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Datenanalyse mit R und dplyr

In diesem Projekt wirst du das erfolgreiche Datenanalyse-R-Paket "dplyr" so benutzen, dass deine nächste Datenanalyse oder -transformation zum Erfolg wird!

Wir werden dich in die Lage versetzen, mit dem dplyr-Paket in R eigene Datenanalyse-Pipelines zu bauen, die dir analytische Einblicke in einen Datensatz offenbaren.

In diesem Projekt wirst du das erfolgreiche Datenanalyse-R-Paket "dplyr" so benutzen, dass deine nächste Datenanalyse oder -transformation zum Erfolg wird!

Wir werden dich in die Lage versetzen, mit dem dplyr-Paket in R eigene Datenanalyse-Pipelines zu bauen, die dir analytische Einblicke in einen Datensatz offenbaren.

Während des Projekts wirst du einen Datensatz flexibel sortieren, interessante Beobachtungen herausfiltern, wichtige Variablen auswählen, diese dann modifizieren oder neue Informationen aus ihnen generieren, und nicht zuletzt Statistiken für Gruppen in den Daten erzeugen. Das beste: Dieses Data Mining kannst du schnell und einfach in der R-Konsole erledigen, ohne dich anstrengend durch ein unübersichtliches BI-Interface klicken zu müssen.

Dieses angeleitete Projekt ist gut geeignet für Anfänger. Du wirst in der Lage sein, selbstständig mit großen unbekannten Datensätzen zu jonglieren und relevante Informationen aus großen Daten zu erzeugen.

Wenn dir also demnächst in der freien Wildbahn ein Datensatz begegnet und du dich fragst, welche interessanten und nützlichen Informationen darin verborgen sind, dann hast du nach diesem Kurs das Handwerkszeug dazu!

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What's inside

Syllabus

Projekt-Überblick

In diesem Projekt wirst du das erfolgreiche Datenanalyse-R-Paket „dplyr“ so benutzen, dass deine nächste Datenanalyse oder -transformation zum Erfolg wird! Am Ende dieses Projekts wirst du in der Lage sein, mit dem dplyr-Paket in R eigene Datenanalyse-Pipelines zu bauen, die dir analytische Einblicke in einen Datensatz offenbaren. Während des Projekts wirst du einen Datensatz flexibel sortieren, interessante Beobachtungen herausfiltern, wichtige Variablen auswählen, diese dann modifizieren oder neue Informationen aus ihnen generieren, und nicht zuletzt Statistiken für Gruppen in den Daten erzeugen. Das beste: Dieses Data Mining kannst du schnell und einfach in der R-Konsole erledigen, ohne dich anstrengend durch ein unübersichtliches BI-Interface klicken zu müssen.Dieses angeleitete Projekt ist gut geeignet für Anfänger. Du wirst in der Lage sein, selbstständig mit großen unbekannten Datensätzen zu jonglieren und relevante Informationen aus großen Daten zu erzeugen. Wenn dir also demnächst in der freien Wildbahn ein Datensatz begegnet und du dich fragst, welche interessanten und nützlichen Informationen darin verborgen sind, dann hast du nach diesem Projekt das Handwerkszeug dazu!

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Dieses angeleitete Projekt ist gut geeignet für Anfänger

Durch den Kurs lernst du, Datenanalyse-Pipelines in R mit dplyr zu erstellen

Du lernst, Datensätze flexibel zu sortieren, interessante Beobachtungen herauszufiltern, wichtige Variablen auszuwählen und zu modifizieren

Mithilfe des Kurses kannst du Statistiken für Gruppen in den Daten erstellen

Die Kursinhalte sind gut geeignet, um eigenständig mit großen unbekannten Datensätzen zu arbeiten

Du erlernst, relevante Informationen aus großen Datenmengen zu extrahieren

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Datenanalyse mit R und dplyr with these activities:

Teilnahme an Meetup-Gruppen oder Konferenzen zu Datenanalyse mit `dplyr`

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Die Teilnahme an Meetup-Gruppen oder Konferenzen ermöglicht den Austausch mit anderen Fachleuten und erweitert das Verständnis von Datenanalyse mit `dplyr`.

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Suche nach Meetup-Gruppen oder Konferenzen, die sich mit Datenanalyse mit `dplyr` befassen.
Nim teil und vernetze dich mit anderen Teilnehmern.

