Raw data must be processed so it can be transformed into meaningful insights. This process is known as data analysis.
The data analysis process typically involves five key steps:
Asking the right questions
Collecting the data
Cleaning the data
Exploring the data to find patterns and insights
Presenting the results
Data is only as good as the questions being asked. So, before starting the data collection process, it is important to clearly know what you are looking for. Having a clear understanding of the questions will save time and resources.
Once the questions are defined, the next step is to collect the data. Data can come from various sources, including:
Company databases
Publicly available open data
Private data that can be purchased
One technique for collecting data from the vast ocean of information on the web is called web scraping. There are software tools available to help you scrape the web. If you have a basic understanding of HTML, you can gain greater control over the type of information extracted from web pages.
Additionally, there are APIs. Web APIs provide access to data from third-party platforms over the internet. For example, there are APIs for platforms like Facebook, YouTube, Wikipedia, and many others.
Raw data is rarely ready for immediate analysis. It is often messy and can present several challenges, such as:
Missing values
Duplicates
Incorrect data types
Anomalies or extreme values (also known as outliers)
Exploring the data is where the process becomes more interesting. This stage involves finding patterns, connections, insights, and sometimes making predictions. Depending on the questions to be answered, different techniques can be used, such as data visualisations, statistical analysis, or more advanced methods like machine learning.
The final step is communicating the findings. At this stage, communication skills are very important. Data visualisation and storytelling are valuable tools that will help you turn insights into actionable outcomes.
Cheers, Irina 😊
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