Softskills is the key to career opportunities in technology

In the tech world, it's not all about writing code, knowing languages or programmes. That's only part of it, what we would call technical skills or hardskills. The other is the softskillswhich play a fundamental role. Many tend to focus exclusively on the technical, neglecting these skills that are fundamental to professional success.

According to Harvard Business Review, the 58% of technology managers have dismissed technically sound candidates for lack of communication skills.

What are softskills?

Common Softskills: those that we can all learn and improve, such as communication, time management, teamwork, critical thinking, adaptability, problem solving, flexibility, feedback, strategic thinking, crisis management, empathy, etc.

Inborn Softskills: those that are unique and non-transferable because they relate to our character. They are the favourites of headhunters, because they predict our behaviour in the company. They are, for example, analytical thinking, conceptual thinking, self-confidence, achievement orientation, information seeking and understanding of the organisation.

They are more than just a means to get a job. It is the key to our professional self-knowledge. If we know what we excel at, we will know what sectors and roles we should focus on. our professional career. For this reason, we make available the softskills in technology more valuable in each role.

Softskills in Cybersecurity

The world of cyber security is not just about detecting vulnerabilities or responding to incidents. It requires a critical thinking The role is also a strong one for analysing threats, identifying suspicious patterns and making decisions under pressure. In addition, a fundamental part of this role is the communication. Risks often need to be explained to non-technical people, such as managers or customers, so the ability to translate complex information into simple terms is key.

A softskill innate fundamental in this field is the self-confidence. Cybersecurity professionals must rely on their judgement to make quick decisions at critical moments and take responsibility for protecting sensitive data.

Softskills in DevOps

DevOps is the union of development and operations, which is why the teamwork is at the heart of the discipline. DevOps practitioners collaborate with multiple teams, from developers to system administrators, which demands a communication and the ability to facilitate working together. In addition, the DevOps environment is constantly changing, with new tools and methodologies emerging all the time, so that the adaptability is crucial.

This is in addition to the success orientationa softskill innate that enables DevOps professionals to stay focused on continuous improvement, process optimisation and efficient task automation.

Softskills in Software development

Software development is, at its core, the problem solving through the code. Having the flexibility The ability to approach challenges from different perspectives is what differentiates a good programmer from an exceptional one. In addition, receive feedback is part of day-to-day work, whether in code reviews or team retrospectives, so the ability to accept and apply constructive criticism is indispensable.

The conceptual thinking is key for frontend developers, as it allows them to visualise how different pieces of software fit together in a larger system and to develop innovative solutions. While the analytical thinking is highly valued by backend developers because it allows them to decompose complex problems, optimise code performance and design efficient architectures that ensure system scalability and stability.

Softskills in Data Analytics and Data Science

In the field of data analysis, it is not enough to obtain valuable insights; you need to know how to communicate them effectively. A good analyst does not just present graphs and numbers, but tells a story with the data so that decision-makers understand the information and act on it. In addition, the analytical thinking is essential for accurately cleaning, interpreting and modelling data; and the ability to search for information is what allows analysts to delve deeper into data beyond the obvious, identifying hidden patterns and creating effective predictive models.

Softskills in Cloud Computing and Software Architecture

Software architects and cloud professionals must design solutions that are scalable and sustainable in the long term. This implies a strategic thinking to anticipate potential problems and optimise resources from the outset. In addition, when infrastructure failures do occur, it is crucial to remain calm and crisis management effectively to minimise the impact on the business.

A softskill innate key in this field is the understanding of the organisation. Understanding how business structures work, the power dynamics and the interests of different teams allows you to design solutions that truly align with the needs of the business. In addition, the empathy will be a good ally in this process.

Now that we know our skills, how do we transfer them?

It is important to explain technical concepts in a clear and accessible way. Not all recruiters are going to have the range of technical terms that we have mastered as tech specialists. A good method for structuring responses in interviews is the STAR framework (Situation, Task, Action, Result), which allows us to talk about concrete data and increase the impact of our communication. For example, instead of saying "we optimised the code", you can explain "we reduced the load time by 30%, improving user retention".

Communication, as well as all the common softskills, It gets better with practice. You will see your growth as you accumulate interviews, but you can put it into practice by participating in activities such as hackathons or class groups, even with your friends or sports environment. And get used to receiving constructive feedback and collaborating effectively with others.

