18 Data Analytics Tools You Need to Improve Your Skills

If you trade stocks, distribute and sell products, or develop marketing strategies, you likely understand the power of data. Today’s data shapes nearly every decision we make and can determine a company’s path to success.

We swim in data (2.5 quintillion bytes are created daily, according to some estimates), and we are constantly developing faster and more automated ways to collect, organize, store, clean, retrieve, and analyze all of it. Thus, keeping current with all the data analytics languages, frameworks, and tools can be overwhelming.

Data analytics usually requires an ever-expanding vocabulary and resume to remain competitive, and it’s a field that needs more data experts. According to the World Economic Forum, nearly all of the top 20 emerging jobs deal with data. In fact, the top three jobs are data analyst/scientist, AI and machine learning specialist, and big data specialist. Further, careers in data can be lucrative. For instance, data scientists earn a mean salary of $103,930 in the U.S., according to the Bureau of Labor Statistics, with several states (including New York) offering mean salaries above $120,000.

Ready to boost your data analytics skills? Then join us in learning about the top data analytics tools that may benefit you in the future.

How Do I Choose Which Data Analytics Tools to Learn?

The World Economic Forum’s 2020 Future of Jobs report indicates that more than 90 percent of organizations in digital communications, education, financial services, transportation, and mining and minerals will adopt big data analytics technologies by 2025. At least 80 percent of companies in agriculture and food production, automobile production, healthcare, and oil and gas production are doing so as well.

These employers are usually looking for professionals versed in big data tech, but where do you start? Perhaps you work for a business, have a strong product to sell, and understand your customer base well. You may work with data daily, have a good feel for using it to make business decisions, and know how it can build success.

But there’s a catch: the sheer amount of data — and ways to store, analyze, and visualize it — can be overwhelming. Data analytics tools change and adapt. Developers make them faster, add more features, and build them to handle ever-growing amounts of data. Choosing the right tool is like choosing the right road to your destination quickly and easily. But which one?

That’s where you have to be selective. You can’t (and probably don’t want to) learn how to use every programming language, framework, or tool for data analytics. What’s important is to gain the skills you need to make yourself more competitive in the job market.

Consider which data analytics skills you need for specific career paths. For instance, the Initiative for Analytics and Data Science Standards analyzed LinkedIn job listings to determine the skills most requested among employers. Here are some of the findings:

Many data analytics tools can make you more valuable and in-demand for potential employers. Seek out those that are most important to your field. Professionals who work with databases should consider learning SQL and its variations. Python is powerful for data scientists and analysts. JavaScript and Tableau are used for data visualization. And Excel remains an important all-purpose tool.

By combining mastery of these important skills with your business experience and expertise, your research and communication skills, and your professional network, you can make yourself a leading contender in the job market. So which data analytics skills should you consider learning? Here are our picks.

18 Essential Data Analytics Tools You Should Master

The top tools, frameworks, and languages used in data analytics today cover a variety of business needs and technological approaches. Some require little programming experience to operate, while others take time, study, and practice to master.

Again, don’t feel compelled to add every one of these to your resume. Even veteran programmers don’t have the time or energy to become fluent in every data analytics tool. Consider those that best fit your industry, your professional needs, and your job title.

JavaScript

JavaScript is known best as a scripting language for web pages. It makes sites come alive by adding interactive elements such as animation, buttons and menus, and forms for surveys or data entry. JavaScript is ubiquitous in web design — developers have made it the most-used language for nine straight years, according to Stack Overflow.

JavaScript carries versatility beyond building web pages. It is used in game development, APIs, and in apps that work with databases. Data analysts who want to query customers can use JavaScript to build survey apps that gather information. No wonder Hack Reactor calls JavaScript “the best coding language to learn.”

Mozilla, whose co-founder Brendan Eich developed JavaScript, has a wealth of information for beginners about the programming language.

CSS

CSS, or Cascading Style Sheets, a companion to HTML, gives your web pages a spark. Consider HTML to be the language that builds the substance of your page and CSS brings style to that substance.

With CSS, developers define the look and feel of their page. Type sizes and fonts, color, column width and spacing, page layout, and design all are controlled with CSS. This language works in conjunction with HTML, so once you’re comfortable with the basics of HTML, you can add CSS to your repertoire. Knowing CSS will enable you to add style and dynamism to your data visualizations.

Getting started is easy. Mozilla offers another easy-to-follow introduction to CSS. In addition, Freecodecamp has built a series of courses that detail how to construct a webpage using HTML and CSS.

Tableau

“Data culture” is a key phrase at Tableau, which built its platform to give people and businesses the power to harness their data.

Tableau makes free and paid versions of its software, which can import, analyze, and visualize data from almost any source. Users can input data from spreadsheets, databases, or data warehouses. They can combine data from multiple sources, build visual representations, and discover trends.

