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Ronald Guymon
and
Ashish Khandelwal
Nearly every aspect of business is affected by data analytics. For businesses to capitalize on data analytics, they need leaders who understand the business analytic workflow. This course addresses the human skills gap by providing a foundational set of data processing skills that can be applied to many business settings.
In this course you will use a data analytic language, R, to efficiently prepare business data for analytic tools such as algorithms and visualizations. Cleaning, transforming, aggregating, and reshaping data is a critical, but inconspicuous step in the business analytic workflow.
Read more
Nearly every aspect of business is affected by data analytics. For businesses to capitalize on data analytics, they need leaders who understand the business analytic workflow. This course addresses the human skills gap by providing a foundational set of data processing skills that can be applied to many business settings.
In this course you will use a data analytic language, R, to efficiently prepare business data for analytic tools such as algorithms and visualizations. Cleaning, transforming, aggregating, and reshaping data is a critical, but inconspicuous step in the business analytic workflow.
Read more
Nearly every aspect of business is affected by data analytics. For businesses to capitalize on data analytics, they need leaders who understand the business analytic workflow. This course addresses the human skills gap by providing a foundational set of data processing skills that can be applied to many business settings.
In this course you will use a data analytic language, R, to efficiently prepare business data for analytic tools such as algorithms and visualizations. Cleaning, transforming, aggregating, and reshaping data is a critical, but inconspicuous step in the business analytic workflow.
As you learn how to use R to prepare data for analysis you will gain experience using RStudio, a powerful integrated development environment (IDE), that has many built-in features that simplify coding with R.
As you learn about the business analytic workflow you will also consider the interplay between business principles and data analytics. Specifically, you will explore how delegation, control, and feasibility influence the way in which data is processed. You will also be introduced to examples of business problems that can be solved with data automation and analytics, and methods for communicating data analytic results that do not require copying and pasting from one platform to another.
Register for this course and see more details by visiting:
OpenCourser.com/course/q7b3nh/introduction
What's inside
Syllabus
Course Overview and Module 1: How Do I Get Started Using a Data Analytic Language to Solve Business Problem?
In this module you will be introduced to (1) the role of data analytics in business domains, and (2) R and RStudio.
Read more
Syllabus
Course Overview and Module 1: How Do I Get Started Using a Data Analytic Language to Solve Business Problem?
In this module you will be introduced to (1) the role of data analytics in business domains, and (2) R and RStudio.
Read more
Module 2: How Do I Get to Know My Data and Share It With Others?
In this module you will explore whether data is an asset and how to explore a dataset.
Module 3: How Can I Use Functions to Help with Data Preparation?
This module starts with a discussion on the importance of assembling data for business analytic purposes, and then illustrates data transformation using Tidyverse, a group of useful R packages.
Module 4: How Do I Preprocess Data?
In this module, we will learn how delegation, feasibility, and control influence the level at which data is aggregated. We then focus on performing a variety of data preprocessing tasks to prepare data for use in visualizations and algorithms.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Covers data analytics workflow, which is a skill required almost in every business area
Develops data processing skills and knowledge applicable to many business settings
Teaches how to use R, a data analytic language, and RStudio, an integrated development environment (IDE)
Focuses on data preparation and workflow principles to help learners understand the interplay between business and analytics
Involves hands-on experience in cleaning, transforming, aggregating, and reshaping data using R
May require additional software or hardware, which could pose financial barriers for some learners
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Reviews summary
Demanding r analytics course
Learners say this course is a great way to start learning R, but they say that it requires some background in coding. Students who are new to R are especially positive about the course, and they appreciate the well-prepared assignments. However, the third and fourth modules seem rushed and less well-planned than the first two.
Suitable for beginners, but may be challenging for those without coding background.
"Its a good course to learn business analytics with R"
"Excellent, very intuitive course, a good way to start your journey with R and analytic career."
"This class was great, you get to learn the basics of R, I have no previous experience with programming languages and I feel in a much better place, after taking this class."
Assignments and quizzes are effective learning tools.
"very useful class"
"I enjoyed creating R Markdown documents which I consider an elegant way to present data."
