What is data analysis?

The world possesses endless amounts of data available to work. Big companies like Google and Microsoft use data to make decisions, but these are not the only places to do this.

Data Analysis is also used by small businesses, retail companies, in medicine and even in the sports world. It is a universal language and more important than ever.

What is data analysis?

Data analysis is the process of evaluating data using statistical or analytical tools to discover useful information. Some of these tools are programming languages ​​like R or Python. Microsoft Excel is also popular in the world of data analysis.

When data is collected and sorted by these tools, the results will be interpreted to make decisions. The final results can be distributed in summary form or in visual form such as charts or graphs.

The process of presenting data in a visual form is called data visualization. Data visualization tools make the job easier. Programs like Tableau or Microsoft Power BI give you lots of pictures that can bring data to life.

There are several methods of data analysis including data mining, text analytics, and business intelligence.

How is data analysis performed?

Data analysis is a big topic and may include some of the following:

1. Define goals : Start by outlining some clearly defined goals. In order to get the best results from the data, the goals have to be clear.
2. Ask questions : Find out the questions you want to answer with data. For example, do red sports cars crash more often than other vehicles? Figure out which data analysis tools will get the best results for your question.
3. Data collection : Collect useful data to answer questions. In this example, data can be collected from a variety of sources such as DMV or police accident reports, insurance claims, and admission details.
4. Data Scrubbing : Raw data can be collected in a number of different formats, with lots of invalid and cluttered things. The data needs to be 'cleaned' and converted so data analysis tools can import it. This step is very important.
5. Data analysis : Import this new 'clean' data into data analysis tools. These tools allow you to explore data, find patterns and answer what-if questions (what happens, if .). This is where you find results!
6. Draw conclusions and make predictions : Draw conclusions from your data. These conclusions can be summarized in a report, visual chart or both to get correct results.