Then what?

The raw data is usually not in the most appropriate format and it undergoes some form of processing. The size of raw data is usually quite big. Thus, we need software tools to transfer and process it.

The required tools change depending on how and where you want to keep the data. Another critical decision point is how to access the stored data.

Once you are all set with maintaining the data properly and efficiently, it is time to converting it to value. This is where the algorithms and statistics come into play.

Statistics allow us to understand and explore the data. It is of crucial importance for creating robust and accurate products to draw meaningful insights from the data.

Although the fundamental concepts and principals are the same, how you apply data science to different domains might change. Thus, domain knowledge is an important asset in the process of converting data to value.

Last but not least, your results or the value you created will not be impactful if not explained clearly. You will need to use tools to deliver the results and demonstrate the entire process. Data visualization tools are great resources for this step.

Continue reading: https://towardsdatascience.com/what-does-it-really-mean-to-learn-data-science-6a419cc3e699?source=rss—-7f60cf5620c9—4

Source: towardsdatascience.com