Best Data Science Tools For Data Scientists

By Techfunnel Author - Last Updated on July 10, 2020
Tools for Data Science

The cogent need to make statistics unified, analyze data, learn through the machine and their related methods for the purpose of understanding and analyzing actual phenomenon with data that has led to the birth of data science.

Data Science is an integrative field that makes use of scientific methods, processes, algorithms, and systems for the extraction of knowledge and insight from both structured and unstructured data. It makes use of techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, and information science.

In 2015, The American Statistical Association identified both distributed and parallel systems, statistics, and machine learning and database management as the three foundational and professional communities of data science. Data science cannot function at all without its tools.

So, what are the data science tools we have today?

Below is a list of some of the best tools for data science.

  • BigML

    This is one of my favorite Data Science tool I personally use to make machine learning simply for me. This worldwide tool has been designed to run in the cloud or on-premises for the operationalizing of machine learning in organizations making it easy to solve and automate classification and cluster analysis.

  • Bokeh

    This tool aims to build modern web browsers for presentation. It also helps users create dashboards, interactive plots, and data applications easily. The best part is that it’s totally free.

  • Clojure

    Clojure has been designed to merge an efficient infrastructure with an interactive development of a scripting language for programming that is multithreaded. This tool is unique because it is a compile language that remains dynamic with every feature supported at runtime.

  • Excel

    This Microsoft office package is a very familiar tool that scientists rely on to quickly sort, filter and work with their data. It is on almost every computer device you come across so data scientists from all over the world can get to work easily.

  • ForecastThis

    ForecastThis is a huge tool in the grasp of data scientists that makes predictive model selection automated. The company behind this tool is constantly striving to make deep learning relevant for finance and economics by enabling quantitative analysts, investment managers and data scientists to make use of their own data for the purpose of generating robust forecasts and the optimization of complex future objectives.

  • Java

    Java, Oh Java! Old but Gold. This tool is a language that has a very broad user base. It helps data scientists to create products and frameworks involving distributed systems, machine learning, and data analysis.

    Java is very convenient for people to use. This has given it comparison with other great data science tools like  R and Python.

  • Jupyter

    Dubbed from the planet Jupiter, Jupyter, as its name implies, has been designed to function all around the world. It has made provision for a multi-language interactive computing environment.

    It has a notebook which is an open-source web application allowing data scientists to create and share documents that contain live codes, visualizations, equations, and explanatory tests.

  • Logical Glue

    Logical Glue is an award-winning tool that enables the user to learn machine language on an artificial intelligence platform. It could not have won an award if not for its key benefit of increasing productivity and profit for organizations through a process of bringing your insights to life for your targeted audience.

  • MySQL

    MySQL is a very popular open-source database. What some people do not know is that it is also a great tool for data scientists to use to access data from their database. It has been used alongside Java for more efficiency.

    It can store and structure your data in a very organized manner giving you no hassle at all. It supports data storage needs for production systems. It has also been enabled with the feature of querying data after designing the database.

  • Narrative Science

    Narrative science is a great tool for data scientists that has helped organizations maximize the impact of their data with intelligent and automated narratives generated by advance narrative language generation (NLG).

    This tool is capable of turning your data into actionable and powerful assets for making more efficient decisions thereby making the workers in your organization understand and act on data.

  • NumPy

    NumPy is a tool that is well suited for scientific uses as it contains a powerful N-dimensional array object with sophisticated broadcasting functions, and it is totally free. It is a fundamental package whose full potential can only be realized when used alongside with Python. It is also a multidimensional container of generic data.

  • OpenRefine

    Once Google Refine, Open Refine is now an open-source project that is supported and funded by anyone who wishes to. As its name implies, it is an extraordinarily powerful tool used by data scientists to clean up, transform and extend data with web services before linking it to databases.

    It has also been designed with the capability to reconcile and match data, link and extend datasets with a range of web services and upload cleaned data to a central database.

  • Pandas

    Pandas is a great data science tool , equipped with an open source library, whose aim is to deliver high performance, easy to use data structures and data analyzing tools for the python programming language.

