Introduction

Data has taken centre stage in strategic decision-making and planning of organisational strategies in the modern digital era. Modern businesses are banking heavily on data scientists and analytics professionals to help them reach data-driven decisions. If we follow the market stats, data scientists are expected to witness a rise of 200% by the year 2026. India will have more than 11 million job opportunities in data science over the next 5 years from now. In other words, the domain of data science extends highly lucrative opportunities for aspiring professionals. However, to build a successful career in the data sector, aspirants must get acquainted with the latest top 10 data science tools.

Importance of the tools for professionals

Dealing with a huge chunk of data is a daunting task. This is where the most popular data science tools come to the rescue. Data science professionals use a diverse set of tools to gain valuable predictions and insights that facilitate accurate decision-making. These data science tools range from data visualisation tools to analytics tools, and more. As mentioned above, aspiring data professionals must be well-versed in all major data science to build a rewarding career in the domain.
Below are some of the hand-picked data science tools that you should master to stay ahead in the data science market.

Top 10 Data Science Tools

1. Apache Hadoop

Definition

Apache Hadoop is one of the popularly used open-source tools for storing and processing huge data. This is essential for repositories in big data and can process large sets of both structured and unstructured data. Hadoop in Data Science is particularly helpful to manage a large set of data.


Features

Below mentioned are some of the key features that make Hadoop one of the most reliable and powerful tools in data analytics:


Open source

The open source software signifies that the source code is available easily and understandable. An individual can also make necessary modifications as per the requirements.


High availability

Hadoop provides a high availability of data on the Hadoop cluster.


Data locality

Data Locality assures faster processing. The computation logic is moved near the data instead of moving it to computation logic.


Ease of use

The processing part is managed by Hadoop itself and hence is easy to use for developers.


Higher Processing speed

Hadoop allows way faster processing of large sets of data than conventional database systems.


Sound Security measures

Hadoop has in-built security factors such as encryption, authorisation, and authentication. It enables strong data protection and ensures that only authorised users can access it.


Better integration with tools

Hadoop provides integration support with other popular tools such as Apache Storm, Apache Flink, and Apache Spark.



Benefits:

• Compatibility with varied data sources
• Cost-effective
• Lower network traffic
• Highly scalable
• High Flexibility
• High throughput
• Supports multiple languages



2. Tableau

Definition

Tableau is one of the widely used tools for data visualization tasks.


Features

Some of the popular features of Tableau are as follows:

Tableau Dashboard

The dashboard provides informative visualisation and provides an overview of the data in story format.


Sharing & Collaborating

Data sharing is way easier with Tableau, courtesy of better dashboards and visualizations. Users also receive feedback and review instantly. Moreover, the tool supports better data analysis.


In-memory and live data:

Tableau supports better connectivity among the external and real-time sources of data for better data extractions.


Ask data

The Ask Data in Tableau supports altering the data to perform Google searches.


Data sources

Some of the data sources are data warehouses on cloud data, big data, and relational database. Tableau is compatible with multiple data connectors including SQL, Cloudera, MYSQL, Hadoop, and others.


Robust security

Tableau is equipped with robust security protocols such as Kerberos, Active Directory, etc., for mighty data security. The tool follows tight security measures regarding permission and authentication methods.


Predictive analysis

Tableau enables forecasting that enables obtaining data projections based on a few parameters.


Advanced visualisation

The bullet charts, pie charts, barcharts, histogram, gantt charts provide better visibility of the data.



Benefits:

Some of the benefits of Tableau are as below:
• Data visualization
• Creates interactive visualization quickly
• Easy implementation
• Handling of huge datasets
• Compatible with different scripting language
• Has a responsive dashboard and provides mobile support



3. TensorFlow

Definition

TensorFlow is an open-source and end-to-end platform designed for machine learning projects. Widely used by data scientists and professionals, the tool helps to create easy graphs to portray data flow. These graphs demonstrate a representation of statistical and mathematical operations and also allow to monitor the overall process with essential metrics.


Features

Some of the important features of TensorFlow are as below:

Flexible

Tensorflow supports resolving complicated topologies with Keras API and pipelines of data input.


Responsive construct

Unlike SciKit and Numpy, TensorFlow provides easy visualization of the different parts of the graph.


Open source

The open source platform of TensorFlow enables the user to manipulate and easily access the library.


