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Martin Burger
Learn how to explore new data sets in R by applying a structured and established data exploration blueprint.
Read more
Learn how to explore new data sets in R by applying a structured and established data exploration blueprint.
Read more
Learn how to explore new data sets in R by applying a structured and established data exploration blueprint.
Do you want to learn how data exploration can be implemented in R? Without data exploration, the whole data analysis process gets inefficient and slow, but follow a good data exploration process and you'll be guided to valuable insights. In this course, Exploring Your First Data Set with R, you will learn how new datasets are explored and analyzed in a quick and efficient way. First, you will learn the methods outlined, following a logical succession, which are applicable in most standard data frames. Then, you will discover how the process is divided into 3 steps: summary statistics, distribution checks, and relation analysis. These steps build on each other and you will find out which variables are worth further analysis and where variable dependencies exist. Finally, you will gain the knowledge of the ground work for machine learning and final data presentation.
When you’re finished with this course, you’ll have the skills to properly structure and conduct data exploration in R.
Register for this course and see more details by visiting:
OpenCourser.com/course/nua7kr/exploring
What's inside
Syllabus
Course Overview
Background on Exploratory Data Analysis
First Level Data Exploration
Statistical Tests to Confirm Initial Findings
Read more
Syllabus
Course Overview
Background on Exploratory Data Analysis
First Level Data Exploration
Statistical Tests to Confirm Initial Findings
Read more
Looking Ahead and Summary
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches data exploration in R, which is a valuable skill for data analysts
Guides learners through a structured and established data exploration blueprint
Suitable for beginners who are new to data exploration in R
Taught by Martin Burger, an experienced instructor in data analysis
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Save Exploring Your First Data Set with R to your list so you can find it easily later:
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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 Exploring Your First Data Set with R with these
activities:
Read 'Data Exploration with R' by Hadley Wickham
Show steps
Provides a comprehensive understanding of data exploration techniques in R, enhancing the student's foundational knowledge.
View
R for Data Science: Import, Tidy, Transform,...
on Amazon
Show steps
-
Read through the book thoroughly, taking notes and highlighting important concepts.
-
Apply the techniques learned in the book to your own data exploration projects.
Gather resources on data exploration in R
Show steps
Encourages students to actively seek and organize relevant resources, fostering self-directed learning and knowledge building.
Show steps
-
Compile a list of useful books, articles, and online resources on data exploration in R.
-
Create a digital or physical notebook to store and organize the gathered resources.
-
Review the resources regularly to reinforce your understanding of data exploration techniques.
Review data visualization techniques
Show steps
Helps students brush up on data visualization methods in R, which is a key aspect of data analysis.
Browse courses on
Data Visualization
Show steps
-
Go over the basics of data visualization, including different types of charts and graphs.
-
Practice creating visualizations using R code.
-
Explore different data visualization libraries in R, such as ggplot2.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Explore data exploration best practices
Show steps
Exposes students to industry best practices in data exploration, broadening their knowledge and improving their approach.
Show steps
-
Review online tutorials and articles on data exploration best practices.
-
Attend webinars or workshops on data exploration techniques.
-
Follow expert practitioners on social media to learn from their experiences.
Join a study group or online forum for data exploration
Show steps
Promotes collaboration and knowledge sharing among students, enhancing their understanding of data exploration concepts.
Show steps
-
Identify and join a study group or online forum focused on data exploration.
-
Actively participate in discussions and ask questions.
-
Share your own knowledge and experiences with other group members.
Practice data exploration techniques in R
Show steps
Provides students with hands-on experience in applying data exploration techniques in R, enhancing their practical skills.
Show steps
-
Work through guided exercises on data exploration using R.
-
Complete coding challenges that involve exploring and analyzing data.
Participate in a data exploration workshop
Show steps
Offers students a structured environment to learn and practice data exploration techniques under the guidance of experienced instructors.
Show steps
-
Identify and register for a relevant data exploration workshop.
-
Actively participate in the workshop sessions and ask questions.
-
Implement the techniques learned in the workshop on your own projects.
Develop a data exploration plan for a real-world dataset
Show steps
Allows students to apply their data exploration knowledge to a practical scenario, fostering a deeper understanding of the process.