    It is flexible, fast and has expressive data structures that make working with relational and labeled data easy and intuitive. It has a data analysis and manipulation tool that is available in a variety of languages. What more? It is Free.

  • RapidMiner

    According to statistics, there is more productivity for data scientists when they use RapidMiner as it is a unified platform for machine learning, data prep, and model deployment. It can run data science workflow directly inside Hadoop with RapidMiner Radoop.

  • Redis

    This data science tool is a data structure server that data scientists use as a cache, database and message broker. It is an open-source, in-memory data structure store that supports hashes, strings, and lists among others.

(Download Whitepaper: Data Science at Scale)

  • Cascading

    This data science tool is an application development platform for data scientists that build Big Data applications on Apache Hadoop. It enables users to solve simple and complex data problems because it boasts of a unique computation engine, systems integration framework, data processing, and scheduling capabilities. It runs on and can be ported between MapReduce, Apache Tea, and Apache Flink.

  • DataRobot

    This tool is an advanced machine learning automation platform, DataRobot makes data scientists build better predictive models faster. Keep up with the ever-expanding ecosystem of machine learning algorithms easily when you use DataRobot.

    DataRobot is constantly expanding and has  vast set of diverse, best-in-class algorithms from leading sources. You can test, train, and compare hundreds of varying models with one line of code or a single click.

    Also, it automatically identifies top pre-processing and feature engineering for each modeling technique. It even uses hundreds and even thousands of servers as well as multiple cores within each server to parallelize data exploration, model building, and hyper-parameter tuning.

  • Apache storm

    It is a tool for data scientists that are involved in handling distributed and fault-tolerant real-time computation. It tackles stream processing, continuous computation, distributed RPC, and more.

    It is a free and open-source tool that can reliably process unbounded data streams for real-time processing. It can be used with any programming language and even cases like real-time analytics, online machine learning, continuous computation, distributed RPC, ETL, and more.

    It has the ability to process more than one million tuples processed per second per mode as it integrates with your existing queueing and database technologies.

  • Iphyton

    Interactive Python tools are a growing project with expanding language-agnostic components coupled with a rich architecture for interactive computing. It is an open-source tool for data scientists and it supports Python 2.7 and 3.3 or newer.

    It is a kernel for Jupyter and it has support for interactive data visualization and use of GUI toolkits. It can load flexible, embeddable interpreters into your own projects and it has easy-to-use high-performance parallel computing tools.

  • KNIME Analytics Platform.

    KNIME is an open platform tool for navigating complex data freely.  KNIME Analytics Platform is an open solution for data-driven innovation to help data scientists uncover data’s hidden potential, mine for insights, and predict futures.

    It can deploy quickly and scale more than 1,000 modules easily. There are hundreds of ready-to-run examples with a comprehensive range of integrated tools. It also offers the widest choice of advanced algorithms available.

  • RStudio

    This is a tool for data scientists that is open source and enterprise-ready. This highly professional software for the R community makes R easier to use as it Includes a code editor, debugging, and visualization tools, Integrated development environment (IDE) for R, Includes a console, syntax-highlighting editor supporting direct code execution and tools for plotting, and workspace management.

    It is available in open source and commercial editions and runs on the desktop or in a browser connected to RStudio Server or Studio Server Pro.


    Pxyll is another open platform tool and it  is the fastest way to integrate Python and Excel. The code you input runs in-process to ensure the best possible performance of your workbooks.

  • TIBCO Spitfire

    It drives digital business by enabling better decisions and faster, smarter actions. The Spotfire solution is a tool for data scientists that addresses data discovery, data wrangling, predictive analytics, and more.

    TIBCO is a secure, governed, enterprise-class analytics platform with built-in data wrangling and it can deliver AI-driven, visual, geo, and streaming analytics. It is equipped with smart visual data discovery with shortened time-to-insight and its data preparation features empower you to shape, enrich, and transform data and create features and identify signals for dashboards and actions.

  • TensorFlow

    It is a flexible, fast, scalable open-source machine learning library for research and production. Data scientists usually use TensorFlow for numerical computation using data flow graphs.

