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Predictive Analytics & Forecasting · LearnSpace
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Predictive Analytics & Forecasting

Курс от Coursera test skills
Уровень не указан≈ 21.8 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

Learn how to design, build, and evaluate predictive models through this comprehensive course in the Data Analytics Skill Path. You will develop critical competencies including designing conceptual, logical, and dimensional data models; applying normalization and indexing to optimize relational databases; and analyzing query execution plans to identify and resolve performance bottlenecks. You will also gain expertise in data quality and governance by performing reconciliation, enforcing schemas in transformation pipelines, implementing root cause analysis through data lineage, and creating automated dashboards to monitor key data metrics. This course further equips you with advanced technical skills such as building reusable, parameterized scripts for data workflows, applying programmatic logic and regular expressions for complex transformations, and optimizing SQL queries for performance at scale. You will explore data integration through APIs, develop multi-step transformation pipelines, and apply advanced text processing techniques to handle unstructured data. Expanding into analytics, you will learn OLTP vs. OLAP distinctions, design star schemas for reporting, and apply clustering and outlier handling strategies to support analytical models. Finally, you will gain exposure to emerging applications of Generative AI in data engineering to enhance automation, scalability, and innovation. The curriculum blends academic rigor with industry-oriented practice, drawing on expertise from Edureka, Microsoft, Google, Packt, Arizona State University, Maven Analytics, Coursera Instructor Network, and Johns Hopkins University. You will progress from relational database foundations and advanced SQL, to Python scripting and API integration, to complex data modeling and transformation workflows, and ultimately to creating insightful dashboards in Tableau. Perfect for aspiring data engineers, data managers, and analytics professionals, this course provides the end-to-end knowledge and hands-on skills needed to confidently build scalable, efficient, and high-quality data systems that drive business value.

Навыки, которые вы освоите

Generative AIMachine Learning AlgorithmsDatabase DesignQuery LanguagesMachine LearningDashboardDatabase ManagementDatabase SystemsDashboard CreationSupervised LearningModel EvaluationTableau SoftwareData ModelingData IntegrationData QualityPredictive ModelingData GovernancePredictive AnalyticsSQLClassification Algorithms

Программа курса

9 модулей · 165 учебных материалов

01Personalize your learning path1 материалов
Personalize your learning pathЗадание
02Predictive Modeling and Analysis48 материалов

Regression

Course IntroductionЧтениеModule Resources & Required FilesЧтение

Учитесь у экспертов

Professionals from the Industry

Преподаватель курса

Predictive Analytics & Forecasting
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Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 21.8 ч

9 модулей

Язык: Английский

Субтитры: Арабский, Французский, Узбекский, Украинский, Китайский (Китай), Греческий, Итальянский, Бразильский португальский, Вьетнамский, Нидерландский, Корейский, Немецкий, Пушту, Русский, Тайский, Индонезийский, Шведский, Турецкий, Азербайджанский, Испанский, Хинди, Японский, Казахский, Венгерский, Польский

Часть программы вашего университета
Introduction to Linear RegressionВидео
Assumptions in Linear RegressionВидео
Working of Linear RegressionВидео
Cost function in Linear RegressionВидео
Gradient Descent in Linear RegressionВидео
Demonstration of Linear Regression: Building ModelВидео
Demonstration of Linear Regression: Testing the ModelВидео
Logistic RegressionВидео
Cost function in Logistic RegressionВидео
Gradient Descent in Logistic RegressionВидео
Importance of Sigmoid FunctionВидео
Demonstration: Logistic Regression - Data ProcessingВидео
Demonstration: Logistic Regression - Model ExecutionВидео
Regularization in RegressionЧтение
Practice Quiz : RegressionЗадание

