Python 数据分析初学者训练营 - 一站式

上次更新时间：2024-11-24

课程售价: 2.9 元

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课程内容

2. Python Programming Fundamentals - Level 1

1. Why Python (免费)
2. Your First Python Code Getting Started (免费)
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

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Python 数据分析初学者训练营 - 一站式

Python Data Analysis Bootcamp for Beginners - All in One

上次更新时间：2024-11-24 09:46

课程售价：2.9元

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课程内容

11个章节 , 56个讲座

2. Python Programming Fundamentals - Level 1

1. Why Python (免费)
2. Your First Python Code Getting Started (免费)
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