    It has a flexible architecture for deploying computation to one or more CPUs or GPUs in a desktop, server, or mobile device with one API along with the nodes in the graph that represent mathematical operations.

    While the graph edges represent the multidimensional data arrays communicated between them and it is ideal for conducting machine learning and deep neural networks but applies to a wide variety of other domains.

  • Shiny

    It is a web application framework for R by RStudio that data scientists use to turn analyses into interactive web applications. It is an ideal tool for data scientists who are inexperienced in web development.

    The good thing is that no HTML, CSS, or JavaScript knowledge is required as it is an easy-to-write app that can combine R’s computational power with the modern web’s interactivity. You can use your own servers or RStudio’s hosting service.

  • SciPy

    This Data Science tool is a  Python-based ecosystem of open-source software intended for math, science, and engineering applications. Its  Stack includes Python, NumPy, Matplotlib, Python, the SciPy library, and more. The SciPy library provides several numerical routines.

  • Scikit-learn

    This tool is an easy-to-use, general-purpose machine learning for Python. Most data scientists prefer scikit-learn because it features simple, efficient tools for data mining and data analysis. It is also accessible to everyone and reusable in certain contexts. It is built on NumPy, SciPy, and Matplotlib.

  • Scala

    Scala is a tool for data scientists looking to construct elegant class hierarchies to maximize code reuse and extensibility. The tool empowers users to implement class hierarchies’ behavior using the higher-order function.

    It has a modern multi-paradigm programming language designed to express common programming patterns concisely and elegantly. It smoothly integrates features of object-oriented and functional languages. It supports higher-order functions and allows functions to be nested.

  • Octave

    This is a scientific programming language that is a useful tool for data scientists looking to solve systems of equations or visualize data with high-level plot commands. Octave’s syntax is compatible with MATLAB, and its interpreter can be run in GUI mode, as a console, or invoked as part of a shell script.

  • NetworkX

    It is a Python package tool for data scientists. You can create, manipulate, and study the structure, dynamics, and functions of complex networks with NetworkX. It has data structures for graphs, digraphs, and multigraphs with abundant standard graph algorithms. You can generate classic graphs, random graphs, and synthetic networks.

  • Natural Language Toolkit

    It is a  leading platform for building Python programs as it is a tool for working with human language data. This tool is helpful for inexperienced data scientists and data science students working in computational linguistics using Python. It provides easy-to-use interfaces to more than 50 corpora and lexical resources.

  • MLBase

    UC Berkeley’s AMPLab developed MLBase as an open-source project that makes distributed machine learning easier for data scientists. It consists of three components which are MLib, MLI, and ML Optimizer. MLBase can implement and consume machine learning at scale more easily.

  • Matplotlib

    This Data Science tool is a Python 2D plotting library that produces publication-quality figures in a variety of hardcopy formats and interactive environments across platforms. It is used by Data scientists in Python scripts, the Python and IPython shell, the Jupyter Notebook, web application servers, and four graphical user interface toolkits.

    It has the ability to generate plots, histograms, power spectra, bar charts, error charts, scatterplots, and more with a few lines of code.

(Also read: Why Data Science Technology is Bigger than Big Data)


    This is a senior high-level language and interactive environment for numerical computation, visualization, and programming. It is a powerful tool for data scientists and it serves as the language of technical computing and is useful for math, graphics, and programming.

    It is designed to be intuitive thereby allowing you to analyze data, develop algorithms, and create models. It combines a desktop environment for iterative analysis and design processes with a programming language capable of expressing matrix and array mathematics directly.

  • GraphLab Create

    This tool is used by data scientists and developers to build state-of-the-art data products via machine learning. This machine learning tool helps users build intelligent applications end-to-end in Python as it Simplifies the development of machine learning models.

    It also incorporates automatic feature engineering, model selection, and machine learning visualizations specific to the application. You can identify and link records within or across data sources corresponding to the same real-world entities.