"Everything about the course (instructor, contents, quizzes and assignments) was very fine"
Concise explanations with many examples make learning easier.
"Great course! Concise explanations with many examples, well prepared assignments."
"excellent . the videos are easy to understand."
"The course is fantastic."
Peer review process is not very clear and can lead to unfair or delayed grading.
"Everything about the course (instructor, contents, quizzes and assignments) was very fine except for the last course assignment (the Peer-Graded assignment), which I had to wait for more than 8 days to have it graded."
"The lectures are good, but the peer review process is horrible."
"When I try to review submissions myself I am directed to a project that isn't even the same."
The rushed pace of modules 3 and 4 and the lack of clarity in some lectures could be improved.
"The third and the 4th module felt a bit rushed and did not seemed planned as well as the first two."
"Thanks for the wonderful introduction to data analytics using R. Module 4 needs a bit of work to make the content more relevant."
"This course should disclaim that previous background in coding is strongly recommended."
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 Introduction to Business Analytics with R with these
activities:
Create a comprehensive notes repository
Show steps
Organize and consolidate your course materials, making them easily accessible for review and future reference.
Show steps
-
Gather notes, assignments, quizzes, and exams from all course modules.
-
Categorize and arrange the materials logically, using a note-taking app or physical folders.
-
Review and supplement your notes regularly to reinforce your understanding and identify areas for further study.
Review statistics concepts for data analysis
Show steps
Enhance your understanding of statistical concepts to strengthen your foundation for data analysis.
Browse courses on
Statistics
Show steps
-
Review basic statistics concepts such as probability distributions, hypothesis testing, and regression analysis.
-
Apply statistical techniques to real-world data analysis scenarios.
-
Use statistical software or online tools to perform data analysis and interpretation.
Join a study group
Show steps
Deepen your understanding of data analytic concepts and RStudio by engaging in discussions and problem-solving with peers.
Browse courses on
Data Analytics
Show steps
-
Find or create a study group with fellow learners who share similar interests and goals.
-
Meet regularly to discuss course materials, share insights, and work on projects together.
-
Engage in active listening, asking questions, and seeking clarification to enhance your understanding.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice data cleaning and transformation using R
Show steps
Gain proficiency in data cleaning and transformation techniques to prepare data for analysis.
Browse courses on
Data Cleaning
Show steps
-
Obtain datasets with varying levels of complexity and data quality.
-
Use R functions and packages to clean and transform the data, such as removing duplicates, handling missing values, and standardizing formats.
-
Validate the cleaned and transformed data to ensure its integrity and readiness for analysis.
Solve coding challenges on R coding platforms
Show steps
Sharpen your R programming skills and deepen your understanding of data manipulation and algorithmic techniques.
Browse courses on
R Programming
Show steps
-
Identify relevant coding challenges on platforms like HackerRank or CodeChef that align with course topics.
-
Attempt to solve the challenges independently, and use debugging tools to identify and correct errors.
-
Review solutions and compare your approach with others to learn alternative strategies.
Explore advanced R packages for data visualization
Show steps
Expand your data visualization skills by exploring advanced R packages to create visually appealing and informative data representations.
Browse courses on
Data Visualization
Show steps
-
Identify R packages for data visualization, such as ggplot2, plotly, and dplyr.
-
Follow online tutorials and documentation to learn the syntax and capabilities of these packages.
-
Create interactive and dynamic data visualizations using the learned techniques.
Build a portfolio of data analytics projects
Show steps
Showcase your data analytics skills by creating a portfolio of projects that demonstrate your ability to solve real-world business problems.
Browse courses on
Data Analysis
Show steps
-
Identify real-world business problems that can be addressed using data analytics.
-
Gather and analyze data using appropriate techniques and tools.
-
Present your findings and recommendations in a clear and concise manner.
-
Document your projects, including the problem statement, approach, and results.
Contribute to R open-source projects
Show steps
Gain practical experience in R programming and contribute to the open-source community by participating in R projects.
Browse courses on
Open Source
Show steps
-
Identify R open-source projects that align with your interests and skills.
