Statistics and Data Analysis with R from Coursera

This course is the third course in a 3-part specialization entitled "Statistics and Applied Data Analysis." The course is meant for those familiar with statistics but unfamiliar with the programming language R.

The purpose of this course is to teach learners how to use the popular open-source (and thus, free) integrated development environment RStudio to perform basic and complex statistical calculations.

After an introduction to basic calculations, vector, matrices, data frames, and how to import data from common file types (.xlsx, .csv, .txt), learners are taught how to solve probability and counting problems in R, followed by discrete and continuous probability distribution calculations, one-sample hypothesis tests, and two-sample hypothesis tests (comparisons). Finally, participants will learn how to create regression models in R and perform analysis of variance (ANOVA).

One of the most beneficial aspect of the course are the programming assignments, which are completed online in the R programming language in Jupyter notebooks.

What's inside

Syllabus

Welcome and Introduction to the R Programming Language

Welcome to "Statistics and Data Analysis with R"! In this week, you will be introduced to R and RStudio and will learn how to install and navigate RStudio. You will then learn how to perform basic calculations, use script files, create and work with vectors and matrices, and install and load add-on packages. Finally, you will learn all about data frames and tibbles, how to import data from external files (.xlsx, .csv, and .txt files), and how to work with built-in and user-defined functions. When you are ready, you must pass the Week 1 Graded Quiz in order to access the Week 2 Starter Files and Cheat Sheet. You will need access to these items in order to complete Module 2. You must also pass Assignment 1, which counts towards the final grade in the course.

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what should give you pause

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Teaches how to import data from common file types, such as .xlsx, .csv, and .txt, which is essential for real-world data analysis

Uses Jupyter notebooks for programming assignments, providing an interactive and reproducible environment for statistical analysis

Covers a wide range of statistical techniques, including probability distributions, hypothesis testing, regression, and ANOVA

Requires learners to pass graded quizzes to access subsequent modules, ensuring a solid understanding of the material

Features screencasts with in-video questions, which actively engages learners and reinforces their understanding of the concepts

Uses RStudio, a popular open-source integrated development environment, which is widely used in the field of statistics

Reviews summary

Applying statistics with r and hands-on projects

According to learners, this course offers a solid foundation in applying statistical concepts using the R programming language. Many highlight the hands-on programming assignments completed in Jupyter notebooks as a major strength, finding them well-designed and essential for practical learning. The lectures and instructor explanations are generally seen as clear and easy to follow. However, students consistently note that the course assumes a strong prerequisite knowledge of statistics; those with a rusty or insufficient stats background may struggle and need to supplement heavily. While the course effectively teaches the use of R for stats, it is not ideal for learning statistics itself.

Useful for data-related careers.

"Definitely beneficial for my career."

"Highly relevant for data science/analytics roles."

"Essential for anyone working with data."

Covers standard statistical topics.

"A solid introduction to using R for statistical analysis. The pacing is good, and the topics covered align well with the syllabus."

"The syllabus covers standard topics."

"Learned how to perform common statistical tests and building models in R."

"The structure is logical, building from basics to more complex topics like regression and ANOVA."

Lectures are easy to follow.

"The lectures were clear and concise, and the programming assignments... were fantastic."

"The instructor is very good at explaining complex topics."

"Excellent course! Learned so much... Instructor explanations are easy to follow."

"Lectures are clear, assignments are practical."

Practice problems solidify learning.

"One of the most beneficial aspect of the course are the programming assignments, which are completed online in the R programming language in Jupyter notebooks."

"The hands-on coding and projects are the strongest part of the course for me."

"The programming assignments in Jupyter notebooks were fantastic. They forced me to apply the concepts immediately."

"Assignments are key to learning. They are well-designed and cover all the key functions."

"Assignments provide excellent practice. Highly relevant for data science/analytics roles."

"The assignments are hands-on and challenging in a good way. Learned how to perform many statistical procedures efficiently in R."

Requires solid background in statistics.

"The course is okay, but assumes a stronger statistics background than I had. I found myself constantly referring to external resources..."

"Requires a good understanding of statistics going in."

