With Big Data on the rise, data scientists require specific tools i.e. software and programming languages, in order to perform the type of high-level processing required to store, organize, and analyze these large collections of data. Due to the fact that there are multiple different types of data and ways to both analyze and visualize it, Data Scientists need to learn different tools depending on the type of data that they are analyzing and/or the industry where they work.

Types of Data Science Tools

Data science tools are essentially any program, software, library, or language that can be used to make it easier to create information from data after the data collection process. While some data science tools are focused on one particular purpose, most data science tools are multifaceted, and using them allows you to perform multiple processes, such as data analysis and visualization. The following list offers an overview of the different types of data science tools, as well as how they are used within data science.

Programming Tools

Within the data science industry, Python, R, and SQL are widely cited as the most commonly used tools amongst data scientists. According to survey findings from the data science company Kaggle, “Of the data professionals who identified as a data scientist, 93% used Python, 57% used SQL and 41% used R.” In addition to SQL, R, and Python there are many programming languages that have their own unique libraries and packages that can be used for multiple purposes.

With programming languages, you can actually design your own code to do whatever data processes that you need, such as analyzing, visualizing, and creating models for different types of data. Knowledge of a programming language is a versatile data science skill due to the number of uses that the tool can include.

Data Analysis Tools

When working with large stores of data, you cannot easily analyze the data on your own. Many data scientists will upload their data into a program for more in-depth analysis. While programming tools can be used for data analysis, there are a variety of tools that you can use to perform some form of data or statistical analysis to find patterns or emerging themes in a dataset without advanced knowledge of programming. The most popular data analysis tools within data science are statistical analysis software (SAS) tools such as SPSS, Stata, and SAS. While these are primarily viewed as data analysis tools, each of these tools can also be used for statistical modeling and data visualization.

Data Visualizations Tools

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After the data is collected and analyzed, many industry professionals present their findings to an audience through creating a data visualization. Data visualization tools take the data that has been collected, cleaned, and uploaded into a program or data analysis tool, in order to create an image, map, word cloud, graph, chart, or some other visual expression of what the data science professional or program found in the data. Data visualizations tools are especially useful within industries where Data Scientists have to convince or explain the data to an audience. And, while many data analysis tools and programs also allow you to visualize data, some tools that primarily focus on data visualization are Tableau and Google Charts. In addition, you can use any spreadsheet program, such as Microsoft Excel or Google Sheets to create simple and straightforward data visualizations.

Database Design Tools

Generally, big data is stored in a database or platform that was designed to make the process of examining and analyzing the data easier. Database design tools are unique in that they are structured to make it easier for data science students and professionals to organize and search through a large dataset as well as to perform data modeling functions. MySQL is one of the most popular database design tools, as well as LucidChart and Navicat.

Data Sharing and Collaboration Tools

In addition to storing, visualizing, analyzing, and collecting data, individuals working with data might also want to share or work on the data that they have collected with another individual or institution. Data sharing is especially useful when working on team-based projects or between multiple institutions. Due to the sensitive nature of data and concerns around large stores of data being lost, it is important to use data science tools that are made for sharing, which is why Jupyter Notebooks and GUIs like BigML are so useful.

Industry-Specific Data Science Tools

Each industry also has data science tools that are more popular within that particular field or type of work. Data scientists, as well as students and professionals interested in data science, should have some knowledge of the industry-standard of data science tools and software.

The following list includes some of the most popular multipurpose data science tools and the fields/industries in which they are commonly used.

  • SPSS or STATA - These statistical analysis programs are commonly used within research settings, especially within the healthcare industry and the social sciences, with SPSS being very popular within the field of psychology.
  • Microsoft Excel - This spreadsheet program is usually seen within the realms of business and finance, and Excel is not only used to analyze data but also to organize it in ways that are easy to visualize and understand for a non-technical audience.
  • SQL and MySQL - Known for database management and design, MySQL is used within the realms of information and data science and is especially useful for libraries, museums, government, and other institutions or industries that work with archives or long term data storage.
  • MATLAB - As a proprietary programming language, MATLAB is commonly used within the science and engineering fields, both within and outside of academia.
  • Python or R - Programming languages are the most widely used data science tools, and popular open-source programming languages like Python and R can be useful in any field or industry that requires data analysis or visualization.

Want to know more about the latest data science tools?

Then take one of Noble Desktop’s data science courses to learn everything you need to know about the tools and technology that industry experts and data professionals use on the job. For courses that you can take from any location and environment, the live online data science classes offer the most flexibility and convenience. But for students and professionals that require a more traditional in-person classroom experience, you can also find a data science class near you on the Noble Desktop website.