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to Advanced Data analysis is a popular and promising field. It can help us extract valuable information from massive data and provide support for decision-making. So, what are the basic knowledge of data analysis? 1. Basic Statistics Descriptive Statistics: Describe the central tendency (mean, median, mode) and dispersion (variance, standard deviation) of data. Inferential Statistics: Infer the overall characteristics based on sample data.
Common methods include hypothesis testing and confidence intervals Email List Probability Theory: Probability Theory provides a theoretical basis for statistics and helps us understand the possibility of random events. 2. Data Cleaning and Preprocessing Missing Value Processing: Deletion, filling, interpolation and other methods. Outlier Processing: Identify and process outliers, such as outliers, noise data, etc. Data Standardization: Convert data into a unified format and scale. 3. Data Exploratory Analysis (EDA) Data Visualization: Use charts, graphs, etc. to display data distribution, trends and relationships. Feature Engineering: Extract features useful for the model from raw data.
Correlation analysis: Analyze the relationship between variables. 4. Data modeling Regression analysis: Establish a relationship model between dependent variables and independent variables. Classification model: Divide data into different categories. Cluster analysis: Divide data into different groups. Time series analysis: Analyze data that changes over time. 5. Data analysis tools Python: Pandas, NumPy, Scikit-learn and other libraries. R: A powerful tool for statistical calculations and drawing. SQL: Used for data query and manipulation. Excel: Simple data analysis and visualization. Business intelligence tools: Tableau, Power BI, etc. Learning suggestions Combining theory with practice: While learning theoretical knowledge, more hands-on practice can truly master it.
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