The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Hadoop is a very powerful Big Data open-source tool and has got some exceptional features.
Hadoop stores a huge amount of structured and unstructured data in its storage layer called Hadoop Distributed File System
Another robust feature is a software framework and programming model used for processing huge amounts of data called MapReduce
Hadoop is a highly scalable open-source platform where an application can run on over a thousand nodes
Fault tolerance is provided by Hadoop where it controls faults by the process of replica creation
TensorFlow is an end-to-end open-source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML-powered applications. Some very interesting features to highlight are –
TensorFlow offers multiple levels of abstraction so you can choose the right one for your needs
TensorFlow is supportive of an ecosystem of powerful add-on libraries and models to experiment with, including Ragged Tensors, TensorFlow Probability, Tensor2Tensor and BERT
One can easily train and deploy models in the cloud, on-prem, in the browser, or on-device no matter what language one uses
With TensorFlow, we can train multiple Neural Networks and GPUs and create a highly efficient large-scale system models
Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation.
It is incredibly expressive, flexible, and apt for innovative research because of its modular nature
Keras supports almost all the models of a neural network like fully connected, convolutional, pooling, recurrent, embedding, etc. We can even build further complex models using the above-mentioned models
Being a Python-based framework, we can easily debug and explore in Keras
MATLAB is a high-performance programming platform for technical computing. We can use MATLAB for a range of applications, including deep learning and machine learning, signal processing and communications, image and video processing, control systems, test and measurement, computational finance, and computational biology. Let us look at some features this Data Science tool provides.
We can apply domain-specific feature engineering techniques for sensor, text, image, video, and other types of data
Fine-tune machine learning and deep learning models with automated feature selection, model selection, and hyperparameter tuning algorithms
Simulate and train dynamic system behavior with reinforcement learning
Create, modify, and analyze deep learning architectures using apps and visualization tools
Tableau is an interactive data visualization software that helps create descriptive and interesting visuals without coding. This Data Science tool was founded in January 2003 and acquired by Salesforce in August 2019. The software has been designed on the philosophy of “seeing and exploring data” and provide excellent flexibility in designing dashboards. When it comes to an exciting and interactive range of visualizations, user interface layout, visualization sharing, and intuitive data exploration capabilities, Tableau can be a suitable tool to select. A few features of this popular tool are –
Tableau allows deployment on a local server or Amazon Web Services (AWS), Google Cloud Platform or Microsoft Azure
We can definitely select Tableau as a choice for Data Visualization tool to handle large amount of data
Tableau provides integration with R and Python
Tableau offers free one-year Tableau licenses to students at accredited academic institutions through Tableau for Students program
Apache Spark™ is a unified analytics engine for large-scale data processing. It is an improvement over Hadoop and works much faster because of its cluster management system. Today Apache Spark is the most actively developed open-source project in Big Data and is common among data science beginners as well. Some features provided by Spark are –
Spark powers a stack of libraries including SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming
Spark runs on Hadoop, Apache Mesos, Kubernetes, standalone, or in the cloud. It can access diverse data sources.
It offers over 80 high-level operators that make it easy to build parallel apps. We can use it interactively from the Scala, Python, R, and SQL shells.
IBM Watson™ Studio is a platform for businesses to prepare and analyze data as well as build and train AI and machine learning models in a flexible hybrid cloud environment. It is one of the top Data Science tools worth learning if you don’t want to sit and code on Python or R. The various amazing features Watson Studio provides are –
Automatically analyze your data and generate candidate model pipelines customized for your predictive models
Use Data Refinery to clean and shape data using a graphical flow editor
Create a notebook file, use a sample notebook or bring your own notebook to Watson Studio
Quickly prepare data and develop models visually with SPSS Modeler in Watson Studio
Full integration with Watson Machine Learning helps you bring your models from Watson Studio into production at scale
Another in Data Science tools that can be leveraged for AI by anyone who does not know programming or Machine Learning is Data Robot. We can build and deploy highly accurate Machine Learning models in a fraction of time. This is essentially a tool that automates the Machine Learning modeling by searching through millions of possible combinations of algorithms, pre-processing steps, features, transformations, and tuning parameters to deliver the best model for the data set and prediction target. Some features to share are
We can drag and drop data sets required for modeling
The tool aids in prediction and gaining insights
Data Robot automatically build, train and evaluate 1000s of models
A very essential part of the Data Science life cycle is Data Acquisition. The information available on the internet is an ocean of data that can be leveraged for the collection of data. It becomes tedious to manually assemble data from various websites and a painful task if we need a huge amount of data to be collected. Free Web Scraping Data Science tools like Octoparse turn very useful in collecting data from websites automatically. Below you can find several of the Octoparse’s features:
Octoparse can handle both static and dynamic websites with AJAX, JavaScript, cookies and etc.
The web scraping tool can even deal with information that is not showing on the websites by parsing the source code
It allows you to export all types of scraped data in TXT, HTML CSV, or Excel formats
Octoparse allows you to run your extraction on the cloud and your local machine
Last but not least in the Data Science tools is the most popular programming language for Data Science for the last couple of years. Python is an object-oriented programming language widely used for almost everything today. All the various steps in the life cycle of Data Science like Exploratory Data Science, Data Visualization, Statistical Modeling, Data Cleaning, etc. can be coded using Python. It has various excellent libraries like Pandas, Matplotlib, NLTK, Numpy, etc. Although Python does not need much elaboration, we have covered Python in comparison with R in our earlier blog R vs Python.
Conclusion –
The list mentioned above is not an exhaustive one and varies vividly. In fact, there are many other tools like H20, Selenium Web driver, Weka, BigML, etc. which can also be in the top 10 list based on different experiences. Do tell us if we have missed any tool which according to you definitely holds a place in the top 10 list. Next, we will talk about the top tools for Automated Machine Learning and on Web Scraping tools. Till then, Happy Learning!!
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