-
Review the project documentation and codebase to understand the project's goals and structure.
-
Propose and implement improvements or bug fixes to the project.
-
Collaborate with other contributors and maintainers to ensure code quality and project progress.
Create a comprehensive notes repository
Show steps
Organize and consolidate your course materials, making them easily accessible for review and future reference.
Show steps
Show steps
Show steps
- Gather notes, assignments, quizzes, and exams from all course modules.
- Categorize and arrange the materials logically, using a note-taking app or physical folders.
- Review and supplement your notes regularly to reinforce your understanding and identify areas for further study.
Review statistics concepts for data analysis
Show steps
Enhance your understanding of statistical concepts to strengthen your foundation for data analysis.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Review basic statistics concepts such as probability distributions, hypothesis testing, and regression analysis.
- Apply statistical techniques to real-world data analysis scenarios.
- Use statistical software or online tools to perform data analysis and interpretation.
Join a study group
Show steps
Deepen your understanding of data analytic concepts and RStudio by engaging in discussions and problem-solving with peers.
Browse courses on
Data Analytics
Show steps
Show steps
Show steps
- Find or create a study group with fellow learners who share similar interests and goals.
- Meet regularly to discuss course materials, share insights, and work on projects together.
- Engage in active listening, asking questions, and seeking clarification to enhance your understanding.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice data cleaning and transformation using R
Show steps
Gain proficiency in data cleaning and transformation techniques to prepare data for analysis.
Browse courses on
Data Cleaning
Show steps
Show steps
Show steps
- Obtain datasets with varying levels of complexity and data quality.
- Use R functions and packages to clean and transform the data, such as removing duplicates, handling missing values, and standardizing formats.
- Validate the cleaned and transformed data to ensure its integrity and readiness for analysis.
Solve coding challenges on R coding platforms
Show steps
Sharpen your R programming skills and deepen your understanding of data manipulation and algorithmic techniques.
Browse courses on
R Programming
Show steps
Show steps
Show steps
- Identify relevant coding challenges on platforms like HackerRank or CodeChef that align with course topics.
- Attempt to solve the challenges independently, and use debugging tools to identify and correct errors.
- Review solutions and compare your approach with others to learn alternative strategies.
Explore advanced R packages for data visualization
Show steps
Expand your data visualization skills by exploring advanced R packages to create visually appealing and informative data representations.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Identify R packages for data visualization, such as ggplot2, plotly, and dplyr.
- Follow online tutorials and documentation to learn the syntax and capabilities of these packages.
- Create interactive and dynamic data visualizations using the learned techniques.
Build a portfolio of data analytics projects
Show steps
Showcase your data analytics skills by creating a portfolio of projects that demonstrate your ability to solve real-world business problems.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Identify real-world business problems that can be addressed using data analytics.
- Gather and analyze data using appropriate techniques and tools.
- Present your findings and recommendations in a clear and concise manner.
- Document your projects, including the problem statement, approach, and results.
Contribute to R open-source projects
Show steps
Gain practical experience in R programming and contribute to the open-source community by participating in R projects.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Identify R open-source projects that align with your interests and skills.
- Review the project documentation and codebase to understand the project's goals and structure.
- Propose and implement improvements or bug fixes to the project.
- Collaborate with other contributors and maintainers to ensure code quality and project progress.
Career center
Learners who complete Introduction to Business Analytics with R will develop knowledge and skills
that may be useful to these careers:
Business Analyst
Business Analyst
Business Analysts use data analysis to help businesses improve their operations, plan for the future, and make better decisions. This course can help you develop the skills you need to become a Business Analyst, including data cleaning, transformation, and aggregation. You will also learn about the business analytic workflow and how to communicate data analytic results effectively.
Data Analyst
Data Analyst
Data Analysts use data to solve business problems. They collect, clean, and analyze data to identify trends and patterns. This course can help you develop the skills you need to become a Data Analyst, including data cleaning, transformation, and aggregation. You will also learn about the business analytic workflow and how to communicate data analytic results effectively.
