Top 10 tools a data scientist should use in 2021
The work of a data scientist centers around the process of extraction of meaningful data from unstructured information and analyzing that data for necessary interpretation. This requires a lot of useful tools. The following are the top 10 most necessary tools that a data scientist needs to know about in 2021.
Python is the most widely used programming language for data science and machine learning and one of the most popular languages overall. The Python open-source project’s website describes it as “an interpreted, object-oriented, high-level programming language with dynamic semantics,” as well as built-in data structures and dynamic typing and binding capabilities. The site also touts Python’s simple syntax, saying it’s easy to learn and its emphasis on readability reduces the cost of program maintenance. The multipurpose language can be used for a wide range of tasks, including data analysis, data visualization, AI, natural language processing, and robotic process automation. Developers can create web, mobile, and desktop applications in Python, too. In addition to object-oriented programming, it supports procedural, functional, and other types, plus extensions written in C or C++.
Jupyter Notebook is an open-source web application that enables interactive collaboration among data scientists, data engineers, mathematicians, researchers, and other users. It’s a computational notebook tool that can be used to create, edit and share code, as well as explanatory text, images, and other information. Jupyter users can add software code, computations, comments, data visualizations, and rich media representations of computation results to a single document, known as a notebook, which can then be shared with and revised by colleagues. As a result, notebooks “can serve as a complete computational record” of interactive sessions among the members of data science teams, according to Jupyter Notebook’s documentation. The notebook documents are JSON files that have version control capabilities. In addition, a Notebook Viewer service enables them to be rendered as static web pages for viewing by users who don’t have Jupyter installed on their systems.
Apache Spark is an open-source data processing and analytics engine that can handle large amounts of data, upward of several petabytes, according to proponents. Spark’s ability to rapidly process data has fueled significant growth in the use of the platform since it was created in 2009, helping to make the Spark project one of the largest open-source communities among big data technologies. Due to its speed, Spark is well suited for continuous intelligence applications powered by near-real-time processing of streaming data. However, as a general-purpose distributed processing engine, Spark is equally suited for extract, transform and load uses and other SQL batch jobs. Spark initially was touted as a faster alternative to the MapReduce engine for batch processing in Hadoop clusters.
Keras is a programming interface that enables data scientists to more easily access and use the TensorFlow machine learning platform. It’s an open-source deep-learning API and framework written in Python that runs on top of TensorFlow and is now integrated into that platform. Keras previously supported multiple back ends but was tied exclusively to TensorFlow starting with its 2.4.0 release in June 2020. As a high-level API, Keras was designed to drive easy and fast experimentation that requires less coding than other deep learning options. The goal is to accelerate the implementation of machine learning models, in particular, deep learning neural networks through a development process with “high iteration velocity,” as the Keras documentation puts it. The Keras framework includes a sequential interface for creating relatively simple linear stacks of layers with inputs and outputs, as well as a functional API for building more complex graphs of layers or writing deep learning models from scratch.
Xplenty, is data integration, ETL, and an ELT platform that can bring all the data sources together. It is a complete toolkit for building data pipelines. This elastic and scalable cloud platform can integrate, process, and prepare data for analytics on the cloud. It provides solutions for marketing, sales, customer support, and developers. Sales solution has the features to understand your customers, for data enrichment, centralizing metrics & sales tools, and for keeping your CRM organized. Its customer support solution will provide comprehensive insights, help you with better business decisions, customized support solutions, and features of automatic Upsell & Cross-Sell. Xplenty’s marketing solution will help you to build effective, comprehensive campaigns and strategies. Xplenty contains the features of data transparency, easy migrations, and connections to legacy systems.
IBM SPSS is a family of software for managing and analyzing complex statistical data. It includes two primary products: SPSS Statistics, a statistical analysis, data visualization, and reporting tool, and SPSS Modeler, a data science and predictive analytics platform with a drag-and-drop UI and machine learning capabilities. SPSS Statistics covers every step of the analytics process, from planning to model deployment, and enables users to clarify relationships between variables, create clusters of data points, identify trends and make predictions, among other capabilities. It can access common structured data types and offers a combination of a menu-driven UI, its command syntax, and the ability to integrate R and Python extensions, plus features for automating procedures and import-export ties to SPSS Modeler. Created by SPSS Inc. in 1968, initially with the name Statistical Package for the Social Sciences, the statistical analysis software was acquired by IBM in 2009, along with the predictive modeling platform, which SPSS had previously bought. While the product family is officially called IBM SPSS, the software is still usually known simply as SPSS.
An open-source framework used to build and train deep learning models based on neural networks, PyTorch, is touted by its proponents for supporting fast and flexible experimentation and a seamless transition to production deployment. The Python library was designed to be easier to use than Torch, a precursor machine learning framework that’s based on the Lua programming language. PyTorch also provides more flexibility and speed than Torch, according to its creators. First released publicly in 2017, PyTorch uses arraylike tensors to encode model inputs, outputs, and parameters. Its tensors are similar to the multidimensional arrays supported by NumPy, another Python library for scientific computing, but PyTorch adds built-in support for running models on GPUs. NumPy arrays can be converted into tensors for processing in PyTorch, and vice versa.
KNIME, for data scientists, will help them in blending tools and data types. It is an open-source platform. It will allow them to use the tools of their choice and expand them with additional capabilities. It is very useful for the repetitive and time-consuming aspects. Experiments and expands to Apache Spark and Big data. It can work with many data sources and different types of platforms.