Show steps
-
Identify a suitable dataset for exploration.
-
Define the research questions and objectives for the exploration.
-
Create a detailed plan outlining the data exploration methods to be used.
-
Execute the data exploration plan and document the findings.
Contribute to open-source projects related to data exploration
Show steps
Provides students with practical experience in applying data exploration techniques to real-world projects, fostering their problem-solving skills and industry relevance.
Show steps
-
Identify open-source projects related to data exploration.
-
Review the project documentation and codebase.
-
Identify areas where you can contribute your skills and knowledge.
-
Submit pull requests with your contributions.
Read 'Data Exploration with R' by Hadley Wickham
Show steps
Provides a comprehensive understanding of data exploration techniques in R, enhancing the student's foundational knowledge.
View
R for Data Science: Import, Tidy, Transform,...
on Amazon
Show steps
Show steps
Show steps
- Read through the book thoroughly, taking notes and highlighting important concepts.
- Apply the techniques learned in the book to your own data exploration projects.
Gather resources on data exploration in R
Show steps
Encourages students to actively seek and organize relevant resources, fostering self-directed learning and knowledge building.
Show steps
Show steps
Show steps
- Compile a list of useful books, articles, and online resources on data exploration in R.
- Create a digital or physical notebook to store and organize the gathered resources.
- Review the resources regularly to reinforce your understanding of data exploration techniques.
Review data visualization techniques
Show steps
Helps students brush up on data visualization methods in R, which is a key aspect of data analysis.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Go over the basics of data visualization, including different types of charts and graphs.
- Practice creating visualizations using R code.
- Explore different data visualization libraries in R, such as ggplot2.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Explore data exploration best practices
Show steps
Exposes students to industry best practices in data exploration, broadening their knowledge and improving their approach.
Show steps
Show steps
Show steps
- Review online tutorials and articles on data exploration best practices.
- Attend webinars or workshops on data exploration techniques.
- Follow expert practitioners on social media to learn from their experiences.
Join a study group or online forum for data exploration
Show steps
Promotes collaboration and knowledge sharing among students, enhancing their understanding of data exploration concepts.
Show steps
Show steps
Show steps
- Identify and join a study group or online forum focused on data exploration.
- Actively participate in discussions and ask questions.
- Share your own knowledge and experiences with other group members.
Practice data exploration techniques in R
Show steps
Provides students with hands-on experience in applying data exploration techniques in R, enhancing their practical skills.
Show steps
Show steps
Show steps
- Work through guided exercises on data exploration using R.
- Complete coding challenges that involve exploring and analyzing data.
Participate in a data exploration workshop
Show steps
Offers students a structured environment to learn and practice data exploration techniques under the guidance of experienced instructors.
Show steps
Show steps
Show steps
- Identify and register for a relevant data exploration workshop.
- Actively participate in the workshop sessions and ask questions.
- Implement the techniques learned in the workshop on your own projects.
Develop a data exploration plan for a real-world dataset
Show steps
Allows students to apply their data exploration knowledge to a practical scenario, fostering a deeper understanding of the process.
Show steps
Show steps
Show steps
- Identify a suitable dataset for exploration.
- Define the research questions and objectives for the exploration.
- Create a detailed plan outlining the data exploration methods to be used.
- Execute the data exploration plan and document the findings.
Contribute to open-source projects related to data exploration
Show steps
Provides students with practical experience in applying data exploration techniques to real-world projects, fostering their problem-solving skills and industry relevance.
Show steps
Show steps
Show steps
- Identify open-source projects related to data exploration.
- Review the project documentation and codebase.
- Identify areas where you can contribute your skills and knowledge.
- Submit pull requests with your contributions.
Career center
Learners who complete Exploring Your First Data Set with R will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use their expertise in math, statistics, and programming to solve complex problems. They are responsible for developing and implementing data-driven solutions that can help businesses improve their operations. This course, Exploring Your First Data Set with R, provides a strong foundation in data exploration, which is a critical skill for Data Scientists. By learning how to explore and analyze data in R, you will be well-prepared to enter this exciting and rapidly growing field.