Tableau is an option for analysts who must work with data but don’t have the time or interest to learn to code. The Tableau community counts more than one million users, and Salesforce acquired the company in 2019. According to Tableau, Verizon, New York City Health & Hospitals, Whole Foods, and Nissan all use the software.

Tableau Public is a free online platform that allows users to create and share data visualizations.

MongoDB

Those who work with document databases likely are familiar with MongoDB, which is one of the most popular database platforms on the market.

In data parlance, a document consists of a field and a value. Consider the construct, “Name: Joe, Age: 21.” The terms “name” and “age” are the fields, followed by their corresponding values. MongoDB’s storage system makes large documents easy to access, index, and query to find information quickly.

MongoDB hosts a free platform that works with multiple programming languages and can be scaled to large projects. Adobe, eBay, Google, Intuit, and eHarmony are among the companies that employ MongoDB.

The best place to learn how to use and get the most out of MongoDB is the company’s site. MongoDB offers a series of free courses to help set up and query a database, use charts, and do much more. MongoDB also offers professional certifications.

Matplotlib

Python’s wide array of libraries helps to make it one of the top programming languages. Matplotlib is a Python library used to create a variety of data visualizations, including animations and those with interactivity.

Matplotlib is free,open-source, and contains code for dozens of visualization formats — from simple line and bar graphs to various style sheets to animations, Matplotlib can display data in myriad ways. Installation instructions are simple, there are multiple tutorials, and third-party packages add even more functionality.

For Python users, Matplotlib is an important companion to visualize data properly.

PostgreSQL/pgAdmin

SQL is a foundational programming language used to store and retrieve data from relational databases. It is considered a high-priority skill among those who work with relational databases. Fortunately, several SQL-based management systems are available to facilitate the process. PostgreSQL is among the most popular databases (No. 2, according to Stack Overflow), and pgAdmin is a key developmental platform that works in conjunction with it.

PostgreSQL is an open-source relational database system that was developed at the University of California at Berkeley in the 1980s. This feature-rich system allows users to define data types, scale their datasets to any size, and write code from different programming languages without recompiling. Apple, Instagram, Reddit, and IMDb are among the many companies that use PostgreSQL. It has become an essential database system.

To make the most of PostgreSQL, tools such as pgAdmin are critical. pgAdmin is a management and administration tool that works on many major web browsers. It can be deployed in a server environment, allowing multiple users access to creating, viewing, and editing all common PostgreSQL objects.

NumPy

Python is considered a powerful data analytics tool in part because of its associated  libraries. Among these libraries is NumPy (Numerical Python), another open-source project that bills itself as the “universal standard for working with numerical data in Python.”

NumPy is a Python library devoted to science and engineering. NumPy terminology can get pretty deep (the ndarray object is central to it), but that’s the target user base. NumPy speeds the process of working with arrays and works with other Python libraries, such as Matplotlib for visualization. It is central to data science and machine learning, containing a robust set of tutorials and guides for those who want to employ it for those purposes.

R

R joins Python on the list of the most popular programming languages in data analytics. R is used extensively in statistical computing to perform a variety of tasks, including modeling, classification, and clustering. As an important companion to its statistical analyses, R features a graphics environment for visualizing and graphing data.

As a popular open-source language, R is rich with packages — more than 18,000 of them, according to the Comprehensive R Archive Network. They help make R an industry must-have for data analytics.

R’s website is filled with tutorials to get the most out of the programming language, which can be applied to multiple scientific disciplines that use data. For instance, companies such as Merck and Pfizer that recruit Ph.D. students often look for those with knowledge of statistics and programming, according to the Association for Psychological Science. Being able to program in R can open doors.

SAS

SAS is one of the world’s leading analytics companies, offering a host of products for a variety of needs, industries, and technologies. SAS sells products for data management, cloud computing, AI and machine learning, marketing, risk management, and much more.

One of SAS’s top products is Viya, a data-management solution that uses AI to help deliver business analysis and transform data into insight. For those using its products, SAS offers a deep collection of training tools and resources, including live classes, webinars, video tutorials, and guides.

What Are the Best Ways to Improve My Data Analytics Skills?

With so many tools, platforms, and languages to learn in data analytics, finding a place to start can be challenging. Which skills should you acquire to perform your current job more efficiently or pursue a new one? And how much time and money are you able to invest in this educational process?

You have several options to learn new data analytics tools, each of which can be the right path. Let’s explore three ways to improve your data analytics skills to determine which might be right for you.

Data Analytics FAQs

Learn More About Data Analytics

Data shapes nearly every corner of our world, and businesses are investing more money and employee resources into analytics. To be competitive, businesses must be data savvy; that also applies to the workforce. Make yourself stand out to top employers by learning how to use some of the key data analytics tools. Learn more about the in-demand skills that can take you places at Columbia Engineering Data Analytics Boot Camp.