Top Data Science Tools

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Have you ever wondered why you should learn Python for Data Science? Is data science a hot career nowadays? Well, the simple answer is, yes! Big data dominates modern times. As you read this article, there are millions of bytes of data being generated. However, the potential of this colossal amount of data lies in how well it is being managed and processed, and that is where data science has a crucial role to play.

Data science is a multidisciplinary field that involves deriving useful insights from data to drive decision making and strategizing, which in turn contribute to growth and prosperity. Data analysts in the past were limited to basic software like Excel for data processing. But with the advancement in computer science and technology, data scientists today have a plethora of sophisticated techniques and methods like machine learning (ML), artificial intelligence (AI), statistics, and applied mathematics at their disposal.

The popularity of data science

Super-fast computers, high computational power, and high-level programming languages have accelerated the popularity of data science in recent years. With that, the demand for data scientists and analysts is on an ever-increasing high. Learning Python for data science is just a baby-step towards mastering data science because there is a lot more you can explore.

What are the top data science tools?

The extraction, manipulation, processing, and analysis of data have benefitted not only the IT sector but other critical industries as well. Data scientists, researchers, analysts, and developers put in enormous amounts of research and effort to collect, study, and make predictions out of the data. But, all this would not have been possible had there not been the different programming languages and statistical tools for carrying out the data operations. Many of these tools are Python-based that reiterates the advantages of learning Python for data science course. So, let's have a look at some of the best data science tools:

  1. SAS

SAS is the acronym for Statistical Analysis System. SAS serves many purposes, including statistical analysis, data mining, business intelligence applications, analysis of clinical trials, analysis of time series, and econometrics. The salient features of SAS include:

  • Strong ability to analyze data
  • 4 generation programming language with flexibility
  • Supports various types of data formats
  • Offers data encryption algorithms
  • Identifies grammatical errors and generates visually appealing reports
  • Apache Hadoop

It is open-source software that facilitates reliable and scalable computing. The salient features of Apache Hadoop are:

  • Availability of standard functions and libraries
  • Its distributed file system splits and distributes large chunks of a file
  • Management of clusters and job scheduling
  • Handling of parallel processing
  • Can be seamlessly integrated with external software solutions and apps
  • TensorFlow

TensorFlow offers advanced algorithms for machine learning, deep learning, and artificial intelligence. It is an open-source toolkit known for its superior performance and high computational abilities. Key features include:

  • Can run on CPUs, GPUs, and more recently on TPU platforms as well
  • It has a responsive construct
  • It is flexible and easily trainable
  • Comes with parallel neural network training, layered components, and feature columns
  • Contains statistical distributions
  • MATLAB

MATLAB is closed-source software that allows algorithmic implementation, matrix functions, and modeling statistical data. Its key features are:

  • Can simulate neural networks and fuzzy logic
  • Offers a numerical computing environment
  • Comes with a powerful graphics library
  • Can be easily integrated with embedded systems
  • Suitable for learning deep learning algorithms
  • Tableau

Tableau is an efficient tool for data visualization that makes use of cloud databases, online cubes for analytical processing, relational databases, and spreadsheets. Its highlights include:

  • Complete end-to-end analytics
  • Can perform advanced calculations with data
  • A well-protected system with minimum security risks
  • A user-friendly interface compatible with all devices
  • Content discoveries are effortless

  • Apache Spark

This analytics engine can handle stream processing and batch processing besides carrying out cluster management. Its APIs are programmable in Java, Python, and R. Key points about it include:

  • Offers speed, and is fault-tolerant, and reusable
  • Facilitates stream processing in real-time
  • Significantly faster compared to Apache Hadoop
  • The ability for efficient cluster management
  • DataRobot

DataRobot is the one-stop-shop for AI building, deployment, and maintenance. This enterprise-level solution offers the following business benefits:

  • MLOps
  • Automated time series
  • Automated machine learning
  • Paxata
  • BigML

BigML aims at simplifying machine learning through building and sharing models and datasets. With a user-friendly GUI, BigML comes with the following abilities:

  • Classification
  • Cluster analysis
  • Regression
  • Forecasting time series
  • Topic modeling and anomaly detection

Need to take a course

A career in data science demands a strong knowledge of different programming languages. Hence, learning Python for a data science course is essential; keeping in mind the numerous benefits that the course can fetch. Let's have a quick look at some of the advantages that come with data science training:

  • Data science skills are in high demand amongst all the premium industries and businesses across the world, enhancing the career path of data science professionals.
  • Data science and big data technologies demand significant proficiency in data management skills. A course in data science prepares candidates for a competitive career in the field of data management.
  • With the skills and expertise to handle big data, candidates can rest assured that they will bag the highest paying job titles like data scientist, data architect, data engineer, and business analyst.
  • Top companies like Amazon, Facebook, and Google hire well-trained data science professionals.
  • Data science courses and training programs offer individual attention to students' requirements.

Considering the growing popularity of data science and related career paths, there can be no better time to learn Python for data science and sign up for a certification course in data science. In this article, we discussed some of the best data science tools without which, data analysis and management would have been impossible. While traditional aids like mathematical and statistical techniques remain, advanced tools, such as those described in the article have helped in furthering the evolution and progress of data science.

Author: Admin

Admin is a professional blogger and digital marketer at 99techpost. She writes about Digital Marketing, Technology, WordPress, SEO, Web Design and Development . You can also follow us on facebook & twitter. Feel free to contact us if you have any queries.

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