Python 数据分析初学者训练营 - 一站式
Python Data Analysis Bootcamp for Beginners - All in One
- 2. Python Programming Fundamentals - Level 1
- 1. Why Python
- 2. Your First Python Code Getting Started
- 3. Variables and naming conventions
- 4. Data types integers, float, strings, boolean
- 5. Type conversion and casting
- 6. Arithmetic operators (+, -, , , %, )
- 7. Comparison operators (, =, =, ==, !=)
- 8. Logical operators (and, or, not)
- 3. Python Programming Fundamentals - Level 2
- 1. Lists creation, indexing, slicing, modifying
- 2. Sets unique elements, operations
- 3. Dictionaries key-value pairs, methods
- 4. Conditional statements (if, elif, else)
- 5. Logical expressions in conditions
- 6. Looping structures (for loops, while loops)
- 7. Defining, Creating and Calling functions
- 4. What is Data Analysis
- 1. Understanding data analysis
- 2. Step-by-step data analysis procedure
- 5. Clean Dataset for Integrity and Validity
- 1. Importing dataset into Jupyter Notebook
- 2. Imputing missing values with SimpleImputer
- 3. Finding and dealing with inconsistent data
- 4. Identify and assign correct dataset
- 5. Dealing with duplicate values
- 6. Manipulate Data to Increase the Functionality
- 1. Sorting and arranging dataset
- 2. Conditional Filtering of dataset
- 3. Merging extra data with the dataset
- 4. Concatenating variables within dataset
- 7. Explore dataset and generate significant insights
- 1. What is exploratory data analysis
- 2. Frequency and percentage analysis
- 3. Descriptive analysis for numeric data
- 4. Grouping analysis - numeric measure by nominal data
- 5. Pivot table - a tabulation of insights
- 6. Crosstabulation - categorical vs categorical data
- 7. Correlation - numeric vs numeric data
- 8. What is Statistical Data Analysis
- 1. Various aspects of hypothesis testing
- 2. Confidence level, significance level, p-value
- 3. Steps in hypothesis testing
- 9. Transforming Data into Normal Distribution
- 1. Test normality of numeric data
- 2. Square root transformation method
- 3. Logarithm transformation method
- 4. Boxcox transformation method
- 5. Yeo-johnson transformation method
- 10. Statistical Analysis and Hypothesis Testing
- 1. One sample T-test
- 2. Independent sample T-test
- 3. One way analysis of variance (ANOVA)
- 4. Chi-square test for independence
- 5. Pearson correlation analysis
- 6. Linear regression analysis
- 11. Understanding Python Errors
- 1. Module not found error
- 2. Syntax error
- 3. Key error
- 4. Index error
- 5. Attribute error
- 6. Value error
- 7. Type error
- 12. Handling Errors in Python
- 1. Debugging errors in seconds
- 2. Enhancing python codes