"Honestly, I struggled a lot. The course moved too fast for me, and the statistical concepts weren't explained well enough..."

"Make sure your statistics foundation is solid."

"But the stats review is minimal. If you aren't comfortable with hypothesis testing, confidence intervals, regression theory etc., you'll need to supplement heavily."

"It delivers on teaching R for stats, but not on teaching stats with R if that makes sense."

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 Statistics and Data Analysis with R with these activities:

Review Introductory Statistics

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Reinforce foundational statistical concepts to better understand the more advanced topics covered in the course.

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Review key statistical terms and definitions.
Work through practice problems on descriptive statistics.
Familiarize yourself with basic probability concepts.

Review 'The Art of Statistics: How to Learn from Data'

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Gain a deeper understanding of the underlying statistical principles by reading a book that emphasizes conceptual understanding.

View The Art of Uncertainty: How to Navigate Chance,... on Amazon

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Read the chapters related to descriptive statistics and probability distributions.
Take notes on the key concepts and examples provided in the book.
Relate the concepts to the R functions used in the course.

Review 'R for Data Science'

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Enhance your R programming skills by working through the examples and exercises in a popular data science book.

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

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Work through the chapters on data transformation and visualization.
Practice using the dplyr and ggplot2 packages.
Apply the techniques to the datasets used in the course assignments.

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Practice R Syntax for Data Manipulation

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Solidify your understanding of R syntax for data manipulation, which is crucial for completing assignments.

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Complete online R tutorials focusing on data frames and tibbles.
Practice importing data from different file formats (.csv, .txt, .xlsx).
Write R scripts to perform basic data cleaning and transformation tasks.

Create a Cheat Sheet for Hypothesis Testing

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Synthesize your knowledge of hypothesis testing by creating a cheat sheet summarizing key concepts and R functions.

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Summarize the different types of hypothesis tests covered in the course.
List the R functions used for each type of test.
Include examples of how to use the functions with sample data.

Follow Tutorials on Regression Modeling in R

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Deepen your understanding of regression modeling techniques in R by following guided tutorials.

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Find tutorials that cover simple linear, polynomial, and multilinear regression.
Implement the regression models in R using the datasets provided in the tutorials.
Analyze the results and compare them with the tutorial's explanations.

Analyze a Public Dataset with R

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Apply your knowledge of R and statistical analysis to a real-world dataset to solidify your skills.

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Find a public dataset relevant to your interests.
Import the data into R and perform exploratory data analysis.
Apply the statistical techniques learned in the course to answer specific research questions.
Document your analysis and findings in a report.

Career center

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

Data Analyst

A data analyst uses statistical methods to analyze data and provide insights. This role involves working with datasets, performing statistical calculations, and creating visualizations to communicate findings. The course, Statistics and Data Analysis with R, is particularly relevant because it teaches how to use R, a popular programming language for data analysis. The course covers key statistical concepts such as probability distributions, hypothesis testing, and regression models; these are essential skills for a data analyst. This course's focus on practical applications using R makes it directly applicable to the kind of work a data analyst does. The use of programming assignments completed in Jupyter notebooks provides essential hands-on experience. Learners will find that skills in importing data, basic calculations, and regression modeling will be useful in this career.

See salaries and explore the career path for Data Analyst

Statistician

A statistician develops and applies statistical theories and methods to solve real-world problems. The work involves designing experiments and analyzing data to draw meaningful conclusions. Statistics and Data Analysis with R is highly relevant because it provides a practical foundation in statistical computing using R. A statistician performs many tasks that align with the concepts taught in the course, such as calculating probabilities, performing hypothesis testing, and creating regression models. This course introduces how to use R to accomplish these tasks. One who wishes to pursue a career as a statistician will find that the course's detailed approach to performing statistical analyses in R will help build a solid base. The programming assignments performed in Jupyter notebooks give practical experience.

