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Principles of Data Science · LearnSpace
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Principles of Data Science

Курс от Packt
Начальный≈ 34.6 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

In this course, you'll gain essential skills to transform raw data into actionable insights, covering the full data science lifecycle, from preparation to advanced machine learning techniques. By focusing on modern models and ethical considerations, you'll be prepared to make informed data-driven decisions in real-world scenarios. This course emphasizes hands-on learning with practical examples and real-world applications to enhance your understanding of data science. You'll learn how to apply machine learning techniques to real-life problems and refine your coding and statistical skills. What makes this course unique is its balance of theory and practice, combining foundational concepts with modern advancements in data science, including ethical issues related to AI. You'll work on actionable case studies that allow you to immediately apply what you learn. This course is perfect for aspiring data scientists who have basic programming or math skills. It is ideal for beginners looking to build a strong foundation in data science. Prior knowledge of Python will be helpful but not necessary.

Навыки, которые вы освоите

Data EthicsResponsible AIApplied Machine LearningData ScienceProbabilityPython ProgrammingData IntegrityData ArchitectureMachine Learning MethodsData VisualizationProbability & StatisticsStatistical MethodsPandas (Python Package)Machine LearningData ProcessingBayesian StatisticsData MappingData WranglingStatisticsData Literacy

Программа курса

15 модулей · 121 учебных материалов

01Data Science Terminology8 материалов

Lesson 1

Course OverviewВидеоData Science Terminology - Overview VideoВидеоIntroductionЧтениеPredicting COVID-19 with Machine LearningЧтение

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Packt - Course Instructors

Преподаватель курса

Principles of Data Science
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Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 34.6 ч

15 модулей

Язык: Английский

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

Часть программы вашего университета
Computer ProgrammingЧтение
Some More TerminologyЧтение
Case Study What's in a Job DescriptionЧтение
Foundations of Data ScienceЗадание
02Types of Data8 материалов

Lesson 1

Types of Data - Overview VideoВидеоIntroductionЧтениеExample Coffee Shop DataЧтениеDigging DeeperЧтениеMathematical Operations Allowed at the Ordinal LevelЧтениеPractice: Classifying Data and Choosing Your AnalysisDIALOGUEMeasures of VariationЧтениеData Classification and Measurement ConceptsЗадание
03The Five Steps of Data Science9 материалов

Lesson 1

The Five Steps of Data Science - Overview VideoВидеоIntroductionЧтениеExploring the DataЧтениеExploring the DataЧтениеDataFramesЧтениеPractice: Plan Your Attack on a Messy DatasetDIALOGUEFiltering in pandasЧтениеTitanicЧтениеThe Data Science Process and Its Core ElementsЗадание
04Basic Mathematics7 материалов

Lesson 1

Basic Mathematics - Overview VideoВидеоIntroductionЧтениеSummationЧтениеLogarithms/ExponentsЧтениеPractice: Analyze Recommendation System DesignsDIALOGUELinear AlgebraЧтениеFoundations of Mathematical Concepts in Data ScienceЗадание
05Impossible or Improbable A Gentle Introduction to Probability8 материалов

Lesson 1

Impossible or Improbable A Gentle Introduction to Probability - Overview VideoВидеоIntroductionЧтениеBayesian Versus FrequentistЧтениеCompound EventsЧтениеHow to Utilize the Rules of ProbabilityЧтениеPractice: Apply Probability Rules in ContextDIALOGUEComplementary EventsЧтениеExploring Probability FundamentalsЗадание
06Advanced Probability8 материалов

Lesson 1

Advanced Probability - Overview VideoВидеоIntroductionЧтениеMore Applications of Bayes' TheoremЧтениеRandom VariablesЧтениеPractice: Classifying Random VariablesDIALOGUETypes of Discrete Random VariablesЧтениеExample WeatherЧтениеProbability and Statistical ReasoningЗадание
07What Are the Chances? An Introduction to Statistics9 материалов

