There is a lot of data analysis software out there. This article is designed to help you understand the most common data analysis software available, and will help you gain the skills you will need to land an entry level data analysis position.
Today, there is more data than ever. In fact, according to research conducted by Statista, the global volume of data created, captured, copied, and consumed is projected to reach 181 zettabytes by 2025 . That’s a lot of data that’s going to need to be sorted, cleaned, analyzed, and visualized.
Just as there is a lot of data, so too is there a lot of software and tools available to work with it Cloud-based data warehouses store huge amounts of business data. Some programs are specially designed for data modeling, and software visualizes data in bright colors and diagrams. Faced with such an overwhelming choice, many inevitably wonder where they should start.
We’re here to help. Rather than listing all the tools, you might encounter in your professional career, we’ve compiled seven essential data analysis software you should know as you begin your data journey. Here, you’ll encounter some of the most common data analysis software, learn what each does, and discover why it matters. At the end, you’ll even find a suggested course to help you gain the skills you’ll need to land an entry-level data analyst position.
Microsoft Excel is one of the most common software used for data analysis. In addition to offering spreadsheet functions capable of managing and organizing large data sets, Excel also includes graphing tools and computing capabilities like automated summation or “AutoSum.” Excel also includes Analysis ToolPak, which features data analysis tools capable of performing variance, regression, and statistical analysis.
Excel’s simplicity and versatility make it a powerful data analysis tool suitable for managing, sorting, filtering, cleaning, analyzing, and visualizing data. If you’re just starting out in data science, you should consider learning more about Excel to prepare for your future career.
Python is routinely ranked as the most popular programming language in the world today .
Unlike other programming languages, Python is relatively easy to learn and can be used for a wide range of tasks, including software and web development, and data analysis. In the world of data, Python is used to streamline, model, visualize, and analyze data using its built-in data analytics tools. One of the key features of Python that appeals to data analytics professionals is its many libraries, such as Pandas and Numpy, which offer a variety of powerful tools for many analytics needs.
Early professionals should learn Python to ensure that they have a firm grasp of one of the most important programming languages used in data today.
Learn to Program and Analyze Data with Python. Develop programs to gather, clean, analyze, and visualize data.
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R is an open-source programming language used for statistical computing and graphics.
Like Python, R is considered a relatively easy-to-learn programming language. Typically, it’s used for statistical analysis, data visualization, and data manipulation. The statistical focus of R means that it’s well-suited to statistical calculations, while the visualization tools included within R make it a great language for creating compelling graphics like scatter plots and graphs.
Alongside Python, R is one of the most important programming languages used in data analysis. If you’re considering a career in data, then you might want to spend time learning R.
Read more: Python or R for Data Analysis: Which Should I Learn?
Tableau is a data visualization software used primarily for business analytics and business intelligence.
Tableau is undoubtedly one of the most popular data visualization platforms in the world of business, particularly because it features an easily understood user interface and seamlessly turns data sets into comprehensible graphics. While business users enjoy it because of its ease of use, data analysts like it because it packs powerful tools that can perform advanced analytics functions like segmentation, cohort analysis, and predictive analysis.
Data visualization is important because it allows data analysts to convey their findings to colleagues and stakeholders who might not otherwise understand them. If you’re considering a future in either business analytics or intelligence, then you might consider learning Tableau to prepare for the professional workplace.
MySQL is an open-source relational database management system (RDBMS) used for storing application data, particularly web-based ones. Popular among websites, MySQL has been used by such popular websites as Facebook, Twitter, and YouTube.
In data, a Structured Query Language (SQL) is used for managing relational database management systems, which use relational databases usually structured into tables. As a result, data professionals use MySQL to store data securely and perform routine data analysis. While the program has some limitations, MySQL typically fits well within many businesses' existing data systems.
In particular, you should consider learning MySQL if you would like to work in tech on web applications.
SAS is a well-known suite of statistical analysis software developed by the SAS Institute for various analytical purposes, including business intelligence, advanced analytics, and predictive analytics.
Analysts use SAS to retrieve, report, analyze, and visualize data. Business intelligence analysts and data analysts more broadly like SAS because it brings together a variety of powerful analytic tools in one place and has an intuitive graphical user interface (GUI) that makes it easy to use. Furthermore, SAS is a reliable software suite that allows data analysts to perform much of their work – from managing data to cleaning and modeling it.
Learn SAS to prepare for positions focused particularly on business intelligence and analytics or if you want to become familiar with a software suite that can handle most of what a data analyst might need to do.
Jupyter Notebook is a web-based interactive environment used to share computational documents or “notebooks.” Data analysts use Jupyter Notebooks to write and run code, clean data, data visualization, machine learning, statistical analysis, and many other forms of data analysis. Furthermore, Juypter Notebook allows users to combine data visualizations, code, comments, and numerous different programming languages in one place, allowing for an improved space to document a data analysis process and share them with others.
Whatever your professional data goals, you will likely benefit from using a tool like Jupyter Notebook to work through data problems and share your work with others.
A career in data analysis begins with gaining the skills you need to get the job done. Start your data journey today with one of Coursera’s many data analysis professional certificates or specializations by industry leaders like Google.
Designed for those without experience, Google’s Data Analytics Professional Certificate will have you job-ready in less than 6 months. Learn job-relevant skills like how to clean and organize data for analysis, complete analysis and calculations using spreadsheets, and critical tools like SQL, R programming, and Tableau.
This is your path to a career in data analytics. In this program, you’ll learn in-demand skills that will have you job-ready in less than 6 months. No degree or experience required.
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Spreadsheet, Data Cleansing, Data Analysis, Data Visualization (DataViz), SQL, Questioning, Decision-Making, Problem Solving, Metadata, Data Collection, Data Ethics, Sample Size Determination, Data Integrity, Data Calculations, Data Aggregation, Tableau Software, Presentation, R Programming, R Markdown, Rstudio, Job portfolio, case study
1. .Statista. “Volume of data/information created, captured, copied, and consumed worldwide from 2010 to 2025, https://www.statista.com/statistics/871513/worldwide-data-created/.” Accessed August 11, 2022.
2. TIOBE. “TIOBE INDEX, https://www.tiobe.com/tiobe-index/.” Accessed August 11, 2022.
This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.