Top 25 Data Science Tools to be Used in 2024

A list of 25 most popular data science tools used in successful businesses to build machine learning models, develop complex statistical algorithms, and perform other advanced data science tasks.

1. Apache Spark 

It is an open-source analytics and data processing engine that efficiently manages enormous data amounts. Its speed makes it suitable for continuous intelligence applications. It enables timely data streaming processes. 91% of users prefer Apache due to its high performance, while 77% use it because of its ease of use. 

Apache Spark is perfectly suited for tasks like extracting, transforming, and loading data, making it one of the best data science tools. It can also perform multiple SQL batch jobs. Often, data scientists use Apache with Hadoop, but it can operate solo against other data stores and file systems. 

Key features: High-speed processing, fast unified analytics engine, huge ML libraries

Pros: 

• Real-time streaming
• Supports SQL apps
• It is flexible

Cons: 

• Unavailability of dedicated file management.
• Glitches with small files.

Cost: $399/year

2. Talend 

This data science tool is known to build software solutions for application integration and data preparation. The best qualities of Talend include updated statistics, early cleansing, smooth scalability, better collaboration, efficient management, faster designing, and native code access. Talend has a vast community of users that can be incredibly helpful for you as you can get constant support and guidance. The advanced tool is built keeping the present and future needs of the data science field in mind.

Key features: Cloud storage, Enterprise application integration, Unified platform.

Pros:

• It is good for showcasing.
• It is language-independent.

Cons: 

• It’s not self-contained.
• Not preferable for collaboration.

Cost: $12,000/year

3. D3.js

Another data science tool you can consider is D3.js (Data-Driven Documents). It works as a JavaScript library, helping users to create custom data visualizations in the internet browser. Instead of using its native graphical vocabulary, the tool utilizes web standards, including CSS, HTML, and scalable vector graphics. In simple words, this data science tool is flexible and dynamic and needs minimum effort to create visual data representations. 

Key features: Declarative programming, Code reusability, curve generating functions.

Pros:

• D3.js is a highly data-driven tool. 
• It’s the most specialized and appropriate tool for data visualizations.  
• It offers a fantastic community. 

Cons: 

• Needs to improve documentation.
• It lacks high-quality creative visualization charts.

Cost: $108/ year.

4. Go Spot Check

It is one of the most powerful data science tools best suited for field teams, allowing them to collect and distribute data instantly. It works as a BI and analytics platform that users can leverage to gather timely details and run a quick analysis. It helps business leaders to make well-thought operational decisions. 

The tool works in just 3 steps: 

• Creating
• Collecting
• Analyzing

Users can examine data in real-time and access its dashboard to monitor the progress and performance of work.

Key features: Reporting, Data Structuring, integration and filtering, mobile sharing.

Pros:

• Offers advanced analysis.
• Provides a smart tool for form creation.
• Facilitates real-time data collection. 

Cons: 

• Limited history retention.
• Learning its functions is quite challenging.. 

Cost: $300/year

5. IBM SPSS

The tool can effectively manage and analyze complex statistical data. It provides two main products: 

• SPSS Statistics: It is a tool to perform data reporting and visualization. 
• SPSS Modeler: A predictive analytics platform. It includes machine learning and drag-and-drop UI capabilities. 

SPSS Statistics enables users to perform all the analytics steps beginning from planning to deployment of the model. It also helps users clarify the relations between variables, identify trends, create data point clusters, and make predictions.

Key features: Visual graphing and reporting, advanced data preparation, linear regression analytics.

Pros: 

• It can predict categorical outcomes.
• It can manage large amounts of data.
• Facilitates smooth user interface.

Cons: 

• Services are almost the same as Excel.
• It has limited functionality.

Cost: $1,188 to $8,450/year

6. Mozenda

It is a business web-scraping cloud-based platform. Business leaders can efficiently gather and organize web data in the most affordable manner. It has a user-friendly UI and a point-to-click interface. It comes in two parts: 

• With an application for building the data extraction project. 
• A web console for running agents, organizing results, and exporting data. 

It’s simple to integrate and enables users to deliver results in the XML, CSV, JSON, or TSV format. 

Key features: Data Import/Export API, Web data extraction, Multiple data sources.

Pros:

• Offers best customer support experience. 
• Maintains a consolidated data feed for multiple sources. 
• It can manage and automate the entire data collection process.