Data Analyst
Data Analyst
Data Analysts are responsible for collecting, cleaning, and analyzing data to identify trends and patterns. The insights they provide help businesses make informed decisions. This course, Exploring Your First Data Set with R, provides a solid foundation in data exploration, which is a critical skill for Data Analysts. By learning how to explore and analyze data in R, you will be well-equipped to enter this in-demand field.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for designing, developing, and deploying machine learning models. They use their expertise in machine learning algorithms, programming, and data analysis to create models that can solve real-world problems. This course, Exploring Your First Data Set with R, provides a solid foundation in data exploration, which is a critical skill for Machine Learning Engineers. By learning how to explore and analyze data in R, you will be well-equipped to enter this in-demand field.
Statistician
Statistician
Statisticians use their expertise in mathematics and statistics to collect, analyze, and interpret data. They work in a variety of fields, including healthcare, finance, and marketing. This course, Exploring Your First Data Set with R, provides a solid foundation in data exploration, which is a critical skill for Statisticians. By learning how to explore and analyze data in R, you will be well-equipped to enter this in-demand field.
Data Engineer
Data Engineer
Data Engineers are responsible for building and maintaining the infrastructure that supports data analysis. They design and implement data pipelines, databases, and other data management systems. This course, Exploring Your First Data Set with R, provides a strong foundation in data exploration, which is a critical skill for Data Engineers. By learning how to explore and analyze data in R, you will be well-prepared to enter this in-demand field.
Data Visualization Specialist
Data Visualization Specialist
Data Visualization Specialists use their expertise in data analysis and visualization to create clear and concise data visualizations. They work with stakeholders to communicate complex data in a way that is easy to understand. This course, Exploring Your First Data Set with R, provides a solid foundation in data exploration, which is a critical skill for Data Visualization Specialists. By learning how to explore and analyze data in R, you will be well-prepared to enter this in-demand field.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use their expertise in mathematics, statistics, and programming to develop and implement financial models. They work in a variety of industries, including investment banking, hedge funds, and asset management. This course, Exploring Your First Data Set with R, provides a solid foundation in data exploration, which is a critical skill for Quantitative Analysts. By learning how to explore and analyze data in R, you will be well-prepared to enter this in-demand field.
Actuary
Actuary
Actuaries use their expertise in mathematics, statistics, and finance to assess and manage risks. They work in a variety of industries, including insurance, pensions, and healthcare. This course, Exploring Your First Data Set with R, provides a solid foundation in data exploration, which is a critical skill for Actuaries. By learning how to explore and analyze data in R, you will be well-prepared to enter this in-demand field.
Market Researcher
Market Researcher
Market Researchers use their expertise in data collection and analysis to understand consumer behavior. They work with businesses to develop and implement marketing strategies. This course, Exploring Your First Data Set with R, provides a solid foundation in data exploration, which is a critical skill for Market Researchers. By learning how to explore and analyze data in R, you will be well-prepared to enter this in-demand field.
Management Consultant
Management Consultant
Management Consultants use their expertise in business analysis and data analysis to help organizations improve their operations. They work with clients to identify problems, develop solutions, and implement change. This course, Exploring Your First Data Set with R, provides a solid foundation in data exploration, which is a critical skill for Management Consultants. By learning how to explore and analyze data in R, you will be well-prepared to enter this in-demand field.
Data Scientist Intern
Data Scientist Intern
Data Scientist Interns work under the supervision of experienced Data Scientists to gain hands-on experience in data analysis and machine learning. This course, Exploring Your First Data Set with R, provides a solid foundation in data exploration, which is a critical skill for Data Scientist Interns. By learning how to explore and analyze data in R, you will be well-prepared to enter this in-demand field.
Business Analyst
Business Analyst
Business Analysts use their expertise in data analysis and business processes to help organizations improve their operations. They work with stakeholders to identify problems, develop solutions, and implement change. This course, Exploring Your First Data Set with R, provides a strong foundation in data exploration, which is a critical skill for Business Analysts. By learning how to explore and analyze data in R, you will be well-prepared to enter this in-demand field.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use their expertise in mathematics, statistics, and computer programming to solve complex problems in a variety of industries. They develop and implement solutions that can improve efficiency, productivity, and profitability. This course, Exploring Your First Data Set with R, provides a solid foundation in data exploration, which is a critical skill for Operations Research Analysts. By learning how to explore and analyze data in R, you will be well-prepared to enter this in-demand field.
