We may earn an affiliate commission when you visit our partners.
Course image
Microsoft
Enroll now

What's inside

Syllabus

Getting Started with R and Visual Studio Code
In this first module, you'll become familiar with R, a language used to organize, analyze, and visualize data in your workplace. You will set up your workspace in Microsoft Visual Studio Code, learn how to navigate the basic interface, and start writing your first simple commands. This module will guide you step-by-step from the very start.
Read more

Save this course

Create your own learning path. Save this course to your list so you can find it easily later.
Save

Career center

Learners who complete R Programming Essentials with Microsoft Tools will develop knowledge and skills that may be useful to these careers:
Statistical Programmer
A Statistical Programmer applies programming skills, often using languages like R, to implement statistical analyses and generate reports, particularly in research, clinical trials, or government sectors. This role demands precision in handling data and executing complex statistical methods. The R Programming Essentials with Microsoft Tools course is directly relevant for individuals aspiring to become a Statistical Programmer. It immerses learners in R fundamentals, including managing different data types and writing functions, which are critical for building robust statistical code. The focus on importing, organizing, and cleaning messy data, alongside creating straightforward reports, aligns perfectly with the day-to-day tasks. Furthermore, the course's emphasis on version control with Git and GitHub, along with documenting coding steps, ensures that learners develop professional practices for reproducible and auditable statistical work. This course helps prepare learners to confidently tackle real-world statistical programming tasks.
Data Analyst
A Data Analyst extracts, cleans, and interprets data to help organizations make informed decisions. This role involves sifting through complex datasets, identifying trends, and presenting findings in clear, understandable reports. The R Programming Essentials with Microsoft Tools course provides a strong foundation for aspiring Data Analysts, teaching how to organize, analyze, and visualize data effectively. Learners will master R basics, including data structures and functions, crucial for efficient data manipulation. The module on working with data, covering importing, tidying, and summarizing information, directly prepares one for the practical challenges of a Data Analyst role. Using version control with Git and GitHub, along with AI assistance, enhances project management skills, invaluable for collaborative data projects. This course helps build essential R programming proficiency, a highly sought-after skill for analyzing and presenting insights in various industries.
Research Assistant
A Research Assistant provides support to researchers, often involving data collection, organization, and preliminary analysis across academic, scientific, or market research settings. Their work is crucial in laying the groundwork for deeper investigations. The R Programming Essentials with Microsoft Tools course offers valuable skills for a Research Assistant, particularly in handling quantitative data. The course teaches fundamental R programming concepts, including managing variables and data structures, which is essential for processing research data efficiently. The module focused on importing, organizing, and tidying data, along with producing simple summaries, directly prepares one for common tasks encountered in research. Moreover, the emphasis on version control with Git and GitHub helps learners manage research projects systematically and collaborate effectively, ensuring data integrity and reproducibility, which are paramount in research environments.
Product Analyst
A Product Analyst examines product usage, feature performance, and user behavior data to provide insights that guide product development and strategy. This role is central to understanding what makes a product successful. The R Programming Essentials with Microsoft Tools course provides highly relevant skills for a Product Analyst. The course teaches fundamental R programming, including handling various data structures like tables and writing functions, which are essential for processing product metrics and A/B test results. The modules on importing, organizing, and tidying data, along with producing straightforward reports, directly prepare one for the analytical tasks required to present actionable insights to product teams. Using version control with Git and GitHub also helps manage successive analyses and collaborate effectively on product data projects, making this course a strong asset for entering this data-driven field.
Quantitative Analyst
A Quantitative Analyst, often found in finance or economics, uses mathematical and statistical methods, often implemented with programming languages like R, to analyze financial markets, risk, and investment strategies. This role typically requires an advanced degree. The R Programming Essentials with Microsoft Tools course establishes a foundational understanding of R programming for individuals aiming for a Quantitative Analyst role. The course covers essential concepts like variables, data structures, and functions, which are critical for expressing complex quantitative models. Skills in importing, cleaning, and organizing data in R are directly applicable to preparing financial datasets for analysis. While this course focuses on essentials, it helps build a technical bedrock in a language widely used in quantitative analysis, potentially serving as a stepping stone towards roles that demand sophisticated statistical programming and modeling.
Financial Data Analyst
A Financial Data Analyst specializes in processing and interpreting financial datasets to identify trends, assess risks, and inform investment decisions for banks, hedge funds, or corporations. This role requires strong analytical and programming capabilities. The R Programming Essentials with Microsoft Tools course helps build a foundation in R programming, which is frequently used for statistical analysis and modeling in finance. The course covers essential concepts like variables, data structures, and writing functions, all critical for manipulating complex financial data. Learning to import, organize, and tidy data, then perform simple calculations and create reports, directly equips one for common tasks such as analyzing market trends or portfolio performance. The final project, applying learned skills to a realistic workplace challenge, further enhances practical problem-solving abilities invaluable to a Financial Data Analyst.
Business Intelligence Analyst
A Business Intelligence Analyst transforms raw business data into actionable insights, helping companies understand performance, identify opportunities, and make strategic decisions. This often involves data extraction, transformation, loading, and visualizing trends. The R Programming Essentials with Microsoft Tools course equips aspiring Business Intelligence Analysts with core R programming skills for data handling. The ability to import, organize, and tidy data, as taught in the course, is fundamental when preparing datasets for analysis and reporting. Learning to perform simple calculations and create clear reports directly supports the objective of presenting business insights. Additionally, the practice of completing a final R programming project that addresses a realistic workplace challenge helps develop a practical, solution-oriented mindset essential for building effective business intelligence solutions.
Junior Data Scientist
