8 Data Science Applications To Consider In 2023

Because of the increasing volume and complexity of enterprise data, as well as its central role in decision-making and strategic planning, organisations are investing in the people, processes, and technologies required to gain valuable business insights from their data assets. This includes a number of tools that are commonly used in data science applications.

According to Wavestone’s NewVantage Partners unit’s annual survey, 87.8% of chief data officers and other IT and business executives from 116 large organisations said their investments in data and analytics initiatives, such as data science programmes, will increase in 2022. According to a report on the Data and Analytics Leadership Executive Survey published in January 2023, 83.9% expect further increases this year despite the current economic conditions.

According to the survey, 91.9% of responding organisations received measurable business value from their data and analytics investments in 2022, and 98.2% expect their planned 2023 spending to pay off. Many strategic analytics goals, however, remain aspirational: only 40.8% of respondents said they compete on data and analytics, and only 23.9% have established a data-driven organisation.

As data science teams build their portfolios of enabling technologies to help them achieve their analytics goals, they have a wide range of tools and platforms to choose from. Here’s a list of 18 top data science tools that may be able to help you with your analytics, listed alphabetically with details on their features and capabilities – as well as some potential limitations.

1. Spark by Apache

According to supporters, Apache Spark is an open-source data processing and analytics engine that can handle large amounts of data – up to several petabytes. Spark’s ability to process data quickly has fuelled significant growth in its use since its inception in 2009, contributing to the Spark project becoming one of the largest open-source communities among big data technologies.

Spark’s speed makes it ideal for continuous intelligence applications that require near-real-time processing of streaming data. However, because Spark is a general-purpose distributed processing engine, it is equally suited for extract, transform, and load operations as well as other SQL batch jobs. Indeed, Spark was originally marketed as a faster alternative to the MapReduce engine for batch processing in Hadoop clusters.

Spark is still frequently used in conjunction with Hadoop, but it can also be used independently against other file systems and data stores. It includes a large set of developer libraries and APIs, including a machine learning library and support for key programming languages, making it easier for data scientists to get started quickly with the platform.

2. js

D3.js is a JavaScript library that can be used to create custom data visualisations in a web browser. It is commonly referred to as D3, which stands for Data-Driven Documents, and it employs web standards such as HTML, Scalable Vector Graphics, and CSS rather than its own graphical vocabulary. D3’s creators describe it as a dynamic and flexible tool that generates visual representations of data with minimal effort.

D3.js allows visualisation designers to bind data to documents using the Document Object Model and then manipulate the documents using DOM manipulation methods. It was first released in 2011 and can be used to create various types of data visualisations with features like interaction, animation, annotation, and quantitative analysis.

However, D3 contains over 30 modules and 1,000 visualisation methods, making it difficult to learn. Furthermore, many data scientists lack JavaScript proficiency. As a result, they may prefer a commercial visualisation tool such as Tableau, leaving D3 to be used more by data visualisation developers and specialists who are also members of data science teams.

3. IBM SPSS for Windows

IBM SPSS is a software suite for managing and analysing large amounts of complex statistical data. SPSS Statistics, a statistical analysis, data visualisation, and reporting tool, and SPSS Modeler, a data science and predictive analytics platform with a drag-and-drop UI and machine learning capabilities, are its two main products.

SPSS Statistics covers every step of the analytics process, from planning to model deployment, and allows users to, among other things, clarify relationships between variables, create clusters of data points, identify trends, and make predictions. It supports common structured data types and provides a menu-driven user interface, its own command syntax, and the ability to integrate R and Python extensions, as well as features for automating procedures and import/export ties to SPSS Modeler.

The statistical analysis software, developed by SPSS Inc. in 1968 under the name Statistical Package for the Social Sciences, was acquired by IBM in 2009, along with the predictive modelling platform that SPSS had previously purchased. While the product family is officially known as IBM SPSS, the software is still commonly referred to as SPSS.

4. Julia

Julia is an open source programming language that can be used for numerical computing, machine learning, and other data science applications. Julia’s four creators stated in a 2012 blog post that they set out to design one language that addressed all of their needs. A major goal was to avoid writing programmes in one language and then converting them to another for execution.

