Boxuegu-Data Analysis Course for Everyone

Course Catalog:

├──Chapter 1 Overview of the Career of Data Analyst

| ├──01. What is the financial prospect of a data analyst? [3].mp4 6.78M

| ├──02. Who is suitable for data analysis_[3].mp4 12.93M

| ├──03.Critical knowledge of data analysts_[3].mp4 29.04M

| └──04.Main responsibilities of a data analyst_[3].mp4 20.58M

├──Chapter 2 Concepts and Ideas of Data Analysis and Data Mining

| ├──Section 1 Basic Concepts

| | ├──01. Data analysis and data mining definition.mp4 129.88M

| | ├──02. The hierarchy of data analysis and data mining.mp4 63.28M

| | ├──03. Three elements of data analysis and data mining.mp4 227.64M

| | └──04. Summary of this section.mp4 41.30M

| ├──Section 2 Exploratory Data Analysis

| | ├──01. How to describe traffic data.mp4 204.82M

| | ├──02. Principles of Visual Display.mp4 19.81M

| | └──03. Summary of this section.mp4 26.52M

| ├──Section 3 Prediction and Classification

| | ├──01. Conceptual model and process of prediction and classification.mp4 87.00M

| | ├──02. Classification and prediction: linear regression.mp4 135.32M

| | ├──03. Logistic regression.mp4 223.60M

| | ├──04. Decision Tree Algorithm.mp4 124.01M

| | ├──05. Support Vector Machine.mp4 105.06M

| | ├──06. Naive Bayes.mp4 88.14M

| | └──07. Summary of this section.mp4 25.72M

| └──Section 4 Clustering and Dimensionality Reduction

| | ├──01. Basic concepts of clustering algorithms.mp4 100.08M

| | ├──02. Hierarchical Clustering.mp4 87.79M

| | ├──03. K-means clustering.mp4 74.11M

| | ├──04. Dimensionality reduction model-PCA.mp4 61.44M

| | └──05. Summary of this section.mp4 2.13M

├──Chapter 3 Basic Statistics and SPSS Software Application

| ├──Section 1 Descriptive Statistics Description

| | ├──01. The purpose of statistical analysis.mp4 39.60M

| | ├──02. Key concepts of statistical analysis.mp4 17.91M

| | ├──03. Four measurement scales.mp4 151.23M

| | ├──04. Central tendency-mean.mp4 58.42M

| | ├──05. Central tendency - median and mode.mp4 36.14M

| | ├──06. Discrete Trend-Range and Variance.mp4 132.21M

| | ├──07. Case operation-How to realize the trend of dispersion and concentration.mp4 137.79M

| | └──08. Summary of this section.mp4 7.21M

| ├──Section 2 Hypothesis Testing_Statistical Judgment

| | ├──01. The essence of statistics.mp4 39.43M

| | ├──02. Two major theorems of statistics.mp4 46.30M

| | ├──03. Statistical judgment-sampling error and standard error.mp4 61.26M

| | ├──04. Statistical Inference-t Distribution.mp4 85.49M

| | ├──05. Statistical Inference-Parameter Estimation.mp4 72.08M

| | ├──06. Statistical Inference-Hypothesis Testing.mp4 127.05M

| | └──07. Summary of this section.mp4 20.70M

| ├──Section 3 Sampling Method

| | ├──01. Statistical process.mp4 8.58M

| | ├──02. The concept of sampling.mp4 6.80M

| | ├──03. Sampling method and non-sampling method.mp4 93.78M

| | ├──04. Characteristics of sampling survey and census.mp4 39.51M

| | ├──05. Non-sampling survey.mp4 67.10M

| | ├──06. Three types of non-sampling surveys.mp4 236.94M

| | ├──07. Handling of no-response errors.mp4 15.64M

| | ├──08. Sampling process.mp4 43.71M

| | ├──09. Sampling unit and sampling frame.mp4 25.95M

| | ├──10. Sampling format.mp4 166.50M

| | ├──11. Probability sampling-simple sampling and systematic sampling.mp4 49.71M