See salaries and explore the career path for Statistician

Quantitative Analyst

A quantitative analyst, often called a quant, develops and implements mathematical and statistical models used in finance and other industries. These models help with decision making, risk management, and trading strategies. The course, Statistics and Data Analysis with R, helps prepare for this professional role by focusing on statistical analysis using R, which is a vital tool for a quantitative analyst. The course's syllabus includes regression models, analysis of variance, hypothesis tests, probability, and other quantitative techniques. Those who wish to pursue this career would find the course's practical, hands-on approach to statistical computing valuable. The exercises using Jupyter notebooks will help build confidence in modeling data. Note that this career path often requires advanced degrees.

See salaries and explore the career path for Quantitative Analyst

Research Scientist

Research scientists conduct experiments and analyze data to further scientific knowledge. Statistics and Data Analysis with R is a helpful course, as it teaches how to use R to perform statistical computations that are necessary in data analysis. Research scientists often need skills in hypothesis testing, regression analysis, and probability calculations, covered in this course. This course also teaches how to use R to import data, perform calculations, and create statistical models. Such practical, hands-on training will be useful for a career as a research scientist. The practical experience gained from the programming assignments in Jupyter notebooks will be particularly relevant. Note that this career path typically requires advanced degrees.

See salaries and explore the career path for Research Scientist

Market Research Analyst

A market research analyst studies market conditions to assess the potential sales of a product or service. This professional role includes collecting and analyzing data, often using statistical techniques to identify trends and patterns. The course, Statistics and Data Analysis with R, will be helpful by providing training in R, a commonly used language for data analysis. This professional path requires knowledge of hypothesis testing, regression models and probability distributions; these are all areas covered in the course. This course introduces how to use R to import data, perform calculations, and create statistical models. The practical experience provided through programming assignments in Jupyter notebooks will be particularly relevant to those who desire a career in market research analysis.

See salaries and explore the career path for Market Research Analyst

Bioinformatician

A bioinformatician develops and uses computational methods to analyze biological data. This role requires a strong understanding of both biology and statistics, and also the ability to use programming languages to analyze data. The course, Statistics and Data Analysis with R, will help develop skills in statistical analysis using the R programming language, which is often used by bioinformaticians. This course includes concepts such as hypothesis testing, probability calculations, and regression models, which are useful for analyzing biological data. The practical programming exercises performed in Jupyter notebooks provide crucial hands-on experience. Those who wish to pursue a career in bioinformatics will find this course may be particularly useful. Note that this career path often requires advanced degrees.

See salaries and explore the career path for Bioinformatician

Financial Analyst

A financial analyst provides financial advice and recommendations to organizations. This involves analyzing financial data, creating financial models, and assessing risk. This job requires data analysis and statistical skills. The course Statistics and Data Analysis with R may be useful since it teaches the use of R for statistical calculations. The course covers various statistical concepts, such as hypothesis tests and regression modeling, which are used by a financial analyst. The course provides practical experience with programming assignments in Jupyter notebooks. One who wishes to enter this professional career role may find this course a useful place to begin.

See salaries and explore the career path for Financial Analyst

Business Intelligence Analyst

A business intelligence analyst interprets data to identify trends and provide insights that can help businesses make better decisions. This role involves analyzing data, creating reports, and communicating findings to stakeholders. The course Statistics and Data Analysis with R may be useful for this role as it introduces the use of R for statistical analysis. A business intelligence analyst will likely use tools to create models and perform tests, and the course teaches how to do this in R. The programming assignments in Jupyter notebooks provide practical training. Those pursuing this career path should consider this course to learn core data analysis skills.

See salaries and explore the career path for Business Intelligence Analyst

Operations Research Analyst

An operations research analyst uses mathematical and statistical methods to solve complex problems and improve operational efficiency. This role requires strong analytical and problem-solving skills. The course, Statistics and Data Analysis with R, is moderately relevant because it introduces statistical calculations using R. The course covers techniques such as hypothesis testing and modeling, and this may be useful for those who wish to enter a career in operations research. The course gives practical experience with programming assignments in Jupyter notebooks. One who seeks a career in this field may find this course a helpful place to begin.

