❓Tools Data Analysts Use

Hey friends,

What technical tools does a data analyst use on the job? And how do you know which tools to use for analysis? I’ll be answering these 2 question below so that you have insight into what a data analyst does!

What technical tools does a data analyst use on the job?

The most common technical tools a data analyst uses are Excel, SQL, and a BI visualization tool. Depending on the role, it may require more or less Python as well. My recommendation would be to find the technical requirements of roles you want to apply for and work backwards from there.

As a start, if you don’t have a specific job role in mind, watch How I'd become a data analyst as I list the common skills there.

How do you know which technical tools to use for analysis?

This is a trickier question to answer. I talk about this in, Think like a data analyst in tech. Basically the tools you use will really depend on many factors. This includes:

Type of Data You’re Working With: The type, size, and complexity of the data being analyzed plays a large role in what tool to use. SQL is great for structured data whereas Python is better for unstructured data. Excel also works great for quick analysis of smaller datasets but has limitations on it’s ability to work with larger datasets.

Analytical Requirements: For simple queries and reporting tasks, SQL will work but for advanced analytics or complex data transformations Python may be necessary.

Tool Familiarity and Availability: The type of tools you use will vary widely depending on the company. For example, some companies use Excel as their main BI tool whereas others like mine do not use much of Excel at all.

Scalability: The scalability and integration requirements of the analysis also will determine what tool to use. When working with big data and considering the performance of processing data analysis tasks, cloud technology tools like Apache Spark or Hadoop may be a better choice.

My tip would be, don’t worry about learning it all and just focus on learning the skills and moving on. Remember “progress over perfection”.

Talk soon,

Agatha

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