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Introduction to Applied Business Analytics · LearnSpace
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Introduction to Applied Business Analytics

Курс от University of Illinois Urbana-Champaign
Начальный≈ 14 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

Nearly every aspect of business is affected by data analytics. For businesses to capitalize on data analytics, they need leaders who understand the business analytic workflow. This course addresses the human skills gap by providing a foundational set of data processing skills that can be applied to many business settings. In this course you will use Python, a widely adopted data analytics language, to efficiently prepare business data for analytic tools such as algorithms and visualizations. Cleaning, transforming, aggregating, and reshaping data is a critical, but inconspicuous step in the business analytic workflow. As you learn how to use Python to prepare data for analysis, you will gain experience using integrated development environments (IDEs) that simplify coding, support data exploration, and help you share results effectively. As you learn about the business analytics workflow you will also consider the interplay between business principles and data analytics. Specifically, you will explore how delegation, control, and feasibility influence the way in which data is processed. You will also be introduced to examples of business problems that can be solved with data automation and analytics, and methods for communicating data analytic results that do not require copying and pasting from one platform to another.

Навыки, которые вы освоите

Data-Driven Decision-MakingBusiness AnalysisData AnalysisPython ProgrammingData PresentationData PreprocessingAnalytics

Программа курса

4 модулей · 84 учебных материалов

01Course Introduction and Module 1: How Do I Get Started Using a Data Analytic Language to Solve Business Problems?32 материалов

About the Course

Course IntroductionВидеоMeet Professor Ron GuymonВидеоSyllabusЧтениеGlossaryЧтение

Учитесь у экспертов

Ronald Guymon

Senior Lecturer of Accountancy

Introduction to Applied Business Analytics
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Обучение на Coursera

≈ 14 ч

4 модулей

Язык: Английский

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

Часть программы вашего университета
About the Discussion ForumsЧтение
Orientation QuizЗадание
Online Education at Gies College of BusinessЧтение
The Impact of the Gies CommunityВидео
Welcome! Please Tell Us About YourselfPLUGIN

About Your Classmates

Getting to Know Your ClassmatesОбсуждениеUpdate Your ProfileЧтение

Module 1 Information

Module 1 OverviewЧтениеModule 1 ReadingsЧтениеModule 1 IntroductionВидео

Module 1 Lectures

Overview of Business AnalyticsВидеоExamples of Business AnalyticsВидеоFACT Framework ВидеоWhy Python for Business Analytics?ВидеоPython AccoutrementsВидеоPython and Integrated Development Environments (IDEs)ВидеоInstalling Python and JupyterLab: Start HereЧтениеInstalling Python Using JupyterLab Desktop (Recommended for Windows and Mac Users)ВидеоInstalling Python Using Homebrew and Pyenv (For advanced Mac users)ВидеоInstalling Python from Python.org for Windows (For advanced Windows users)ВидеоAn Example Workflow With JupyterLabВидеоTour of JupyterLabВидеоUsing Interactive Python Notebook (IPYNB) FilesВидеоTour of Jupyter NotebookВидеоBasic Calculations with PythonВидеоGoogle Colab - An Online Version of Jupyter NotebookВидеоModule 1 ConclusionВидео

Module 1 Graded Activities

Module 1 QuizЗадание
02Module 2: How Can I Frame Business Questions and Data Analytic Questions?17 материалов

Module 2 Information

Module 2 OverviewЧтениеModule 2 ReadingsЧтениеModule 2 IntroductionВидео

Module 2 Lectures

Framing Questions for Actionable InsightВидеоFraming Python QuestionsВидеоFraming Questions for External SourcesВидеоFraming Questions for Python's Built-In DocumentationВидеоFraming Questions About Module Functions and MethodsВидеоFraming Questions About Pandas DataframesВидеоFraming Questions About Python DictionariesВидеоFraming Questions About Python ListsВидеоFraming Questions About Python StringsВидеоActing on the AnswerВидеоActing on the Answer by Running Code ExperimentsВидеоActing on the Answer by Reading Error MessagesВидеоModule 2 ConclusionВидео

Module 2 Graded Activities

Module 2 QuizЗадание
03Module 3: How Can I Explore the Data?16 материалов

Module 3 Information

Module 3 OverviewЧтениеModule 3 ReadingsЧтениеModule 3 IntroductionВидео

Module 3 Lectures

Is Data an Asset?ВидеоAssembling DataВидеоProperties of a Tidy DataframeВидеоData DictionariesВидеоCharacteristics of a Tidy DatasetВидеоExploring Dataframes Using FiltersВидеоExploring Dataframes Using Conditional StatementsВидеоSummary StatisticsВидеоExploring Data with Summary StatisticsВидеоExploring Dataframes with VisualizationsВидеоModule 3 ConclusionВидео

Module 3 Graded Activities

Module 3 QuizЗаданиеModule 3 Peer Reviewed Assignment Взаимная проверка
04Module 4: How Do I Assemble the Data?19 материалов

Module 4 Information

Module 4 OverviewЧтениеModule 4 ReadingsЧтениеModule 4 IntroductionВидео

Module 4 Lectures

Cleaning and Preprocessing the DataВидеоGeneral Data Cleaning Tasks for Columns of a DataframeВидеоGeneral Data Cleaning Tasks for Rows of a DataframeВидеоCleaning String Columns of a DataframeВидеоCleaning Date Columns of a DataframeВидеоDataframe Shape: Wide Versus LongВидеоChanging the Shape of a DataframeВидеоCombining DataframesВидеоCleaning Your CodeВидеоSaving Cleaned DataВидеоModule 4 ConclusionВидео

Module 4 Graded Activities

Module 4 QuizЗадание

Course Conclusion

Congratulations on completing the course!ЧтениеGet Your Course CertificateЧтениеLearn on Your TermsВидеоHow Was the Course?PLUGIN