Überblick über das R-Paket `tidyverse`

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Auffrischen der Grundlagen des R-Pakets `tidyverse`, um ein solides Fundament für den Kurs zu schaffen.

Browse courses on Data Handling

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Überprüfe die Dokumentation des R-Pakets `tidyverse`.
Lösche Codebeispiele aus der Dokumentation und probiere sie in R aus.

Übungen mit dem R-Paket `dplyr`

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Durch das Verfolgen von geführten Tutorials können die Fähigkeiten zur Verwendung des R-Pakets `dplyr` verbessert werden, wodurch die Kursinhalte gefestigt werden.

Browse courses on Data Manipulation

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Finde Tutorials zur Verwendung des R-Pakets `dplyr`.
Folge den Schritten in den Tutorials und probiere die Beispiele in R aus.

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Unterstützung anderer Lernender bei der Verwendung von `dplyr`

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Das Mentoring anderer Lernender bei der Verwendung von `dplyr` festigt das eigene Verständnis des Materials und fördert die Problemlösungskompetenzen.

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Finde Möglichkeiten, andere Lernende zu unterstützen, z. B. in Foren oder Lerngruppen.
Beantworte Fragen und biete Unterstützung bei der Verwendung von `dplyr`.

Career center

Learners who complete Datenanalyse mit R und dplyr will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists use scientific methods, processes, algorithms, and systems to extract knowledge and insights from data in various forms, both structured and unstructured. They focus on developing predictive models and leveraging machine learning techniques to solve complex problems. This course on data analysis with R and dplyr can provide data scientists with foundational skills in data manipulation and analysis, which are essential for building accurate and effective models. By gaining proficiency in the dplyr package, data scientists can streamline data analysis tasks and improve the efficiency of their workflows.

See salaries and explore the career path for Data Scientist

Statistician

Statisticians collect, analyze, interpret, and present data for various purposes, including research, quality control, and decision-making. They use statistical methods and tools to uncover patterns, trends, and relationships in data. This course on data analysis with R and dplyr can enhance the skills of statisticians by providing them with hands-on experience in data manipulation and analysis using the powerful dplyr package. By gaining proficiency in dplyr, statisticians can automate repetitive tasks, improve the accuracy of their analysis, and generate more insightful and reliable results.

See salaries and explore the career path for Statistician

Data Analyst

Data Analysts study data using specialized analytics tools, software, and statistical methods. The insights they extract from this data help businesses and other organizations make informed decisions. Understanding how to use the dplyr package in R can help data analysts manipulate and analyze data to derive meaningful insights, which is essential to the role. This course can help you gain a solid understanding of the dplyr package, which will enhance your data analysis skills and increase your competitiveness in the job market.

See salaries and explore the career path for Data Analyst

Business Analyst

Business Analysts use data analysis techniques to identify problems and opportunities within an organization. They leverage data to develop recommendations for improving business processes, increasing efficiency, and driving growth. This course on data analysis with R and dplyr can provide business analysts with the skills to effectively manipulate, analyze, and interpret data, enabling them to make data-driven decisions and contribute to the overall success of their organizations.

See salaries and explore the career path for Business Analyst

Market Researcher

Market Researchers gather, analyze, and interpret data to understand market trends, consumer behavior, and industry dynamics. They use this information to provide insights and recommendations to businesses, helping them make informed decisions about product development, marketing campaigns, and overall business strategy. This course on data analysis with R and dplyr can equip market researchers with the skills to effectively collect, clean, and analyze data, enabling them to extract valuable insights and make data-driven recommendations that drive business growth.

See salaries and explore the career path for Market Researcher

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment recommendations. They leverage their expertise in data analysis to assess risk, identify investment opportunities, and develop trading strategies. This course on data analysis with R and dplyr can provide quantitative analysts with a strong foundation in data manipulation and analysis techniques, enhancing their ability to extract meaningful insights from financial data. By gaining proficiency in dplyr, they can streamline their workflows and make more informed investment decisions.

See salaries and explore the career path for Quantitative Analyst

Data Engineer

Data Engineers design, build, and maintain data pipelines and infrastructure to support data analysis and machine learning applications. They ensure that data is accessible, reliable, and secure for various stakeholders within an organization. This course on data analysis with R and dplyr can provide data engineers with the skills to effectively manipulate, transform, and analyze data, enabling them to build robust and efficient data pipelines that meet the needs of their organization.