Classification: Decision Tree and Random Forest

Classification in Machine LearningВидеоDecision Tree Part 1: What is Decision Tree?ВидеоDecision Tree Part 2: What is Random Forest?ВидеоBasic Terminologies of Decision TreeВидеоWorking of Decision TreeВидеоBuilding a Decision TreeВидеоAdvantages and Disadvantages of Decision TreeВидеоDemonstration Part 1: Explaining the ScenarioВидеоDemonstration Part 2: Exploring the DataВидеоDemonstration Part 3: Profiling ReportВидеоDemonstration Part 4: Attrition and Univariate GraphВидеоDemonstration Part 5: Data Pre - processingВидеоDemonstration Part 6: Building Decision TreeВидеоDemonstration Part 7:Tree ClassifierВидеоDemonstration Part 8: Pros and ConsВидеоRandom Forest Example Part 1: Ensemble Learning and Bagging ВидеоRandom Forest Example Part 2: Working of Random ForestВидеоPractice Quiz : Classification: Decision Tree and Random ForestЗадание

Model Evaluation and Optimization

Performance Metrics for Regression - MAE and MAPE ВидеоPerformance Metrics for Regression - MSE, RMSE, RMSLE and R-squareВидеоConfusion MatrixВидеоROC and AUCВидеоHyperparameter Tuning and OptimizationВидеоModel SelectionВидеоModel Evaluation ВидеоBias Variance Trade-off ВидеоCross ValidationВидеоDemonstration I: Grid Search - Analyze the DataВидеоDemonstration II: Grid Search - Building ModelВидеоOptuna: A Powerful Tool for Hyperparameter OptimizationЧтениеPractice Quiz : Model Evaluation and OptimizationЗадание
03Skill Assessment 13 материалов

Lesson

Practice for Predictive ModelingЗаданиеLearner Expectations for AssessmentЧтениеCheckpoint 1 of 3: Predictive ModelingЗадание
04Regression & Forecasting36 материалов

Intro to Regression

Regression 101ВидеоFeature Engineering for RegressionВидеоPrediction vs. Root-Cause AnalysisВидео

Regression Modeling 101

Intro to Regression ModelingВидеоLinear RelationshipsВидеоLeast Squared ErrorВидеоUnivariate Linear RegressionВидеоCASE STUDY: Univariate Linear RegressionВидеоMultiple Linear RegressionВидеоNon-Linear RegressionВидеоCASE STUDY: Non-Linear RegressionВидео

Regression Model Diagnostics

Intro to Model DiagnosticsВидеоSample Model OutputВидеоR-SquaredВидеоMean Error Metrics (MSE, MAE, MAPE)ВидеоHomoskedasticityВидеоNull HypothesisВидеоF-Significance

Time-Series Forecasting

Intro to ForecastingВидеоSeasonalityВидеоAuto Correlation FunctionВидеоCASE STUDY: Seasonality with ACFВидеоOne-Hot EncodingВидеоCASE STUDY: Seasonality with One-Hot EncodingВидеоLinear Trending
05Develop Regression Models24 материалов

Overview

OverviewЧтение

Train Linear Regression Models

Linear RegressionВидеоLinear Regression in Machine LearningВидеоMatrices in Linear RegressionВидеоNormal EquationВидео Guidelines for Training Linear Regression ModelsЧтениеTraining a Linear Regression ModelЛабораторная

Train Tree-Based Models for Regression

Regression Using Decision Trees and Ensemble ModelsВидео Guidelines for Training Regression Trees and Ensemble ModelsЧтениеTraining Regression Trees and Ensemble ModelsЛабораторная

Train Forecasting Models

ForecastingВидеоAutoregressive Integrated Moving Average (ARIMA)Видео Guidelines for Training Forecasting ModelsЧтение

Tune Regression Models

Cost FunctionВидеоRegularizationВидеоRegularization TechniquesЧтениеGradient DescentВидеоGrid/Randomized Search for RegressionВидео Guidelines for Tuning Regression ModelsЧтениеTuning Regression Models

Evaluate Regression Models

Mean Squared Error (MSE) and Mean Absolute Error (MAE)ВидеоCoefficient of DeterminationВидео Guidelines for Evaluating Regression ModelsЧтениеEvaluating Regression ModelsЛабораторная
06Skill Assessment 23 материалов

Lesson

Practice for AssessmentЗаданиеLearner Expectations for AssessmentЧтениеCheckpoint 2 of 3: Model Building and EvaluationЗадание
07Workflow for Building Complex Models24 материалов