A Junior Data Scientist applies statistical analysis, machine learning methods, and programming to extract insights and build predictive models from large datasets. This role works under supervision, contributing to various stages of the data science pipeline. The R Programming Essentials with Microsoft Tools course helps build a foundational understanding vital for a Junior Data Scientist. It covers core R programming, data structures, and functions, which are prerequisites for more advanced statistical modeling and machine learning. The modules on importing, cleaning, and organizing data in R are directly applicable to the essential data preparation phase of any data science project. Furthermore, the course's introduction to version control with Git and GitHub, along with AI-assisted coding, fosters professional development practices crucial in collaborative data science environments, making it a valuable starting point.
Marketing Analyst
A Marketing Analyst leverages data to understand consumer behavior, evaluate campaign effectiveness, and identify market trends to inform marketing strategies. This role requires adeptness in collecting, scrutinizing, and interpreting diverse marketing datasets. The R Programming Essentials with Microsoft Tools course provides fundamental R programming skills for an aspiring Marketing Analyst, including how to handle various data structures and perform calculations, which are directly applicable to analyzing marketing data from channels like social media, websites, or sales. Learning to import, organize, and create straightforward reports within R helps prepare one to generate insights on campaign performance or customer segmentation. The practical, hands-on projects, culminating in a complete R programming project, develop problem-solving abilities vital for tackling real-world marketing challenges with data.
Operations Research Analyst
An Operations Research Analyst uses advanced analytical methods, including statistical modeling and optimization, to improve complex systems and processes across various industries. This role often requires an advanced degree. The R Programming Essentials with Microsoft Tools course provides foundational R programming and data handling skills for individuals interested in becoming an Operations Research Analyst. The course's focus on variables, data structures, conditional statements, and functions is essential for building the analytical models used in operations research. Skills in importing, cleaning, and summarizing data in R are directly applicable to preparing real-world operational datasets. While this course provides foundational programming expertise, it helps build a strong analytical toolkit in R, a language frequently employed in developing and testing optimization algorithms and simulations.
Environmental Data Scientist
An Environmental Data Scientist analyzes complex environmental datasets, such as climate patterns, pollution levels, or ecological surveys, to identify trends, build predictive models, and inform conservation or policy decisions. The R Programming Essentials with Microsoft Tools course provides foundational R programming skills highly valuable for an Environmental Data Scientist. R is a prominent language in environmental science for statistical modeling, spatial analysis, and data visualization. The course's focus on variables, data structures, and functions helps build the core programming ability needed to process diverse environmental data. Learning to import, organize, and tidy data, then perform calculations and create reports, directly supports the critical tasks of preparing and interpreting environmental information. This course helps develop the analytical and programming competencies essential for understanding and addressing intricate environmental challenges.
Healthcare Data Analyst
A Healthcare Data Analyst extracts, processes, and interprets data from electronic health records, clinical trials, or administrative systems to improve patient outcomes, operational efficiency, or public health initiatives. The R Programming Essentials with Microsoft Tools course provides highly relevant skills for a Healthcare Data Analyst. R is widely used in healthcare for statistical analysis, research, and reporting. The course teaches essential R programming concepts, including managing data structures and writing functions, which are crucial for handling complex healthcare datasets. The modules on importing, organizing, and tidying data, along with creating straightforward reports, directly prepare one for the challenges of preparing and summarizing health information. The emphasis on project work and version control further helps develop the structured approach necessary for managing sensitive healthcare data projects effectively and ethically.
Bioinformatician
A Bioinformatician applies computational tools and statistical methods to analyze large biological datasets, such as genomic or proteomic information, advancing our understanding in biology and medicine. This role typically requires an advanced degree. The R Programming Essentials with Microsoft Tools course offers valuable skills for an aspiring Bioinformatician. R is a widely used language in bioinformatics for statistical analysis and data visualization. The course's emphasis on fundamental R programming, including data structures and functions, provides a necessary foundation for processing complex biological data. Skills in importing, organizing, and tidying data are directly transferrable to handling large-scale biological datasets. The practice of version control with Git and GitHub also supports collaborative research and ensures reproducibility in scientific endeavors, making this course a potential starting point for developing the core programming competencies relevant to the field.
Epidemiologist
An Epidemiologist investigates the patterns and causes of diseases and health outcomes in populations, often using statistical methods and programming to analyze public health data. This role typically requires an advanced degree. The R Programming Essentials with Microsoft Tools course offers valuable skills for an aspiring Epidemiologist. R is a powerful tool for statistical analysis and data visualization in public health research. The course helps build a foundation in R programming, covering essential concepts like variables, data structures, and functions, which are crucial for manipulating and analyzing epidemiological datasets. The modules on working with data, specifically importing, organizing, and tidying information, along with creating straightforward reports, directly prepare one for the practical data challenges faced by an Epidemiologist. This course helps develop core computational skills, aiding in the investigation and interpretation of health trends.
Reporting Analyst
A Reporting Analyst is responsible for designing, developing, and maintaining regular reports that present key business metrics and performance indicators to stakeholders. This role involves strong data manipulation and presentation skills. The R Programming Essentials with Microsoft Tools course provides highly relevant skills for a Reporting Analyst. R is an excellent tool for automating data extraction, transformation, and report generation. The course teaches fundamental R programming concepts, including handling various data structures and writing reusable functions, which are pivotal for efficient report creation. The module on working with data, specifically importing, organizing, tidying, and summarizing information, directly prepares one for crafting clear and accurate reports. The final project, involving planning and completing an R programming project to solve a workplace challenge, helps develop the practical skills needed to deliver impactful reporting solutions.

