13 Best Data Science Tools To Use In 2021

Have you ever wondered what’s the key process behind advances in technologies like Machine Learning and Augmented Reality? The answer is Data Science.

Data science is a field of study that unifies statistics, informatics, data analysis, and various related methods to understand complex patterns hidden inside structured or unstructured data.

It utilizes theories and techniques drawn from many different fields within the context of information science, computer science, and domain knowledge.

Today, there are tons of advanced tools available on the internet to extract knowledge from different types of data. However, not all of them are worth trying.

In this article, we have gathered some of the best data science tools that can be used by researchers and business analysts to generate valuable insights.

Before we start, we want to clarify that this list contains only Data Science tools and not the programming languages or scripts for implementing Data Science. 

9. DataRobot

Pros
  • Perfect for scaling up machine learning capabilties
  • Contains a massive library of open-source and proprietary models
  • Solves the Hardest data science problems
  • Provides fully explainable AI through human-friendly visual insights
Cons
  • Quite expensive compared to other tools

Price: Depends on the project’s size and complexity | Free-trial available

DataRobot is integrated with hundreds of the latest machine learning algorithms, which provides you full transparency and control over the model building and deployment process.

It starts by letting you choose the most suitable model to deploy from numerous possibilities. Using the DataRobot API, you can quickly put any model into production, regardless of whether you need batch deployments, real-time forecasts, or scoring on Hadoop. You may need to add few lines of code to customize the process.

In addition to emphasizing techniques such as transfer learning and machine learning, DataRobot also includes functions that ensure business value like profit curves, data-driven forecasts, and one-click deployment with governance.

The platform can be used to solve a wide range of data science problems, ranging from forecasting sales for millions of products to working with complex genomic data.

8. Alteryx

Pros
  • Intuitive interface
  • Ready-to-use predictive modeling templates
  • Visualizing complex queries
  • Drag-and-drop data prep, blending, and analytics
  • Integrated OCR and text analytics
Cons
  • Expensive
  • Assisted modeling features require additional license

Price: Starts at $2300 per year per user | 30-day free trial available

Alteryx unifies analytics, machine learning, data science, and process automation in one, end-to-end platform. It takes data from hundreds of platforms (including Oracle, Amazon, and Salesforce), letting you spend more time analyzing and less time searching.

You can explore data while creating, accessing, and selecting features with a visual programming interface — Analytic Process Automation. It makes it feasible to make granular changes in individual analytic building blocks, using prebuilt configuration options or by adding your own Python or R code in the analytic workflow.

Alteryx allows you to rapidly prototype machine learning models and pipelines with automated model-training building blocks. It helps you easily visualize the data throughout your entire problem-solving and modeling journey. How? It automatically creates tables, charts, and reports from any step in your process.

The platform is designed for companies of all sizes. If you are a mid-size business, it can help you find new insights and deliver high-impact outcomes.

7. H2O.ai

Pros
  • Distributed, in-memory machine learning
  • Easy to deploy large models
  • Automate the machine learning workflow
  • Works on existing big data infrastructure
Cons
  • Limited data processing options
  • Lack of documentation

Price: Depends on the project’s size and complexity | 14-day free trial available

H2O is an open-source, distributed in-memory machine learning tool with linear scalability. It supports almost all popular statistical and machine learning algorithms, including generalized linear models, deep learning, and gradient boosted machines. It takes data directly from Spark, Azure, Spark, HDFS, and various other sources into its in-memory distributed key-value store.

To build models, you can use either R/Python programming language or H2O Flow (a graphical notebook) that doesn’t require any coding.

H2O AutoML makes it easy to train and evaluate machine learning models. This helps you automate data science tasks (such as algorithm selection, iterative modeling, hyperparameter tuning, feature generation, and model assessment) and focus more on crucial problems. 

The platform is extremely popular among Python and R communities, and used by more than 18,000 organizations.

6. D3.js

Pros
  • Lightweight and fast
  • Gives you complete control of your data visualization
  • Works with web standards like SVG and HTML
  • Many built-in reusable functions and function factories
Cons
  • Documentation can be improved a little more

Price: Free

D3.js (short for Data-Driven Documents) is a JavaScript library for creating dynamic, interactive data visualizations in web browsers. It uses selective pre-designed functions to create SVG objects, customize them, or add dynamic effects to them. You can attach large datasets to these SVG objects to create text/graphic diagrams and charts.

D3 has no standard visualization format. It allows you to design anything from a Pie chart and graphs to HTML tables and geospatial maps.

The data can be in different formats like CSV or JSON. You can even write JavaScript code to read other data formats, or reuse code through a broad collection of official and community-developed modules.

5. Project Jupyter

Pros
  • Lightweight and easy to use
  • Great support for Python Math libraries
  • Predefined visualizations models
  • Easy to edit and track data flows
  • Automatically creates checkpointst
Cons
  • Complex to handle multiple kernels
  • Limited collaboration scope

Price: Free

Project Jupyter is a collection of open-source, interactive web tools, which data scientists can use to combine software code, computational output, multimedia resources, and explanatory text in a single document.