Lesson 1

What Are the Chances? An Introduction to Statistics - Overview VideoВидеоIntroductionЧтениеHow Do We Obtain and Sample Data?ЧтениеRandom SamplingЧтениеHow Do We Measure Statistics?ЧтениеPractice: A Tale of Two ClassesDIALOGUEThe Coefficient of VariationЧтениеCorrelations in DataЧтениеStatistical Foundations and Data InterpretationЗадание
08Advanced Statistics9 материалов

Lesson 1

Advanced Statistics - Overview VideoВидеоIntroductionЧтениеSampling DistributionsЧтениеHypothesis TestsЧтениеAssumptions of the One-Sample T-TestЧтениеPractice: Apply & Interpret a Hypothesis TestDIALOGUEType I and Type II ErrorsЧтениеExample of a Chi-Square Test for Goodness of FitЧтениеStatistical Inference and Testing ConceptsЗадание
09Communicating Data8 материалов

Lesson 1

Communicating Data - Overview VideoВидеоIntroductionЧтениеLine GraphsЧтениеBox PlotsЧтениеPractice: Analyze and Improve a Data DashboardDIALOGUESimpson's ParadoxЧтениеVerbal CommunicationЧтениеData Communication and Visualization FundamentalsЗадание
10How to Tell if Your Toaster is Learning - Machine Learning Essentials9 материалов

Lesson 1

How to Tell if Your Toaster is Learning - Machine Learning Essentials - Overview VideoВидеоIntroductionЧтениеML Isn't PerfectЧтениеHeart Attack PredictionЧтениеPractice: Frame a Machine Learning ProblemDIALOGUEULЧтениеPredicting Continuous Variables with Linear RegressionЧтениеAdding More PredictorsЧтениеMachine Learning FundamentalsЗадание
11Predictions Don't Grow on Trees or Do They9 материалов

Lesson 1

Predictions Don't Grow on Trees or Do They - Overview VideoВидеоIntroductionЧтениеUnderstanding Decision TreesЧтениеDummy VariablesЧтениеPractice: Analyze Feature Engineering Trade-offsDIALOGUEDiving Deep into ULЧтениеAn illustrative example beerЧтениеFeature Extraction and PCAЧтениеText Analysis and Model InterpretationЗадание
12Introduction to Transfer Learning and Pre-Trained Models6 материалов

Lesson 1

Introduction to Transfer Learning and Pre-Trained Models - Overview VideoВидеоIntroductionЧтениеNSPЧтениеPractice: Applying Transfer Learning in Project ScenariosDIALOGUETL with BERT and GPTЧтениеFoundations of Transfer Learning and Pre-trained ModelsЗадание
13Mitigating Algorithmic Bias and Tackling Model and Data Drift10 материалов

Lesson 1

Mitigating Algorithmic Bias and Tackling Model and Data Drift - Overview VideoВидеоIntroductionЧтениеTypes of BiasЧтениеMeasuring BiasЧтениеMitigating Algorithmic BiasЧтениеPractice: Evaluating Bias Mitigation StrategiesDIALOGUEBias in LLMsЧтениеEmerging Techniques in Bias and Fairness in MLЧтениеSources of Data DriftЧтениеEthical Considerations in Machine LearningЗадание
14AI Governance8 материалов

Lesson 1

AI Governance - Overview VideoВидеоIntroductionЧтениеMastering Data GovernanceЧтениеDocumentation and Cataloging the Unsung Heroes of GovernanceЧтениеPractice: Design an AI Governance StrategyDIALOGUENavigating the Intricacy and the Anatomy of ML GovernanceЧтениеBeyond Training Model Deployment and MonitoringЧтениеExploring AI Governance and Data ManagementЗадание
15Navigating Real-World Data Science Case Studies in Action5 материалов

Lesson 1

Navigating Real-World Data Science Case Studies in Action - Overview VideoВидеоIntroductionЧтениеPreliminary Data ExplorationЧтениеText Embeddings Using Pretrainedmodels and OpenAIЧтениеEthical Challenges in Data Science ApplicationsЗадание