Reading list

We've selected 50 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 R Programming Essentials with Microsoft Tools.
Full-lifecycle AI coding workflows with GitHub Copilot span architectural planning, SQL query generation, automated test suites, and CI/CD scripting. Tom Taulli balances productivity gains with candid coverage of licensing risks, security scanning, and human code review.
The third edition of the essential guide to data wrangling with pandas, NumPy, and Jupyter in Python. Updated for newer versions of the libraries, this book remains a core resource for anyone performing data analysis in Python. It widely used textbook and reference for its clear explanations and practical examples.
Often referred to as "CLRS," this book comprehensive and widely-used textbook for undergraduate and graduate-level algorithms courses. It provides in-depth knowledge of data structures and algorithms, including their analysis and implementation. While it assumes some mathematical familiarity, it's an encyclopedic reference valuable for both learning and as a long-term resource.
Comprehensive guide to the Git version control system. It covers a wide range of topics, including basic concepts, advanced workflows, and best practices. It valuable resource for experienced Git users who want to improve their skills.
Provides an in-depth look at Visual Studio Code, including how to use it to build web applications, mobile apps, and data science projects.
Foundation model architecture, context window management, retrieval-augmented generation, and evaluation pipelines explain how coding assistants like GitHub Copilot operate under the hood. Chip Huyen equips engineers to build and audit production AI developer tooling.
Context structuring, few-shot exemplars, chain-of-thought reasoning, and deterministic output formatting directly improve GitHub Copilot Chat and inline code generation. Phoenix and Taylor provide battle-tested patterns for steering large language models in code-heavy tasks.
Side-by-side benchmarks pitting human problem-solving against GitHub Copilot and ChatGPT reveal exactly where AI coding assistants excel and where subtle bugs hide. David Mertz uses twenty-four tricky regex puzzles to sharpen developer verification and prompt refinement skills.
This comprehensive guide covers all aspects of version control with Git, making it a valuable resource for developers looking to master GitHub's core functionality and underlying concepts.
Offers a comprehensive treatment of data structures and algorithms, suitable for academic settings. It covers various data types and algorithms for sorting, searching, and processing. The book is known for its detailed explanations and includes an online portal with source code, making it a strong resource for solidifying understanding.
Provides a foundational understanding of the fundamental principles of data science and the data-analytic thinking necessary for extracting value from data in a business context. It is highly relevant for undergraduate business analytics programs and working professionals. It serves as a useful reference for understanding the business applications of data analysis and is commonly used as a textbook.
Provides a comprehensive overview of the R programming language, covering its syntax, data structures, and functions. It is an excellent resource for beginners who want to learn the basics of R.
Large language model APIs, function calling, and agentic tool execution share the exact capabilities powering GitHub Copilot Workspace and extensions. Caelen and Blete guide developers through building code-aware assistants and automated developer workflows.
Visual mental models of commits, branches, staging areas, and remote pull requests enable developers to checkpoint and review AI-generated diffs with confidence. Anna Skoulikari provides the essential version-control foundation for collaborating on GitHub.
This textbook is suitable for advanced data structures or introductory graduate-level algorithm analysis courses. It bridges the gap between foundational data structures and more advanced analysis techniques. The book provides a rigorous and in-depth analysis of algorithms and their implementation in C++.
Focuses on the crucial skill of communicating insights from data effectively through compelling visualizations. is highly relevant for all levels, emphasizing the importance of clear and impactful data presentation. It is valuable additional reading that complements technical data analysis skills.
Tactical programming that prioritizes speed over clean abstractions quickly buries teams in complexity—a danger amplified by uncritical use of AI autocomplete. John Ousterhout teaches developers how to design deep modules and clear interfaces before prompting for implementation.
Bridges the gap between statistical theory and its practical application in data science using R and Python. It focuses on the statistical concepts most relevant to data scientists and provides code examples. It's a useful reference and learning resource for those applying statistics in their work.
Offers a new way of thinking about data science and ethics informed by intersectional feminist thought. It challenges existing power structures within data and explores how data can be used to work towards justice. It's highly relevant for contemporary discussions around fairness and bias in data analysis.
Characterization tests, dependency-breaking seams, and sprout methods allow engineers to wrap untested code in safety harnesses before asking GitHub Copilot to refactor it. Michael Feathers provides the defensive testing playbook every AI-augmented developer needs.