These skills will not only help you excel at work, but also help you build lasting professional relationships.

Boost your Technology Career with the Right Softskills

Success in the tech world depends not only on the mastery of tools and languages, but also on the ability to communicate, adapt and solve problems effectively.

If you want to stand out in the labour market and learn how to apply these skills in real environments, IMMUNE Technology Institute offers you innovative training programmes in cybersecurity, software development, cloud computing and data science.

📍 Find out more about our programmes and take your career to the next level: tech.immune.institute

🔹 Which softskills do you think are most valuable in your sector? Let us know what you think in the comments! 👇

Student experience Official Master's Degree in Data Science and Business Analytics

We spoke with Cristina, a student at the Official Master's Degree in Data Science and Business Analytics of IMMUNE with the collaboration of the Metropolitan University of Education, Science and Technology of Panama UMECIT to find out about their experience.

What was your professional situation before you started your Master's degree?

I got my bachelor's degree a year ago, in September, so I hadn't really entered the world of work yet. So I decided to look for a specialisation option and that's when I started looking for options and I found this master's degree.

Why did you choose to train in data science?

My undergraduate degree was in finance, and within this sector I work in the following areas databases extensive. I wanted to delve a little deeper into this subject to be able to look for alternatives other than the typical specialisation that is done in my field, which is administrative, I wanted a master's degree that would lead me to be at the forefront, so to speak, something current.

I wanted an education that would make me stand out from the rest and something that I would enjoy.

How are you finding the experience?

It has been a challenge because, as I said before, I have a degree in bail bonds and I didn't know how to handle the subject. I had some basics in Python, but not much more. However, in the training all the basics are given at the beginning.

Thanks to the teachers that we have all the classes here, all the support material, it has made it a little bit easier for us to go deeper into the subjects.

What do you think of the professors of the Master in Data Science and Business Analytics?

They are people who have a vocation for teaching, you can tell, and I am very grateful to them because they are open to questions and to any concerns that may arise.

They are always attentive and have a lot of availability, patience and, above all, mastery of the sector.

What do you think of the methodology?

The methodology of the Master is very good and accessible.

It has the possibility to watch the recorded classes, which supports those of us who work and cannot see them at the moment and watch them on a recorded version.

Also, the teachers are always there to support us whenever we need them.

Really the methodology is optimal for the people we work with.

How has IMMUNE accompanied you throughout the process?

The accompaniment I have received from IMMUNE has been very good.

I am Ecuadorian, and I applied for the Official Master's Degree in Data Science and Business Analytics for a scholarship through the Higher Secretariat for Science and Technology and Innovation of my country.

During the process, an IMMUNE advisor helped me throughout the whole process. Also, throughout the process I have had different IMMUNE workers who have been attentive and have helped me.

Would you recommend IMMUNE Technology Institute?

Of course I do, with my eyes closed. My experience has been magnificent.

I have already told my university colleagues about this master's degree, and I have recommended them to train in non-traditional fields and with institutions that teach this excellently.

Database: the heart of the digital age

The databases are at the heart of the digital era, allowing organisations to store, organise y manage data efficiently. Since their invention, they have evolved to adapt to the changing needs of information storage and access, and today they are more relevant than ever. In this article, we will explore the different types of databases and provide examples to illustrate how they are integrated in various contexts and systems, such as Excel and SQL databases, as well as innovative distributed database systems.

Types and examples of databases

The databases vary in complexity, from simple spreadsheets to advanced systems for large corporations. Common examples include customer databases for businesses, academic records in universities and inventory management systems in retail.

Databases in Excel

Although it is not a traditional database system, Excel is widely used to manage data in the form of spreadsheets. Its accessibility and ease of use make it ideal for small businesses and personal use.

SQL databases

The language SQL (Structured Query Language) is essential for interacting with relational databases, allowing users to perform queries and modify data efficiently. Some tata types in SQL include INTEGER, VARCHAR, and DATE, among others, which define the nature of the data that can be stored in a column.

Types of databases that exist

There are several types of databaseseach with its own characteristics and optimal uses:

RelationalThe data are organised in tables related to each other by keys.
Non-Relational (NoSQL)including document, graph and key-value databases, suitable for large volumes of unstructured data.
DistributedThey run on multiple computers simultaneously and are essential for large-scale applications.

Distributed databases

The distributed databases increase efficiency by splitting the data load across multiple servers. This improves performance and offers greater resilience to failures compared to centralised systems.

