There are no two ways about it, data is the fuel of the modern corporate economy. Everyone from the social media giant Meta to Amazon and even relatively small businesses rely extensively on the power of data collection to increase their profit margins, grow their influence, and ultimately dominate their markets.
Why is this the case, and what is data science?
Well, firstly, the reason why data is so important to collect is because it provides businesses with valuable insights about their markets and customers. This information can help companies craft better offers, tweak their marketing language and style, see which products are performing well and which ones aren’t, as well as how their prospects and customers are evolving their behavior, wants and desires, and buying trends.
So, this is why data science has become the buzzword of the modern age. As you might have guessed, data science is the collecting and analyzing of this information on behalf of companies.
This is everything you need to know about data science – from data to insights:
Understanding the Data Landscape
Firstly, it is crucial for you to understand the data science landscape as a whole, so let’s start with the basics.
Every business (and individual to a certain extent) needs quality data. Without the information that you collect being accurate, up-to-date, and extensive, you cannot form any tangible conclusion or take the right actions.
What’s more, you could end up forming completely the wrong conclusions and wreck your business (and reputation) overnight as a result. It only takes a few details to be inconclusive for you to paint the wrong picture from a data set, so data scientists are there to ensure the information is of sound origin.
Data scientists also provide proven models to analyze and view the data. This is almost like having glasses to read tiny text through. Without it, you may struggle to understand what the data means, or what actions you should take off the back of it.
Data cleaning techniques
The next element of the data science process is when data scientists clean the information. This is vital because there can be inconsistencies, errors in data collection, missing values, and random freak results, which threaten to skew the overall conclusion.
This is a tricky part of the process because you need to know exactly what is okay to take out or clean up, and what needs to be left in to paint a rounded picture.
For example, data scientists might use imputation to replace random outlier results with estimated results, which are more in line with the overall result.
Exploratory Data Analysis (EDA)
While the name might be bewilderingly jargon-infested, this step in the process is when data scientists start to form meaningful patterns, relationships, or patterns in the data set, helping the customer make sense of the information uncovered.
They do this through a combination of data science models and algorithms, uncovering underlying conclusions that might not be immediately obvious to the naked eye.