| | ├──12. Probability sampling-pps sampling.mp4 115.68M

| | ├──13. Probability Sampling-Stratified Sampling.mp4 21.97M

| | ├──14. Non-probability sampling-area sampling, time sampling and telephone sampling.mp4 61.76M

| | └──15. Summary.mp4 22.62M

| └──Section 4 General Model

| | ├──Practical questions

| | ├──1. t-test.mp4 19.08M

| | ├──2. t-test-case practice.mp4 181.35M

| | ├──3. F test.mp4 34.93M

| | ├──4. F test-case practice.mp4 93.26M

| | ├──5. Correlation analysis.mp4 21.58M

| | ├──6. Correlation Analysis-Case Practice.mp4 44.81M

| | ├──7. Linear regression.mp4 40.77M

| | ├──8- Linear Regression-Case Practice.mp4 89.32M

| | └──9. Summary of this section.mp4 47.01M

├──Chapter 4 Basics of Data Preprocessing

| ├──Section 1 Preparation before data analysis

| | ├──1. Statistical workflow.mp4 24.80M

| | ├──2. Statistical preparation.mp4 100.21M

| | ├──3. Data inspection points.mp4 153.32M

| | ├──4. Preparation for open questions.mp4 173.26M

| | └──5. Summary of this section.mp4 11.30M

| ├──Section 2 Data Cleansing

| | ├──1. The concept and process of data cleaning.mp4 39.00M

| | ├──2. Field selection and data quality report.mp4 100.61M

| | ├──3. Main work of data cleaning.mp4 108.24M

| | ├──4. Error value and abnormal value processing method.mp4 142.40M

| | ├──5. Missing value processing method.mp4 233.95M

| | ├──6. Processing of abnormal values ​​and missing values.mp4 169.79M

| | └──7. Summary of this section.mp4 11.46M

| ├──Section 3 Data Normalization

| | ├──1. Data conversion.mp4 236.81M

| | ├──2. Data discretization and data expansion.mp4 153.58M

| | ├──3. Data merging and splitting.mp4 210.56M

| | └──4. Summary of this section.mp4 5.74M

| └──Homework.txt 0.06kb

├──Chapter 5 MySQL Tutorial

| ├──Section 1 Introduction to SQL

| | ├──1. SQL Introduction.mp4 82.03M

| | ├──2. Establish database.mp4 71.82M

| | ├──3. Create data tables and constraints.mp4 101.50M

| | ├──4. Insert and change.mp4 108.79M

| | └──5. Summary of this section.mp4 9.74M

| ├──Section 2 Basic Query Statements

| | ├──1. Basic query statement.mp4 158.66M

| | └──2. Summary of this section.mp4 6.65M

| ├──Section 3 Cross-Query and Sub-Query

| | ├──1. Aggregate functions and cross-queries: group by.mp4 119.38M

| | ├──2. Subquery (in, not in) & fuzzy matching Like.mp4 144.84M

| | └──3. Summary of this section.mp4 3.10M

| ├──Section 4 Practice Table Query

| | ├──1. Join table query.mp4 247.05M

| | └──2. Summary.mp4 14.25M

| └──After-class exercises

| | ├──Title.txt 0.35kb

| | └──Homework materials.rar 144.33kb

├──Chapter 6 Excel Analysis and Visualization

| ├──Section 1 Introduction to Excel

| | └──1. Introduction to Excel.mp4 55.95M

| ├──Section 2 Excel Function Skills

| | ├──1. Introduction to functions.mp4 84.28M

| | ├──2. Search functions - vlookup and hlookup.mp4 208.29M

| | ├──3. Search functions - INDEX and MATCH.mp4 45.41M

| | ├──4. Statistical function.mp4 252.21M

| | ├──5. Logical functions (Part 1) - if, anda and or.mp4 123.57M

| | ├──6. Logical Function (Part 2).mp4 113.60M

| | ├──7. Date functions and text functions.mp4 190.06M

| | └──8. Summary of this section.mp4 21.51M

| ├──Section 3 Excel Quick Processing Skills

| | ├──1. Macro Techniques.mp4 262.60M

| | ├──2. Pivot table and selective paste.mp4 184.97M