Which are the most widely used in the world

Some of the most used databases include MySQLPostgreSQL, MongoDB and Oracle. These systems are noted for their robustness, flexibility and wide industry adoption:

MySQLwidely used in web applications, it is known for its ease of use and efficiency.
PostgreSQLpreferred for its compliance with standards and extensibility.
MongoDBNoSQL: represents NoSQL databases, popular for their scalability and flexibility.
Oracleis used by large companies, it stands out for its performance and advanced features.

Data and its manipulation

In all database systems, the DATA TYPES define the type of information that can be stored. These include numeric, text and binary types, each with their own rules and operations.

If you are interested in going deeper into the world of SQL databases and their manipulation, IMMUNE offers specific programmes such as the Data Science Master which approaches it from a holistic approach, the Bootcamp Data Analysis where you will learn in a practical way how to use databases. It is also possible to learn in a more specialised way how to displaying the data, programming SQL o PowerBI through short courses.

Why go for databases and their learning?

Firstly, because they are fundamental in today's society, being an essential part of our society. profile in high demand. More and more data is being handled in more and more massive ways, playing a key role in the decision making strategic.

The evolution of the DBs has taken us from simple flat files to complex distributed databases capable of operating across global networks. With the growing importance of big data and artificial intelligence, the ability to manage and analyse large data sets is more valuable than ever. This is reflected in the demand for professionals with advanced skills in SQL databases, NoSQL, and in the implementation of distributed systems.

In today's ecosystem, the basics in Excel remain a valuable tool because of their simplicity and accessibility. However, for managing large volumes of data and more complex applications, the SQL bases and the NoSQLwhich offer greater efficiency, speed and security. These tools enable businesses to extract value from their data, enhancing their ability to compete in an increasingly data-driven marketplace.

In addition, with the emergence of the distributed databasesIn addition, organisations can now enjoy a level of scalability and availability that was unthinkable just a decade ago. This not only improves responsiveness to market demands, but also opens up new possibilities for innovation and collaboration on a large scale.

The BBDD are more than just containers of information; they are the engine that drives innovation in almost every aspect of modern society. From mobile applications to global financial systems, their impact is pervasive. At IMMUNE, we recognise that mastering databases is essential to the future of technology and we offer an education that prepares students to lead this transformation.

Find out more about our Data Science courses using the form below!

Data mining: the analytics revolution in the age of big data

La minería de datos se ha convertido en la columna vertebral de la analítica moderna. Este proceso, que implica el data processing and its procesamiento, permite descubrir patrones significativos y conocimientos que pueden propulsar una empresa hacia el éxito. En un mundo impulsado por un volumen inmenso de información, la capacidad de filtrar y utilizar datos relevantes es más crítica que nunca.

¿Qué es la minería de datos?

La minería de datos, ¿qué es en realidad? Es una disciplina que combina técnicas de analysis y minería de datos, estadística y sistemas de información para transformar grandes bloques de datos en información valiosa. Esta información puede entonces ser utilizada para tomar decisiones basadas en datos, optimizar procesos y prever tendencias futuras.

La importancia de los datos calificados

Una fase crucial en la minería de datos es la calidad y preparación de los mismos. Datos precisos y bien estructurados son fundamentales para el análisis eficaz. En IMMUNE, entendemos que la calidad de los datos iniciales determina la calidad de los insights obtenidos. Por ello, en nuestro Data Science Master, enfatizamos la importancia de la limpieza y el pre-procesamiento de los datos.

Técnicas y herramientas empleadas

Existen diversas herramientas de minería de datos diseñadas para explorar grandes bases de datos, identificar patrones y establecer relaciones. Entre las herramientas más destacadas se incluyen plataformas de minería de datos y machine learning, que utilizan algoritmos para predecir comportamientos y tendencias.

Técnicas de minería de datos

The técnicas de minería de datos son variadas y dependen del objetivo específico del análisis. Algunos ejemplos incluyen la clasificación, la regresión, la segmentación y la detección de anomalías. Para entender mejor una de estas técnicas, te recomendamos leer Qué es el clustering y cómo comprender los datos.

Aplicaciones prácticas del data mining

Un ejemplo habitual de la aplicación de la minería de datos es su uso en el sector financiero, donde las instituciones aplican modelos predictivos para evaluar riesgos de crédito.

