Data science is a fast-paced field. New data science applications come out every year. To be in the data science field, you need to know the latest skills, software and tools.
This data science guide walks you through some of the most popular data science tools and resources. This guide will help data science students and data scientists.
The best way to explore these topics is with a degree in data science. If you are interested, learn more about the best online bachelor’s degrees in data science programs.
Every data science job is different. But all data scientists and data science students should know data science tools like:
This is not a complete “top data science tools 2022” list. Instead, it includes five of the most popular data science tools today.
Python is one of the most commonly used programming languages by data scientists. Python is a top data science tool because:
Python has the flexibility, functionality, and wide support to take on data science projects of all kinds.
If you need to collect, clean, analyze, and present data, then R is a top choice. R is free and is designed for statistical computing and graphics.
One of R’s best features is its open source code. Thousands of user-made code packages expand R’s capabilities. Many commercial code libraries extend the functionality of R too.
Like Python, R is popular because of its ease of use. R runs on MacOS, Windows, and various UNIX systems.
Excel is not the flashiest option on this list, but it is the most widely used data science tool. Because it is used by so many people, it remains one of the best data science tools in 2022.
Excel is easy to learn and use. Excel has a large suite of tools for organizing and analyzing data, and is used by most organizations. Excel is also a powerful data visualization tool. It works with SQL. Excel can clean up data as well.
Java is one of the oldest data science software tools on this list. Java does not have some newer features of languages like Python and R. However, Java is easy to learn, and is platform-independent.
Data scientists like Java because it is an object-oriented language with a large library of data science tools. Java is also architecture-neutral and extremely secure.
SAS must be on any list of the top data science tools. SAS is closed source and proprietary, which makes it relatively expensive.
Many data scientists use SAS because it is good for advanced data analytics, data management, and business intelligence. SAS is also used for data mining, machine learning, risk management, and predictive analytics.
The tools below are not always required but could be useful in your work as a data scientist.
Data scientists mainly use Matlab for mathematical modeling, numerical computing, and data visualization.
The benefit of Matlab is that it has a roughly 40-year history. Matlab is widely known, widely used, and well-supported.
Matlab is also used for these tasks:
You can use add-ons, native applications, and user-generated applications to add to Matlab’s functions.
Matplotlib is also popular with data scientists. It is used along with Python to create data visualizations.
Matplotlib has a hierarchical structure, which allows you to create data visualizations with high-level commands. It can also be used for complex data plotting using low-level commands.
The code base for Matplotlib is quite large, which can be difficult to handle. However, the payoff is that there are many uses for generating static and dynamic data visualizations.
According to its fans, one of the best features of Apache Spark is that it handles huge amounts of data. Spark can process data quickly. Apache Spark is growing in popularity because of this speed.
Spark is highly functional and useful. Spark can be used for:
Spark also supports machine learning APIs that improve your ability to make predictions based on the data.
Data Robot is one of the most important machine learning data science tools.
Data Robot automates machine learning, makes it faster, and lets users generate high quality predictive models. In fact, you can compare different predictive models with just one line of code.
Data Robot also has benefits like ease of use, simple deployment process, and integration with Python.
Since D3.js is used in a web browser, it follows HTML, CSS, and other web standards. It can be used for everything from creating animations to conducting quantitative analyses.
There are some excellent books on data science. Whether you want to improve your existing skills, learn how to use new software, or improve your understanding of data science as a whole, this short list of recommended data science books should help:
For more great reads on the subject, check out the 25 Most Influential Books in Computer Science.
And if you’re ready to take a deep dive in a specific area of concentration, check out the best master’s degrees in data science.
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