See salaries and explore the career path for Operations Research Analyst

Actuary

An actuary assesses and manages financial risks using advanced statistical and mathematical techniques. This role requires a deep understanding of probability, statistics, and financial principles. The course, Statistics and Data Analysis with R, may be useful by helping build a foundational understanding of statistical computing using R. The actuary role requires skills in probability, hypothesis testing, and regression models, and this is what this course covers as part of its syllabus. Those who wish to enter this professional career role may find this course to be a helpful introduction to data analysis tools and statistical methods. Note that this career path often requires advanced degrees and certifications.

See salaries and explore the career path for Actuary

Research Associate

A research associate supports research projects by collecting, analyzing, and interpreting data and assisting in the preparation of research reports. This position requires data analysis skills and the ability to use statistical software such as R. The course, Statistics and Data Analysis with R, may be helpful for this career, as it provides training on how to perform statistical calculations. The course covers probability, hypothesis testing, regression models, and similar statistical concepts that may be applicable to this role. The programming assignments using Jupyter notebooks provide hands-on experience using R. A research associate may find that this course provides a foundation for data analysis work.

See salaries and explore the career path for Research Associate

Epidemiologist

An epidemiologist studies the patterns, causes, and effects of health conditions in specific populations. This role requires strong data analysis and statistical skills. The course, Statistics and Data Analysis with R, may be useful because it teaches the use of R, which is often used in epidemiological research. The course covers important statistical concepts, such as probability distributions, hypothesis testing, and regression models. An epidemiologist may find the programming assignments in Jupyter notebooks helpful for understanding how to perform statistical analysis in R. Note that this professional field often requires an advanced degree.

See salaries and explore the career path for Epidemiologist

Survey Statistician

A survey statistician designs and analyzes surveys to collect data and address important research questions. This position requires a solid understanding of statistical methods and sampling techniques. The course, Statistics and Data Analysis with R, may be helpful by providing training in data analysis using R, which is commonly used for this role. The course covers probability and hypothesis tests, which are useful to survey statisticians. The programming assignments using Jupyter notebooks will help those who wish to enter this career role feel comfortable performing statistical calculations. Note that this professional role often requires an advanced degree.

See salaries and explore the career path for Survey Statistician

Clinical Data Manager

A clinical data manager oversees the collection, management, and analysis of data in clinical trials. This position requires knowledge of data management principles and statistical analysis. The course, Statistics and Data Analysis with R may be helpful because it introduces learners to statistical computing using the R programming language. The course provides training in hypothesis testing and regression modeling, which may be used during the analysis phase of clinical trials. The practical experience gained from exercises in Jupyter notebooks will be relevant for those who wish to pursue a career as clinical data manager.

See salaries and explore the career path for Clinical Data Manager

Financial Risk Analyst

A financial risk analyst assesses the potential risk that a business, investment, or financial system may face. This role requires understanding statistical methods and financial markets. The course, Statistics and Data Analysis with R, may be useful because it helps to build a foundation in statistical analysis using R. The course covers statistical concepts such as hypothesis testing and regression models. The practical training through the use of Jupyter notebooks may help those who desire to enter this career path. One who wishes a career as a financial risk analyst may find it a helpful place to begin.

See salaries and explore the career path for Financial Risk Analyst

Reading list

We've selected two 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 Statistics and Data Analysis with R.

R for Data Science

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Comprehensive guide to using R for data science, covering data manipulation, visualization, and modeling. It provides practical examples and exercises to reinforce learning. This book is commonly used as a textbook at academic institutions. It adds more depth to the course by providing a more detailed explanation of R functions and techniques.

R for Data Science: Import, Tidy, Transform,...

Paperback

(Deutsch) R für Data Science: Daten importieren, bereinigen,...

Kindle Edition

$$$

The Art of Uncertainty: How to Navigate Chance,...

Save

Provides a conceptual understanding of statistics, focusing on the art of data analysis rather than just the mechanics. It helps build a strong foundation for understanding the statistical methods used in the course. While not an R-specific book, it provides the necessary statistical background to better understand the application of R in data analysis. It is more valuable as additional reading to provide a broader context.

The Art of Uncertainty: How to Navigate Chance,...

Kindle Edition

Statistics and Data Analysis with R

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What's inside

Syllabus

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Reviews summary

Applying statistics with r and hands-on projects

Activities

Career center

Reading list

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