Visualiser

Tensorflow offers the ability to inspect the various representations and also allows necessary modifications while debugging.


Statistical distribution

The TensorFlow library carries multiple distribution functions such as Gamma, Bernoulli, Uniform, and Chi2.


Layered components

TensorFlow functions like tf.contrib.layers carry out layered functionalities in terms of biases and weights. This helps to provide dropout layer, batch normalisation, and other operations.


Columns with features

Tensorflow has columns that can serve as intermediaries between the estimators and raw data. Hence it helps to bridge the input in the model.


Neural network training

The easy pipelining in neural networks helps to train multiple GPUs and neural networks. This makes the model effective and efficient on a larger scale.


Easy to train

TensorFlow can be easily trained on GPU and CPU for distributed computing.



Benefits:

The benefits of TensorFlow are as follows:
• Easy development of the models
• Can carry out complex numerical computations
• Excellent op-level graphs
• Ideal library management
• Easy computation and deployment with GPU
• Supports Keras
• Consist of pre-trained datasets and models
• Open source platform



4. Power BI

Definition

Power BI is SaaS[based popular Business Intelligence and data visualisation tool. It is an aggregation of data connectors, software services, and apps that is used for fetching data from multiple sources. The data extends insightful reports that are used by business users.


Features

Attractive visualisation:

Power BI offers a range of options for attractive visualisations such as line chart, bar chart, ribbon chart, scatter chart, pie chart, donut chart and others.


Dataset filtration:

Dataset is utilized for creating visualizations and can be filtered into smaller subsets.


Flexible tiles:

It is the single block that is composed of visualization in the tool dashboard. It is segregated into informative visualization which can be placed anywhere across the dashboard.


Dashboards can be customised

The visualization collection across the dashboard offers significant insights regarding data that can be printed as well as shared.


Informative reports

The visual reports on Power BI have a combination of visualisations that portray a structured presentation of the data.


Navigation pane

The navigation pane in Power BI is equipped with options for dashboards, reports, and datasets.


Get data

Power BI allows the selection of a range of data that are added every month.



Benefits

Some of the significant benefits of Power BI are as follows:
• It is composed of a personalized and rich dashboard
• Provides constant innovation
• Simplicity of usage
• The report is published securely
• No speed or memory constraint
• Technical support is not needed



5. Matplotlib

Definition

Matplotlib is a powerful open-source tool in Python library for data visualisation, such as plotting graphs.


Features

Versatility

Matplotlib provides a wide variety of plots, including line plots, scatter plots, bar plots, histograms, pie charts, and more. It can be used for a broad range of scientific, engineering, statistical, and data visualisation tasks.


Customisation

Matplotlib allows customisation of colours, line styles, markers, labels, axes, and other visual elements. This flexibility facilitates easy creation of publication-quality plots.


Multiple Backends

The tool supports multiple backends for rendering plots. This allows users to generate visualizations in various formats, such as PNG, PDF, SVG, and more. It also supports interactive backends for use in GUI applications.


Object-oriented API

Thanks to this feature, Matplotlib users always enjoy more fine-grained control over the elements of a plot. This approach is useful for creating complex and customised visualisations.


Matplotlib.pyplot Interface

The pyplot module provides a simple and convenient interface for creating and customising plots. It is widely used for quick and interactive plotting, especially in Jupyter notebooks.


Subplots and Figures

Matplotlib helps to create multiple subplots within a single figure. This allows users to display multiple plots in a grid or other arrangements.


Integration with NumPy

Matplotlib seamlessly integrates with NumPy, a fundamental package for scientific computing in Python. This integration makes it easy to plot data stored in NumPy arrays.


Animations

Matplotlib helps data scientists to create advanced animated visualizations for attractive data illustration.


LaTeX Compatibility

Matplotlib supports LaTeX to facilitate easy formatting of text and mathematical expressions in titles, labels, and annotations. This particular feature helps to create easily legible plots for scientific publications.



Benefits

• Provides simpler approaches to access larger sets of data.
• Supports easy navigation
• Supports different data representation methods
• Easy accessibility with high quality images
• Numerous applications
• Creates advanced visualization
• Customisable and extensive
• Easy data analysis
• Backed by a huge community



6. KNIME

Definition

KNIME analysis allows users to visualise, access, blend, and analyse data. It is a highly versatile aide for ML, data analytics, and other data-driven tasks.


