The modern-day data analyst uses computer software programs to perform data analysis tasks. Several data analysis tools serve different analysis purposes. A successful data analyst should have in-depth knowledge and understanding of the inner workings of these tools. Using this understanding, data analysts can select the appropriate tool for a given data analysis task.

This paper will discuss six types of data analysis tools: spreadsheets, databases, self-service data visualization, programming languages, big data tools, and cloud.

Spreadsheets are suitable for data collection. The two categories of spreadsheets are Excel and Google Sheets. Spreadsheets are structured such that they have fields in which users can enter data. Specifically, Google Sheets are great for data collection because they are centralized, and Google Forms can easily be shared online with respective parties (Nguyen, 2018). Data collected through spreadsheets can then be uploaded to databases. However, spreadsheets could be better for data cleaning.

Databases can be relational, column, document, or graph. Relational databases are the most commonly referred to type of databases. Their relational databases include Microsoft Access, SQL Server, and Oracle. Databases are not suitable for analysis (Nguyen, 2018). They are, however, suitable for data collection. Relational databases are designed for data storage and transactions. They play a role in data analysis by allowing querying and generation of samples, from stored data, for analysis.

Self-service data visualization tools bring the analyst experience to non-analysts. Such tools include Tableau, Power BI, Qlik Sense, and AWS Quicksight. These tools are simplified so that users can visualize data without undergoing technical training (Nguyen, 2018). Power BI is available as a free download, but sharing the results requires a paid service. Tableau and Qlik Sense are paid, while AWS Quicksight offers a cheaper alternative for online cloud-based analytical tools.

Programming languages for data analysis comprise R and Python. The two languages are successors to older tools such as Matlab, SPSS, and SaaS, which were more expensive. The two languages are appropriate for simple data analysis tasks and complex data science tasks (Nguyen, 2018). R and Python are open source, thus offering lower costs. The above tools are suitable for performing analysis tasks relating to moderately small data (ProjectPro, 2023). Next is the analysis of big data.

Big data implies vast volumes of data that a single computer may not handle, requiring multiple computers to share the process (Nguyen, 2018) simultaneously. This data type uses distributed computing, where several computers operate as clusters. Examples of such computing are Hadoop, Data Lakes, and Spark (ProjectPro, 2023). Data formats that require huge memory, such as videos and images, necessitate big data solutions.

Cloud-based solutions are not software running locally on a computer. It is usually accessed through the Internet. Multiple computers may be used to store and share processes. Examples of cloud are AWS, Microsoft Azure, and Google Cloud Platform. Cloud is cheap and straightforward for a business just setting up an IT infrastructure. Cloud is also suitable for requiring big data (Nguyen, 2018). Because the cloud can be scaled up or down easily or switched off without much hardware and software sunk cost, cloud-based solutions are some of the best computing solutions. Cloud is great at big data because it accesses multiple databases with different data sets.

A data science professional should choose the appropriate tool for the right task. Simple spreadsheets are great for data collection. Databases provide an excellent source for stored data. Self-service visualization tools enable non-technical users to visualize data. Programming languages are suitable for large data tasks. Big data tools are good for analyzing big data, for example, data from the cloud. The cloud is an excellent source of big data. Each tool is valid at a different cycle of the data analytics process.