Data analysis and mining: learn basic statistical principles and data analysis methods, including data cleaning, data visualization, feature engineering, machine learning algorithms, etc.
Big data processing and storage technology: Learn big data processing framework (such as Hadoop and Spark) and distributed storage system (such as HDFS), and understand the principle and technology stack of big data processing.
Database technology and management: learn the basic principles of database design and management, including the use of relational databases (such as MySQL and Oracle) and NoSQL databases (such as MongoDB and Redis).
Data warehouse and business intelligence: learn the construction and maintenance of data warehouse and the use of business intelligence tools to help enterprises conduct data analysis and decision support.
Data security and privacy protection: learn the basic concepts and technologies of data security, including data encryption, rights management, risk assessment, and other laws and regulations related to privacy protection.
You can also learn some related programming languages and tools, such as Python, R, SQL and so on. , as well as knowledge about deep learning and artificial intelligence, to meet the development needs of big data.
The concept of big data
Big data refers to a large-scale, highly complex and diverse data collection. It has three characteristics: big data usually refers to a huge amount of data, which cannot be managed, processed and analyzed by traditional data processing tools. These data come from various sources, including sensor data, social media data, log data and so on.
Big data is often generated and flowed at high speed, which requires real-time or near-real-time processing and analysis in a short time. For example, financial transactions, network traffic, etc. All these need to be processed quickly in order to make real-time decisions. Big data includes not only structured data (such as tables in a database), but also semi-structured data and unstructured data. These different types of data need to be processed and analyzed by specific technologies and algorithms.