Rounak Jain Apr 15, 2020 No Comments
Data Science today is a well-established career. People across the globe from various profiles are switching to a career in the field of Data Science and Artificial Intelligence. It is worth noting here that this field is regularly interrupted by new and improved tools and programming languages, both licensed or open-source. It becomes prerogative that the aspirants keep themselves abreast with the latest to ensure they stay relevant in this highly demanding field. Hence in this article, we are going to talk about the top 10 Data Science tools in 2020 that everyone should be aware of.
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.
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 –
Fig – Companies using TensorFlow
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.
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 –
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 –
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 –
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
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:
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.
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!!