  • ggplot2

    ggplot2 was developed by Hadley Wickham and Winston Chang as a plotting system for R that is based on the grammar of graphics. With ggplot2, data scientists c avoid many of the hassles of plotting while maintaining the attractive parts of base and lattice graphics and producing complex multi-layered graphics easily.

    It helps you to create new types of graphic tailored to your needs which will help you and others understand your data thereby making you produce elegant data for data analysis.

  • Gawk

    It is an operating system that enables you to use a computer without software “that would trample your freedom.” They created Gawk, an awk utility that interprets a special-purpose programming language.

    It empowers users to handle simple data-reformatting jobs using only a few lines of code. It allows you to search files for lines or other text units containing one or more patterns. It is data-driven rather than procedural making it easy to read and write programs.

  • Fusion Tables

    Fusion Tables is a cloud-based data management service focusing on collaboration, ease-of-use, and visualizations. Since it is an experimental app, Fusion Tables is a data visualization web application tool for data scientists that empowers you to gather, visualize, and share data tables.

    You can make a map in minutes and Search thousands of public Fusion Tables or millions of public tables from the web that you can import to Fusion Tables. Lastly, you can Import your own data and visualize it instantly thereby Publishing your visualization on other web properties.

  • FeatureLabs

    Feature Labs is designed to develop and deploy intelligent products and services for your data. They work mainly with data scientists. It integrates with your data to help scientists, developers, analysts, managers, and executives discover new insights and gain a better understanding of how your data forecasts the future of your business. It features On-boarding sessions tailored to your data and uses cases to help you get off to an efficient start.

  • DataRPM

    This Data Science tool is the “industry’s first and only cognitive predictive maintenance platform for industrial IoT. DataRPM is the recipient of the 2017 Technology Leadership Award for Cognitive Predictive Maintenance in Automotive Manufacturing from Frost & Sullivan.

    It uses patent-pending meta-learning technology, an integral component of Artificial Intelligence, to automate predictions of asset failures and runs multiple live automated machine learning experiments on datasets.

  • D3.js

    D3.js was created by Mike Bostock. It is used by Data scientists as a JavaScript library for manipulating documents based on data, to add life to their data with SVG, Canvas, and HTML. It lays emphasis on web standards to gain full capabilities of modern browsers without being tied to a proprietary framework and combines powerful visualization components and a data-driven approach to Document Object Model (DOM) manipulation. It can also bind arbitrary data to a DOM and then apply data-driven transformations to the document.

  • Apache Spark

    It delivers “lightning-fast cluster computing.” A very wide range of big organizations use Spark to process large datasets, and this data scientist tool can access diverse data sources such as HDFS, Cassandra, HBase, and S3.

    It is designed with advanced DAG execution engine to support acyclic data flow and in-memory computing, has more than 80 high-level operators that make it simple to build parallel apps, can be used interactively from the Scale, Python, and R shells and it powers a stack of libraries including SQL, DataFrames, MLlib, GraphX, and Spark Streaming.

  • Apache Pig

    This tool is a  platform designed for analyzing large datasets. It consists of a high-level language for expressing data analysis programs that is coupled with infrastructure for evaluating such programs.

    Since Pig programs’ structures can handle significant parallelization, they can tackle large datasets. The Infrastructure consists of a compiler capable of producing sequences of Map-Reduce programs for which large-scale parallel implementations already exist and a language layer including a textual language called Pig Latin.

  • Apache Mesos

    As a cluster manager, Apache Mesos provides efficient resource isolation and sharing across distributed applications or frameworks. It abstracts CPU, memory, storage, and other resources away from physical or virtual machines to enable fault-tolerant, elastic distributed systems to be built easily and run effectively.

    It is built using principles similar to that of the Linux kernel but at a different level of abstraction and it runs on every machine and provides applications like Hadoop and Spark with APIs for resource management and scheduling completely across datacenter and cloud environments. It has non-disruptive upgrades for high availability.

  • Apache Mahout

    An open-source tool. Apache Mahout aims at enabling scalable machine learning and data mining. To be specific, the project’s goal is to “build an environment for quickly creating scalable performant machine learning applications.” It has a Simple, extensible programming environment and framework for building scalable algorithms Including a wide variety of pre-made algorithms for Scala + Apache Spark, H2O, and Apache Flink.

