Most Popular Data Science Tools | A Complete Beginners Guide | REAL-TIME Examples

• In this article you will get
• 1.What is Data Science?
• 2.Why Data Science?
• 3.Data Science Tools
• 4.Conclusion

What is Data Science?

Data science is explained , unsurprisingly, as “the study of a data.” That certainly narrows it is down! Fortunately, there’s more to it. The purpose of a data science is to gain knowledge and insights from all the kinds of data, structured or unstructured.The process includes the creating and implementing ways of recording, analyzing, and storing a data to glean useful information effectively. A Data science isn’t computer science; the latter focuses on creating the algorithms and programs for processing and recording data, while data science focuses on the analysis and may not even need a computer.

Why Data Science?

Since so much of life is be digitized data, it’s only logical to the study data science. But there are particular reasons why understanding data science and applying it to more careers is be good idea:

Fraud prevention: Data scientists can identify the anomalous data and create methodologies for predicting fraudulent incidents.

Informed decisions: Accurate, useful data helps to management make better decisions, ones that have increased likelihood of success.

Better customer experiences: The more appropriate and relevant data that are businesses have on their customers, better they can tailor the experience for every consumer.

Data Science Tools

Let’s take look at those tools and their advantages now, placed in an alphabetical order:

Algorithms.io.

This tool is the machine-learning (ML) resource that takes a raw data and shapes it into the real-time insights and actionable events, particularly in context of machine-learning.

Advantages:

• It’s on cloud platform, so it has all SaaS advantages of scalability, security, and infrastructure.
• Makes a machine learning simple and accessible to the developers and companies.

Apache Hadoop

This open-source framework creates a simple programming models and distributes an extensive data set processing across thousands of computer clusters. Hadoop works equally well for the research and production purposes. Hadoop is a perfect for high-level computations.

Advantages:

• Open-source.
• Highly scalable.
• It has more modules available.
• Failures are handled at application layer.

Apache Spark

Also called “Spark,” this is all-powerful analytics engine and has a distinction of being the most used a data science tool. It is known for offering the lightning-fast cluster computing. Spark accesses varied a data sources such as Cassandra, HDFS, HBase, and S3. It can also simply handle large datasets.

Advantages:

• Over 80 high-level operators simplify a process of parallel app building.
• Can be used an interactively from a Scale, Python, and R shells.
• An Advanced DAG execution engine supports in-memory computing and also acyclic data flow.

BigML

This tool is the another top-rated data science resource that provides the users with a fully interactable, cloud-based GUI environment, ideal for the processing ML algorithms. And can create a free or premium account depending on needs, and web interface is simple to use.

Advantages:

• An affordable resource for the building complex machine learning solutions.
• Takes a predictive data patterns and turns them into intelligent, practical applications usable by anyone.
• It can run in a cloud or on-premises.

D3.js

D3.js is the open-source JavaScript library that lets make interactive visualizations on a web browser. It emphasizes the web standards to take full advantage of all of features of modern browsers, without being bogged down with the proprietary framework.

Advantages:

• D3.js is based on very famous JavaScript.
• Ideal for the client-side Internet of Things (IoT) interactions.
• Useful for a creating interactive visualizations.

Data Robot

This tool is explained as an advanced platform for an automated machine learning. Data scientists, executives, IT professionals, and software engineers use it to help them build better quality for predictive models, and do it faster.

Advantages:

• With just a single click or line of code, and can train, test, and compare many various models.
• It features of Python SDK and APIs.
• It comes with the simple model deployment process.

Excel

Yes, even this ubiquitous old database workhorse gets a some attention here, too! Originally developed by the Microsoft for spreadsheet calculations, it has a gained widespread use as a tool for the data processing, visualization, and sophisticated calculations.

Advantages:

• Can sort and filter the data with one click.
• Advanced Filtering function lets filter data based on a favorite criteria.
• Well-known and also found everywhere.

ForecastThis

If data scientist who needs automated predictive model selection, then this is a tool ForecastThis helps investment managers, data scientists, and quantitative analysts to use their in-house data to be optimize their complex future objectives and also create robust forecasts.

Advantages:

• Simply scalable to fit any size challenge.
• Includes the robust optimization algorithms.
• A Simple spreadsheet and API plugins.

Google BigQuery

This is a more scalable, serverless data warehouse tool created for the productive data analysis. It uses a Google’s infrastructure-based processing power to the run super-fast SQL queries against append-only tables.

Advantages:

• Extremely fast.
• Keeps costs down since users need only pay for the storage and computer usage.
• Easily scalable.

Java

Java is classic object-oriented programming language that’s been around for a years.

Advantages:

• Suitable for a large science projects if used with the Java 8 with Lambdas.
• Java has extensive suite of tools and libraries that are perfect for the machine learning and data science.
• Simple to understand.

MATLAB

MATLAB is the high-level language coupled with an interactive environment for a numerical computation, programming, and visualization. MATLAB is the powerful tool, a language used in a technical computing, and ideal for the graphics, math, and programming.

Advantages:

• An Intuitive use.
• It analyzes a data, creates models, and develops algorithms.
• With just few simple code changes, it scales analyses to run on a clouds, clusters, and GPUs.

MySQL

Another familiar tool that enjoys the widespread popularity, MySQL is one of the most popular open-source databases available today. It’s ideal for accessing the data from databases.

Advantages:

• Users can simply store and access data in the structured manner.
• Works with the programming languages like Java.
• It’s open-source relational database management system.

NLTK

Short for a Natural Language Toolkit, this open-source tool works with the human language data and is a well-liked Python program builder. NLTK is ideal for the rookie data scientists and students.

Advantages:

• Comes with the suite of text processing libraries.
• Offers over the 50 easy-to-use interfaces.
• It has active discussion forum that offers a wealth of new information.

Rapid Miner

This data science tool is the unified platform that incorporates data prep, machine learning, and model deployment for making a data science processes simple and fast. It enjoys heavy use in manufacturing, telecommunication, utility, and banking industries.

Advantages:

• All of resources are located on one platform.
• GUI is based on the block-diagram process, simplifying these blocks into the plug-and-play environment.
• Uses visual workflow designer to the model machine learning algorithms.

SAS

This data science tool is designed are especially for the statistical operations. It is the closed-source proprietary software tool that specializes in handling and analyzing a massive amounts of data for the large organizations. It’s well-supported by its company and more reliable. Still, it’s a case of getting what pay for a because SAS is expensive and best suited for the large companies and organizations.

Advantages:

• Numerous analytics functions covering an everything from the social media to automated forecasting to location data.
• It features are interactive dashboards and reports, letting user go straight from the reporting to analysis.
• Contains the advanced data visualization techniques like auto charting to present compelling results and data.

Conclusion

In a today’s data-driven world, data plays the crucial role in a survival of any organization in this competitive era. By utilizing the data, data scientists provide impactful insights to key decision-makers of organizations. This is the almost impossible to imagine without use of above-listed powerful data science tools.It offers a way for analyzing data, creating interactive visualizations with the aesthetics, and developing powerful and automated predictive models using machine learning algorithms, which ease process of extracting and delivering valuable insights from a raw and seemingly useless data.