The Data Science Toolkit in cse data science is the art of drawing and visualizing useful insights from data. It is the process of collecting, analyzing, and modeling data to solve problems related to the real world. To implement the operations users are required to have to use such tools to manipulate the data and entities to solve the issues. There are pre-defined functions, algorithms, and a user-cooperative Graphical User Interface (GUI). As humans know data science has a very fast execution process, and one tool is not enough to implement this.

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, individuals can work with the number of clusters of nodes.

● 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.

2. SAS (Statistical Analysis System)

SAS is a statistical tool developed by the 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 cse data science. It is used in areas like Data Mining, Statistical Analysis, Business Intelligence Applications, Clinical Trial Analysis, and Econometrics & Time-Series Analysis.

Latest Version: SAS 9.4

● It is a suite of well-defined tools.
● It has a simple but effective GUI.
● It provides a Granular analysis of textual content.

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.

Latest Version: Apache Spark 2.4.5

● It offers data cleansing, transformation, model building & evaluation.
● It can work in-memory making it extremely fast for processing data and writing to disk.
● It provides many APIs that facilitate repeated access to data.

4. Data Robot

Data Robot 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 the AI. Data Robot’s Prediction Explanations can help understand the reasons behind machine-learning model results.

● Highly Interpretable.
● It can make 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, and was recently acquired by Salesforce.

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. Big ML

Big ML, founded in 2011, is a cse data science tool that provides a fully intractable, cloud-based GUI environment that you can use for processing Complex Machine Learning Algorithms. The main goal of using Big ML 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: Big ML Winter 2020

● It specializes in predictive modeling.
● It can export models via JSON PML and PMML making 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 the Google Brain team, is a free and open-source software library for data and differentiable programming across a range of tasks. It provides an environment for building and training models, and deploying platforms such as computers, smartphones, and servers, to achieve 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 in 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.

Latest Version: Jupyter Notebook 6.0.3

● It provides an environment to perform data cleaning, statistical computation, visualization, and create predictive machine learning models.