Cons: 

• High price.
• Not preferred for complex data points.

Cost: $3500/year

7. Julia

Julia is known as an open-source programming language utilized for machine learning, numerical computing, and various types of data science applications. The advanced data science tool provides a premium dynamic language with such a performance that matches with statically typed languages like Java and C. Though users don’t need to define the data types in programs, an option is available if they want to do so. Its execution speed is faster due to the use of numerous dispatch approaches at runtime. As of January 1st, 2022, for Julia + packages, there are over 250,000 GitHub stars.

Key features: Metaprogramming, Just-in-time compiler, MIT licensed.

Pros:

• It facilitates high-performance. 
• The language is perfect for interactive use. 
• Easily expresses functional and object-oriented programming patterns via multiple dispatch. 

Cons: 

• Shows low responsiveness.
• Difficulty in sharing programmes.

Cost: Free of cost

8. Octoparse

The tool is for Windows, known to be a client-side web scraping software. Without coding, it can convert semi-structured or unstructured data from sites into a structured data set. It is one of the most appropriate data science tools for people who do not have a programming background. Its web scraping template is simple to use yet one of the most powerful features. It inputs the target keywords/websites in the parameters over the pre-formatted tasks. 

Key features: Automatic pagination, IP rotation, Configurable workflow.

Pros:

• It can save data in XML, CSV, HTML.
• Speeds up the process of data extraction through blocking the ads.
• Facilitates built-in templates.

Cons: 

• User-interface needs improvement. 
• Works only for Windows. 

Cost: $4,899/year

9. Jupyter Notebook

The tool allows researchers, data engineers, mathematicians, data scientists, and even general users to carry out interactive collaboration among themselves. This computational notebook application creates, edits, and shares code. It also generates explanatory images, texts, and other information. Users can add software code, comments, computations, computation result-rich media representations, and data visualizations to a sole document called the notebook.

Key features: 40+ programming languages, Code-autocompletion, Live presentation.

Pros:

• It can display running code cells outputs. 
• Codes are easy to read. 
• Prepares a more structured program. 

Cons: 

• Can not be modularised. 
• Poor security.

Cost: Free of cost

10. OnBase

It works as a solitary business information platform that manages content, tasks, and cases. This data science tool operates by centralizing the business content at a secure location. Further, users can extract relevant data whenever they want. By implementing OneBase, organizations can become more capable, agile, and efficient. Thus, users can expect increased productivity, better customer service, and reduced risk across their business operations. 

Key features: Enterprise content management, Business process management, Case management.

Pros:

• It provides configurable solutions.
• Best option for administrators with no technical background.
• It can add and expand solutions. 

Cons: 

• Hard to navigate.
• Document correction workflow is not smooth. 

Cost: $25,000 (one time payment).

11. Keras

This programming interface allows data scientists to easily access and utilize ML platforms. This open-source deep learning system and API is written using Python. The system contains a sequential interface that can create simple linear layers’ stacks, including outputs and inputs. It will also include a functional API to create more complex graph layers. Users can write deep learning models/programs from scratch. 

Key features: ML algorithm library, document classification, model training.

Pros: 

• It has a high-level interface.
• It can run conveniently on both GPU and CPU. 
• It supports most of the neural network modes. 

Cons: 

• Sometimes it gives low-level backend errors.
• Its data pre-processing tools are not satisfying. 

Cost: N/A

12. PyTorch

The tool is mainly used to create and train deep learning models depending on neural networks. Its proponents tout it to enable flexible and quick experimentation resulting in smooth deployment from transition to production. This Python-based library is simple to use and runs as a precursor ML framework based on the programming language named Lua. It is considered a better performer than Torch. 

Key features: Production ready, End-to-end, ML framework. 

Pros: 

• It’s simple to code. 
• It’s faster and flexible.
• It supports CPU and GPU. 

Cons: 

• Lacks a proper coherent model.
• It does not provide visualization interfaces. 

Cost: Free of cost

13. Domino Data Lab 

It can automate DevOps for data science. It enables users to put their time and efforts into researching and testing better ideas at a much faster pace. The automatic tracking of the processes allows reusability, reproducibility, and collaboration. Domino is a unique data science tool that provides visibility into computing usage, data science products, projects, etc., to assist in team management as it evolves. 

Key features: Integrated workflows, Cloud-hosted infrastructure.

