Data Science Tools & Trends and Books for Data Science

Top Data Scientist Tools

Every data science job is different. But all data scientists and data science students should know data science tools like:

• Python
• R
• Excel
• Java
• SAS

This is not a complete “top data science tools 2022” list. Instead, it includes five of the most popular data science tools today.

Python

Python is one of the most commonly used programming languages by data scientists. Python is a top data science tool because:

• Its simple syntax makes it easy to learn.
• It offers dynamic semantics.
• It can be used for many different tasks like data visualization, data analysis, and AI.
• It supports object-oriented programming.
• It can be used to create mobile and desktop applications.

Python has the flexibility, functionality, and wide support to take on data science projects of all kinds.

R

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

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

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

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.

Other Data Science Tools and Techniques

The tools below are not always required but could be useful in your work as a data scientist.

Matlab

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:

• Machine learning and deep learning
• Big data analytics
• Data preparation
• Algorithmic design
• Predictive modeling

You can use add-ons, native applications, and user-generated applications to add to Matlab’s functions.

Matplotlib

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.

Apache Spark

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:

• Extracting data
• Batch processing
• Stream processing
• SQL batch jobs

Spark also supports machine learning APIs that improve your ability to make predictions based on the data.

Data Robot

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.

D3.js

D3.js is an open source JavaScript library that’s used to create data visualizations.

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.

D3 has benefits like ease of use and flexibility. D3.js can be difficult to learn if you don’t have expertise in JavaScript.

Books for Data Scientists

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:

Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python 2nd Edition

Authored by Peter Bruce, Andrew Bruce, and Peter Gedeck, this is a good beginner book for learning about data analysis, statistical methods, and classification techniques.

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems 2nd Edition

This technical handbook by Aurélien Géron explores how to build intelligent systems and master techniques for machine learning.

Naked Statistics

This New York Times bestseller makes data science interesting and funny. Author Charles Wheelan explores how you can answer all sorts of questions with the right data and statistical tools.

Inflection Point: How the Convergence of Cloud, Mobility, Apps, and Data Will Shape the Future of Business

This book, written by Scott Stawski, offers interesting insights into the ways that data science tools and technologies are forever changing the way businesses operate.

The Art of Statistics: Learning from Data

Author David Spiegelhalter explores how statistics can be easily manipulated, sometimes for the worse. This book outlines the principles used to gain understanding from data.

For more great reads on the subject, check out the 25 Most Influential Books in Computer Science.