PACE in machine learning: The plan and analyze stages

PACE in machine learningВидеоPlan for a machine learning projectВидеоMore about planning a machine learning projectЧтениеOvercome challenges and learn from your mistakesВидеоAnalyze data for a machine learning modelВидеоIntroduction to feature engineeringВидеоExplore feature engineeringЧтениеSolve issues that come with imbalanced datasetsВидеоMore about imbalanced datasetsЧтениеAnnotated follow-along guide: Feature engineering with PythonЛабораторнаяFeature engineering and class balancingВидеоActivity: Perform feature engineeringЛабораторнаяExemplar: Perform feature engineeringЛабораторнаяTest your knowledge: PACE in machine learning: The plan and analyze stagesЗадание

PACE in machine learning: The construct and execute stages

Introduction to Naive BayesВидеоNaive Bayes classifiersЧтениеAnnotated follow-along guide: Construct a Naive Bayes model with PythonЛабораторнаяConstruct a Naive Bayes model with PythonВидеоKey evaluation metrics for classification modelsВидеоMore about evaluation metrics for classification modelsЧтение

Review: Workflow for building complex models

Glossary TermsЧтение
08Python Demos and Case-Studies on Machine Learning (ML) Algorithm Fundamentals23 материалов

Python Demos and Case-studies on Machine Learning(ML) Algorithm Fundamentals

Machine Learning Algorithms Architecture - PART IВидеоMachine Learning Algorithms Architecture - PART IIВидеоMachine Learning Types & Algorithm Selection StrategyВидеоBias and Variance - Trade-off - PART IВидеоBias and Variance - Trade-off - PART IIВидеоMachine Learning Strategies for Business Improvement – An Overview (Healthcare, Banks, Industries) - PART IВидеоMachine Learning Strategies for Business Improvement – An Overview (Healthcare, Banks, Industries) - PART IIВидеоPreparing Data for Optimization in Production Manhours - Demo with EDA procedures - PART IВидеоPreparing Data for Optimization in Production Manhours - Demo with EDA procedures - PART IIВидеоSupervised Machine Learning Algorithm- Principle and types - PART IВидеоSupervised Machine Learning Algorithm- Principle and types - PART IIВидеоSupervised Machine Learning Algorithm- Principle and types - PART IIIВидеоRegression Algorithm - Principle & Practicing exercise on Salary Prediction - PART IВидеоRegression Algorithm - Principle & Practicing exercise on Salary Prediction - PART IIВидеоRegression Algorithm - Principle & Practicing exercise on Salary Prediction - PART IIIВидеоClassification algorithm-Decision tree algorithm for EV vehicle purchase - PART IВидеоClassification algorithm-Decision tree algorithm for EV vehicle purchase - PART IIВидеоClassification algorithm-Decision tree algorithm for EV vehicle purchase - PART IIIВидеоClassification algorithm-Decision tree algorithm for EV vehicle purchase - PART IVВидеоImplementation framework of ML algorithms – Lung Cancer Prediction - PART IВидеоImplementation framework of ML algorithms – Lung Cancer Prediction - PART IIВидеоFuture of COBOT – An application of ML in Oil & Gas industry - PART IВидеоFuture of COBOT – An application of ML in Oil & Gas industry - PART IIВидео
09Skill Assessment 33 материалов

Lesson

Practice for Time Series ForecastingЗаданиеLearner Expectations for AssessmentЧтениеCheckpoint 3 of 3: Time Series ForecastingЗадание
Видео
T-Values & P-ValuesВидео
MulticollinearityВидео
Variance Inflation FactorВидео
RECAP: Sample Model OutputВидео
Видео
CASE STUDY: Seasonality with Linear TrendВидео
SmoothingВидео
CASE STUDY: SmoothingВидео
Non-Linear TrendsВидео
CASE STUDY: Non-Linear TrendВидео
Intervention AnalysisВидео
CASE STUDY: Intervention AnalysisВидео
Лабораторная
Activity: Build a Naive Bayes modelЛабораторная
Exemplar: Build a Naive Bayes modelЛабораторная
Test your knowledge: PACE in machine learning: The construct and execute stagesЗадание