eLearning courses Data science

Our Data Science eLearning courses for all those who deal with computer data.

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Corsi per la trasformazione tecnologica, analitica e digitale delle imprese

Data science - Introduzione ai Big data e all'analisi dei dati - 1 ora

20,00 €

Data science - Big data e analisi dei dati - 1 ora

20,00 €

What is Data Science?

Data science is a discipline that uses scientific methods, processes, algorithms and systems to extract value from data. Data science shows trends and produces insights that companies can use to make more targeted decisions and create more innovative products and services.

Who is the Data Scientist?

Data Scientists are experts able to extract insights from huge amounts of structured and unstructured data in order to help define or meet specific needs and business objectives. Data is the basis of innovation, but its value derives from the information that data scientists can obtain and based on which to act. Data scientists combine skills in various disciplines, including statistics, computer science and business economics, to analyze data collected from the Web, smartphones, customers, sensors and other sources.

What does a Data Scientist do?

The main goal of a data scientist is to organize and analyze large amounts of data, often using dedicated software. The final results of a data analysis must be simple enough to be understood by all stakeholders involved.

How important is Data Scientist?

The role of data scientists in data analysis is becoming increasingly important as companies increasingly rely on Big Data and analytics to support decision-making processes and Cloud technologies, automation and machine learning as fundamental components of their IT strategies.

How to become a Data Scientist?

Data Scientists are hyper-specialized figures; they are always graduates often with a Master's degree or a PhD, mainly in Engineering, Computer Science, Economics, Mathematics and Statistics. Their training path continues with specialization courses outside the university environment. To become a Data Scientist, you need to have heterogeneous skills, ranging from technology to market and business knowledge, to the ability to use machine learning techniques and programming languages.

What are the skills of Data Scientist?

An essential prerequisite required by companies for a Data Scientist is the ability to use at least one programming language, in particular R or Python. In many cases, however, the ability to develop and implement machine learning algorithms is required. Finally, the ability to communicate and present results to business users is required, and is reported in 36% of the offers analyzed. More specifically, the five skills needed to become data scientists are:

  • Programming: the most important capacity of a data scientist, a skill that adds value to data science skills. The programming improves the skills in the statistical field, allows to analyze large data sets and offers the possibility to create one's own tools.
  • Quantitative analysis: important skill for the analysis of large data sets, the quantitative analysis improves the ability to perform experimental analyzes, scale the data strategy and implement machine learning.
  • Understanding of the product: Understanding the products helps to perform quantitative analyzes, and also allows you to predict the behavior of a system, establish metrics and improve debugging skills.
  • Communication: probably the most important soft skill in every sector. Strong communication skills help exploit all other skills.
  • Teamwork: Much like communication, teamwork is essential for a successful career. It requires generosity, the ability to receive feedback and share one's knowledge with others.

Come la data science sta trasformando il modo di fare business?

Le aziende utilizzano la data science per trasformare i dati raccolti da innumerevoli canali in un vantaggio competitivo ridefinendo i prodotti e i servizi. Ad esempio, le aziende analizzano i dati raccolti dai call centre per identificare i clienti propensi all'abbandono e utilizzano strategie di marketing per tentare di fidelizzarli. Le aziende di logistica analizzano i modelli di traffico, le condizioni meteorologiche e altri fattori per migliorare la velocità di consegna e ridurre i costi. Le aziende farmaceutiche analizzano i dati degli esami clinici e i sintomi segnalati per aiutare i medici a diagnosticare le malattie in anticipo e trattarle in modo più efficace.

What is the Data Science work process?

To extract information and insight from structured or non-structured data, data science uses a rigorous scientific process, which can be summarized as follows:

  • Definition of the problem: confronting those who are experiencing it if this does not concern us directly.
  • Data collection: the data needed to solve the problem can come from company databases, from web scraping operations or from any other source.
  • Data processing: data errors are corrected at this stage or transformations are performed to obtain additional data from the data
  • Model creation: the field that studies how to find relationships in data is called data mining and to do so it uses, but are not, machine learning techniques.
  • Presentation of the results: after having drawn the conclusions of the case, we need to show them adequately using storytelling together with graphics and other types of visualizations.

What are Insights?

The main task of a data scientist is to explore the data. On the basis of specific questions - typically required by the business and relating, for example, to the production or sales trend or to the reorganization of resources - the data scientist becomes a real investigator and puts into practice all his analytical creativity. Armed with technological tools and machine learning algorithms, it is able to examine and scientifically predict correlations between phenomena that are invisible on first analysis. Its goal is to obtain the most accurate insights to provide the business with a precise overview of the problem to be solved.

What are data products?

Can the data produce other useful data? Of course yes! The data is also used to enhance the data, but also to orient them. Amazon, Netflix, Spotify - but also the Gmail antispam filter - use applications developed by data scientists on a daily basis that exploit more and more artificial intelligence to allow machines to build recommendation engines (which suggest what to buy, what to look at, or to learn which communications we do not wish to receive.

What business sectors require data scientists?

Each sector has its own Big Data assets to analyze. According to BLS research.

  • Business: data today influences corporate strategy in almost all companies, which therefore need an expert to derive value from information. Business data analysis can impact decisions about efficiency, inventory, production errors, customer loyalty, and so on.
  • E-commerce: now that websites collect more data on purchases, data scientists can help e-commerce companies improve customer service, recognize trends and develop services or products .
  • Finance: Account data, credit and debit transactions and transactions are fundamental in the financial industry. But in this field, data scientists also deal with fraud detection, security and compliance.
  • Public Administration: Big Data helps government agencies and the Public Administration to make decisions and monitor the general satisfaction of citizens. As with the financial sector, security and compliance are a fundamental part of the work of data scientists.
  • Science: scientists have always managed data, but the technological tools available today make it possible to better collect, share and analyze data from experiments. Data scientists can help with this process.
  • Social networking: data collected by social networks is used to define targeted advertising partners, improve customer satisfaction, recognize trends, improve functionality and services. The analysis of posts, tweets and blogs can help companies to constantly improve the services offered.
  • Healthcare: electronic medical records are almost the norm for healthcare facilities. In this area, data scientists can help improve health services, recognize trends that might otherwise go unnoticed, ensure safety and compliance
  • Telecommunications: all electronic devices collect data, which must be stored, managed, maintained and analyzed. Data scientists help companies identify bugs, improve products and maintain high customer satisfaction by offering the services they require.