Imagine that you own an ice cream shop and you notice that on Sundays, especially when the temperature is high, sales increase exceptionally. By being aware of these two variables that directly affect your business, you can prepare to make the most of the situation, right?
The example above demonstrates how analyzing variables can make a big difference in your company - but they are not always so obvious. On the contrary: there are several patterns and trends in your customers' behavior that can be of great value for decision-making, but they simply go unnoticed because they are too discreet.
So, we come to the question: how can we see relationships, patterns and insights that can make a difference in the results of your business? The answer is Data Science, widely used by successful companies such as Netflix, Amazon and Uber. In an online class exclusively for LIT students, Prof. Dr. Daniel Bergmann, responsible for the courses Multivariate Data Analysis for Decision Making and Quantitative Methods Applied to Business , explained what this information can do for a company.
Understand what Data Science applied to business is estonia whatsapp data and how it works
Explaining Data Science in companies
The professor explains that data science is the combination of statistics, modeling, computing and business. In practice, several techniques are used to analyze a database with diverse data and, from there, identify trends and patterns. In this way, it is possible to reach important insights for assertive decision-making that would not be visible to the naked eye. "The main idea is to associate variables to find predictions of your benchmark or manage a trend", he highlights.
Furthermore, the data provides feasible conclusions, giving a much more realistic view of the fact analyzed. This is an ongoing process that is constantly being refined, always proposing improvements to the business.
How to put Data Science into practice?
First, you need to have some kind of database. The more information about customers or the company, the better. You can use well-known tools like Excel to manage these numbers, but the professor recommends using Python, as it is a programming language capable of handling a large amount of data without losing agility.
It is also necessary to master techniques such as regression, correlation and clustering, for example. They will help to discover whether certain hypotheses are correct or not, and also demonstrate results that may be considered unexpected.