I statistics and mathematics:
Statistics and mathematics are the basis of big data analysis, in which statistics provides methods for data analysis and interpretation, and mathematics provides tools for data modeling and prediction. Learning statistics and mathematics helps to understand the characteristics and analysis methods of data, and can use related tools to process and mine data.
The following statistical and mathematical knowledge and skills are required:
1, probability theory and mathematical statistics: master the basic concepts of probability theory and mathematical statistics, such as random variables, probability distribution, parameter estimation, hypothesis testing, etc.
2. linear algebra: master the concepts and algorithms of linear algebra, such as matrix operation, eigenvalue, vector space, etc.
3. Calculus: Master the basic concepts and algorithms of calculus such as limit, derivative and integral.
4. Mathematical optimization: master the methods of mathematical optimization, such as linear programming, integer programming, dynamic programming, etc.
Second, computer science and programming:
Computer science and programming are the core skills of big data analysis, in which computer science provides tools for data processing and analysis, while programming can realize algorithms for data processing and analysis. Learning computer science and programming helps to understand the principles and methods of big data processing and analysis, and can use relevant tools to process and analyze data.
Specifically, you need to master the following computer science and programming knowledge and skills:
1, computer system basics: master the basic principles and components of computer systems, such as operating system, network system, database system, etc.
2, programming language: master at least one programming language, such as Python, Java, R, etc.
3. Data structures and algorithms: master commonly used data structures and algorithms, such as arrays, linked lists, trees and graphs.
4. Database technology: master the basic principles and operations of the database, such as SQL language and database design.
Third, data cleaning and analysis:
Data cleaning and analysis are the key skills of big data analysis, in which data cleaning aims to remove noise and abnormal values in data, and data analyzer can deeply mine and analyze data. Learning data cleaning and analysis is helpful to understand the characteristics and analysis methods of data, and can use related tools to mine and analyze data.
Specifically, you need to master the following knowledge and skills of data cleaning and analysis:
1. data preprocessing: master the basic methods of data preprocessing, such as missing value processing and abnormal value processing.
2. Data visualization: master the basic methods of data visualization, such as chart drawing and data reporting.