En el ámbito de la salud, se utiliza para mejorar la atención al paciente y predecir brotes epidémicos.

Pero, la minería de datos no se limita a sectores financieros o de salud; su impacto en el comercio electrónico es igualmente transformador. Las empresas utilizan la minería para personalizar las experiencias de compra, optimizar las cadenas de suministro y aumentar la retención de clientes.

Al analizar patrones de compra y comportamientos de navegación, las empresas pueden crear ofertas personalizadas que aumentan significativamente las conversiones.

¿Cómo se relacionan la minería de datos y Big Data?

La relación entre la minería de datos y big data es más que estrecha: es simbiótica. El big data ofrece un vasto universo de información, mientras que la minería de datos proporciona las herramientas analíticas necesarias para interpretar esa información y convertirla en conocimiento accionable.

En este extenso océano de datos, es crucial no solo recolectar la información, sino también extraer patrones e información que ayuden a tomar decisiones críticas de negocio. Las avanzadas técnicas de inteligencia artificial y aprendizaje automático juegan un papel integral en este proceso, automatizando y perfeccionando esta búsqueda de patrones y correlaciones que de otro modo serían inextricables. Estos métodos se han vuelto indispensables en la era del big data, permitiendo a las empresas no solo reaccionar a los cambios del mercado, sino anticiparlos.

Entender el papel que juegan estas tecnologías en la transformación de los datos brutos en estrategias de negocios es crucial, y es una área explorada por los expertos en los campos de la ciencia de datos y la analítica avanzada.

Qué beneficios y ventajas presenta la minería de datos

Descubrimiento de patrones ocultos y relaciones inesperadas: la minería de datos es esencial para identificar patrones no evidentes y conexiones entre variables en grandes conjuntos de datos. Esta habilidad es crucial en áreas como la detección de fraudes, donde los patrones ocultos pueden indicar actividades sospechosas.
Toma de decisiones basada en datos: al proporcionar insights basados en datos, la minería de datos permite a las empresas tomar decisiones más informadas y basadas en evidencia. Esto lleva a una mejor asignación de recursos, estrategias de marketing más efectivas y una planificación empresarial más acertada.
Aumento de la eficiencia operativa: las organizaciones pueden usar la minería de datos para optimizar sus procesos operativos, reduciendo costos y mejorando la eficiencia. Por ejemplo, en la gestión de la cadena de suministro, puede prever la demanda de productos, ayudando a evitar tanto el exceso de stock como las situaciones de stock insuficiente.
Mejora de la experiencia del cliente: la minería de datos ayuda a las empresas a entender mejor las necesidades y preferencias de sus clientes. Esto se traduce en una mejora en la personalización de los servicios, lo que a su vez aumenta la satisfacción del cliente y fomenta la lealtad a la marca.
Predicción de tendencias futuras: con la capacidad de analizar grandes volúmenes de datos históricos, la minería de datos es fundamental para predecir tendencias futuras. Esto es especialmente valioso en mercados volátiles y para anticipar cambios en el comportamiento del consumidor.
Innovación en productos y servicios: la minería de datos puede revelar necesidades no satisfechas o nuevos nichos de mercado, impulsando la innovación en productos y servicios. Las empresas pueden utilizar estos insights para desarrollar soluciones más adecuadas y competitivas.
Gestión del riesgo mejorada: en sectores como las finanzas y el seguro, la minería de datos es crucial para evaluar y gestionar riesgos. Al analizar patrones de datos históricos, las empresas pueden identificar riesgos potenciales con mayor precisión y tomar medidas preventivas.

Por qué aprender data mining

La minería de datos representa una ventaja competitiva inestimable en la era digital. Permite a las organizaciones no solo sobrevivir sino prosperar en un mercado saturado de información. Con la implementación de técnicas de análisis y minería de datos, las empresas pueden prever tendencias, mejorar la toma de decisiones y desbloquear nuevos caminos hacia la innovación.

En IMMUNE, comprendemos el valor de los datos y ofrecemos programas formativos de vanguardia, como nuestros másteres en Data Science, Artificial Intelligence and Data Science y Data Science y Business Analytics, para capacitar a los profesionales en las habilidades necesarias para liderar esta transformación.

Find out more about our Data Science courses using the form below!

What is a Data Centre and how does it work?

The Data Centres play a key role in storing, processing and distributing data on a large scale, enabling the functionality of essential cloud services. Essentially they are the backbone that supports the modern connectivityThe range of applications ranges from everyday applications to business-critical operations.