| | ├──3. Format adjustment techniques.mp4 149.13M

| | ├──4. Search and Positioning & Data Validity Techniques.mp4 276.50M

| | ├──5. Shortcut key related skills.mp4 64.12M

| | └──6. Summary of this section.mp4 24.31M

| ├──Section 4 Excel Visualization Techniques

| | ├──1. How to make a picture.mp4 154.00M

| | ├──2. How to make a combination chart.mp4 151.30M

| | ├──3. Bar chart variant.mp4 132.19M

| | ├──4. How to start the data map.mp4 95.59M

| | └──5. Summary of this section.mp4 7.47M

| └──After-class exercises

| | ├──After-class exercises.docx 412.69kb

| | ├──Nezha.png 129.28kb

| | ├──Homework materials (1).rar 144.33kb

| | └──Homework materials.rar 144.33kb

├──Chapter 7 Advanced Learning

| ├──Section 1: Selection of multivariate analysis methods

| | ├──1. Unsupervised analysis and supervised analysis.mp4 31.93M

| | └──2. Principles of unsupervised analysis.mp4 38.37M

| ├──Section 2 Factor Analysis

| | ├──1. Factor analysis usage scenarios.mp4 26.96M

| | ├──2. The concept and analysis process of factors.mp4 72.83M

| | ├──3. Estimation of the number of factors.mp4 65.87M

| | ├──4. Rotation of factor axis.mp4 59.08M

| | ├──5. Factor interpretation and factor score calculation.mp4 53.58M

| | ├──6. Case practice.mp4 118.97M

| | └──7. How to use factor analysis for evaluation.mp4 89.31M

| ├──Section 3 Cluster Analysis

| | ├──1. Cluster analysis usage scenarios.mp4 120.50M

| | ├──2. Cluster analysis algorithm.mp4 79.91M

| | ├──3. Non-hierarchical clustering K-means.mp4 112.92M

| | ├──4. K-means case practice.mp4 223.04M

| | └──5. Second-order clustering.mp4 32.96M

| ├──Section 4 Correspondence Analysis

| | ├──1. Purpose of Correspondence Analysis and Interpretation of Results.mp4 98.03M

| | └──2. Correspondence Analysis Case Practice.mp4 128.91M

| ├──Section 5 Multidimensional Scaling Analysis

| | ├──1. Concept and usage scenarios.mp4 81.03M

| | ├──2. Multidimensional Scaling Analysis Example.mp4 130.62M

| | ├──3. Case 1: Assign seats based on student scores.mp4 54.71M

| | ├──4. Case 2: Assign seats based on students' test scores.mp4 26.13M

| | ├──5. Case 3: Judging competitiveness based on the similarity of mobile phones.mp4 27.91M

| | └──6. The shortcomings of multidimensional scaling and alternative methods.mp4 30.85M

| ├──Section 6 Time Series Analysis

| | ├──1. Time series usage scenarios.mp4 6.45M

| | ├──2. Two types of time series.mp4 6.39M

| | ├──3. Time Series Model ARIMA.mp4 10.12M

| | ├──4. Processing methods in time series.mp4 97.16M

| | └──5. Case practice-forecasting of factors affecting sales of a supermarket chain.mp4 153.00M

| ├──Section 7 Logistic

| | ├──1. Usage scenarios and theoretical background.mp4 96.33M

| | └──2. Logistic case practice-factors affecting user churn and new user prediction.mp4 288.51M

| └──After-class exercises

| | ├──Advanced Statistical Methods Assignment Data.xlsx 11.05kb

| | └──Title.txt 0.14kb

├──Chapter 8 Classic Data Mining Algorithms

| ├──Section 1 Data Mining Basics and Data Stratified Sampling

| | ├──1. Familiar data mining cases in life.mp4 31.16M

| | ├──2. Data preparation and data segmentation method.mp4 12.24M

| | ├──3. The connection and difference between data analysis and data mining.mp4 84.83M