Features

Open Source

Being an open-source platform, KNIME allows users to access and modify the source code. This helps to foster a collaborative environment and encourages active contributions from the community.


Graphical Workflow Design

KNIME uses a visual programming approach where users can design data workflows by dragging and dropping nodes onto a canvas. This makes it easy to understand and modify data processing pipelines.


Vast Node Repository

KNIME carries a suite of pre-built nodes for various data manipulation, analysis, and visualization tasks. Users can also create custom nodes or use nodes developed by the community.


Integration Capabilities

KNIME supports integration with a wide range of data sources, including databases, flat files, web services, and more. It also provides connectors to popular data science and machine learning tools, such as R and Python.


Data Exploration and Visualisation

The platform offers tools for exploring and visualizing data directly within the workflow. Users can create interactive visualisations and gain insights into the data during the analysis process.


High Scalability

KNIME is designed for high scalability. The tool can be used for both large and small-scale data, as per the specific needs of the user. It supports distributed computing environments and can be deployed on cloud platforms.


Execution and Automation

KNIME workflows can be executed in a batch mode or scheduled to run at specific times. This enables automation of data analysis processes, making it suitable for production environments.


High Extensibility

KNIME’s extensibility feature allows users to integrate their own functionality through scripting or by developing custom nodes. This flexibility makes it adaptable to a variety of use cases.


Collaboration and Sharing

KNIME workflows and components can be shared with others, promoting collaboration within a team or the larger community. This sharing extends to the exchange of best practices and reusable components.


Advanced Analytics and Machine Learning

KNIME provides a range of tools for advanced analytics and machine learning. It supports popular machine learning libraries and frameworks, making it suitable for data science and predictive modelling.



Benefits

• Improved scalability with sophisticated data handling
• Export and import of the workflows
• Parallel execution
• Command line version
• Simple and high extensibility via API for the plug-in extensions



7. Rapidminer

Definition

It is one of the powerful data science platforms that creates a collective implication on model operation, model deployment, and data mining.


Features

Data Preparation

It includes tools for data cleansing, transformation, and preprocessing to prepare data for analysis.


User-Friendly Interface

RapidMiner features a breezy interface and can be used by users with varying levels of technical expertise.


Drag-and-Drop Design

Users can create and customise analytical workflows through easy drag-and-drop interface. This makes it both easier and faster to build data analysis pipelines.


Machine Learning and Predictive Analytics

RapidMiner supports a wide range of machine learning algorithms for classification, regression, clustering, and association analysis.


Integration of R and Python

Users can leverage the power of R and Python by integrating scripts and code seamlessly into their RapidMiner workflows.


Text Mining and Natural Language Processing

It includes tools for analysing and extracting valuable insights from unstructured text data.


Big Data Integration

RapidMiner can handle and analyse large datasets by integrating with big data technologies such as Apache Hadoop and Spark.


Automated Machine Learning

The platform offers automated machine learning capabilities, allowing users to automatically select and optimize machine learning models for their specific use cases.


Model Validation and Evaluation

RapidMiner provides tools for validating and evaluating machine learning models, including cross-validation, confusion matrices, and other performance metrics.


Real-Time Analytics

RapidMiner supports real-time data analytics and enables users to build models that can make predictions on streaming data.


Deployment Options

RapidMiner models can be deployed in various environments, including cloud-based platforms and on-premises infrastructure.



Benefits

• RapidMiner has several procedures specifically in the selection of attributes
• It provides immense flexibility to the user
• Allows integration of algorithm of the tools



8. Apache Spark

Definition

Apache Spark is an open source and distributed system that helps to process large datasets.


Features

Fault tolerance

Can handle node failures of workers.


Speed

Assists applications running on the Hadoop that can run faster on memory by 100x and on disk by 10x.


Real-time stream processing

With Spark, the streaming jobs can be written with language-integrated API to the stream processing.


Reusable

The code can be utilised for joining the streaming data and batch processing against available historical data.


Support multiple languages

Most of the API of Spark are available in R, Python, Scala, and Java.


Advanced analytics

It has evolved as a de-facto standard in the segment of data science and data processing.



