What Software Do Data Analysts Use?

What Does a Data Analyst Do?

As more and more data is being created, the corresponding need for those who can manage it is increasing as well. Data Analysts are tasked with taking huge stores of data and working to locate information and trends about past and current practices, as well as offer predictions about what is likely to happen at a company or business based on these numbers. Data Analysts transform large sets of data into engaging visualizations that are accessible to all members of a team or business. These clear and engaging visual aids can be used to help employers make sound business decisions.

What Tools Should a Data Analyst Learn?

Those working with data must wear many hats; they have to have an understanding of how to collect and clean data, patience for analyzing large sets of data, a knack for storytelling, and a passion for finding ways to visualize their findings so that they can be shared with others and ultimately acted upon. This article will explore some of 2021’s core tools for each step of the data analytics process, as well as their primary industry uses.

Tools for Data Storage

Modern data storage has been around for nearly one hundred years. In the time since, it has evolved from storing information magnetically on tape to the vast database management systems and cloud storage options currently in use to retain digital data.

Here are a few of the most common ways data is being stored in 2021:

• MySQLis a relational database management system. Whereas SQL Server is a licensed Microsoft product users have to pay to use, MySQL is free and open-source. MySQL also differs from SQL Server in terms of the languages it supports, as well as storage space, query cancellation capabilities, and capacity for data file manipulation.
• Apache Hadoopis a free software framework based on Java that clusters large amounts of data in order to store it.
• NoSQL databases are non-tabular and are designed to store data in a different manner than it is stored in relational tables. They are able to easily scale with significant amounts of data and large user loads, and offer users flexible schemas.

Tools for Data Mining

Data mining involves uncovering relationships and patterns in large datasets. This advanced form of data analysis draws from AI, statistics, and machine learning to help users locate important information. Those who are skilled at data mining can offer their organization insights about customer needs, as well as suggestions for how to cut down on costs and increase revenue.

Here are a few data mining tools that are popular among Data Analysts in 2021:

• Oracle: Considered to be the international leader in database software, Oracle provides users with multiple data mining algorithms that can be used for classification, regressing, prediction, and anomaly detection.
• KNIME: This platform is open-source and free. Its pre-built components allow users to quickly model without the need to enter any code. KNIME has an intuitive user interface that’s perfect for modeling and production endeavors.
• Rapid Miner: This software platform is commonly used by Data Scientists and Data Analysts for several stages of data modeling, such as for data preparation and cleaning, exploratory analysis, and data visualizations. Rapid Miner is particularly useful for deep learning, text mining, machine learning, and predictive analysis.
• SAS Enterprise Miner: This data management and analytics platform was designed to simplify data mining and aid with the process of transforming huge datasets into actionable insights. SAS’s extensive set of algorithms can be used to explore or prepare data, as well as to create complex descriptive or predictive models.

Tools for Statistical Analysis

Statistics plays an important role in the data analytics process. This branch of mathematics involves studying and manipulating data in order to represent specific characteristics. The data used can be quantitative or qualitative.Statistics provides users with a means for collecting, reviewing, and analyzing data, as well as a way to draw conclusions from this data and ultimately make better business decisions. Applying statistical methods to data analytics is a vital process, one that fuels new discoveries, informs the decision-making process, and helps predict what is to come.

The following list contains some of the currently most-used statistical tools for data analytics:

• Ris becoming an increasingly popular programming language for those working with big data. This powerful scripting language is often used for statistical computing and graphics. It’s also able to handle complex datasets, which makes it a go-to language for anyone working in data analytics or data science. R is generally seen as more approachable than Python for non-developers because users can create a statistical model and compelling visualization with only a few lines of code. R is open-source, offers simple data wrangling options, powerful graphics, and reproducible research.
• MATLABis a programming language and analytics platform commonly used by Scientists and Engineers. It has applications in image processing, computational biology, deep learning, and machine learning. In order to use MATLAB successfully, it’s important to have a background in coding.
• Microsoft Excelis a spreadsheet software application used for organizing data, making calculations, working with graphic tools, and designing PivotTables. Its ease of use and simple interface make Excel the industry-standard for spreadsheets.Excel has many professional usages across industries, in particular in office jobs or careers that require organizing, sorting, and keeping track of numbers. Some of this software’s most common uses are for data entry, time management, financial analysis, accounting, programming, and charting.