Their ability to ensure continuous availability, data security and scalability makes them essential infrastructures for businesses, organisations and individual users. In addition, their evolution towards virtual environments provides a level of flexibility and efficiency unprecedented.

But do you really know What is a Data Centre? So that you can get to know all the ins and outs of these technological nerve centres that support a large part of our daily digital life, we explain below everything you need to know about the data centre, what it is and how it works, and what the difference is between the Virtual data centre and traditional systems.

What is a Data Centre?

A Data Centre, also known as Cloud Centerrepresents the dynamic core of the digital infrastructure, where large volumes of information are concentrated and processed, guaranteeing the security and availability of data.

However, its function goes beyond being a mere digital store, as it is the technological epicentre that facilitates connectivity and underpins the delivery of online services, powering critical applications and supporting operations in various sectors. The fundamental pillar of the efficient and consistent functioning of contemporary society in the digital environment.

How does a Data Centre work?

In the heart of a Data Center servers are located, high-performance devices that run applications and store data. These servers are organised into racks and are connected to an intricate high-speed network which facilitates fast data transfer.

The robustness of the physical infrastructure is further strengthened by the implementation of uninterruptible power supply (UPS) systems and generatorsThe new system ensures a constant availability of services even in emergency situations.

This combination of advanced technology y back-up measures guarantees not only speed and efficiency in data processing, but also reliability and continuity of hosted services, thus consolidating its essential role in the modern digital fabric.

Advantages of a Data Centre

The Virtual Data Center offer very interesting benefits that allow critical operations to be carried out and connectivity to be maintained without interruption, ensuring constant availability of services and efficient data management.

Its design is centred on the energy efficiency and in the implementation of back-up measures also contribute to optimal performance, ensuring the viability and success of digital operations in various sectors.

Scalability: Data Centres can scale up to meet the growing demand for data, offering a flexible solution for businesses and any type of organisation.
Security: have very strong security measures in place to ensure data integrity, from advanced monitoring systems to restricted access.
Energy efficiency: Most data centres are designed to be energy efficient, applying practices and using technology to reduce energy consumption.
Continuous availability: Hardware redundancy and backup measures ensure that services hosted in a Virtual Data Centre are available virtually all the time, avoiding unplanned outages.

Virtual Data Centre versus traditional infrastructure

If you are wondering whether it is better to opt for a Virtual Data Centre or maintain a traditional infrastructure, here are the main differences between the two systems.

Flexibility: A Virtual Data Centre allows for greater flexibility by providing resources on demand, avoiding investment in fixed hardware.
Costs: traditional infrastructure can require a significant upfront investment, whereas a Virtual Data Centre allows a pay-as-you-go model, reducing costs in the long run.
Simplified management: resource management in a virtual environment is more agile and centralised, simplifying operations compared to traditional infrastructure.
Adaptability to technological evolution: Virtual Data Centres have a greater ability to adjust and adopt new technologies in an agile manner, which is a strategic advantage.
Resource elasticity: Virtual Data Centres allow resources to be scaled according to the needs of the moment. In this way, companies can handle peaks in demand without reducing efficiency or incurring unnecessary costs.
Remote access and collaboration: Data centre virtualisation facilitates remote access to resources, enabling the efficient collaboration of geographically distributed teams. This not only improves operational efficiency, but also allows for greater flexibility in remote working and collaboration.

Examples of Data Centres

The following examples illustrate the diversity and importance of data centres in providing digital services on a global scale.

Google Data Center: Google's global infrastructure, powered by strategically distributed data centres, ensures rapid delivery of services such as search, email and cloud storage.
Amazon Web Services (AWS): AWS operates an extensive network of data centres to support storage and computing solutions.
Microsoft Azure: With a large number of data centres around the world, Azure provides a comprehensive platform for developing and deploying applications in the cloud.

Become a Cloud Computing Expert with IMMUNE

As we have just seen, in the era of the Cloud Computing, Data centres have become the backbone of our digital age, providing the infrastructure needed to ensure connectivity, storage and data processing. They are also constantly being upgraded to ensure our digital experiences are seamless and secure.

Therefore, it is not surprising that professional profiles related to this sector have experienced a significant demand in recent years. If you are already working in the technological world and want to specialise in this branch, the Master in Cloud Computing Online is ideal for this. You will learn how to design, implement and manage cloud-based infrastructures, with a thorough understanding of the fundamentals of data centres as essential elements for the implementation of cloud solutions.