Although it has been around for decades, its popularity has exploded over the past couple of years. Jupyter offers various products to develop open-source software, open standards, and services for interactive computing.

  • Jupyter Notebook lets you create and share documents that contain live equations, code, visualizations, and narrative text.
  • Jupyter Kernels handles multiple requests, such as code execution and inspection, and provides a reply.
  • JupyterLab provides building blocks (terminal, file browser, text editor, rich outputs, etc.) in an intuitive user interface.
  • JupyterHub supports multiple users by spawning, managing, and proxying multiple singular Jupyter Notebook servers.

You can use these tools (free of cost) to perform numerical simulation, data cleaning, statistical modeling, data visualization, and much more, right from your browser.

4. Apache Spark

Pros
  • Robust and fault tolerant
  • Efficiently implements machine learning models for larger data sets
  • Can source data from multiple data sources
  • Multiple language support
Cons
  • High learning curve
  • Poor data visualization

Price: Free

Apache Spark is an open-source data-processing engine built for large data sets. It uses a state-of-the-art DAG scheduler, a query optimizer, and an efficient execution engine to achieve high performance for both batch and streaming data. It can run workloads up to 100 times faster.

Spark powers a bunch of libraries including GraphX, MLlib for machine learning, Spark Streaming, and SQL and DataFrames. All these libraries can be seamlessly merged into a single application.

This tool features a hierarchical master-slave architecture. The “Spark Driver” is the master node that manages multiple worker (slave) nodes and delivers data results to the application client.

The fundamental structure in Spark is Resilient Distributed Datasets, a fault-tolerant collection of components that can be distributed among several nodes in a cluster and worked on in parallel.

It provides more than 80 high-level operators, making it easy to develop parallel applications. Furthermore, you can also use Spark interactively from R, Python, Scala, and SQL shells.

3. IBM SPSS Statistics

Pros
  • Automated data preparation
  • Enables precise modeling of linear and non-linear relationships
  • Anomaly detection and forecasting
  • Support for R algorithms and graphics
Cons
  • Most features are available in paid versions
  • Interface looks outdated

Price: Starts at $99 per month | 30-day free trial available

SPSS Statistics is a powerful statistical software platform that allows you to make the most of the valuable information your data provides. It is designed to solve business and research problems through detailed analysis, hypothesis testing, and predictive analytics.

SPSS can read and write data from spreadsheets, databases, ASCII text files, and other statistics packages. It can read and write to external relational database tables via SQL and ODBC.

Most of the key features of SPSS are accessible via pull-down menus. You can use the 4GL command syntax language to simplify repetitive tasks and handle complex data manipulations and analyses.

Market researchers, data miners, governments, and survey companies extensively use this platform to understand data, analyze trends, validate assumptions, and make accurate conclusions.

2. RapidMiner

Pros
  • Comes with rich set of Machine Learning algorithms
  • Intuitive GUI
  • Full automation where needed
  • Extensions to link other useful tools
  • Comprehensive tutorials
Cons
  • Graphs are a bit old fashioned
  • Large datasets take time to process

Price: Free

Developed on an open core model, RapidMiner supports all steps of the machine learning method, including data preparation, result visualization, model validation, and optimization.

In addition to its own collection of datasets, RapidMiner provides several options to set up a database in the cloud for storing massive amounts of data. You can store and load data from various platforms such as NoSQL, Hadoop, RDBMS, and more.

Common tasks like data pre-processing, visualization, and cleaning can be performed via drag-and-drop options without having to write a single line of code.

RapidMiner’s library (which contains over 1,500 functions and algorithms) ensures the best model for any use case. It also comes with pre-designed templates that can be utilized in common use cases such as fraud detection, predictive maintenance, and customer churn.

The platform is extensively used to develop business and commercial software, as well as for rapid prototyping, education, training, and research. More than 700,000 analysts use RapidMiner to increase revenue, reduce operating costs, and avoid risks.

1. Apache Hadoop

Pros
  • Highly scalable scalable as it operates in a distributed environment
  • Redundant design ensures fault tolerance
  • Can be used in a cloud environment or commodity hardware
  • Store data in any format
Cons
  • Less efficient than other modern frameworks
  • Requires significant expertise to set up, maintain, and upgrade

Price: Free

Hadoop is an ecosystem of open-source utilities that fundamentally changes the way businesses store, process, and analyze data. Unlike conventional platforms, it allows many different types of analytic workloads to run on the same data, at the same time, at large scales on industry-standard hardware.

Hadoop distributes large datasets and analytics jobs across nodes in a computing cluster, converting them into smaller workloads that can be executed in parallel. It can handle both structured and unstructured data and scale up from a single machine to thousands of devices.

This tool has five main modules:

  • Hadoop Distributed File System (HDFS) can store large data sets across nodes in a fault-tolerant manner.
  • Yet Another Resource Negotiator (YARN) is responsible for planning tasks, managing cluster resources, and scheduling jobs running on Hadoop.
  • MapReduce is the big data processing engine and programming model that enables the parallel computation of large data sets.
  • Hadoop Common consists of libraries and utilities required by other Hadoop modules.
  • Hadoop Ozone is an object store optimized for billions of small files.