  • Apache Kafka

    Apache Kafka is built to efficiently processes streams of data in real-time. Data scientists make use of this tool to build real-time data pipelines and streaming apps because it empowers them to publish and subscribe to streams of records, store streams of records in a fault-tolerant way, and process streams of records as they occur. It runs as a cluster on one or more servers and the cluster stores stream of records in categories called topics.

  • Apache Hive

    Apache Hive started as a subproject of Apache Hadoop and now is a top-level project itself. Apache Hive is a data warehouse software that assists in reading, writing, and managing large datasets that reside in distributed storage using SQL. It can project structure onto data already in storage and a command-line tool is provided to connect users to Hive.

  • Apache HBase

    Apache HBase is a scalable, distributed, big data store. This open-source tool is used by data scientists when they need random, realtime read/write access to Big Data. Apache HBase provides capabilities similar to Bigtable on top of Hadoop and HDFS. It is a Distributed Storage System for Structured Data that has linear and modular scalability. It strictly and consistently reads and writes.

  • Apache Hadoop

    This Data Science tool is an open source software for reliable, distributed, scalable computing. A framework that allows the distributed processing of large datasets across clusters of computers, the software library uses simple programming models.

    It is appropriate for research and production. It is designed to scale from single servers to thousands of machines. The library can detect and handle failures at the application layer instead of relying on hardware to deliver high-availability.

  • Apache Giraph

    Giraph is an iterative graph processing system designed for high scalability. It began as an open-source counterpart to Pregel but adds multiple features beyond the basic Pregel model. Data scientists use it to “unleash the potential of structured datasets at a massive scale.”

    It has Master computation, Sharded aggregators, Edge-oriented input, Out-of-core computation, Steady development cycle and growing community of users.


    This tool is a LumenData Company providing machine learning as a service for streaming data from connected devices. The tool  turns raw data into real-time insights and actionable events so that companies are in a better position to deploy machine learning for streaming data.

    It simplifies the process of making machine learning accessible to companies and developers working with connected devices. Its Cloud platform also addresses the common challenges with infrastructure, scale, and security that arise when deploying machine data.

  • Trifacta

    Trifacta makes provision for three products for data wrangling and data preparation. It may be used by individuals, teams, and organizations as it  will help in exploring, transforming, cleaning, and joining the desktop files together. It  is an advanced self-service platform for data preparation.

  • Alteryx

    This is another great data science tool. It provides a platform to discover, prep, and analyze the data. Also, it helps you to find deeper insights by deploying and sharing the analytics at scale. It allows you to discover the data and collaborate across the organization.

    It also has functionalities to prepare and analyze the model. Alteryx will allow you to centrally manage users, workflows, and data assets, and to embed R, Python, and Alteryx models into your processes.


    With 130,000 data scientists and approximately 14,000 organizations, the community is growing at a strong pace. is an open-source tool that is aimed at making data modeling easier.

    It has the ability to implement a majority of the Machine Learning algorithms including generalized linear models (GLM), Classification Algorithms, Boosting Machine Learning and so on. It provides support for Deep Learning and It also provides support to integrate with Apache Hadoop to process and analyze huge amounts of data.

  • Tableau

    This tool is the most popular data visualization tool used in the market. It gives you access to breaking down raw, unformatted data into a processable and understandable format. Visualizations created using Tableau can easily help you understand the dependencies between the predictor variables.

    These tools are very functional and effective, so why not include them to your work and witness a tremendous change.

Techfunnel Author | is an ambitious publication dedicated to the evolving landscape of marketing and technology in business and in life. We are dedicated to sharing unbiased information, research, and expert commentary that helps executives and professionals stay on top of the rapidly evolving marketplace, leverage technology for productivity, and add value to their knowledge base.

Techfunnel Author | is an ambitious publication dedicated to the evolving landscape of marketing and technology in business and in life. We are dedicate...

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