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Big Data Courses

170 Courses

California Institute of Technology Northeastern University

Database to AI: Practical Data Analytics Integration

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California Institute of Technology Udemy

AWS Analytics - Athena Kinesis Redshift QuickSight Glue

Unlock the potential of AWS Analytics with our comprehensive course covering key topics such as Athena, Kinesis, Redshift, QuickSight, and Glue. Gain insights into the latest trends in Data Science, develop robust Data Lakes, and master Machine Learning techniques. Our course is designed for professionals looking to enhance their knowledge in.
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California Institute of Technology XuetangX

人工智能教育应用

人工智能被视为继蒸汽机、电力、互联网之后最有可能带来新的产业革命浪潮的技术，而教育领域则是人工智能技术影响最为深刻的领域之一。无论你是在校学生还是一线教师，只要对人工智能和教育感兴趣，都可以来学习这门课程。这门课程将介绍人工智能的核心技术与教育创新应用场景，帮助你深刻认识人工智能对教育体系的变革与推动作用，以及未来人工智能在教育领域的发展趋势.
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California Institute of Technology XuetangX

数据科学导论

新技术如云计算、大数据、物联网和人工智能彻底改变了我们对数据的理解，带来了许多新问题，而这些问题在传统理论中尚无解决方案。每个领域现在已发展出许多新的学科如农业大数据、工业大数据等，从学科角度探讨大数据的挑战和解决方案。因此，我们亟需更新我们的知识结构。 课程介绍数据科学的基本概念、理论，并展示其应用和发展前景。学生将掌握获取、处理、管理、分析和.
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California Institute of Technology XuetangX

工业互联网

1. 课程所属学科专业 《工业互联网》属于交叉学科课程，涵盖多个专业领域：0802 机械工程, 0804 仪器科学与技术, 0808 电气工程, 0809 电子科学与技术, 0810 信息与通信工程, 0811 控制科学与工程, 0812 计算机科学与技术, 0822 轻工技术与工程, 0835 软件工程, 0837 安全科学与工程, 0839 网络空间安全, 0854 电子信息, 0855 机械, 1201管理科学与工程, 1205 信息资源.
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California Institute of Technology XuetangX

Computer Fundamentals: Problem Solving Using Computational Thinking

The "Computer Fundamentals: Problem Solving Using Computational Thinking" course is designed to enhance students’ practical skills in addressing problems through computational thinking approaches. In today's world, marked by the rapid advancement of technologies like artificial intelligence, the Internet of Things, big data, and blockchain, the.
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California Institute of Technology XuetangX

长江商学院季波教授领衔：师生共话数字经济时代的创业机遇

大数据、人工智能、物联网、区块链、云计算等技术迅猛发展，正在改变我们的日常生活、学习与思考方式。数字化已成为时代不可逆转的趋势，是企业未来生存的关键。面对数字时代，哪些新商业模式可行？传统企业如何顺利完成数字化转型？如何把握数字经济时代的发展机遇？ 6月25日，学堂在线国际MBA中心推出“国际MBA中心师生圆桌对话”系列直播，首场聚焦数字经济时代的创业机遇.
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California Institute of Technology XuetangX

大学计算机基础（艺术类）

本课程专为艺术类高校设计，重点在于教授计算机基础知识，以适应当代技术发展并满足艺术类学生的特定需求。通过该课程，学生不仅能学习计算机基础知识和概念，还能掌握应用和操作技能。 课程内容依据教育部颁布的《计算机基础教学基本要求》，涵盖计算机领域的各个方面，包括计算机的发展及其应用、系统组成、操作系统基础、办公软件使用、网络及其应用、计算机前沿技术、信.
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California Institute of Technology XuetangX

数字素养与社会经济

本课程结合趣味科普，让学生从数据和技术的角度重新认识世界。从经济角度分析网红及其他现象，全面了解数字经济，具备大数据思维，提升数字素养。此为数字化社会的核心素养，是公民生存的基本能力，也是21世纪劳动者和消费者的关键技能。课程强调“双重”技能：数字技能和专业技能，避免成为数字时代的信息盲。 《数字素养与社会经济》课程分为数字经济的基础、经济模式、发展.
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California Institute of Technology XuetangX

E-Government

The E-Government course provides an in-depth look at electronic government, blending social governance, business environment, and engineering disciplines like big data and information systems. Aimed at developing interdisciplinary talents, it covers management and technology aspects. Starting with an overview of the evolution and significan.
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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!