Is an excellent starting point for anyone new to data analysis or statistics. It demystifies core statistical concepts without relying heavily on mathematical formulas, making it highly accessible for high school and undergraduate students. It provides a strong foundation in the intuition behind statistical analysis and helps readers understand how data can be used and misused. This is valuable background reading that builds prerequisite knowledge.
Practical guide to using R for data science. It covers topics such as data wrangling, exploratory data analysis, and machine learning.
Practical guide to using R for data analysis and visualization. It covers a wide range of topics, from data wrangling and exploratory data analysis to statistical modeling and graphics.
Practical guide to using R for data analysis and visualization. It covers a wide range of topics, from data wrangling and exploratory data analysis to statistical modeling and graphics.
Problem decomposition, specification writing, and systematic code reading replace rote syntax memorization when programming alongside GitHub Copilot. Computer science educators Leo Porter and Daniel Zingaro teach learners how to prompt, test, and debug AI-generated functions safely.
Provides a guide to creating effective and aesthetically pleasing data visualizations. It delves into the principles behind good visualization design, helping readers make informed choices about how to represent their data. It valuable reference for anyone creating visualizations, from students to professionals.
Provides a comprehensive overview of the Perforce Helix version control system. It covers a wide range of topics, including basic concepts, advanced workflows, and best practices. It valuable resource for experienced Perforce Helix users who want to improve their skills.
Provides a comprehensive introduction to artificial intelligence, covering topics such as machine learning, natural language processing, and computer vision. It is suitable for students with a strong background in mathematics and computer science.
Provides a comprehensive introduction to machine learning, covering topics such as supervised learning, unsupervised learning, and reinforcement learning. It is suitable for students with a strong background in mathematics and computer science.
Provides a comprehensive introduction to deep learning, covering topics such as neural networks, convolutional neural networks, and recurrent neural networks. It is suitable for students with a strong background in mathematics and computer science.
Provides a comprehensive introduction to partial differential equations, covering topics such as the heat equation, the wave equation, and the Laplace equation. It is suitable for students with a strong background in calculus and linear algebra.
Explores the intersection of artificial intelligence and coding, providing practical guidance on how to leverage AI techniques to enhance the coding process. It is written by a renowned expert in artificial intelligence, making it a valuable resource for those seeking a deep understanding of the topic.
IDE integration, inline ghost-text completions, Copilot Chat slash commands, and unit test generation receive hands-on coverage across enterprise languages. Kurt Dowswell demonstrates how to engineer comment-driven prompts, refactor legacy logic, and enforce security guardrails.
Teaches probability and statistics using a computational approach with Python. It's ideal for students and professionals with programming experience who want to understand statistical concepts by doing. It helps solidify understanding through hands-on application.
Step-by-step exercises pair core programming fundamentals with GitHub Copilot completions, chat refactoring, and automated unit testing. Scott McCoy and Mary Delamater show students and working developers how to guide Copilot without surrendering code comprehension.
Securing codebases built with generative assistants requires defending against probabilistic failure modes, injected dependencies, and non-deterministic outputs. Stephen R. Jordan outlines modern application security controls tailored for AI-augmented engineering teams.
Serves as an introduction to designing algorithms and includes a comprehensive catalog of algorithms and data structures. It's a valuable reference for understanding different algorithmic techniques and when to apply them. It is suitable for both students and practitioners.
Concise and easy-to-use reference guide to Git. It covers all the essential commands and concepts, and great resource for quick lookups.
A timeless classic that remains highly relevant today. exposes common ways statistics can be manipulated or misinterpreted, fostering a critical eye essential for anyone working with data. It's valuable for all levels, from high school to professional, as it highlights the importance of data integrity and ethical considerations. This serves as crucial additional reading to develop data literacy.
Offers a very approachable and illustrated guide to algorithms, making it excellent for beginners. It uses diagrams and clear explanations to introduce fundamental concepts like sorting and searching. It's a great starting point before diving into more theoretically dense texts.
This textbook offers a rigorous introduction to data structures and algorithms with implementations in C++. It covers a wide range of topics and is suitable for undergraduate computer science programs.