See salaries and explore the career path for Data Engineer

Machine Learning Engineer

Machine Learning Engineers build, deploy, and maintain machine learning models to solve complex problems in various industries. They leverage their expertise in data analysis and programming to develop algorithms that can learn from data and make predictions. This course on data analysis with R and dplyr can provide machine learning engineers with a solid foundation in data manipulation and analysis techniques, enabling them to prepare data effectively for machine learning models. By gaining proficiency in dplyr, they can streamline their data preprocessing tasks and improve the accuracy and performance of their machine learning models.

See salaries and explore the career path for Machine Learning Engineer

Software Engineer

Software Engineers design, develop, and maintain software systems. They use programming languages and tools to create applications that meet the needs of users. This course on data analysis with R and dplyr can provide software engineers with the skills to effectively analyze and process data within their software applications, enabling them to develop more robust and user-friendly systems. By gaining proficiency in dplyr, they can automate repetitive data manipulation tasks and improve the efficiency of their software development process.

See salaries and explore the career path for Software Engineer

Data Architect

Data Architects design and manage data systems to meet the needs of an organization. They ensure that data is stored, organized, and accessible in a way that supports efficient data analysis and decision-making. This course on data analysis with R and dplyr can provide data architects with a strong foundation in data analysis techniques and data management best practices. By gaining proficiency in dplyr, they can effectively analyze data to inform their data architecture decisions and ensure that the data systems they design meet the evolving needs of their organization.

See salaries and explore the career path for Data Architect

Business Intelligence Analyst

Business Intelligence Analysts use data analysis techniques to extract insights from data and identify opportunities for improvement within an organization. They present their findings to decision-makers to help them make informed decisions and drive business growth. This course on data analysis with R and dplyr can provide business intelligence analysts with the skills to effectively analyze and interpret data, enabling them to derive meaningful insights and make data-driven recommendations that contribute to the success of their organization.

See salaries and explore the career path for Business Intelligence Analyst

Financial Analyst

Financial Analysts use data analysis techniques to evaluate financial performance, make investment recommendations, and advise clients on financial matters. They leverage their expertise in data analysis to assess risk, identify investment opportunities, and develop financial plans. This course on data analysis with R and dplyr can provide financial analysts with a strong foundation in data analysis techniques, enabling them to effectively analyze financial data and make informed investment decisions. By gaining proficiency in dplyr, they can streamline their data analysis tasks and improve the accuracy and reliability of their financial analysis.

See salaries and explore the career path for Financial Analyst

Risk Analyst

Risk Analysts identify, assess, and manage risks within an organization. They use data analysis techniques to evaluate potential risks, develop mitigation strategies, and ensure compliance with regulatory requirements. This course on data analysis with R and dplyr can provide risk analysts with the skills to effectively analyze and interpret data, enabling them to make informed risk assessments and develop robust risk management strategies. By gaining proficiency in dplyr, they can streamline their data analysis tasks and improve the efficiency of their risk management processes.

See salaries and explore the career path for Risk Analyst

Actuary

Actuaries use mathematical and statistical models to assess risk and uncertainty in various fields, including insurance, finance, and healthcare. They use data analysis techniques to evaluate risk, develop pricing models, and make recommendations on risk management strategies. This course on data analysis with R and dplyr can provide actuaries with a strong foundation in data analysis techniques, enabling them to effectively analyze data and make informed risk assessments. By gaining proficiency in dplyr, they can streamline their data analysis tasks and improve the accuracy and reliability of their actuarial models.

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Biostatistician

Biostatisticians use statistical methods to analyze data in the biomedical field. They collaborate with medical researchers to design and conduct studies, analyze data, and interpret results. This course on data analysis with R and dplyr can provide biostatisticians with a strong foundation in data analysis techniques, enabling them to effectively analyze biomedical data and make informed conclusions. By gaining proficiency in dplyr, they can streamline their data analysis tasks and improve the efficiency of their research.

See salaries and explore the career path for Biostatistician

Reading list

We've selected 12 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Datenanalyse mit R und dplyr.

R for Data Science

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Dieses Buch ist ein umfassender Leitfaden für die Verwendung von R für die Datenanalyse. Es deckt alle wichtigen Aspekte von R ab, von der Datenaufbereitung bis zur Visualisierung.

Datenanalyse mit R und dplyr

What's inside

Syllabus

Good to know

Save this course

Activities

Career center

Reading list

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