In this article, we will learn about some of the Data Science Tools used by Data Scientists to carry out their data operations. We will understand the key features of the tools, benefits they provide and comparison of various data science tools.

Introduction to Data Science

Data Science has emerged out as one of the most popular fields of 21st Century.

In order to do so, he requires various tools and programming languages for Data Science to mend the day in the way he wants. We will go through some of these data science tools utilizes to analyze and generate predictions.

Top Data Science Tools

Here is the list of 14 best data science tools that most of the data scientists used.

1. SAS

SAS are specifically designed for statistical operations. SAS is a closed source proprietary software that is used by large organizations to analyze data. SAS uses base SAS programming language for performing statistical modeling.

While SAS is highly reliable and has strong support from the company, it is highly expensive and is only used by larger industries.

2. Apache Spark

Apache Spark or simply Spark is an all-powerful analytics engine and it is the most used Data Science tool. Spark is specifically designed to handle batch processing and Stream Processing.

It comes with many APIs that facilitate Data Scientists to make repeated access to data for Machine Learning, Storage in SQL, etc. It is an improvement over Hadoop and can perform 100 times faster than MapReduce.

Spark does better than other Big Data Platforms in its ability to handle streaming data. This means that Spark can process real-time data as compared to other analytical tools that process only historical data in batches.

Spark offers various APIs that are programmable in Python, Java, and R.

Spark is highly efficient in cluster management which makes it much better than Hadoop as the latter is only used for storage. It is this cluster management system that allows Spark to process applications at a high speed.

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BigML is another widely used Data Science Tool. It provides a fully interactable, cloud-based GUI environment that you can use for processing Machine Learning Algorithms. BigML provides standardized software using cloud computing for industry requirements.

Through it, companies can use Machine Learning algorithms across various parts of their company.

BigML specializes in predictive modeling. It uses a wide variety of Machine Learning algorithms like clustering, classification, time-series forecasting, etc. It allows interactive visualizations of data and provides you with the ability to export visual charts on your mobile or IOT devices.

Furthermore, BigML comes with various automation methods that can help you to automate the tuning of hyperparameter models and even automate the workflow of reusable scripts.

4. D3.js

Javascript is mainly used as a client-side scripting language. D3.js, a Javascript library that allows you to make interactive visualizations on your web-browser. With several APIs of D3.js, you can use several functions to create dynamic visualization and analysis of data in your browser.

Another powerful feature of D3.js is the usage of animated transitions. D3.js makes documents dynamic by allowing updates on the client side and actively using the change in data to reflect visualizations on the browser.

Overall, it can be a very useful tool for Data Scientists who are working on IOT based devices that require client-side interaction for visualization and data processing.

5. MATLAB

MATLAB is a numerical computing environment for processing mathematical information. It is a closed-source software that facilitates matrix functions, algorithmic implementation and statistical modeling of data. MATLAB is most widely used in several scientific disciplines.

In Data Science, MATLAB is used for simulating neural networks and fuzzy logic. Using the MATLAB graphics library, you can create powerful visualizations. MATLAB is also used in image and signal processing.

This makes it a very versatile tool for Data Scientists as they can tackle all the problems, from data cleaning and analysis to more advanced Deep Learning algorithms.

Furthermore, MATLAB’s easy integration for enterprise applications and embedded systems make it an ideal Data Science tool.

6. Excel

It is the most widely used Data Analysis tool. Microsoft developed Excel mostly for spreadsheet calculations and today, it is widely used for data processing, visualization, and complex calculations.

Excel is a powerful analytical tool for Data Science.

Excel comes with various formulae, tables, filters, slicers, etc. You can also create your own custom functions and formulae using Excel. While Excel is not for calculating the huge amount of Data, it is still an ideal choice for creating powerful data visualizations and spreadsheets.

You can also connect SQL with Excel and can use it to manipulate and analyze data. A lot of Data Scientists use Excel for data cleaning as it provides an interactable GUI environment to pre-process information easily.

7. ggplot2

ggplot2 is an advanced data visualization package for the R programming language. The developers created this tool to replace the native graphics package of R and it uses powerful commands to create illustrious visualizations.

