8 Most Used Data Science Tools

Data Science is the art of drawing and visualizing useful insights from data. Basically, it is the process of collecting, analyzing, and modeling data to solve problems related to the real-world. To implement the operations we have to use such tools to manipulate the data and entities to solve the issues. With the help of these tools, no need to use core programming languages in order to implement Data Science. There are pre-defined functions, algorithms, and a user-friendly Graphical User Interface (GUI). As we know that Data Science has a very fast execution process, one tool is not enough to implement this.

Most Frequent Used Tools For Data Science

1. Apache Hadoop

Apache Hadoop is a free, open-source framework by Apache Software Foundation authorized under the Apache License 2.0 that can manage and store tons and tons of data. It is used for high-level computations and data processing. By using its parallel processing nature, we can work with the number of clusters of nodes. It also facilitates solving highly complex computational problems and tasks related to data-intensive.

• Hadoop offers standard libraries and functions for the subsystems.
• Effectively scale large data on thousands of Hadoop clusters.
• It speeds up disk-powered performance by up to 10 times per project.
• Provides the functionalities of modules like Hadoop Common, Hadoop YARN, Hadoop MapReduce.

2. SAS (Statistical Analysis System)

SAS is a statistical tool developed by SAS Institute. It is a closed source proprietary software that is used by large organizations to analyze data. It is one of the oldest tools developed for Data Science. It is used in areas like Data Mining, Statistical Analysis, Business Intelligence Applications, Clinical Trial Analysis, Econometrics & Time-Series Analysis.

Latest Version: SAS 9.4

• It is a suite of well-defined tools.
• It has a simple but most effective GUI.
• It provides a Granular analysis of textual content.
• Easy to learn and execute as there is a lot of available tutorials with appropriate knowledge.
• Can make visually appealing reports with seamless and dedicated technical support.

3. Apache Spark

Apache Spark is the data science tool developed by Apache Software Foundation used for analyzing and working on large-scale data. It is a unified analytics engine for large-scale data processing. It is specially designed to handle batch processing and stream processing. It allows you to create a program to clusters of data for processing them along with incorporating data parallelism and fault-tolerance. It inherits some of the features of Hadoop like YARN, MapReduce, and HDFS.

Latest Version: Apache Spark 2.4.5

• It offers data cleansing, transformation, model building & evaluation.
• It has the ability to work in-memory makes it extremely fast for processing data and writing to disk.
• It provides many APIs that facilitate repeated access to data.

4. Data Robot

DataRobot Founded in 2012, is the leader in enterprise AI, that aids in developing accurate predictive models for the real-world problems of any organization. It facilitates the environment to automate the end-to-end process of building, deploying, and maintaining your AI. DataRobot’s Prediction Explanations help you understand the reasons behind your machine learning model results.

• Highly Interpretable.
• It has the ability to making the model’s predictions easy to explain to anyone.
• It provides the suitability to implement the whole Data Science process at a large scale.

5. Tableau

Tableau is the most popular data visualization tool used in the market, is an American interactive data visualization software company founded in January 2003, was recently acquired by Salesforce. It provides the facilities to break down raw, unformatted data into a processable and understandable format. It has the ability to visualize geographical data and for plotting longitudes and latitudes in maps.

Latest Version: Tableau 2020.2

• It offers comprehensive end-to-end analytics.
• It is a fully protected system that reduces security risks to the maximum state.
• It provides a responsive user interface that fits all types of devices and screen dimensions.

6. BigML

BigML, founded in 2011, is a Data Science tool that provides a fully interactable, cloud-based GUI environment that you can use for processing Complex Machine Learning Algorithms. The main goal of using BigML is to make building and sharing datasets and models easier for everyone. It provides an environment with just one framework for reduced dependencies.

Latest Version: BigML Winter 2020

• It specializes in predictive modeling.
• It has ability to export models via JSON PML and PMML makes for a seamless transition from one platform to another.
• It provides an easy to use web-interface using Rest APIs.

7. TensorFlow

TensorFlow, developed by Google Brain team, is a free and open-source software library for dataflow and differentiable programming across a range of tasks. It provides an environment for building and training models, deploying platforms such as computers, smartphones, and servers, to achieving maximum potential with finite resources. It is one of the very useful tools that is used in the fields of Artificial Intelligence, Deep Learning, & Machine Learning.

Latest Version: TensorFlow 2.2.0

• It provides good performance and high computational abilities.
• Can run on both CPUs and GPUs.
• It provides features like easily trainable and responsive construct.

8. Jupyter

Jupyter, developed by Project Jupyter on February 2015 open-source software, open-standards, and services for interactive computing across dozens of programming languages. It is a web-based application tool running on the kernel, used for writing live code, visualizations, and presentations. It is one of the best tools, used by scratch level programmers & data science aspirants, by which they can easily learn and adapt the functionalities related to the Data Science field.

Latest Version: Jupyter Notebook 6.0.3

• It provides an environment to perform data cleaning, statistical computation, visualization and create predictive machine learning models.
• It has the ability to display plots that are the output of running code cells.
• It is quite extensible, supports many programming languages, easily hosted on almost any server.