| | ├──4. Modeler software introduction.mp4 43.96M

| | └──5. How to implement data stratified sampling in Modeler.mp4 143.68M

| ├──Section 2 Naive Bayes

| | ├──1. Naive Bayes Principle.mp4 77.21M

| | ├──2. Naive Bayes algorithm process.mp4 34.33M

| | ├──3. Naive Bayesian Algorithm Example.mp4 58.45M

| | ├──4. Advantages and disadvantages of the naive Bayes algorithm.mp4 114.41M

| | └──5. Case Practice - Modeling Using Bayesian Networks.mp4 96.41M

| ├──Section 3 Decision Tree

| | ├──1. Decision tree usage scenario.mp4 3.88M

| | ├──2. Decision Tree Algorithm (1)——ID3.mp4 19.40M

| | ├──3. Decision Tree Algorithm (2)——C4.5.mp4 45.49M

| | ├──4. Decision Tree Algorithm (3)——Regression Tree CART.mp4 50.17M

| | ├──5. Decision Tree Algorithm (4)——CHAID.mp4 11.05M

| | ├──6. Preventing overfitting problems.mp4 6.37M

| | └──7. How to make a decision tree using Modeler.mp4 141.23M

| ├──Section 4 Neural Networks

| | ├──1. The composition of neural network.mp4 87.75M

| | ├──2. Calculate the error function and correct the accident weight.mp4 30.30M

| | ├──3. The relationship between neural networks and other analyses.mp4 17.95M

| | └──4. Case Practice.mp4 28.39M

| ├──Section 5 Support Vector Machine

| | ├──1. Introduction to the principle of support vector machine.mp4 21.16M

| | ├──2. Linearly separable and linearly inseparable.mp4 11.01M

| | └──3. Case Practice.mp4 60.86M

| ├──Section 6 Ensemble Algorithms and Model Evaluation

| | ├──1. The purpose and method of integrated algorithm.mp4 15.20M

| | ├──2. The calculation principle of Bagging and Bosting.mp4 170.23M

| | ├──3. Model evaluation based on confusion matrix.mp4 40.12M

| | ├──4. Draw the GAIN curve and Lift curve in Modeler.mp4 19.94M

| | └──5. Learning materials expansion.mp4 27.78M

| └──After-class exercises

| | ├──After-class exercises.txt 0.18kb

| | └──Homework materials.rar 313.64kb

├──Chapter 9 Introduction to R language and basic analysis

| ├──Section 1 Basic Operations of R Language

| | ├──1. Introduction to R language.mp4 124.09M

| | ├──10. Discrete random variable distribution and continuous random variable distribution.mp4 274.86M

| | ├──2. Basic operations of R language.mp4 146.18M

| | ├──3. Introduction to data structure of R language.mp4 100.36M

| | ├──4. Basic operations of vectors and matrices.mp4 281.98M

| | ├──5. Data frame operation.mp4 341.38M

| | ├──6. Loop control flow——for&while.mp4 108.99M

| | ├──7. Conditional selection control flow——if.mp4 68.31M

| | ├──8. Custom function.mp4 77.09M

| | └──9. Introduction to R language functions and applications of probability distribution.mp4 206.44M

| ├──Section 2 Descriptive Data Analysis in R

| | ├──1. Exploratory Data Analysis - Central Tendency and Dispersion Tendency.mp4 220.06M

| | ├──2. Exploratory Data Analysis - Introduction to Correlation Coefficient and Function.mp4 244.92M

| | └──3. Exploratory Data Analysis - Hypothesis Testing.mp4 41.23M

| ├──Section 3 R language regression algorithm

| | ├──1. Introduction to basic regression algorithms and related Hahn numbers (Part 1).mp4 363.71M

| | ├──2. Introduction to basic regression algorithms and related Hahn numbers (Part 2).mp4 272.55M