It is the most widely used library that Data Scientists use for creating visualizations from analyzed data.Ggplot2 is part of tidyverse, a package in R that is designed for Data Science.

One way in which ggplot2 is much better than the rest of the data visualizations is aesthetics. With ggplot2, Data Scientists can create customized visualizations in order to engage in enhanced storytelling.

Using ggplot2, you can explain your data in visualizations, add text labels to data points and boost intractability of your graphs.

8. Tableau

Tableau is a Data Visualization software that is packed with powerful graphics to make interactive visualizations. It is focused on industries working in the field of business intelligence.

The most important aspect of Tableau is its ability to interface with databases, spreadsheets, OLAP (Online Analytical Processing) cubes, etc. Along with these features, Tableau has the ability to visualize geographical data and for plotting longitudes and latitudes in maps.

Along with visualizations, you can also use its analytics tool to analyze data. Tableau comes with an active community and you can share your findings on the online platform. While Tableau is enterprise software, it comes with a free version called Tableau Public.

9. Jupyter

Project Jupyter is an open-source tool based on IPython for helping developers in making open-source software and experiences interactive computing. Jupyter supports multiple languages like Julia, Python, and R.

It is a web-application tool used for writing live code, visualizations, and presentations. It is also a powerful tool for storytelling as various presentation features are present in it.

Using Jupyter Notebooks, one can perform data cleaning, statistical computation, visualization and create predictive machine learning models. It is 100% open-source and is, therefore, free of cost.

10. Matplotlib

Matplotlib is a plotting and visualization library developed for Python. It is the most popular tool for generating graphs with the analyzed data. It is mainly used for plotting complex graphs using simple lines of code. Using this, one can generate bar plots, histograms, scatterplots etc.

Matplotlib has several essential modules. One of the most widely used modules is pyplot. It offers a MATLAB like an interface. Pyplot is also an open-source alternative to MATLAB’s graphic modules.

Matplotlib is a preferred tool for data visualizations and is used by Data Scientists over other contemporary tools.

11. NLTK

Natural Language Processing has emerged as the most popular field in Data Science. It deals with the development of statistical models that help computers understand human language.

These statistical models are part of Machine Learning and are able to assist computers in understanding natural language through several of its algorithms. Python language comes with a collection of libraries called Natural Language Toolkit (NLTK) developed for this particular purpose only.

NLTK is widely used for various language processing techniques like tokenization, stemming, tagging, parsing and machine learning.

It has a variety of applications such as Parts of Speech Tagging, Word Segmentation, Machine Translation, Text to Speech Speech Recognition, etc.

12. Scikit-learn

Scikit-learn is a library-based in Python that is used for implementing Machine Learning Algorithms. It is simple and easy to implement a tool that is widely used for analysis and data science.

It supports a variety of features in Machine Learning such as data preprocessing, classification, regression, clustering, dimensionality reduction, etc

13. TensorFlow

TensorFlow has become a standard tool for Machine Learning. It is widely used for advanced machine learning algorithms like Deep Learning. Developers named TensorFlow after Tensors which are multidimensional arrays.

It is an open-source and ever-evolving toolkit which is widely known for its performance and high computational abilities. TensorFlow can run on both CPUs and GPUs and has recently emerged on more powerful TPU platforms.

This gives it an unprecedented edge in terms of the processing power of advanced machine learning algorithms.

Due to its high processing ability, Tensorflow has a variety of applications such as speech recognition, image classification, drug discovery, image and language generation, etc.

14. Weka

Weka or Waikato Environment for Knowledge Analysis is a machine learning software written in Java. It is a collection of various Machine Learning algorithms for data mining. Weka consists of various machine learning tools like classification, clustering, regression, visualization and data preparation.

It is an open-source GUI software that allows easier implementation of machine learning algorithms through an interactable platform.

You can understand the functioning of Machine Learning on the data without having to write a line of code. It is ideal for Data Scientists who are beginners in Machine Learning.

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Summary

We saw how data science requires a vast array of tools. The tools for data science are for analyzing data, creating aesthetic and interactive visualizations and creating powerful predictive models using machine learning algorithms.

Most of the data science tools deliver complex data science operations in one place. This makes it easier for the user to implement functionalities of data science without having to write their code from scratch.

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