Data Science is related with extracting, manipulating, processing and generating predictions out of data. In order to perform these tasks, we need various statistical tools and programming languages. In this article, we are going to share some of the well known Data Science Tools used by Data Scientists to carry out their data operations. We will try to understand the main features of the tools, benefits they can provide.
Brief Introduction To Data Science
Data Science has emerged out as one of the most popular fields of computer world. Companies are hiring Data Scientists to help them gain insights about the market and to improve their products. Data Scientists work as decision makers and are largely responsible for analyzing and processing a large amount of unstructured and structured data. In order to do so, he requires various specially designed tools and programming languages for Data Science to perform the task in the way he wants. Data scientists uses these data science tools to analyze and generate predictions.
Top Data Science Tools
Here is the list of best data science tools that most of the data scientists used.
SAS is one of those data science tools which are specifically designed for heavy statistical operations. It is a closed source proprietary software that is used by large organizations to analyze data these days. SAS uses base SAS programming language which for performing statistical modeling. It is widely used by data science professionals and companies working on reliable commercial software. SAS offers numerous statistical libraries and tools that a Data Scientist can use for modeling and organizing their huge data. It is highly reliable and has strong support from the company that is why it is highly expensive and is only used by larger industries. Also, SAS pales in comparison with some modern open-source tools. SAS has several libraries and packages but dome are not available in the base pack and can require an expensive upgradation.
2. Apache Spark
Apache Spark or simply Spark is an all-powerful tool with analytics engine and it is one of the most used Data Science tool around the globe. 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 has many Machine Learning APIs that can help Data Scientists to make powerful predictions with the given data.
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. But the most powerful conjunction of Spark is with Scala programming language which is based on Java Virtual Machine and is cross-platform in nature.
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 application at a high speed.
It is another tool widely used by Data Science professionals. BigML provides a great and fully intractable, cloud-based GUI environment that you can use for processing Machine Learning Algorithms. It provides a standardized software using cloud computing for industry requirements. Through it, companies can use Machine Learning algorithms across various parts of their company. For example, it can use this one software across for sales forecasting, risk analytics, and product innovation. BigML specializes in predictive modeling. It uses a wide variety of Machine Learning algorithms like clustering, classification, time-series forecasting, etc.
BigML provides an easy to use web-interface using Rest APIs and you can create a free account or a premium account based on your data needs. 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.
You can combine this with CSS to create illustrious and transitory visualizations that will help you to implement customized graphs on web-pages. 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.
MATLAB is a multi-paradigm 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. It also helps in automating various tasks ranging from extraction of data to re-use of scripts for decision making. However, it suffers from the limitation of being a closed-source proprietary software.
Probably Excel the most widely used tool for Data Analysis. Microsoft developed Excel specially for spreadsheet calculations but today, it is also used for data processing, visualization, and complex calculations. Excel is a robust analytical tool for Data Science.
Excel comes with various predefined formulas, tables, filters etc. You can also create your own custom functions and formulas using Excel. Excel is not for calculating the huge amount of Data like other tools, but 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 your data. So many Data Scientists are using Excel for data manipulation as it provides an easy and intractable GUI environment to pre-process information easily.
ggplot2 is an advanced software for data visualization for the R programming language. The developers created this tool to replace the native graphics package of R language. It uses powerful commands to create great illustrious visualizations. It is the widely used library that Data Scientists use for creating appealing 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 annotate your data in visualizations, add text labels to data points and boost intractability of your graphs. You can also create various styles of maps such as choropleths, cartograms, hexbins, etc. It is the most used data science tool.
Tableau is a Data Visualization software that is packed with powerful graphics to make interactive and appealing visualizations. It is focused on needs of 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 creating 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 with other users. While Tableau is enterprise software, it comes with a free version called Tableau Public.
Project Jupyter is a IPython based open-source tool for helping developers in making open-source software and experiences interactive computing. Jupyter has support for multiple languages like Julia, Python, and R. It is one the best web-application tool used for writing live code, visualizations, and presentations. Jupyter is a widely popular tool that is designed to address the requirements of Data Science.
It is an interactable environment through which Data Scientists can perform all of their responsibilities. 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. There is an online Jupyter environment called Collaboratory which runs on the cloud and stores the data in Google Drive.
Matplotlib is a plotting and visualization library developed for Python. It is the most popular choice of data scientists 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. As a matter of fact, NASA used Matplotlib for illustrating data visualizations during the landing of Phoenix Spacecraft. It is also an ideal tool for beginners in learning data visualization with Python.
Data science requires a vast variety of tools. The tools for data science are for analyzing data, creating aesthetic and interactive attractive visualizations and creating robust predictive models using machine learning algorithms. Most of the data science tools mentioned above, deliver complex data science operations in one place. This makes it easier for the user or data scientist to implement functionalities of data science without having to write their code from scratch.