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Data Preparation and Analysis · LearnSpace
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Data Preparation and Analysis

Курс от Illinois Tech
Средний≈ 79.7 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

This course introduces the necessary concepts and common techniques for analyzing data. The primary emphasis is on the process of data analysis, including data preparation, descriptive analytics, model training, and result interpretation. The process starts with removing distractions and anomalies, followed by discovering insights, formulating propositions, validating evidence, and finally building professional-grade solutions. Following the process properly, regularly, and transparently brings credibility and increases the impact of the results. This course will cover topics including Exploratory Data Analysis, Feature Screening, Segmentation, Association Rules, Nearest Neighbors, Clustering, Decision Tree, Linear Regression, Logistic Regression, and Performance Evaluation. Besides, this course will review statistical theory, matrix algebra, and computational techniques as necessary. This course prepares students ready for and capable of the data preparation and analysis process. Besides developing Python codes for carrying out the process, students will learn to tune the software tools for the most efficient implementation and optimal performance. At the end of this course, students will have built their inventory of data analysis codes and their confidence in advocating their propositions to the business stakeholders. Required Textbook: This course does not mandate any textbooks because the lecture notes are self-contained. Optional Materials: A Practitioner's Guide to Machine Learning (abbreviated PGML for Reading) Software Requirements: Python version 3.11 or above with the latest compatible versions of NumPy, SciPy, Pandas, Scikit-learn, and Statsmodels libraries. To succeed in this course, learners should possess a basic knowledge of linear algebra and statistics, basic set theory and probability theory, and have basic Python and SQL skills. A few courses that can help equip you with the database knowledge needed for this course are: Introduction to Relational Databases, Relational Database Design, and Relational Database Implementation and Applications.

Навыки, которые вы освоите

Model TrainingModel EvaluationDecision Tree LearningCorrelation AnalysisProbability & StatisticsLogistic RegressionData CleansingData AnalysisMachine LearningMachine Learning AlgorithmsData VisualizationData ProcessingMachine Learning MethodsData PresentationStatistical MethodsData PreprocessingApplied Machine LearningAnalyticsExploratory Data AnalysisStatistical Analysis

Программа курса

9 модулей · 144 учебных материалов

01Module 1: Process of Preparing and Analyzing Data23 материалов

Course Welcome

Course OverviewВидеоInstructor IntroductionВидеоSyllabusЧтениеData FilesЧтение

Учитесь у экспертов

Ming-Long Lam

Преподаватель курса

Jawahar Panchal

Professor

Data Preparation and Analysis
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Обучение на Coursera

≈ 79.7 ч

9 модулей

Язык: Английский

Субтитры: Арабский, Французский, Узбекский, Итальянский, Бразильский португальский, Корейский, Немецкий, Испанский, Японский, Казахский

Часть программы вашего университета
Meet and Greet DiscussionОбсуждение

Module 1 Introduction

Module 1 IntroductionВидеоModule 1 IntroductionЧтениеModule 1 Python Lab - VS CodeЛабораторная

Lesson 1: Why do We Analyze Data?

Big Data and IEEE 754ЧтениеWhy Do We Analyze DataВидеоWhy Do We Analyze Data QuizЗадание

Lesson 2: The Process of Data Analysis

CRISP-DM2ЧтениеThe Process of Data Analysis - Part 1ВидеоThe Process of Data Analysis - Part 2ВидеоThe Process of Data Analysis QuizЗадание

Lesson 3: The First Step of Knowing Your Data

Selecting the Bin Size of a Time HistogramЧтениеThe First Step of Knowing Your Data - Part 1ВидеоThe First Step of Knowing Your Data - Part 2ВидеоThe First Step of Knowing Your Data - Part 3ВидеоThe First Step of Knowing Your Data - Part 4ВидеоKnowing Your Data QuizЗадание

Module 1 Summative Assessment

Module 1 Summative AssessmentЗадание

Module 1 Summary

Module 1 SummaryЧтение
02Module 2: Measure and Visualize Correlation17 материалов

Module 2 Introduction

Module 2 IntroductionВидеоModule 2 IntroductionЧтениеModule 2 Python Lab - VS CodeЛабораторная

Lesson 1: Correlation of Continuous Features

Chicago Taxi Trip DataЧтениеDiscover and Measure Associations - Part 1ВидеоDiscover and Measure Associations - Part 2ВидеоCorrelation of Continuous Features QuizЗадание

Lesson 2: Correlation of Mixed Types Features

Correlation with PythonЧтениеMeasure Associations - Part 1ВидеоMeasure Associations - Part 1 (Continued)ВидеоCorrelation of Mixed Types FeaturesЗадание

Lesson 3: A Means To An End for Feature Screening

Eta-squaredЧтениеMeasure Associations - Part 2ВидеоMeasure Associations - Part 2 (Continued)ВидеоMeans to an End for Feature Screening QuizЗадание

Module 2 Summative Assessment

Module 2 Summative AssessmentЗадание

Module 2 Summary

Module 2 SummaryЧтение
03Module 3: Market Basket Analysis16 материалов

Module 3 Introduction

Module 3 IntroductionВидеоPGML Chapter 3ЧтениеModule 3 Python Lab - VS CodeЛабораторная

Lesson 1: The Purpose of Market Basket Analysis

Cross-SellingЧтениеWhat is in Your Basket - Part 1ВидеоWhat is in Your Basket - Part 2ВидеоMarket Basket Analysis QuizЗадание

Lesson 2: Association Rules Discovery

Apriori Algorithm and Association RulesЧтениеHow Are Association Rules Discovered - Part 1ВидеоHow Are Association Rules Discovered - Part 2ВидеоAssociation Rules Discovery QuizЗадание