Julia achieves this by combining the convenience of a high-level dynamic language with performance comparable to statically typed languages like C and Java. Users are not required to define data types in programmes, but they have the option to do so. The use of a multiple dispatch approach at runtime also contributes to faster execution.

Julia 1.0 was released in 2018, nine years after work on the language began; the most recent version is 1.8.4, with a 1.9 update now available for beta testing. Because Julia’s compiler differs from the interpreters in data science languages like Python and R, new users “may find Julia’s performance unintuitive at first,” according to the documentation. However, “once you understand how Julia works, it’s easy to write code that’s nearly as fast as C,” it claims.

5. Jupyter Notebook 5

Jupyter Notebook is an open-source web application that allows for interactive collaboration among data scientists, data engineers, mathematicians, researchers, and other users. It’s a computational notebook tool for writing, editing, and sharing code, as well as explanatory text, images, and other data. Jupyter users, for example, can combine software code, computations, comments, data visualisations, and rich media representations of computation results into a single document known as a notebook, which can then be shared and revised by colleagues.

As a result, notebooks “can serve as a complete computational record” of interactive sessions among data science team members, according to the documentation for Jupyter Notebook. The notebook documents are JSON files that support version control. A Notebook Viewer service also allows them to be rendered as static webpages for users who do not have Jupyter installed on their systems.

Jupyter Notebook has its roots in the programming language Python; it was originally part of the open source IPython interactive toolkit project before being split off in 2014. Jupyter gets its name from a loose combination of Julia, Python, and R; in addition to those three languages, Jupyter has modular kernels for dozens more. JupyterLab, a newer web-based UI that is more flexible and extensible than the original, is also included in the open source project.

6. Keras

Keras is a programming interface that makes it easier for data scientists to access and use the TensorFlow machine learning platform. It’s a Python-based open source deep learning API and framework that runs on top of TensorFlow and is now integrated into that platform. Keras previously supported multiple back ends, but with the release of 2.4.0 in June 2020, it became exclusively tied to TensorFlow.

Keras was created as a high-level API to enable quick and easy experimentation that requires less coding than other deep learning options. The goal is to accelerate the implementation of machine learning models, specifically deep learning neural networks, through a development process with “high iteration velocity,” as described in the Keras documentation.

The Keras framework includes both a sequential interface for building relatively simple linear stacks of layers with inputs and outputs and a functional API for building more complex graphs of layers or writing deep learning models from scratch. Keras models can run on CPUs or GPUs and be deployed across multiple platforms, including web browsers and mobile devices running Android and iOS.

7. Matlab

Matlab is a high-level programming language and analytics environment for numerical computing, mathematical modelling, and data visualisation that has been developed and sold by software vendor MathWorks since 1984. It’s primarily used by traditional engineers and scientists to analyse data, design algorithms, and create embedded systems for wireless communications, industrial control, signal processing, and other applications, frequently in conjunction with a companion Simulink tool that provides model-based design and simulation capabilities.

While Matlab isn’t as popular in data science applications as languages like Python, R, and Julia, it does support machine learning and deep learning, predictive modelling, big data analytics, computer vision, and other data scientist work. The platform’s data types and high-level functions are intended to accelerate exploratory data analysis and data preparation in analytics applications.

Matlab, which stands for matrix laboratory, is considered relatively simple to learn and use. It includes prebuilt applications but also allows users to create their own. It also includes a toolbox library with discipline-specific software and hundreds of built-in functions, such as the ability to visualise data in 2D and 3D plots.

8. Matplotlib

Matplotlib is an open-source Python plotting library that is used in analytics applications to read, import, and visualise data. Matplotlib allows data scientists and other users to create static, animated, and interactive data visualisations in Python scripts, the Python and IPython shells, Jupyter Notebook, web application servers, and various GUI toolkits.

The library’s large code base can be difficult to master, but it’s organised in a hierarchical structure that allows users to build visualisations primarily using high-level commands. The module pyplot, which provides a “state-machine environment” and a set of simple plotting functions similar to those found in Matlab, is at the top of the hierarchy.

Matplotlib, which was first released in 2003, also includes an object-oriented interface that can be used in conjunction with pyplot or on its own; it supports low-level commands for more complex data plotting. The library is primarily intended for creating 2D visualisations, but it also includes a toolkit with 3D plotting capabilities.