Data Analysis with R from Coursera

The R programming language is purpose-built for data analysis. R is the key that opens the door between the problems that 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. You will then 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 and evaluate and tune its performance. By following this process, you can be sure that your data analysis performs to the standards that you have set, and you can have confidence in the results.

You will build hands-on experience by playing the role of a data analyst who is analyzing airline departure and arrival data to predict flight delays. 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 model.

Watch the videos, work through the labs, and add to your portfolio. Good luck!

Note: The pre-requisite 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

Syllabus

Introduction to Data Analysis with R

All data analysis starts with a problem that you need to solve and understanding your data and the types of questions you can answer about it are key aspects of this. The R programming language provides you with all the tools you need to conduct powerful data analysis, providing the conduit between your data and the real-world problems you want to solve. In this module, you’ll review a type of problem that you can solve in R and the underlying data that forms the basis for your analysis. You’ll also learn about the R packages for data analysis, which provide a powerful set of tools that you’re likely to use in everyday data analyses. Finally, you’ll see how to import data and gain basic insights from the dataset.

Data Wrangling

Data wrangling, or data pre-processing, is an essential first step to achieving accurate and complete analysis of your data. This process transforms your raw data into a format that can be easily categorized or mapped to other data, creating predictable relationships between them, and making it easier to build the models you need to answer questions about your data. This module provides an introduction to data pre-processing in R and then provides you with the tools you need to identify and handle missing values in your dataset, transform data formats to align them with other data you may want to compare them to, normalize your data, create categories of information through data binning, and convert categorical variables into quantitative values that can then be used in numeric-based analyses.

Exploratory Data Analysis

Exploratory data analysis, or EDA, is an approach to analyzing data that summarizes its main characteristics and helps you gain a better understanding of the dataset, uncover relationships between different variables, and extract important variables for the problem you are trying to solve. The main question you are trying to answer in this module is: "What causes flight delays?" In this module, you’ll learn some useful exploratory data analysis techniques that will help answer this question.

Model Development in R

You have identified the problem that you’re trying to solve and have pre-processed the dataset you’ll use in your analysis, and you have conducted some exploratory data analysis to answer some of your initial questions. Now, it’s time to develop your model and assess the strength of your assumptions. In this module, you will examine model development by trying to predict the arrival delay of a flight using the Airline dataset. You’ll learn regression techniques for determining the correlation between variables in your dataset, and evaluate the result both visually and through the calculation of metrics.

Model Evaluation

You have a firm understanding of your data and have pre-processed it to ensure the best possible outcomes. And you have conducted exploratory data analysis and developed your model. Everything looks good so far, but how can you be certain your model works in the real world and performs optimally? In this module, you’ll learn how to use the tidymodels framework to evaluate your model. Tidymodels is a collection of packages for modeling and machine learning using tidyverse principles. Using these packages, you’ll learn how to cross-validate your models, identify potential problems, like overfitting and underfitting, and handle overfitting problems using a technique called regularization. You’ll also learn how to tune your models using grid search.

Project

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Provides a strong foundation for beginners in R programming who want to learn data analysis

Develops essential data wrangling skills and techniques

Provides practical experience through hands-on labs

Facilitated by experienced instructors recognized for their work in data analysis

Teaches industry-relevant skills and knowledge in data analysis

Covers a comprehensive range of topics in data analysis with R

Reviews summary

Beginner-friendly and comprehensive data analysis with r course

Learners say that the Data Analysis with R course on Coursera is an excellent resource for beginners seeking to grasp foundational concepts of the field. According to reviews, this course empowers learners to acquire practical data analysis techniques, utilizing the R programming language. Students appreciate the engaging assignments and well-structured curriculum that facilitates a deeper understanding of data modeling, wrangling, normalization, and machine learning algorithms. Overall, this course is highly well received, providing a comprehensive learning experience for aspiring data analysts.

Ideal for beginners with little to no data analysis knowledge.

"It is excellent course. I recommend for all that do not have a lot of knowledge and experience in data analysis with R Programming."

"IBM's courses are very professional, allowing students to fully acquire knowledge"

"Perfect course to progress in data science with R. Progressif and well structured for a good work to understanding the data"

Instructors provide clear explanations with relevant visual aids and demonstrations.

