Shulou(Shulou.com)06/03 Report--

Chapter 1 course introduction [giving away relevant e-books + in-class codes] this chapter first introduces what this course is, what features it has, what can be learned, how to arrange the content, what basis is needed, and whether it is suitable for learning this course. Then an overview of data analysis, so that we have an overall understanding of the meaning and role of data analysis, so that we have a basic concept and understanding of what we are going to do next. .. 1-1 course Guide 1-2 Overview of data Analysis Chapter 2 where does the data come from? How do you get here? In this chapter, we will introduce the general means of data acquisition. It mainly includes data warehouse, crawling, data filling, log, burying point, calculation and other means. At the same time, we will also introduce several commonly used data websites for your reference and learning. 2-1 data warehouse 2-2 monitoring and crawling 2-3 filling, burying points, logs, calculation 2-4 data learning website chapter 3 single-factor exploration analysis and data visualization with data, how to get started? In this chapter, we will introduce part of exploratory analysis-single factor exploratory analysis and visualization. We will take the basic knowledge of statistical theory as the starting point to learn outlier analysis, comparative analysis, structural analysis and distribution analysis. At the same time, introduce the case-HR human resources analysis table, which will be used in the following chapters, and use theoretical and visual methods to complete the preliminary analysis of the table. .. 3-1 data case introduction 3-2 centralized trend Off-trend 3-3 data distribution-skewness and kurtosis 3-4 sampling theory 3-5 coding implementation (based on python2.7) 3-6 data classification 3-7 outlier analysis 3-8 comparative analysis 3-9 structure analysis 3-10 distribution analysis 3-11 Satisfaction Level analysis 3-12 LastEvaluation analysis 3-13 NumberProject analysis 3-14 AverageMonthlyHours analysis 3-15 TimeSpendCompany analysis 3-16 WorkAccident analysis 3- 17 Left Analysis 3-18 PromotionLast5Years Analysis 3-19 Salary Analysis 3-20 Department Analysis 3-21 simple Comparative Analysis Operation 3-22 Visualization-histogram 3-23 Visualization-histogram 3-24 Visualization-Box Chart 3-25 Visualization-Line Chart 3-26 Visualization-Pie Chart 3-27 Chapter 4 summary of multi-factor exploration and analysis And? In this chapter, we introduce another part of exploratory analysis-multi-factor compound exploratory analysis. We also take the basic statistical knowledge as the starting point to learn the analysis methods of mutual influence and cooperation among multiple factors, such as cross analysis, grouping analysis, correlation analysis, component analysis and so on. At the same time, take the HR human resources analysis table as an example to further explore. .. 4-1 hypothesis test 4-2 chi-square test 4-3 variance test 4-4 correlation coefficient 4-5 linear regression 4-6 principal component analysis 4-7 coding 4-8 cross analysis method and implementation 4-10 correlation analysis and implementation 4-10 factor analysis and implementation 4-12 Chapter 5 summary of theoretical data pre-processing has been understood, use it! Don't worry, process it first. In this chapter, we will introduce the main contents of feature engineering, focusing on the main contents of data cleaning and data feature preprocessing, including data cleaning, feature acquisition, feature processing (including referencing, normalization, standardization, etc.), feature dimensionality reduction, feature derivation. The quality of preprocessing directly affects the effect of the following model. 5-1 Overview of feature engineering 5-2 data sample acquisition 5-3 outliers processing 5-4 labeling 5-5 feature selection 5-6 feature transformation-referenced 5-7 feature transformation-discretized 5-8 feature transformation-normalized and standardized 5-9 feature transformation-digitized 5-10 feature transformation-normalized 5-11 feature dimensionality reduction-LDA5-12 feature derivation 5-13 HR table Feature preprocessing for 15-14 HR tables-25-15 this chapter summarizes Chapter 6 Mining Modeling to use the data! In this chapter, we will introduce the main contents of data mining and modeling. It mainly includes the establishment and practice of five kinds of models, which are: classification model (KNN, naive Bayes, decision tree, SVM, integration method, GBDT... ), regression model and classification of regression ideas (linear regression, logistic regression [also known as Luigi regression, logical regression. Transliteration difference], neural network, regression tree) Clustering models (K-means, DBSCAN, hierarchical clustering,... 6-1 machine learning and data modeling 6-2 training set, verification set, Test set 6-3 classification-KNN6-4 classification-naive Bayesian 6-5 classification-decision tree 6-6 classification-support vector machine 6-7 classification-ensemble-random forest 6-8 classification-ensemble-Adaboost6-9 regression-linear regression 6-10 regression-classification-logical regression 6-11 regression-classification-artificial neural network-16-12 regression-classification-artificial neural network-26-13 Regression-regression Tree and lifting Tree 6-14 clustering-Kmeans-16-15 clustering-Kmeans-26-16 clustering-DBSCAN6-17 clustering-hierarchical clustering 6-18 clustering-Graph splitting 6-19 Association-Association rules-16-20 Association rules-Association rules-26-21 semi-supervised-label Propagation algorithm 6-22 Chapter 7 summarizes which model is better to evaluate? In the previous chapter, we learned a lot of models, a dataset, which can be modeled with a variety of models, so which model is good, we need something indexable to help us make decisions. In this chapter, we will introduce the use of confusion matrix and corresponding indicators, ROC curve and AUC value to evaluate the classification model; MAE, MSE, R2 to evaluate the regression model; RMS, contour coefficient to evaluate the clustering model. In the chapter 7-1 Classification Evaluation-confusion Matrix 7-2 Classification Evaluation-ROC, AUC, lifting Map and KS figure 7-3 regression Evaluation 7-4 unsupervised Evaluation Chapter 8 Summary and Outlook, we will review the entire content of this course and take a fresh look at our data analysis work from multiple perspectives. Finally, we will learn what other aspects can be developed after learning this course. 8-1 course Review and Multi-Angle data Analysis 8-2 what else can big data do after studying this course?

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