Analyzing Data with R from edX

The R programming language is purpose-built for data analysis. R is the key that opens the door between the problems you want to solve with data and the answers you need to meet your objectives. This course starts with a question, and then walks you through the process of answering it through data. You will first learn important techniques for preparing (or wrangling) your data for analysis. Then you will learn how to gain a better understanding of your data through exploratory data analysis, helping you to summarize your data and identify relevant relationships between variables that can lead to insights. ****

Once your data is ready to analyze, you will learn how to develop your model, evaluate it and tune its performance. By following this process, you can be sure that your data analysis performs to the standards that you have set, so that you can have confidence in the results. ****

By playing the role of a data analyst who is analyzing airline departure and arrival data to predict flight delays, you will build hands-on experience delivering insights using data. Using an Airline Reporting Carrier On-Time Performance Dataset, you will practice reading data files, preprocessing data, creating models, improving models, and evaluating them to ultimately choose the best one to use.

Note: The prerequisite for this course is basic R programming skills. For example, ensure that you have completed a course like Introduction to R Programming for Data Science from IBM.

What's inside

Learning objectives

Prepare data for analysis by handling missing values, formatting and normalizing data, binning, and turning categorical values into numeric values.
Conduct exploratory data analysis using descriptive statistics, data grouping, analysis of variance (anova), and correlation statistics.

Develop a predictive model using various regression methods.
Evaluate a model for overfitting and underfitting conditions and tune its performance using regularization and grid search.

Prepare data for analysis by handling missing values, formatting and normalizing data, binning, and turning categorical values into numeric values.
Conduct exploratory data analysis using descriptive statistics, data grouping, analysis of variance (anova), and correlation statistics.
Develop a predictive model using various regression methods.
Evaluate a model for overfitting and underfitting conditions and tune its performance using regularization and grid search.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Builds on your basic R programming knowledge to further develop your programming and data analysis skills

Employs numerous forms of media to illustrate concepts, including videos, readings, and group discussions

Develops a strong foundation for beginners to gain practical experience with data analysis and predictive modeling techniques

Prerequisites are explicitly stated, ensuring learners have the necessary knowledge before enrolling

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 Analyzing Data with R with these activities:

Review R for Data Science

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Reinforce your understanding of R programming concepts and practical data science techniques by reviewing this comprehensive book, deepening your knowledge and improving your ability to apply R effectively in your projects.

View R for Data Science: Import, Tidy, Transform,... on Amazon

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Read the book thoroughly, focusing on understanding the concepts and techniques presented.
Work through the exercises and examples provided in the book to solidify your understanding.

Participate in a peer discussion forum on R programming

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Connect with fellow learners and engage in discussions about R programming and data analysis, fostering a collaborative learning environment and broadening your perspectives.

Browse courses on R Programming

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Join an online forum or discussion group dedicated to R programming.
Ask questions, share insights, and engage in discussions with other members of the community.

Develop a predictive model for flight delays

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Integrate your knowledge of data wrangling, exploratory data analysis, and regression modeling to build a predictive model for flight delays, showcasing your proficiency in end-to-end data analysis.

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Use regression techniques, such as linear regression or logistic regression, to build a predictive model.
Evaluate the model's performance using metrics like accuracy, precision, and recall.
Tune the model's parameters to improve its performance.

Show all three activities

Career center

Learners who complete Analyzing Data with R will develop knowledge and skills that may be useful to these careers:

Data Analyst

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See salaries and explore the career path for Data Analyst

Machine Learning Engineer

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See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data scientists use data to solve complex problems. They use a variety of techniques, including data analysis, machine learning, and artificial intelligence. This course can help data scientists build the skills they need to succeed in their roles. The course covers topics such as data preparation, exploratory data analysis, model development, and model evaluation.

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Statistician

Statisticians collect, analyze, interpret, and present data. They use statistical methods to draw conclusions from data. This course can help statisticians build the skills they need to succeed in their roles. The course covers topics such as data preparation, exploratory data analysis, statistical modeling, and statistical inference.

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Business Analyst

Business analysts use data to identify and solve business problems. They work with stakeholders to understand their needs and develop solutions. This course can help business analysts build the skills they need to succeed in their roles. The course covers topics such as data analysis, data visualization, and business communication.

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Data Engineer

Data engineers design, build, and maintain data systems. They work with data analysts and data scientists to ensure that data is available and accessible. This course can help data engineers build the skills they need to succeed in their roles. The course covers topics such as data architecture, data storage, and data processing.

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Software Developer

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Database Administrator

Database administrators design, build, and maintain databases. They work with data analysts and data scientists to ensure that data is stored and managed efficiently. This course can help database administrators build the skills they need to succeed in their roles. The course covers topics such as database design, database administration, and database security.

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Quantitative Analyst

Quantitative analysts use mathematical and statistical methods to analyze financial data. They develop models to predict financial risks and returns. This course can help quantitative analysts build the skills they need to succeed in their roles. The course covers topics such as financial data analysis, financial modeling, and risk management.

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Market Researcher

Market researchers collect, analyze, and interpret data about markets and consumers. They use this data to help businesses make decisions about products, services, and marketing strategies. This course can help market researchers build the skills they need to succeed in their roles. The course covers topics such as market research methods, data analysis, and market segmentation.

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Operations Research Analyst

Operations research analysts use mathematical and statistical methods to solve problems in business and industry. They develop models to optimize processes and improve efficiency. This course can help operations research analysts build the skills they need to succeed in their roles. The course covers topics such as optimization, simulation, and data analysis.

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Actuary

Actuaries use mathematical and statistical methods to assess risk and uncertainty. They develop models to predict future events and calculate insurance premiums. This course can help actuaries build the skills they need to succeed in their roles. The course covers topics such as risk management, insurance, and financial mathematics.

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Health Economist

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Epidemiologist

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Biostatistician

Biostatisticians use statistical methods to analyze biological and medical data. They develop models to predict health outcomes and evaluate the effectiveness of treatments. This course can help biostatisticians build the skills they need to succeed in their roles. The course covers topics such as biostatistics, epidemiology, and clinical research.

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