Overall, Hadoop incorporates emerging data formats (such as social media sentiment and clickstream data) and helps analysts make better real-time data-driven decisions.

Other Equally Great Data Science Tools

10. Tableau

Best for: small businesses to visualize data and generate meaningful insights

Tableau is a visual analytics platform that allows you to see and understand data. It offers a wide range of data source options you can connect to and fetch data from.

The best thing about Tableau is that it doesn’t require any coding or technical skill to extract meaningful insights. You can use its UI-based functions to generate custom dashboards and analyze reports. Due to its ease of use and advanced visualizations, Tableau has garnered interest among data scientists, analysts, business executives, and teachers.

11. Databricks Lakehouse

Best for: Data scientists and engineers to collaborate across all workloads

Databricks Lakehouse unifies all your data, analytics, and AI workloads in a single platform. It makes it feasible to use Business Intelligence tools directly on the source data, reducing latency and improving cost efficiency.

The platform supports a broad range of workloads, including machine learning, SQL, analytics, and more. It offers seamless integration with AWS, Azure, and Google Cloud.

Built on open-source and open standards, Databricks’ native collaborative capabilities enhance your ability to work across teams and innovate faster. All in all, it will accelerate your data science vision and help you see beyond the roadmap.

12. TIBCO Data Science

Best for: Students and academics to build sophisticated data science, statistics, and machine learning workflows.

From data preparation and model creation to deployment and monitoring, TIBCO Data Science tools allow you to automate tedious tasks and build business solutions, using machine learning algorithms.

The desktop-based UI features more than 16,000 functions, which you can use to create sophisticated advanced analytics workflows. There are also options to integrate R, Python, and other nodes within the pipelines.

In addition, the built-in nodes give you access to graph, text analytics, time-series, regression, neural networks, statistical process control, and multivariate statistics.

TIBCO also offers extensive support for enterprise governance in industries like healthcare, pharma, manufacturing, finance, and insurance.

13. Weka

Best for: solving real-world data mining problem

Weka is a set of visualization tools and algorithms for data analysis and predictive modeling. They all are available for free under the GNU General Public License.

More specifically, Weka contains tools for data pre-processing, classification, regression, clustering, and visualization. For people who haven’t coded for a while, Weka, with its graphical user interface, provides an easy transition into the world of data science.

Users can experiments with their datasets by applying different algorithms to see which model gives the best result. Then they can use visualization tools to inspect the data.

Read: 15 Best Test Data Generation Tools

Frequently Asked Questions

What’s the difference between data science, AI, and ML?

Data Science is a broad field of study that involves pre-processing, analysis, and visualization of structured and unstructured data. The insights gained from data are then applied to a wide range of application domains.

Artificial Intelligence means teaching a machine to mimic human behavior in some way. The goals of AI research include knowledge representation, planning, learning, reasoning, natural language processing, perception, and the ability to manipulate objects.

Machine learning is a subset of AI that focuses on how to use data and algorithms to imitate the way humans learn. The more data (also called training data) the ML model gets, the more accurately it makes predictions without being explicitly programmed to do so.

Read: Artificial Intelligence vs. Machine Learning vs. Deep Learning | The Difference

What are the steps involved in data science?

Data science involves six iterative steps.

  1. Plan: Define a project and its estimated results.
  2. Build a data model: Use an appropriate data science tool to create machine learning models.
  3. Evaluate: Use evaluation metrics and visualization to measure model performance against new data.
  4. Explain (in simple terms) the internal mechanics of machine learning models.
  5. Deploy the well-trained model in a secure and scalable environment.
  6. Monitor the model to ensure that it is working properly.
What to consider before selecting a data science tool?

Following are the key features you should consider in a data science platform:

  • It should allow multiple users to work together on the same model
  • Should include support for the latest open-source applications
  • Must be scalable
  • Should be able to automate tedious tasks
  • Should have the capability to easily deploy models into production
How data science helps business?

Data science plays a major role in analyzing the health of businesses. It extracts valuable information from raw data and predicts the success rate of the company’s products and services. It also helps in identifying inefficiencies in manufacturing processes, targeting the right audience, and recruiting the right talent for the organization.

Some sectors use data science to increase the security of their business and protect sensitive information. Banks, for example, use machine learning algorithms to detect fraud based on customer’s usual financial activities. These algorithms have been proven far more effective and accurate in identifying frauds than manual investigations.

Read: 12 Largest Data Centers In The World [By Size]

According to the GlobalNewswire report, the global data science platform market will reach $224 billion by 2026, growing at a CAGR of 31 percent.

Written by
Varun Kumar

Varun Kumar is a professional science and technology journalist and a big fan of AI, machines, and space exploration. He received a Master's degree in computer science from GGSIPU University. To find out about his latest projects, feel free to directly email him at [email protected] 

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1 comment
  • Hello Varun,
    Extremely overall quite fascinating post. This is the awesome list data science tools. I was searching for this sort of data and delighted in perusing this one. Continue posting. A debt of gratitude is in order for sharing. Thank you.