Lesson 3: Application of Association Rules Discovery

What Can Association Rules Tell Me - Part 1ВидеоWhat Can Association Rules Tell Me - Part 2Видео

Module 3 Summative Assessment

Module 3 Summative AssessmentЗадание

Module 3 Summary

Module 3 SummaryЧтениеInsights from an Industry Leader: Learn More About Our ProgramЧтение
04Module 4: Partitioning, Segmenting, and Clustering of Observations18 материалов

Module 4 Introduction

Module 4 IntroductionВидеоPGML Chapter 4 ЧтениеModule 4 Python Lab - VS CodeЛабораторная

Lesson 1: Partition Observations for Training Models

Sampling TechniquesЧтениеPartition Observations for Training Models - Part 1ВидеоPartition Observations for Training Models - Part 2ВидеоPartition Observations for Training Models QuizЗадание

Lesson 2: Create Segments of Observations for Business Reasons

RFMЧтениеCreate Segments of Observations for Business Reasons - Part 1ВидеоCreate Segments of Observations for Business Reasons - Part 2ВидеоSegments of Observations QuizЗадание

Lesson 3: Create Clusters of Observations that Share Common Feature Values

ClusteringЧтениеPut Observations with Similar Feature Values in Clusters - Part 1ВидеоPut Observations with Similar Feature Values in Clusters - Part 2ВидеоPut Observations with Similar Feature Values in Clusters - Part 3ВидеоClustering QuizЗадание

Module 4 Summative Assessment

Module 4 Summative AssessmentЗадание

Module 4 Summary

Module 4 SummaryЧтение
05Module 5: Linear Regression18 материалов

Module 5 Introduction

Module 5 IntroductionВидеоLinear Regression Analysis ЧтениеModule 5 Python Lab - VS CodeЛабораторная

Lesson 1: Linear Regression Model

Least Squares Regression ЧтениеLinear Regression Model​ - Part 1ВидеоLinear Regression Model​ - Part 2ВидеоLinear Regression Model QuizЗадание

Lesson 2: Feature Selection (a.k.a. Model Selection)

Forward and Backward Stepwise RegressionЧтениеForward Selection - Part 1ВидеоForward Selection - Part 2ВидеоFeature Selection QuizЗадание

Lesson 3: Feature Importance

Shapley ValuesЧтениеFeature Importance -​ Part 1ВидеоFeature Importance -​ Part 2ВидеоFeature Importance -​ Part 3ВидеоFeature Importance QuizЗадание

Module 5 Summative Assessment

Module 5 Summative AssessmentЗадание

Module 5 Summary

Module 5 SummaryЧтение
06Module 6: Binary Logistic Regression16 материалов

Module 6 Introduction

Module 6 IntroductionВидеоPGML Chapter 6ЧтениеModule 6 Python Lab - VS CodeЛабораторная

Lesson 1: Logistic Regression

Predictive AnalyticsЧтениеLogistic Regression -​ Part 1ВидеоLogistic Regression -​ Part 2ВидеоLogistic Regression QuizЗадание

Lesson 2: Feature Selection (a.k.a. Model Selection)

Forward SelectionЧтениеForward SelectionВидеоForward Selection QuizЗадание

Lesson 3: The Blessing and the Curse of Too Many Predictors

Best R-squared for Logistic RegressionЧтениеInterpret Model and Assess Performance -​ Part 1ВидеоInterpret Model and Assess Performance -​ Part 2ВидеоBlessing and the Curse of Too Many Predictors QuizЗадание

Module 6 Summative Assessment

Module 6 Summative AssessmentЗадание

Module 6 Summary

Module 6 SummaryЧтение
07Module 7: Decision Trees - The CART Algorithm17 материалов

Module 7 Introduction

Module 7 IntroductionВидеоPGML Chapter 5ЧтениеModule 7 Python Lab - VS CodeЛабораторная

Lesson 1: Motivation of Decision Trees

CARTЧтениеMotivation of Decision Trees -​ Part 1ВидеоMotivation of Decision Trees -​ Part 2ВидеоMotivation of Decision Trees QuizЗадание

Lesson 2: The CART Algorithm

CART as an EquationЧтениеThe CART Algorithm -​ Part 1ВидеоThe CART Algorithm -​ Part 2ВидеоThe CART Algorithm QuizЗадание

Lesson 3: Cluster Profiling

Decision Trees for ClusteringЧтениеCluster Profiling -​ Part 1ВидеоCluster Profiling​ - Part 2ВидеоCluster Profiling QuizЗадание

Module 7 Summative Assessment

Module 7 Summative AssessmentЗадание

Module 7 Summary

Module 7 SummaryЧтение
08Module 8: Evaluating the Performance of Models18 материалов

Module 8 Introduction

Module 8 IntroductionВидеоPGML Chapter 7, 8 ЧтениеModule 8 Python Lab - VS CodeЛабораторная

Lesson 1: Metrics for Prediction Models

OutliersЧтениеPrediction ModelsВидеоMetrics for Prediction Models QuizЗадание

Lesson 2: Metrics for Classification Models

ROC CurveЧтениеNominal Classification ModelsВидеоMetrics for Classification Models QuizЗадание

Lesson 3: Charts for Classification Models

Using Lift AnalysisЧтениеBinary Classification Models -​ Part 1ВидеоBinary Classification Models -​ Part 2ВидеоBinary Classification Models -​ Part 3ВидеоBinary Classification Models -​ Part 4ВидеоBinary Classification Models -​ Part 5Видео

Module 8 Summative Assessment

Module 8 Summative AssessmentЗадание

Module 8 Summary

Module 8 SummaryЧтение
09Summative Course Assessment1 материалов

Summative Course Assessment

Summative Course AssessmentЗадание
Charts for Classification Models QuizЗадание