Data and Big Data technologies refer to a range of techniques and tools used in large-scale data processing and analysis. These technologies include many aspects of data acquisition, storage, management, analysis, and visualization, and are used in a wide range of applications such as data mining, business analytics, healthcare, social media, and other fields. For those studying data and big data technologies, they need to master the following areas:

Database Technology: Understand the principles of database design and implementation, be able to query and manage data using languages such as SQL, and be able to independently complete basic database management tasks.

Programming language: familiar with common programming languages such as Python, Java, etc., and able to use them for data processing and analysis.

Big Data Technology: Knowledge of big data technologies such as Hadoop, Spark, NoSQL, etc., and be able to skillfully use related tools to independently process and analyze big data.

Machine Learning Algorithms: Understand basic machine learning algorithms, such as linear regression, decision tree, neural network, etc., and be able to use tools such as Scikit-learn for model training and prediction.

Data Visualization: Knowledge of the principles and methods of data visualization, and the ability to use tools such as Tableau, R, matplotlib, etc. to present data as meaningful and easy-to-understand graphs and tables.

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In short, data and big data technology is one of the most important areas of the current and future, and learning these technologies requires the mastery of multiple aspects of knowledge. In the learning process, in addition to mastering theoretical knowledge, we should also focus on practice, and constantly update technology and thinking innovation. It is through continuous learning and practice that we can cope with the increasingly complex data environment and get better development in the field of data science.