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Big Data Courses

170 Courses

California Institute of Technology XuetangX

智能社会中的知识产权法

在新文科建设背景下，本课程提供了一个独特的法学学习机会，以人工智能和法律为基础，专注于知识产权法。课程内容紧密跟随智能科技和社会的发展，通过理论与实践的结合，深入探讨智能社会对知识产权法的挑战和变革，同时回应社会热点问题和技术争议。 课程细分为八个章节：包括知识产权法视角下的人工智能、智能社会中的知识产权挑战、著作权法、商业秘密制度、专利法、.
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California Institute of Technology XuetangX

科技与社会

本课程以科技哲学和科学社会学交叉的视野思审视科学技术，特别是在马克思主义指导下，分析现代科技的本质特征和体系结构。同时，总结并概括科技发展的内在动力和发展模式，探索科技的社会功能，反思运行条件，以及社会治理关键问题。 通过学习本课程，学生将实现以下目标： 深刻理解科技发展与个人、社会、人类发展的紧密关联，全面认识科技的正面和负面影响。 初步掌握.
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California Institute of Technology XuetangX

大数据技术与应用

本课程以一系列大数据讲座为主线，突出实战性，激发学生学习兴趣和动力，促进学生理论与实践相结合，启发学生技术创新。注重结合应用实例融会贯通大数据中的理论方法和系统知识（平台、模块、工具），体会运用大数据技术解决实际问题的思路和效果。本课程兼顾信息类和非信息类学生。
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California Institute of Technology XuetangX

管理信息系统-大数据赋能与模式创新

课程教学设计 本课程的教学设计系统且完备，围绕“用好系统”和“造好系统”两个核心主题，分为十章，回答了管理信息系统的“是什么”（第1、2章）、“技术基础是什么”（第3-5章）、“有什么用”（第6章）、及“怎么用”（第9-10章）等问题。从管理和业务人员的视角，探讨如何建设系统（第7-8章），引入了管理信息系统与区块链、元宇宙的结合以及算法在数据洞察中的应用等新颖观点和技.
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California Institute of Technology XuetangX

Cloud Computing and Artificial Intelligence

This course covers the structure and possibilities of Cloud Computing, emphasizing the merging of data with AI services like IoT, key technologies in the Fourth Industrial Revolution. It also delves into the applications of big data processing through text analytics. Participants will gain an understanding and practical skills in the principles o.
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California Institute of Technology XuetangX

Future Automobiles

In the era of global industrial technological reform, characterized by low-carbonization, electrification, intelligence, and connectivity, smart electric vehicles are pivotal as mobile connected nodes, smart mobile terminals, and intelligent energy storage solutions. These vehicles play a crucial role in smart transportation, cities, and energy.
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California Institute of Technology XuetangX

技术创新简史

本课程深入探讨自18世纪的工业革命以来关键领域的技术变革，梳理前三次工业革命的历史经验。课程特别讲述技术创新人物与其对社会的影响，以及为何这些创新会发生的思想历程。从技术人文和社会历史的视角，启发学生对历次工业革命的深刻思考，引领对即将到来的第四次工业革命对人类社会影响的学习与讨论。
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California Institute of Technology XuetangX

清华大学计算机科学与技术系六十周年系庆学术报告（四）大数据

作为清华大学计算机系成立六十周年的庆祝活动之一，该系列学术报告旨在通过邀请国内外学术界与产业界的知名学者和专家，进行专题演讲，介绍国际前沿的研究及产业动态，同时分享他们的战略思考与成果。在交流中找出不足，明确方向，以推动清华大学计算机学科的进一步发展。 六十周年系庆学术报告是一次学术盛宴，参与的演讲者均为各领域的大专家，包括图灵奖得主，以及中外国.
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California Institute of Technology XuetangX

电子商务研究专题

While e-commerce is creating miracles, it is also constantly expanding its frontiers. This course takes the frontiers of the development of e-commerce as the object and discusses the evolutionary laws and ecology of e-commerce through specific topics, including two-sided market theory, long tail theory, Moore's law, Metcalfe's law, Davido's la.
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California Institute of Technology XuetangX

网络与新媒体概论

网络与新媒体概论课程由江西服装学院提供，专门为应用型高校环境设计，旨在使学生掌握新媒体运营与营销技巧。课程利用最新案例来增强学生的技术理解力和营销思维，同时注重课程思政建设，增加了关于新媒体在社会主义民主政治建设中作用的内容，培养学生的媒介素养与价值观。 此课程通过XuetangX平台提供，分类于人工智能课程、大数据课程、数字营销课程、内容营销课程以及社.
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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!