Share

Help others find this course page by sharing it with your friends and followers:

Similar courses

Here are nine courses similar to R Programming Essentials with Microsoft Tools.
Set up a Continuous Integration (CI) workflow in CIrcleCI
Most relevant
Version Control in node.js: Update Your App with GitHub
Most relevant
Working with Version Control
Most relevant
Branching and Merging in Git
Most relevant
Introduction to Website Development Technologies
Most relevant
Introduction to Web Development
Most relevant
ASP.NET Core MVC - Cross-Platform Development
Most relevant
The Ultimate Git Course - with Applications in Unreal...
Most relevant
Become a Software Engineer
Most relevant

Coursera Buyer Protection & Guarantee

Verified partner policies & purchase protections

Accredited University Partner
Free Trial & Audit

7-Day Free Trial & Free Audit

7-day free trial on monthly Coursera Plus subscriptions; free audit option available on most individual courses to explore video lectures and readings at no charge.

Return & Refund Policy

14-Day Refund Guarantee

14-day refund window on annual Coursera Plus subscriptions and single-course purchases prior to certificate issuance. Monthly subscriptions do not offer refunds once billed.

Buyer Protections

Verified Academic & Industry Partner

Accredited university and industry certifications (e.g. Stanford, Google, IBM, Yale), secure SSL payment processing, and verifiable digital certificate links.

Key Purchase Protections
14-day refund window on annual Coursera Plus and single courses
7-day free trial on monthly Coursera Plus subscriptions
Free course audit option for lectures and readings
Verified certificates from top universities & companies
OpenCourser Verified Partner Policy details last reviewed: 2026-10-22
Our mission

OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.

Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.

Find this site helpful? Tell a friend about us.

Affiliate disclosure

We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.

Your purchases help us maintain our catalog and keep our servers humming without ads.

Thank you for supporting OpenCourser.

© 2016 - 2025 OpenCourser