboards, custom and embedded analytics, and self-service visualization as well, all within a scalable and governed framework. The system offers data visualization and discovery for both teams and individuals. Businesses of all different sizes are enabled to explore simple and complex data and find all possible associations in their datasets because of software’s data discovery tools. Users are also allowed to create interactive data visualizations to present the outcome in storytelling form with the help of drag and drop interface. 

Qlik Sense offers a centralized hub that allows every user to share and find relevant data analyses. The solution is capable of unifying data from various databases, including IBM DB2, Cloudera Impala, Oracle, Microsoft SQL Server, Sybase, and Teradata. The Open API allows developers to embed Qlik Sense into new applications and automate data capture. 

Key strengths of Qlik sense are: 

  • Associative model
  • Interactive analysis 
  • Interactive storytelling and reporting  
  • Robust security 
  • Big and small data integration 
  • Centralized sharing and collaboration
  • Hybrid multi-cloud architecture

4. SAS

SAS is a statistical software tool developed for advanced analytics, business intelligence, data management, a criminal investigation, predictive analysis, and data visualization. 

Key features that SAS offers for visual analytics are: 

  • Interactive dashboards, reports, BI and analytics: The tool allows the user to go directly from reporting and exploration, to analysis, to sharing information through different channels with a single interface. 
  • Smart Visualization: The software compellingly presents data and results with advanced data visualization techniques and guided analysis through auto charting. 
  • Location Analytics: Combines traditional data sources with location data for analysis in a geographical context. 
  • Augmented Analytics: Reveals real stories hidden in your data within a few seconds. Automatically shows the user suggestions and identifies related measures.
  • Self-Service Analytics: Automated forecasting, goal seeking, scenario analysis, decision trees, and more are at your fingertips, no matter what skill level the user has. 
  • Text Analytics: The tool enables us to gain insights from social media and other text data, and know whether the sentiment is positive or negative.
  • Self-Service Data Preparation: The user can import their data, join tables, apply essential data quality functions, and more with secure drag-and-drop capabilities.


To sum up, a data scientist must be fluent with a variety of tools and some programming languages as per a data science lifecycle project. Data science tools are crucial for analyzing data, creating powerful predictive models using machine learning algorithms, and creating aesthetic and interactive visualizations.

Most of the tools deliver complex data science operations in one place, thereby making it easy for the user to implement functionalities of data science without the need to write their code from scratch. Also, several other tools cater to the application domains of data science. 

People are also reading:

Simran Kaur Arora

Simran Kaur Arora

Simran works at Hackr as a technical writer. The graduate in MS Computer Science from the well known CS hub, aka Silicon Valley, is also an editor of the website. She enjoys writing about any tech topic, including programming, algorithms, cloud, data science, and AI. Traveling, sketching, and gardening are the hobbies that interest her. View all posts by the Author

Leave a comment

Your email will not be published

Select from the best sales here