Pros:

• It is an open and flexible space. 
• It allows teams to collaborate on projects without any hassle. 
• It comes with integrated security. 

Cons: 

• It’s highly pricy. 

Cost: $75,000/year

14. Matlab

This data science tool focuses on providing robust data visualization. Aside from data visualization, it operates as a top-notch analytics and programming language for mathematical modeling and numerical computing. Mainly conventional scientists and engineers use this tool for analyzing data and designing algorithms. The tool also develops integrated systems to enable wireless communications, signal processing, industrial control, and other applications. 

Key features: Mathematical functions library,  Interactive environment, Text analytics. 

Pros:

• It enables multiple language interfaces. 
• Offers API. 
• It provides in-built graphics. 

Cons: 

• Takes longer to execute. 
• It requires large memory data. 

Cost: $149 (perpetual)

15. Matplotlib platform for data science

It’s a Python plotting library, which is an open-source. It reads, imports, and visualizes information in analytics applications. Data scientists have been using the tool to build animated, static, and interactive data visualizations. Further, users can utilize it in Python, Python scripts, Jupyter Notebooks, GUI toolkits, IPhython shells, etc. 

Key features: Multiple plot types, Diverse graphical representation, 2-D plotting library.  

Pros:

• It supports line graphs, stem plots, histograms etc. 
• It can be used in numerous ways: ipython shells, Python scripts, and Python. 
• It offers high-quality images in different formats including pgf, pdf, png etc.

Cons: 

• Complex infrastructure. 
• Adjusting plots is difficult. 

Cost: Free of cost.

16. KNIME Analytics Platform 

KNIME is among the open and intuitive data science tools. It often integrates new developments regularly. It effectively understands and designs data science workflows and makes reusable elements accessible to everyone. The tool lets users select among 2000 different nodes to create the workflow, set up every analysis step, manage data flow, and ensure work is updated. Moreover, it can connect to a database host as well as data warehouses for integrating data from Apache Hive, Microsoft, Oracle, and more.

Key features:  Intelligent data caching, Integrated deployment, Metadata mapping.

Pros: 

• Most suitable for visually-driven programming. 
• It facilitates hybrid and elastic execution. 
• Offers guided analytics applications. 

Cons: 

• Scalability needs improvement.
• Lacks technical expertise in some functions. 

Cost: Free of cost.

17. NumPy

It stands for Numerical Python, an open-source library for Python programming language. It has been highly in use in areas of engineering, ML and data science applications, and scientific computing. NumPy contains multidimensional routines and array objects. It processes those arrays to support numerous logic and mathematical functions, random number generation, linear algebra, etc. 

Key features: Random number capabilities, Broadcasting functions, N-dimensional array object.

Pros:

• It requires less memory space. 
• It provides improved runtime speed.
• It efficiently deals with linear algebra problems. 

Cons:

• It demands contiguous memory allocation.
• Operational processes are costly.

Cost: Free of cost.

18. Scikit-learn

It’s an ML library that data scientists can access as an open source. It’s built on the NumPy scientific computing libraries and SciPy. It includes matplotlib to plot data. It backs up unsupervised and supervised ML. Users can find multiple models and algorithms known as estimators. In addition to it, it offers selection and evaluation, functionality for model fit, selection + evaluation, processing data, and transforming it. 

Key features: XG boost, Data splitting, Logistic regression.

Pros:

• The scikit-learn tool is very handy and very versatile.
• It has the support of the International online community.
• It comes with an elaborate API documentation.

Cons: 

• Not a suitable option for in-depth learning. 
• It doesn’t support graph algorithms.

Cost: Free of cost

19. Rapid Miner

The tool is best suited for researchers who want faster data analysis and users with no programming background. The users can create procedures, input data into them, and run and present a prediction model. It can efficiently import web apps (nodeJS, flask, android, etc.), ML models, etc. 

Key features: Data exploration, Data prep, Code control. 

Pros:

• It displays powerful visual programming. 
• Accurately evaluates model performance.
• It is extensible via open platform APIs. 

Cons: 

• It has less supportive forums.
• It can slow down your system by acquiring large memory space.

Cost: $7,500 to $54,000/year

20. Pandas

It’s a Python library that helps data scientists analyze and manipulate extracted data. The library is built on top of another Python library called NumPy. Mainly it features these two data structures: 

• Dataframe
• Series

Both of these receive data from multiple inputs like NumPy arrays. A DataFrame is capable of incorporating numerous Series objects as well. 

