Data Science

What is Data Science?

We live in times when each of us generates various data every day. Together, this creates a huge data amount which, without proper processing, only creates chaos. However, with the right tools, they can be very valuable resources. It is thanks to the data that it is possible to study various phenomena, draw conclusions needed to make business decisions, and even predict future events with high probability.

Data Science is a field that combines several sciences – statistics, mathematics, machine learning, and data visualization among others. It’s a scientific approach to meeting your business needs. Data Science-related procedures are very complicated and require both key skills and specialist and industry knowledge.

Data Science – analytics levels 

Data Science can be used for many purposes, taking advantage of the fact that the concept is built on the basis of Data Analytics. Analytics can be carried out at various levels, distinguishing three main groups that complement each other perfectly.

• Descriptive analytics. What happened?

Describes or summarizes raw data in such a way that it can be interpreted by a human. This type of analysis identifies past events. It explains what happened at a given point in a certain time. This enables you to learn from past behaviours and understand how they might affect your outcomes in the future. Most of the statistics we use fall into this category. An example are reports that provide insight into historical data regarding production, finance, sales, operations, inventory, or customers.

• Predictive analytics. What will happen?

It is a tool used to predict future events. Provides insight into what is yet to happen. Of course, all predictions are made within a certain probability – you can never be 100% sure that a predicted event will occur. This type of analysis consists primarily in collecting and grouping the information held, and then filling it based on the most likely scenarios. The basis here is the persistence of recorded data in systems, using various models and calculations to capture the relationships between different data sets. It can be used, for example, to predict consumer behaviour , purchasing patterns or sales activities.

• Prescriptive analytics. How can we achieve it?

It allows you to issue a prescription for a number of different possible actions and determine their impact. In practice, it is about giving advice. The goal is to quantify the effects of future decisions in order to get advice on possible outcomes before decisions are actually made. In general, it foresees many options for the future and allows to assess different possible action outcomes. When properly applied, it can predict not only what will happen, but also why. It goes beyond the descriptive and predictive analysis by recommending different possible ways of acting with possible outcomes. This method uses a combination of business rules, algorithms, machine learning, and computational modelling procedures.

Prescription analytics, as the most complex, is used the least. Many companies do not have the appropriate knowledge and tools to be able to use it in their daily activities. However, when properly implemented, it has a huge impact on the way business decisions are made and the organization financial results.  Withsumm-it , you can effectively implement all types of data analytics and thus make better business decisions, generating a competitive advantage.

With summ-it, you will effectively implement all types of data analytics and make better business decisions, generating a competitive advantage.