Tools for Data Visualization

One of the most useful tools available for presenting complicated material in an accessible way is data visualization. Presenting data in a visual manner makes it easier to understand and faster to process, even for those who aren’t mathematically inclined or trained in analytics. These visual representations of data are not just visually appealing, they also tell a story about the information, allowing audience members to spot outliers, notice trends, and see patterns emerge from data. Visually conveying points is a powerful way to leverage data in order to achieve the desired outcome.

The following are several of 2021’s most popular tools for data visualization:

• Tableauis one of the leading tools for data visualization. Its ease of use and power make this software one of the best options for those working with data. Tableau enables even non-technical users to design customized dashboards and worksheets that present data in a clear and understandable manner. It is able to integrate with hundreds of sources when inputting data and can output dozens of visualization types.Tableau is most often used for data blending, data collaborating, and real-time analysis.
• Infogramis a suite of data visualization tools that provides users with a means of presenting important data and messages in a visually appealing and understandable format. It optimizes a drag-and-drop visualization tool that enables users who don’t have any prior experience with document design to make creative and compelling visualizations. After visualizations are finalized, they can be easily exported into a variety of formats, like PDFs or JPGs, and then shared with other team members or stakeholders. A variety of chart types can be made using Infogram, like word clouds, dual-axis charts, and pyramid charts.
• Zoho Analytics: A self-service data analytics and BI tool that allows users to work with data from many sources. Zoho can blend data and also design cross-functional dashboards and reports.
• Google Chartsis a free data visualization tool for designing interactive charts that can then be displayed online. Because its charting library is JavaScript-based, Google Charts allows users to easily add interactive charts to applications that can be run anywhere on the web. Their chart gallery provides many ready-to-use kinds of charts to meet visualization needs. Google Charts makes it possible to display data visualizations on a website. The charts are powered using data from a database or static data. Seventeen different types of charts can be made using Google Charts, such as timelines, candlestick charts, area charts, and organization charts.

Tools for Business Analytics & Data Reporting

Business analytics is a subset of business intelligence. It is involved with using methods such as predictive analytics,statistical analysis, and data mining in order to find useful information in large datasets. This information is helpful for identifying and predicting trends and various outcomes, which can be reported and shared with stakeholders in order to facilitate better decision-making.

The following list includes some of the most powerful tools for business analytics and data reporting in 2021:

• QlikViewis a classic analytics solution with an associative engine that has transformed the way businesses manage data. It incorporates intuitive visual discovery so that more people can have access to business intelligence insights.
• Microsoft Power BIis a collection of business analytics services that enables users to create data visualizations that can be shared within organizations or on websites. This suite of architectures, technologies, processes, reporting, and data visualization products can be used to extract meaningful information from raw data. Power BI has a clear and easy-to-use interface with which users can design their own dashboards and reports. Those who wish to upload visualizations or reports can easily share them with others using the Power BI service, which has the added perk of refreshing their data each time there’s an update to the underlying dataset. Because Power BI was constructed using Microsoft Excel as a foundation, those who are familiar with Excel can easily transition to using Power BI.
• Jupyter Notebookis an open-sourced, free web application that enables users to create and share documents containing visualizations, equations, text, and live code. It is commonly used to clean and transform data. Jupyter Notebook users can document and run code, review the outcome, and create a data visualization based on the insights gathered without having to leave the environment. One of the main advantages of using Jupyter Notebook is that users can view and execute code in the same user interface, which allows them to make changes to code and to see the results in real-time. In addition to its applications for data visualization, it is also helpful for numerical simulations, statistical modeling, data cleaning, and presentations.

Hands-On Business Intelligence & Data Analytics Classes

For those who want to learn more about how to process, analyze, and ultimately visualize big data, Noble Desktop’s data analytics classes are a great starting point. Courses are offered in New York City, as well as in the live online format in topics like Excel, Python, data science, and data analytics technologies, among others.

In addition, more than 130live online data analytics courses are also available in data analytics and visualization from top providers. Topics offered include FinTech, Excel for Business, and Tableau, among others. Courses range from three hours to six months and cost from $219 to $27,500.

Those who are interested in learning more about data visualization may also want to check out the more than six dozen live online data visualization courses available. Topics include Tableau, Python, Excel, and Power BI, among others. There are also live online Power BI classes currently available for those looking to specialize in this intelligence platform. These interactive classes are taught in real-time and provide all learners with access to an instructor who is teaching live (in-person and online) and is ready to provide feedback and answer questions.

Additionally, Noble’s Data Analytics Classes Near Me tool provides an easy way to locate and browse approximately 400 data analytics classes currently offered in in-person and live online formats. Course lengths vary from three hours to 36 weeks and cost $119-$27,500.