"The instructors' clear explanations, coupled with relevant visual aids and demonstrations, made it easy to grasp complex concepts."

"A comprehensive and useful course. This course is a little bit tough if you are not familiar with statistics."

Provides numerous real-life examples and practical exercises.

"The hands-on approach of the course. The instructors provided numerous real-life examples and practical exercises, allowing me to apply the newly learned techniques directly."

"The assignments and quizzes were challenging yet manageable, and they reinforced my understanding of the material effectively."

Some learners may encounter difficulties with the third-party web app required for the program.

"the 3rd party online web app required to complete the program is completely unusable."

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 Data Analysis with R with these activities:

Volunteer as a Data Science Mentor

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This activity will allow you to give back to the community while reinforcing your own understanding of R and data science.

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Find a volunteer opportunity at a local school or organization
Mentor students or other individuals on R and data science concepts

Attend a Data Science Meetup

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This activity will provide you with an opportunity to connect with other data science professionals and learn about current trends and advancements in the field.

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Find a local Data Science Meetup group
Attend a Meetup and participate in discussions

Participate in an R Workshop

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This activity will provide you with hands-on training on a specific topic related to R, such as data wrangling, machine learning, or visualization.

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Find an R workshop that aligns with your interests
Participate in the workshop and learn from experienced instructors

Two other activities

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Show all five activities

Flight Delay Prediction Project

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This activity will challenge you to apply your data wrangling, exploratory data analysis, and model development skills to a real-world problem.

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Define the problem and objectives
Collect and prepare the data
Explore the data and identify patterns
Develop and evaluate predictive models
Present your findings and recommendations

Contribute to an Open-Source R Project

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This activity will allow you to apply your R skills to a real-world project while contributing to the open-source community.

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Find an open-source R project that interests you
Identify an issue or feature that you can contribute to
Make a pull request with your proposed changes

Career center

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

Data Scientist

The role of a Data Scientist is to utilize data for solving complex business problems. This course emphasizes the process of turning a business problem into a solution using data. As a Data Scientist, you will be involved in all three phases of this process: data wrangling, exploratory data analysis, and model building. All of these aspects are covered comprehensively in this course. Moreover, you will learn about model evaluation, which is critical in the data science workflow. By completing this course, you will gain proficiency in the core competencies of a Data Scientist.

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Statistician

Statisticians play a key role in analyzing data to draw meaningful conclusions and make informed decisions. This course provides a comprehensive foundation in data wrangling, enabling you to effectively clean, prepare, and manage statistical datasets. The exploratory data analysis techniques taught in this course will equip you with the skills to identify patterns, trends, and relationships within data. Additionally, you will gain proficiency in model development and evaluation, empowering you to build and refine statistical models for data analysis and predictive modeling.

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Data Analyst

As a Data Analyst, you will be responsible for transforming raw data into useful insights for your organization. Knowledge of exploratory data analysis, as taught in this course, is pivotal in your role. You will be able to uncover patterns and trends in data to support decision-making. This course delves into model development and evaluation, providing you with the skills to build and refine models. These models can help you predict outcomes and make informed decisions. A solid foundation in data analysis, which this course provides, is paramount for excelling as a Data Analyst.

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Business Intelligence Analyst

Business Intelligence Analysts use data to identify trends, patterns, and insights that help businesses make better decisions. This course provides a solid foundation in data wrangling, ensuring that you can effectively collect, clean, and analyze data from various sources. The exploratory data analysis techniques will equip you with the skills to understand the underlying patterns and relationships in data. Additionally, you will gain proficiency in model development and evaluation, enabling you to build and refine models for data-driven decision-making.

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Operations Research Analyst

As an Operations Research Analyst, your role is to use mathematical and analytical techniques to improve the efficiency of operations within an organization. This course provides a strong foundation in data wrangling, enabling you to collect and process large datasets effectively. The exploratory data analysis techniques taught in this course will help you identify patterns and trends within data, allowing you to make informed decisions. Additionally, you will gain proficiency in model development and evaluation, which are essential skills for developing and refining operational models.

