Data Science Tools and Technologies to consider Using in 2024

Enterprise data is becoming more and more complicated, and because it plays a crucial role in strategic planning and decision-making, businesses are being forced to spend on the people, procedures, and technology necessary to extract meaningful business insights from their data assets. This encompasses an array of instruments frequently employed in data science implementations.

87.9% of chief data officers and senior IT and business executives from 102 large organisations stated that investing in data and analytics is a high priority in an annual poll done by consultancy firm Wavestone. According to a report on the Data and AI Executive Leadership Survey that was released in December 2023, 82.2% of respondents anticipate spending increases this year.

Additionally, according to the poll, 87% of the responding organisations reported that their efforts in data and analytics produced meaningful commercial value in 2023—a tiny decrease from 91.9% in 2022. However, the objectives of strategic analytics improved. Approximately 10% more respondents than in the last study stated that they are competing on data and analytics. Furthermore, more than twice as many people (48.1%) as the previous year (23.9%) believe they have built a data-driven organisation.

Data science teams have a large range of tools and platforms to pick from when they assemble their portfolios of enabling technologies to meet those analytics objectives. 

Here is an alphabetical list of the top 6 data science tools that could help you with the analytics process, along with information about their features, capabilities, and possible drawbacks.

1. Apache Spark

Proponents claim that Apache Spark, an open source data processing and analytics engine, is capable of handling petabytes of data. Since its creation in 2009, Spark has seen a substantial increase in usage due to its speedy data processing, which has led to the creation of one of the biggest open source communities for big data technologies: the Spark project.

Spark excels in continuous intelligence applications that process streaming data almost in real-time due to its speed. It also serves as a versatile distributed processing engine, effectively handling various SQL batch tasks and extract, transform, and load (ETL) applications. Initially, Spark was marketed as a faster batch processing engine for Hadoop clusters compared to MapReduce.

While Spark can operate independently with different file systems and data repositories, data scientists often use it alongside Hadoop. Its broad range of developer libraries and APIs, supporting major programming languages and a machine learning framework, makes it easier for data scientists to utilize the platform effectively.

2. D3.js

Another free resource is the JavaScript framework D3.js, which allows users to create personalised data visualisations in web browsers. Often referred to as D3, or Data-Driven Documents, it does not rely on its own graphical vocabulary, but rather on web standards like HTML, Scalable Vector Graphics, and CSS. The creators of D3 characterise it as a dynamic and adaptable tool that produces visual representations of data with little effort.

With D3.js, visualisation designers can utilise the Document Object Model to tie data to documents, and then utilise DOM manipulation techniques to apply data-driven changes to the pages. Initially launched in 2011, this tool facilitates the creation of diverse data visualisations and offers functionalities like annotation, animation, interaction, and quantitative analysis.

D3 is difficult to master because it has more than 30 modules and 1,000 visualisation techniques. Furthermore, a lot of data scientists lack JavaScript knowledge. As a result, individuals may prefer using a commercial visualization program like Tableau, while data visualization experts and developers within data science teams more frequently use D3.

3. IBM SPSS

A set of software programmes called IBM SPSS is used to organise and examine intricate statistical data. It consists of two main products: SPSS Modeller, a platform for data science and predictive analytics with a drag-and-drop user interface and machine learning capabilities, and SPSS Statistics, a statistical analysis, data visualisation, and reporting tool.

In addition to allowing users to discover patterns, generate data point clusters, make predictions, and clarify relationships between variables, SPSS Statistics covers every stage of the analytics process, from planning to model implementation. It offers a combination of a menu-driven user interface, its own command syntax, and the ability to incorporate R and Python extensions. It can access common structured data formats. It also provides import/export connections to SPSS Modeller and tools for automating processes.

SPSS Inc. developed the statistical analysis software in 1968, initially naming it the Statistical Package for the Social Sciences. IBM purchased the software in 2009 together with the predictive modelling platform that SPSS had previously purchased. Even though IBM SPSS is the official name of the product family, most people still refer to the software as SPSS.

4. Julia

Julia is an open-source programming language used for machine learning, data science applications, and numerical computation. The four people who created Julia stated in a 2012 blog post that their goal was to create a single language that could fulfil all of their requirements. Preventing the need to write programmes in one language and translate them into another before executing them was a major objective.

In order to achieve this, Julia offers the efficiency of statically typed languages like C and Java, along with the ease of use of a high-level dynamic language. Programme users are not required to establish data types, but they can if they so choose. Using a multiple dispatch strategy during runtime contributes to faster execution as well.

Nine years after the language’s development started, Julia 1.0 was made accessible in 2018. The most recent version is 1.9.4, and a 1.10 update is currently available for release candidate testing. Because Julia’s compiler is not like the interpreters of data science languages like Python and R, the documentation states that new users “may find that Julia’s performance is unintuitive at first.” Yet, it asserts, “once you understand how Julia works, it’s easy to write code that’s nearly as fast as C.”

5. Jupyter Notebook

Jupyter Notebook is an open-source web tool that facilitates interactive collaboration between mathematicians, researchers, data scientists, and data engineers. It’s a computational notebook application that lets you write, edit, and distribute code together with explanations, pictures, and other data. For instance, Jupyter users can combine rich media representations of computation results, software code, computations, comments, and data visualizations into a single document called a notebook. They can then share and edit this notebook with their peers.

Because of this, Jupyter Notebook documentation states that notebooks “can serve as a complete computational record” of interactive sessions amongst members of data research teams. Version control is available for the JSON files that make up the notebook documents. Furthermore, users can render notebooks as static web pages using the Notebook Viewer service, making them accessible to users without Jupyter installed on their computers.

Python is the programming language that is the foundation of Jupyter Notebook. Prior to being divided off in 2014, it was a component of the open source IPython interactive toolkit project. Jupyter got its name from a vague blend of Julia, Python, and R. Jupyter provides modular kernels not just for those three languages but for hundreds more. JupyterLab, a more recent web-based user interface that is more adaptable and extendable than the original one, is also part of the open-source project.

6. Keras

Data scientists may more easily access and utilise the TensorFlow machine learning platform with the help of Keras, a programming interface. It’s a Python deep learning framework and API that is open source and operates on top of TensorFlow. It is currently incorporated into that system. Previously supporting several back ends, Keras was only compatible with TensorFlow as of June 2020, when it released version 2.4.0.

Unlike other deep learning alternatives, Keras requires less coding for easy and quick experimentation because it is a high-level API. Through a development approach with “high iteration velocity,” as the Keras documentation describes it, the objective is to expedite the creation of machine learning models, particularly deep learning neural networks.

The Keras framework comes with a functional API for producing more sophisticated graphs of layers or designing deep learning models from scratch, in addition to a sequential interface for building relatively simple linear stacks of layers with inputs and outputs. You can implement and operate Keras models on various platforms, including web browsers, iOS and Android mobile devices, and GPUs.

Conclusion

The demand for qualified data scientists has surged dramatically due to the growing volume of data across all industries. This rise presents a wealth of opportunities for a rewarding and dynamic career. Aspiring professionals can choose from numerous learning paths, including various online data science course in Delhi, Mumbai, Gurgaon and other cities in India, to master both fundamental and advanced aspects of data science.