Benefits

• Sophisticated and easy-to-use platform
• The tool is versatile
• Includes high-level libraries
• Cost-effective
• Achieves higher performance with physical execution and query optimization



9. Excel

Definition

A staple for data scientists and analysts, Excel enables users to visualize, manipulate, and analyse the lump sum set of data.


Features

Better data filtration

Excel offers a wide range of mathematical and statistical formulas for data manipulation.


Data Import and Export:

Excel allows you to import data from various sources, including databases, text files, and online sources.


PivotTables

PivotTables help to ensure dynamic data summarisation and analysis. The tool makes it easier and faster to rearrange and summarise data to identify trends and patterns.


Charts and Graphs

Excel extends a variety of chart types for more pronounced data visualisation. These charts can be easily customised as per the specific requirements of the user.


Data Validation

Excel allows users to set rules to control the type of data entered into cells. This is crucial to maintain data integrity and ensures accurate analysis.


Sorting and Filtering

Excel allows users to sort data alphabetically or numerically. This deep filtering feature enables users to focus on specific subsets of data for analysis.


What-If Analysis

Excel provides tools for scenario analysis and goal seeking. Users will be able to explore how changes to certain values affect the overall dataset.


Solver Add-In

The Solver add-in helps in finding optimal solutions for complex problems by adjusting input values based on specified constraints.


Power Query

Power Query allows for seamless data connectivity, transformation, and shaping from various sources.


Statistical Analysis

Excel includes statistical functions for descriptive statistics, hypothesis testing, and regression analysis.


Data Analysis Tools

Excel offers a long list of data analytics tools, including tools for histogram creation, data sampling, and correlation analysis.


Collaboration and Sharing:

Excel supports collaboration through features like co-authoring and sharing workbooks on cloud platforms like Microsoft 365.



Benefits

• Easy calculation
• Conditional formatting
• Better data organisation through grid structure
• Automation of codes
• Cleaning and transformation of data
• Easy printability of reports
• Saves time with shortcut keys



10. SQL

Definition

Structured Query Language (SQL) is a domain-specific programming language that is designed with a lot of features and functionalities. It is a versatile language that comes in handy in maintaining relational databases.


Features

Data Definition Language (DDL)

SQL includes commands for defining and managing the structure of a database, such as creating tables, altering table structures, and dropping tables.


Data Manipulation Language (DML)

SQL allows users to retrieve and manipulate data stored in a database.


Data Query Language (DQL)

SQL provides a powerful query language for retrieving specific data from a database. The SELECT statement is a fundamental part of DQL.


Transaction Control

SQL allows users to group multiple SQL statements into a single, atomic operation.


Data Integrity

SQL ensures data integrity by enforcing constraints such as primary keys, foreign keys, unique constraints, and check constraints. These constraints help to maintain credibility and accuracy of the data.


Concurrency Control

SQL helps to manage concurrent access to the database by multiple users or applications. It uses techniques like locking to prevent conflicts and maintain data consistency.


Security

SQL databases implement mighty security measures to control access to data and database objects.


Indexes

SQL allows the creation of indexes on tables to improve query performance by speeding up data retrieval. Indexes are a great help while handling large datasets.


Views Creation

SQL supports the creation of views, which are virtual tables derived from one or more underlying tables. Majority of data scientists today prefer “Views” as it helps to simplify complex queries and provide a layer of abstraction.


Stored Procedures and Functions

SQL allows the creation of stored procedures and functions, which are precompiled and stored in the database for reuse. These procedures and functions help to enhance code modularity, reusability, and performance.


Triggers

SQL supports triggers, which are sets of instructions that are automatically executed in response to specific events, such as data modifications. Triggers help to enforce business rules as well as maintain data integrity.


Normalisation

Normalisation is the process of organizing data to minimize data duplication and avoid data anomalies. SQL encourages the normalisation of database tables to reduce redundancy and improve data integrity.



Benefits

• Quick processing of queries
• Interactive framework
• Easy portability
• No coding skill is needed
• Standardized language

Conclusion

Data scientists are one of the highest-paid and in-demand professionals across multiple industries. Senior or mid-level data professionals can earn up to around Rs. 26 LPA. If you are aspiring for a lucrative career, you can join Data science courses and certifications that offer practical training on tools. DataSpace Academy is a leading ed-tech institute that offers job-ready courses on data science, analytics, and business analytics. The academy also extends internship opportunities with capstone projects as well as placement assistance.