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Risk Analyst

As a Risk Analyst, your primary responsibility is to identify, assess, and mitigate risks within an organization. This course provides a solid foundation in data wrangling, allowing you to effectively collect and process large amounts of data from various sources. The exploratory data analysis techniques taught in this course will help you understand the underlying patterns and trends in risk-related data. Additionally, you will gain proficiency in model development and evaluation, enabling you to build and refine models for risk assessment and prediction.

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Machine Learning Engineer

Machine Learning Engineers play a critical role in developing and deploying machine learning models for various applications. This course provides a solid foundation in data wrangling, ensuring that you can effectively prepare and manage data for machine learning algorithms. The exploratory data analysis techniques will equip you with the skills to understand the underlying patterns and relationships in data. Additionally, you will gain proficiency in model development and evaluation, enabling you to build, train, and evaluate machine learning models for real-world problems.

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Data Visualization Analyst

Data Visualization Analysts use visual representations to communicate insights from data. This course provides a foundation in data wrangling, enabling you to effectively prepare and transform data for visualization. The exploratory data analysis techniques will equip you with the skills to understand the underlying patterns and relationships in data. Additionally, you will gain insights into model development and evaluation, which can help you create more effective and informative data visualizations.

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Financial Analyst

Financial Analysts use data to make informed investment decisions and provide financial advice. This course provides a foundation in data wrangling, enabling you to understand the process of data collection, cleaning, and analysis in the financial domain. The exploratory data analysis techniques taught in this course will equip you with the skills to identify patterns and trends in financial data. Additionally, you will gain insights into model development and evaluation, which are essential for building and refining financial models for investment analysis and forecasting.

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Quantitative Analyst

The Quantitative Analyst role involves using mathematical and statistical models to analyze financial data and make investment decisions. This course provides a strong foundation in data wrangling, ensuring that you can efficiently manage and prepare financial datasets. The exploratory data analysis techniques will equip you with the skills to uncover patterns and trends in financial data. Additionally, this course will help you develop proficiency in model development and evaluation, enabling you to build and refine models for financial analysis and prediction.

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Actuary

Actuaries use mathematical and statistical techniques to assess and manage financial risks. This course provides a foundation in data wrangling, enabling you to effectively collect, clean, and analyze large datasets. The exploratory data analysis techniques will equip you with the skills to identify patterns and trends in actuarial data. Additionally, you will gain proficiency in model development and evaluation, which are essential for building and refining actuarial models for risk assessment and pricing.

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Market Researcher

Market Researchers play a vital role in analyzing market data to provide valuable insights for businesses. This course provides you with a strong foundation in data wrangling, allowing you to clean and prepare data effectively. You will also learn exploratory data analysis techniques, which are essential for understanding market trends and consumer behavior. Additionally, this course will help you develop skills in model development and evaluation, enabling you to make data-driven recommendations that drive business growth.

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Data Engineer

The Data Engineer role involves building and maintaining data infrastructure to support data analysis and decision-making. This course provides a foundation in data wrangling, enabling you to understand the process of data ingestion, transformation, and storage. The exploratory data analysis techniques taught in this course will help you develop skills in data exploration and understanding. Additionally, you will gain insights into model development and evaluation, which are essential for ensuring the quality and accuracy of data pipelines.

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Data Architect

The Data Architect role involves designing and implementing data management solutions to meet an organization's data needs. This course provides a foundation in data wrangling, enabling you to understand the principles of data modeling, storage, and retrieval. The exploratory data analysis techniques taught in this course will help you develop skills in data exploration and understanding. Additionally, you will gain insights into model development and evaluation, which are essential for ensuring the quality and integrity of data architecture.

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Business Analyst

A Business Analyst plays a crucial role in examining and recommending solutions for business processes. A background in data analysis empowers you to take on this problem-solving role. This course can help build a foundation for your proficiency in analyzing data. As a Business Analyst, you may also be involved in evaluating models, a competency that you will develop by learning about model evaluation in this course. Business Analysts must possess a deep understanding of their organization's strengths and weaknesses. This is facilitated by the knowledge of data analysis from this course. It will help you identify opportunities for improving efficiency and reducing operational costs through data-driven analysis.

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