Key features:  Input and output tools,  Alignment and indexing, Grouping, Mask data.

Pros:

• Efficient and fast DataFrame object.
• Tools for loading data into in-memory data objects from different file formats.
• It can handle and align missing data.

Cons: 

• Low-quality documentation.
• Poor 3D matrix compatibility.

Cost: N/A

21. Qlik 

This tool works as a visual analytics platform for data science, supporting a number of use cases. Some of the popular usages of Qlik include centrally implemented guided analytics dashboards and applications and embedded and custom analytics. Its scalable and well-organized framework allows self-service visualizations also. The tool is suitable for both individual users and teams. Regardless of their sizes, businesses can explore complex data to discover associations in the datasets using its data discovery tool. 

Key features:  Associative Model, Data Storytelling, Data Preparation and Integration.

Pros: 

• It can perform complex data analysis.
• It enables smooth data interpretation and sharing. 
• It offers better data security. 

Cons: 

• Limited visualization
• Rigid data extraction capabilities

Cost: $360/year

22. Python

One of the popular programming languages in the field of data science is Python. As per its official website, Python can be defined as an object-oriented, interpreted, and top-notch programming language consisting of dynamic semantics. It offers dynamic typing, native data structures, and binding capabilities. Python is known for having the simplest syntax, which makes it much easier to learn. In addition to this, its high readability minimizes the program maintenance costs. As per a finding, 86.7% data science tools users prefer Python. 

Key features: Object-oriented programming, open source, High-level language. 

Pros: 

• It is highly versatile.
• It has straightforward syntax.
• It’s widely in use.

Cons: 

• Requires large memory amounts.
• Comparatively has less speed than other languages.

Cost: Free of cost

23. SAS

SAS can be described as an integrated software. It’s best suited to perform data management, statistical analysis, BI, and advanced analytics. The tool allows users to cleanse, integrate, create and manipulate data. Thus, users can easily analyze data with the help of multiple data science and statistical techniques. SAS is useful for performing numerous tasks ranging from basic data visualization and BI to data mining, risk management, predictive analytics, machine learning, and operational analytics.

Key features: Strong data analysis abilities, flexible with 4GL (Generation Programming Language) and supports various types of data format.

• It’s easily accessible.
• It’s business-focused.
• Provides good user support.

Cons:

• Expensive.
• Poor-quality graphical representation.

Cost: $8,700/year

24. SciPy

SciPy has been supporting scientific computing efficiently as an open-source library for Python. It comes with a mathematical algorithms set and high-level classes/commands for data visualization and manipulation. It also features over a dozen subpackages consisting of utilities and algorithms for multiple functions. These functions involve data integration, optimization, and interpolation. It is also useful to resolve functions related to image statistics and processing, algebraic equations, and differential equations.

Key features: ODE solvers, Signal and image processing, High-level commands.

Pros:

• It comes with modules for optimization.
• It enables integration and interpolation. 
• A suitable tool to solve linear algebra expressions. 

Cons: 

• It’s not simple to learn.

Cost: Free of cost

25. Weka

It is an open-source workbench providing an ML algorithm collection to perform data mining tasks. The algorithms that Weka uses are known as classifiers; these can be implemented directly to data sets without needing any programming. The tool makes it possible by its command-line interface or GUI, providing additional functionality space. Weka is a suitable choice for processes like clustering, classification, association rule mining applications, and regression. Aside from this, it also offers multiple tools for data visualization and processing. 

Key features: Data mining, Data attribute selection, Data connectors. 

Pros:

• It facilitates a simple interface that is easy to use.
• It comes with different types of analyses including decision trees. 
• It can simplify analysis and clustering of data. 

Cons: 

• It’s difficult to integrate with Python.
• Working with Weka is quite difficult. 

Cost: N/A

Takeaway

So what do we think of this new Data Science Era, and the Tools? We have to say that we are looking forward to it, and all the opportunities it will bring in developing a better world. The concerns are obviously there, with how much data surveillance can be done. Yet, as long as we make sure to also take advantage of the analytical, predictive and prescriptive powers of Data Science for the good of humanity, then we believe anything could happen and nothing is beyond reach anymore.

If you need any help with your data science ambitions for your business, talk to Zuci’s data science and analytics engineers for outstanding solutions.

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