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Everything you need to know about Data Science

We commonly talk about Data Science, because today data is a competitive advantage for companies, but what exactly does it mean? We will try to deepen this theme in this essential guide.

 

What is Data Science?


Data Science is the study that concerns the retrieval and analysis of data sets, intending to identify information and correspondences hidden in the unprocessed data, defined as raw. Data Science, in other words, is the science that combines programming skills and mathematical and statistical knowledge to extract meaningful information from data.


Data Science consists of the application of machine learning algorithms to numerical, textual data, images, video, and audio content. The algorithms, therefore, perform specific tasks that concern the extraction, cleaning, and processing of data, generating in turn, data that are transformed into real value for each organization.


Are Data Science and Business Analytics the same?


Often the terms Data Science and Business Analytics are considered synonymous. After all, both the Business Analytics and Data Science activities deal with the data, their acquisition, and the development of models and information processing.

What then is the difference between Data Science and Business Analytics? As the name suggests, Business Analytics is focused on the processing of data, business or sectorial, to extract information useful to the company, focused on its market and on that of its competitors. 


Data Science instead responds to questions about the influence of customer behavior on the company's business results. Data Science combines the potential of data with the creation of algorithms and the use of technology to answer a series of questions. Recently the functions of machine learning and artificial intelligence have evolved and will bring data science to levels that are still difficult to imagine. Business Analytics, on the other hand, continues to be a form of business data analysis with statistical concepts to obtain solutions and in-depth analysis by relating past data to those relating to the present.


Why use Data Science?


The Data Science aims to identify the most significant datasets to answer the questions asked by the companies, elaborate them to extract new data related to behaviors, needs, and trends that are the basis of the data-driven decisions of their managers.

The data thus identified can help an organization contain costs, increase efficiency, recognize new market opportunities and increase competitive advantage.

Can the data produce other useful data? Of course yes! Data Science was created to understand the data and their relationships, analyze them, but above all to extract value and ensure that, properly interrogated and correlated, they generate information that is useful not only to understand the phenomena but above all to orient them.


Data Science is indispensable for companies dealing with digital transformation because it allows them to direct their products or services towards the customer, their purchasing behavior and respond to their needs. Leading companies in the global market, such as Netflix, Amazon, and Spotify use applications developed by Data Scientists. Thanks to artificial intelligence, allow creating recommendation engines that suggest what to buy, what to listen to and which films to see based on the tastes of the individual user. These algorithms are also able to evaluate what were the suggestions that did not affect the user's interest thanks to the machine learning process, which allows refining the proposals more and more and thus increase conversions and optimizing the ROI.

 

The Data Science process

Data Science is mainly used to provide forecasts and trends. It also used to make decisions using tools for predictive analysis, prescriptive analysis, and machine learning.

 

1) Predictive causal analysis

If the data analysis has the purpose of obtaining a prediction that a certain event will occur in the future, it is necessary to apply the predictive causal analysis. Suppose that a bank that provides loans wants to predict the likelihood that customers will repay the loan in the future. In this case, Data Science uses a model that can perform predictive analysis on the customer's payment history to predict whether future payments will be properly received.

 

2) Prescription analysis

On the other hand, if you want to create a model or pattern that applies AI to make decisions autonomously and can constantly update with dynamic self-learning functions, it is certainly necessary to create a prescriptive analysis model. This relatively recent area of ​​Data Science consists of providing advice or directly assuming consequent behavior.


In other words, this model is not only able to predict but suggests or applies a series of prescribed actions. The best example of this is the self-driving car: the data collected by the vehicles are used to optimize the software that drives the car without human intervention. The model will be able to make decisions independently, establishing when to turn, which path to take, when to slow down or break decisively.


3) Machine learning to make predictions


If you have, for example, transactional data from a credit card company and you need to build a model to determine the future trend, you need to use machine learning algorithms through supervised learning. It is called supervised because the data based on which the algorithm can be trained is already available. An example could be the continuous optimization of the voice recognition of Alexa or Google voice assistants.

 

The main phases of the Data Science process

The concrete application of Data Science involves a series of sequential phases, now codified in a sort of process.

 

 

 


1. Knowledge and analysis of the problem


Before starting an analysis project, it is essential to understand the objectives, the context of reference, the priorities and the budget available. In this phase the Data Scientist must identify the needs of those who commission the analysis, the questions to which the project must respond, the data sets already available and those to be found to make the analysis work more effective. Finally, it is necessary to formulate the initial hypotheses, in a research framework open to the answers generated by relating the data, whose combinations can reserve surprises.

 


2. Data preparation


In this phase, the data coming from various sources, generally inhomogeneous, are extracted and cleaning is performed to transform them into elements that can be analyzed. In this phase, an analytical sandbox is needed in which it is possible to perform analyzes for the entire duration of the project. Often we use models in R language to clean, transform and display data. This will help identify outliers and establish a relationship between the variables. Once the data has been cleaned and prepared, it is now possible to perform the data analysis activity by entering them in a data warehouse.

 

 

 


3. Model planning


We then proceed to determine the methods and techniques for identifying the relationships between the variables. These relationships will be the basis of the algorithms that will be implemented for that function. In this phase, we use R, which has a complete set of modeling features and provides a good environment for the construction of interpretative models. SQL analysis services that perform processing using data mining functions and basic predictive models are also useful. Although there are many tools on the market, R is the most used programming language for these activities.


4) The realization of the model

After investigating the nature of the data available and designing the algorithms to be used, it is time to apply the model. This is tested with data sets specifically identified and made available for self-learning of the algorithm. We will evaluate if the existing tools will be sufficient for the execution of the models or we will need a more structured elaboration, then we move on to the optimization of the model and the elaboration is launched.

 


1.Communicating the results


Here is the moment in which the Data Science activity is called to make the relationships identified between the data and the answers to the questions envisaged in the project understandable. In this phase, we reach the objective of the analysis. It is, therefore, necessary to elaborate one or more reports, destined to the managers of the various business functions, making the data emerged from the data science process easily understandable, adopting elements of graphic display, such as infographics and graphics. The text will be understandable even to those who do not have too much experience with data and will simplify their interpretation. It is also useful for those who are involved in product design, marketing management like top managers, who can make data-driven decisions based on data.

 

Conclusions


Data Science is revolutionizing in many sectors. It is just all about to know your client, analyzing his behavior by identifying relationships between data that can turn into predictive results regarding market trends and orientations. Today we are at an early stage, which already allows us to obtain results, but through the development of the IoTData Science, sensors and other tools for data collection will be possible developments now only imaginable.


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