| | ├──3. Model selection.mp4 206.08M

| | └──4. Regression diagnosis.mp4 375.55M

| ├──Section 4 R language classification algorithm

| | ├──1. Logistic regression (Part 1).mp4 336.63M

| | ├──2. Logistic regression (Part 2).mp4 431.91M

| | ├──3. Decision Tree Algorithm.mp4 65.35M

| | ├──4. Decision Tree Pruning.mp4 224.07M

| | └──5. Random Forest.mp4 341.92M

| ├──Section 5 Clustering and Dimensionality Reduction in R

| | ├──1. How to implement hierarchical clustering using R.mp4 468.60M

| | ├──2. How to implement Kmeans clustering method using R.mp4 99.41M

| | ├──3. How to judge the quality of clustering.mp4 171.71M

| | └──4. How to implement PCA dimensionality reduction using R.mp4 342.05M

| └──After-class exercises

| | ├──Scalper details.rar 215.68kb

| | └──After-class exercises.txt 0.34kb

├──Chapter 10 Introduction to Python and Basic Analysis

| ├──Section 1 Overview and Basic Operations

| | ├──1. Introduction to the course and development environment.mp4 80.25M

| | ├──2. Obtaining help documents & basic operations.mp4 233.16M

| | ├──3. Basic operations: integers, decimals, complex numbers & lists, strings, dictionaries.mp4 120.87M

| | ├──4. Custom function.mp4 119.92M

| | ├──5.Implementation of Jupyte common shortcut keys and automatic completion function r.mp4 59.73M

| | └──6. Summary of this section.mp4 9.41M

| ├──Section 2 Numpy

| | ├──1. Create an array from scratch.mp4 229.21M

| | ├──2. Case practice - how to implement 99 multiplication table and slot machine.mp4 113.56M

| | ├──3. Array Operations.mp4 140.86M

| | ├──4. Array calculation.mp4 82.32M

| | ├──5. Array broadcast.mp4 173.79M

| | └──6. Comparison, Masking and Boolean Logic.mp4 113.66M

| ├──Section 3 Pandas

| | ├──1. Sequence and database.mp4 97.54M

| | ├──10. Summary of this section.mp4 11.04M

| | ├──2. Index and slice.mp4 105.59M

| | ├──3. Generate new columns through index operation.mp4 44.32M

| | ├──4. Reading and writing files.mp4 65.38M

| | ├──5. Missing value processing.mp4 106.21M

| | ├──6. Data connection.mp4 146.10M

| | ├──7. Grouping and aggregation.mp4 92.35M

| | ├──8. Pivot table.mp4 125.21M

| | └──9. String processing.mp4 47.02M

| ├──Section 4 Matplotlib and Python drawing

| | ├──1. Basic drawing - line chart and scatter chart.mp4 194.77M

| | ├──2. Basic drawing - histogram and pie chart.mp4 97.36M

| | ├──3. Sub-figures and legends.mp4 93.86M

| | ├──4. Icon settings——labels, table styles and cmap.mp4 206.07M

| | ├──5. Advanced Drawing.mp4 171.06M

| | └──6. Summary of this section.mp4 2.52M

| ├──Section 5 Sklearn and Machine Learning Basics

| | ├──1. Linear regression.mp4 109.66M

| | ├──10. Support Vector Machine - Kernel Function.mp4 144.57M

| | ├──11. How does the support vector machine prevent overfitting.mp4 96.33M

| | ├──12. How to use Python to implement PCA dimensionality reduction algorithm.mp4 227.84M

| | ├──13. How to implement Kmeans clustering using Python.mp4 82.91M

| | ├──14. Summary of this section.mp4 30.38M

| | ├──2. Principles, model implementation and regularization of logistic regression.mp4 222.68M

| | ├──3. Evaluation of logistic regression and optimal number of iterations.mp4 204.24M

| | ├──4. Implementation process of Bayesian classifier.mp4 66.52M

| | ├──5. Naive Bayes Algorithm Case - Handwritten Digit Recognition.mp4 44.51M

| | ├──6. Data preprocessing.mp4 307.29M

| | ├──7. Decision Tree and Random Forest——Entropy and Decision Tree.mp4 86.72M

| | ├──8. Comparison of decision tree and random forest algorithms.mp4 100.08M

| | └──9. Parameter adjustment of random forest.mp4 222.16M

| └──After-class exercises

| | └──After-class exercises.txt 0.40kb

├──Chapter 11 Course Summary Chart

| └──Course Summary.mp4 94.47M

└──Information

| ├──Course practice materials.RAR 1.69M

| ├──Data analysis course that everyone can learn-summary chart.RAR 107.20kb

| └──Data Analyst for Everyone - Lecture Notes (pdf).RAR 21.22M