Financial Analyst
Financial Analyst
Financial Analysts use their expertise in finance and data analysis to evaluate investment opportunities. They work with clients to develop and implement investment strategies. This course, Exploring Your First Data Set with R, provides a solid foundation in data exploration, which is a critical skill for Financial Analysts. By learning how to explore and analyze data in R, you will be well-prepared to enter this in-demand field.
Risk Analyst
Risk Analyst
Risk Analysts use their expertise in mathematics, statistics, and finance to identify and assess risks. They work in a variety of industries, including banking, insurance, and healthcare. This course, Exploring Your First Data Set with R, provides a solid foundation in data exploration, which is a critical skill for Risk Analysts. By learning how to explore and analyze data in R, you will be well-prepared to enter this in-demand field.
For more career information including salaries, visit:
OpenCourser.com/course/nua7kr/exploring
Reading list
We've selected 14 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
Exploring Your First Data Set with R.
Save
A comprehensive reference for R for data science tasks. It contains a wealth of information on data manipulation, visualization, modeling, and more. It assumes the reader has some prior experience with R.
Save
A comprehensive textbook on computer vision. It covers a wide range of topics, including image formation, image processing, and object recognition. It is written in a clear and concise style and is suitable for both beginners and experienced computer vision researchers.
A comprehensive textbook on natural language processing. It covers a wide range of topics, including natural language understanding, natural language generation, and machine translation. It is written in a clear and concise style and is suitable for both beginners and experienced natural language processing researchers.
A guide to machine learning using R. It covers a wide range of topics, including supervised learning, unsupervised learning, and model selection. It is written in a clear and concise style and is suitable for both beginners and experienced data scientists.
Save
A comprehensive guide to data mining using R. It covers a wide range of topics, including data wrangling, feature engineering, and modeling. It is written in a clear and concise style and is suitable for both beginners and experienced data miners.
A textbook on statistical learning methods. It covers a wide range of topics, including supervised learning, unsupervised learning, and model selection. It is written in a clear and concise style and is suitable for both beginners and experienced data scientists.
Save
A comprehensive textbook on speech and language processing. It covers a wide range of topics, including speech recognition, speech synthesis, and natural language processing. It is written in a clear and concise style and is suitable for both beginners and experienced speech and language processing researchers.
A comprehensive graduate-level textbook on statistical inference. It covers a wide range of topics, including point estimation, hypothesis testing, and Bayesian inference. It is written in a clear and concise style and is suitable for both beginners and experienced statisticians.
A comprehensive textbook on reinforcement learning. It covers a wide range of topics, including the theory of reinforcement learning, algorithms for reinforcement learning, and applications of reinforcement learning. It is written in a clear and concise style and is suitable for both beginners and experienced reinforcement learners.
A graduate-level textbook on the foundations of machine learning. It covers a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning. It is written in a clear and concise style and is suitable for both beginners and experienced machine learners.
Save
A look at the misconceptions about data science and how to improve data science practices. also looks at the societal implications of data science.
A guide to deep learning for natural language processing. It covers a wide range of topics, including neural networks, natural language understanding, and machine translation. It assumes the reader has some prior experience with deep learning and natural language processing.
A look at the foundations of statistical inference as well as providing a toolkit of statistical techniques for data analysis. Uses the Stan modeling language.
Save
A book that introduces the basics of causal inference, a branch of statistics that deals with inferring the relationship between cause and effect.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/nua7kr/exploring
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Time
125 hours
Level
Introductory
Via
Pluralsight
Instructor
Martin Burger
Language
English
This course belongs to a
collection
called
Exploratory Data Analysis with R. It's comprised of two courses:
- Exploring Data Visually with R
- Exploring Your First Data Set with R (viewing)
Good to know
Teaches data exploration in R, which is a valuable skill for data analysts
Guides learners through a structured and established data exploration blueprint
Suitable for beginners who are new to data exploration in R
Taught by Martin Burger, an experienced instructor in data analysis
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