Situatie
The world is becoming more and more data-driven, with endless amounts of data available to work with. Big companies like Google and Microsoft use data to make decisions, but they’re not the only ones.
Is it important? Absolutely!
Data analysis is used by small businesses, retail companies, in medicine, and even in the world of sports. It’s a universal language and more important than ever before. It seems like an advanced concept but data analysis is really just a few ideas put into practice.
Solutie
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What Is Data Analysis?
Data analysis is the process of evaluating data using analytical or statistical 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 analytics.
Once data is collected and sorted using these tools, the results are interpreted to make decisions. The end results can be delivered as a summary, or as a visual like a chart or graph.
The process of presenting data in visual form is known as data visualization. Data visualization tools make the job easier. Programs like Tableau or Microsoft Power BI give you many visuals that can bring data to life.
There are several data analysis methods including data mining, text analytics, and business intelligence.
How Is Data Analysis Performed?
concept of big data processing and storage: cloud to database
Data analysis is a big subject and can include some of these steps:
- Defining Objectives: Start by outlining some clearly defined objectives. To get the best results out of the data, the objectives should be crystal clear.
- Posing Questions: Figure out the questions you would like answered by the data. For example, do red sports cars get into accidents more often than others? Figure out which data analysis tools will get the best result for your question.
- Data Collection: Collect data that is useful to answer the questions. In this example, data might be collected from a variety of sources like DMV or police accident reports, insurance claims and hospitalization details.
- Data Scrubbing: Raw data may be collected in several different formats, with lots of junk values and clutter. The data is cleaned and converted so that data analysis tools can import it. It’s not a glamorous step but it’s very important.
- Data Analysis: Import this new clean data into the data analysis tools. These tools allow you to explore the data, find patterns, and answer what-if questions. This is the payoff, this is where you find results!
- Drawing Conclusions and Making Predictions: Draw conclusions from your data. These conclusions may be summarized in a report, visual, or both to get the right results.
Let’s dig a little deeper into some concepts used in data analysis.
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