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Big Data Courses

206 Courses

California Institute of Technology Tecnológico de Monterrey

Inteligencia Artificial y Robótica

La robótica y la inteligencia artificial son factores clave para el éxito y permanencia en el mercado de las diferentes industrias, haciendo de la automatización industrial una realidad. Según el Foro Económico Mundial, roles como Especialistas en Aprendizaje de Máquina, Científicos de Datos o Expertos en Inteligencia Artificial, que demandan ha.
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California Institute of Technology Indiana University

Industry 4.0 for business managers

The world is venturing into the Fourth Industrial Revolution (I4.0), significantly impacting every workforce and organization globally. Enhance your knowledge and develop skills crucial for excelling in the I4.0 era with this Professional Certificate from one of the world's prestigious institutions. The program is designed to shift your perspec.
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California Institute of Technology Delft University of Technology

Data Skills for Artificial Intelligence

Data Skills for Artificial Intelligence Artificial Intelligence and Machine Learning have become integral techniques for most services and products, with data being central to robust and successful AI/ML applications. Without solid data management, AI projects frequently underperform or even fail. This series of MOOCs provides unique insights and.
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California Institute of Technology Udemy

Data Engineering for Beginners: Learn SQL, Python & Spark

Embark on your data engineering journey with our beginner-friendly course designed to master SQL, Python, and Apache Spark (PySpark). Engage in hands-on projects using the powerful Databricks platform hosted on Google Cloud. Offered by Udemy, this course suits aspiring data engineers aiming to build a strong foundation in essential programming.
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California Institute of Technology Udemy

Data Analytics & Data Science for Executives

Enroll now in Data Analytics for Executives to unlock the transformative power of data. Elevate your leadership skills by understanding how to leverage data science, machine learning, and big data technologies for strategic business outcomes. Suitable for executives and business leaders, this comprehensive course provides in-depth knowledge in d.
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California Institute of Technology LinkedIn Learning

Inteligencia artificial más allá de la ingeniería

Embárcate en un viaje fascinante con nuestro curso de "Inteligencia artificial más allá de la ingeniería", diseñado para desmitificar qué es realmente la inteligencia artificial y qué no es. Con una mezcla de explicaciones claras y ejemplos prácticos, este curso te llevará a través del pasado y del presente de la IA, además de presentarte lo.
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California Institute of Technology LinkedIn Learning

Künstliche Intelligenz (KI) – Grundlagen

Vertiefen Sie Ihr Wissen über Künstliche Intelligenz (KI) und Machine Learning (ML) durch einen detaillierten Überblick über deren Geschichte, Techniken und Einsatzbereiche. Dieser Kurs von LinkedIn Learning bietet umfassende Einblicke und Anleitungen für alle Interessierten im Bereich der entwickelnden Technologie, einschließlich Computer Vis.
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California Institute of Technology FutureLearn

Unlocking Media Trends with Big Data Technology

Imagine knowing exactly which film genre will capture audiences’ attention or understanding social media trends before they go viral. Data analysis makes this possible. From the experts at the Communications University of China (CUC), this seven-week course explores the key aspects of big data and helps you develop practical, career-ready skills.
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California Institute of Technology Tecnológico de Monterrey

Impacto de la IA en la Innovación de Negocios

El curso "Impacto de la IA en la Innovación de los Negocios" examina cómo la inteligencia artificial está revolucionando el ámbito empresarial actual. Ofrecido por el prestigioso Tecnológico de Monterrey a través de la plataforma Coursera, este curso te ayudará a comprender y aplicar tecnologías emergentes, desarrollar modelos de negocio inno.
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California Institute of Technology New York University (NYU)

Digital Marketing Tools: Machine Learning and AI

Join the NYU course on Coursera and discover the impact of machine learning and AI on digital marketing. This dynamic course offers businesses the skills to automate decision-making and predict customer behavior using advanced tools. Explore powerful techniques to optimize marketing efforts in real-time and gain insights into machine lear.
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Becoming a Big Data Analyst: A Step-by-Step

Big data analysis is a relatively new, but quite in-demand area of the labour market. The demand for data scientists is constantly growing. Big Data are data sets of very large size, which are also characterised by diversity and high update rate. A big data analyst is a specialist who identifies and investigates patterns in data using special software tools.

Overview of Big Data and AI

The generation and sharing of big data across devices is happening in almost every social sphere. Big Data is used by giants such as Google, Uber, IBM, Amazon to optimise customer experience, reduce the risk of fraud and data security threats. Big Data specialists after big data and ai courses are needed in: marketing, search technology, retail, social media, gaming, personalisation, speech technology, financial institutions and recommendation systems.

Skills You Will Gain

It is not necessary for an analyst to have a university degree in information technology. However, a Data Analyst must understand business processes, understand statistics, perform machine learning, and be able to work with tools.

Types of data analysis:

The duties of the analyst also include tasks on Business Inteligence (BI) and optimisation of processes in production. A specialist should know the methods of analysing business processes: SWOT, ABC, IDEF, BPMN, MTP, PDCA, EPC and others.

Basic Data Analyst skills:

Additionally, the analyst may use Apache Storm, Apache Kinesis, Apache Spark Streaming.

Big Data specialists need to be able to build graphical models using Bayesian and neural networks, clustering and types of analysis. A Data Scientist, Data Analyst or Data Engineer should be skilled in working with Data Lakes, as well as security and Data Governance. Becoming an expert will help you develop each of these skills in depth.

Why Learn Big Data and AI?

In the era of digital transformation, when the amount of data doubles every two years, the art of analysing and using it has become not just an important skill but also a key competitive advantage. In the different fields, traditionally based on knowledge and experience, big data and machine learning course opens new horizons. With the ability to analyse data in depth, we have a tool that allows us to not only respond to current educational needs, but also to predict them, adapting to changing realities faster than ever before.

Career Opportunities and Job Roles

Let's take a look at the main roles and vacancies in Big Data and Data Science.

Data Scientist

A Data Scientist is a specialist who analyses data and develops machine learning with big data to solve business problems. Key responsibilities include:

The Data Scientist should have a strong knowledge of statistics, programming and machine learning.

Data Engineer

The Data Engineer is responsible for building and maintaining the infrastructure for data processing. Key responsibilities include:

The Data Engineer plays a key role in ensuring that data is available and ready for analysis.

Big Data Engineer

The Big Data Engineer develops and maintains systems to process large amounts of data. Key responsibilities include:

The Big Data Engineer should have in-depth knowledge of distributed computing and big data.

Machine Learning Engineer

Machine Learning Engineer specialises in the design and implementation of machine learning models. Key responsibilities include:

The Machine Learning Engineer must have a strong knowledge of machine learning and programming.

Industry Demand

Big Data and AI are two rapidly developing fields that play a key role in today's world. Big Data refers to processing and analysing huge amounts of data that cannot be processed using traditional methods. Data Science, on the other hand, involves the use of statistical methods, machine learning and other technologies to extract knowledge and insights from data. These fields are of great importance to business, science and technology as they enable better informed decision-making and the development of innovative products and services!