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Data analysis knowledge

2024-10-22 16:19:20  128  0  Report

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This mind map serves as a comprehensive guide to understanding 'Data Analysis Knowledge,' covering the essential foundations and advanced techniques in the field. It delves into statistical reserves, including discrete and continuous distributions, hypothesis testing, and parameter estimation methods like the maximum likelihood method. The map also explores linear algebra fundamentals, crucial for data analysis. It highlights the use of languages and tools like R and Python for data preprocessing, visualization, and machine learning. Additionally, it covers algorithm principles, model selection, evaluation, and optimization, providing a holistic view of the data analysis landscape.

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Outline/Content

Fundamentals of Data Analysis

Statistical reserves

Discrete distribution and continuous distribution

Hypothesis testing

Parameter inspection

Nonparametric test

Normality test

Distribution fitting test

Construct your own hypothesis test: the significance of operational activities

Parameter Estimation Method

Maximum likelihood method

Method of Moments Estimation

Linear Algebra

Eigenvalues and eigenvectors

The meaning of matrix operations

Analyzing language and tools

Data preprocessing

Write functions: cross-validation implementation function & Newton's iteration algorithm, etc.

Visualization: ggplot2 regular graph & machine learning visualization results presentation, etc.

Machine Learning Parameter Tuning

Python

Data preprocessing

Python Data Analysis

Implementation of machine learning algorithms

Visualization

Python Algorithm Structure

Web crawlers and natural language processing

Structured Query Language

group by & union

The order of syntax and execution

Hive Optimization: Sort Optimization and Join Optimization, etc.

Principles and Practical Experience of Hadoop

Write execution scripts (Perl and shell)

SPSS, Stata, and Eviews, etc. commercial analytical software

Algorithm Principle

category

Classification algorithm

Regression algorithm

Association analysis

Clustering

Ensemble learning

The principles of boostrap and bagging

Similarities and Applicable Scope

Application: Principles of Random Forest, XGBoost, etc.

Model Selection and Evaluation

Model Applicable Scenarios

Model parameter tuning and evaluation

Knowledge of data analysis techniques

Data collection

Define the analysis dimensions

Extract indicators from various dimensions

Data preprocessing

Format conversion

Variable generation

Missing value handling

Variable discretization

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Modeling Analysis

Test set/training set division

Model Selection

Parameter Selection

Model Evaluation

Accuracy

ROC and AUC

Confusion matrix

Objective function

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Model Optimization and Monitoring

The subsequent fitting situation of the model