Find out more about our Data Science courses using the form below!

ETL: what is it and what are the most commonly used tools?

ETL is a term we find we hear frequently in the world of data management and analytics. Data management has become a basic pillar of business operations and decision making. And, in this context, the ETL becomes a fundamental process. A model that has revolutionised the way in which companies around the world obtain data and manage information to make informed decisions. strategic decisions. But do you know what ETL is for and what it really is?

In this article, we explain in detail how the ETL system works and which tools are most commonly used for process automation, data integration and the creation of efficient workflows.

ETL: what is it and what does it mean?

The acronym ETLwhich stands for Extract, Transform and Load, refers to a critical process in data management. A systematic approach used to move data from different sources, transform it into a useful format and load it into a centralised data warehouse, such as a data warehouse or data warehouse. The aim is to ensure that it is accessible, consistent and ready for analysis.

The extraction involves collecting data from a variety of sources, which may include databases, file management systems, applications, web services and more. An essential step to ensure that the most important data is collected efficiently.

What is the ETL process for?

You know what ETL means and what it is, but do you know what it is for? The ETL processes act as a vital bridge between various data sources and business decision making, facilitating data management, data cleansing and data preparation to maximise the value of information. As a result, the following benefits are achieved:

Integration of data from multiple sources.
Data cleaning and structuring.
Preparation for analysis, eliminating irrelevant data and aggregating information.
Improve query performance.
It automates processes, saving time and reducing errors.
It facilitates decision making based on sound data.

ETL processes: examples

As mentioned above, the close relationship between ETL and data facilitates decision making and efficient information management, which has a positive impact on the business.

There are several ETL examples which show how companies and organisations use the ETL model to collect data from different sources, transform it into useful formats and load it into centralised data warehouses. From data migration to the integration of dispersed information.

Data migration: One ETL case that we may encounter frequently is data migration. For example, when a company decides to change management systems, data may need to be extracted from the old system, adapted to the new system and loaded onto the new platform.
Data integration: Often, data is scattered across different systems. In these cases, ETL development can provide us with a database The use of a unified system to integrate them into a single warehouse considerably facilitates analysis and decision making.
Processing of sales records: an online shop collects sales records across different platforms. By using ETL processes, we can have all data in one format, which makes it easier to analyse sales trends, for example.

What are the most commonly used ETL tools?

In a world where companies are constantly receiving information from multiple sources, the tools for ETL have become a fundamental pillar for managing data, implementing procedures efficiently and making agile decisions. In this way, we can be sure that data is efficiently collected, transformed and loaded into unified databases.

Today, a wide range of ETL tools can be found, ranging from open source platforms to data visualisation tools. The choice will depend on the specific needs of each project and the technological infrastructure available. Some of the most commonly used are the following:

Apache NiFi: an open source tool that enables the automation of data flows between systems. Very popular for data collection and transformation.
Talend: an ETL tool that is very useful for data extraction, transformation and loading. Highly valued for its integration and automation capabilities.
Apache Spark: although primarily used for Big Data processing, it can also function as an ETL tool for large-scale data transformation.
Informatica PowerCenter: offers interesting features for data integration and data transformation.
Microsoft SQL Server Integration Services (SSIS): integrates natively with the Microsoft SQL Server environment. Widely used in Windows environments.
Pentaho: an open source application with a wide range of extraction, transformation and loading options. Highly appreciated for its high level of customisation.
AWS Glue: a service fully managed by Amazon Web Services that facilitates the creation and management of ETL workflows in the cloud.
Google Cloud Dataflow: an online Google Cloud service that enables the creation of large-scale data transformation workflows.

Become an expert in Data Science thanks to IMMUNE

As we have seen, the ETL processes play a fundamental role in data management and analysis. From extracting data from various sources to transforming and loading it into a centralised database, these tools are essential to ensure that data is always available and ready to be analysed. If you are interested in the world of Big Data and want to dedicate yourself to it or continue your training to climb up the career ladder, our Data Analysis, Mining and Visualisation Techniques in Data Science Course will enable you to know and understand the essential steps to extract relevant information to make informed, evidence-based decisions. Give your career a boost and become an expert in Data Science!

If you are looking for technology training fill in the form for more information.