Data Scientist
Data Scientist
Data Scientists use data to build models and algorithms that can solve business problems. They use a variety of statistical and machine learning techniques to extract insights from data. This course can help you develop the skills you need to become a Data Scientist, including data cleaning, transformation, and aggregation. You will also learn about the business analytic workflow and how to communicate data analytic results effectively.
Statistician
Statistician
Statisticians use data to understand the world around us. They collect, analyze, and interpret data to draw conclusions about the world. This course can help you develop the skills you need to become a Statistician, including data cleaning, transformation, and aggregation. You will also learn about the business analytic workflow and how to communicate data analytic results effectively.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use data to optimize business processes. They use a variety of mathematical and modeling techniques to identify inefficiencies and improve operations. This course can help you develop the skills you need to become an Operations Research Analyst, including data cleaning, transformation, and aggregation. You will also learn about the business analytic workflow and how to communicate data analytic results effectively.
Financial Analyst
Financial Analyst
Financial Analysts use data to analyze financial markets and make investment recommendations. They use a variety of financial models and techniques to forecast future financial performance. This course can help you develop the skills you need to become a Financial Analyst, including data cleaning, transformation, and aggregation. You will also learn about the business analytic workflow and how to communicate data analytic results effectively.
Marketing Analyst
Marketing Analyst
Marketing Analysts use data to understand consumer behavior and develop marketing campaigns. They use a variety of market research techniques to collect and analyze data about consumers. This course can help you develop the skills you need to become a Marketing Analyst, including data cleaning, transformation, and aggregation. You will also learn about the business analytic workflow and how to communicate data analytic results effectively.
Product Manager
Product Manager
Product Managers use data to develop and manage products. They work with engineers, designers, and marketers to bring products to market. This course can help you develop the skills you need to become a Product Manager, including data cleaning, transformation, and aggregation. You will also learn about the business analytic workflow and how to communicate data analytic results effectively.
Project Manager
Project Manager
Project Managers use data to plan, execute, and track projects. They work with stakeholders to define project goals, develop project plans, and manage project risks. This course can help you develop the skills you need to become a Project Manager, including data cleaning, transformation, and aggregation. You will also learn about the business analytic workflow and how to communicate data analytic results effectively.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts use data to identify trends and opportunities for businesses. They work with business leaders to develop data-driven insights and make better decisions. This course can help you develop the skills you need to become a Business Intelligence Analyst, including data cleaning, transformation, and aggregation. You will also learn about the business analytic workflow and how to communicate data analytic results effectively.
Data Visualization Specialist
Data Visualization Specialist
Data Visualization Specialists use data to create visual representations of data. They work with designers and engineers to create dashboards, charts, and other visual aids that help people understand data. This course can help you develop the skills you need to become a Data Visualization Specialist, including data cleaning, transformation, and aggregation. You will also learn about the business analytic workflow and how to communicate data analytic results effectively.
Software Engineer
Software Engineer
Software Engineers use data to develop and maintain software applications. They work with designers and other engineers to create software that meets the needs of users. This course may be useful for Software Engineers who want to learn more about data cleaning, transformation, and aggregation. You will also learn about the business analytic workflow and how to communicate data analytic results effectively.
Web Developer
Web Developer
Web Developers use data to create and maintain websites. They work with designers and other developers to create websites that are user-friendly and efficient. This course may be useful for Web Developers who want to learn more about data cleaning, transformation, and aggregation. You will also learn about the business analytic workflow and how to communicate data analytic results effectively.
Database Administrator
Database Administrator
Database Administrators use data to manage databases. They work with engineers and other IT professionals to ensure that databases are running smoothly and efficiently. This course may be useful for Database Administrators who want to learn more about data cleaning, transformation, and aggregation. You will also learn about the business analytic workflow and how to communicate data analytic results effectively.
Information Technology Specialist
Information Technology Specialist
Information Technology Specialists use data to manage IT systems. They work with engineers and other IT professionals to ensure that IT systems are running smoothly and efficiently. This course may be useful for Information Technology Specialists who want to learn more about data cleaning, transformation, and aggregation. You will also learn about the business analytic workflow and how to communicate data analytic results effectively.
For more career information including salaries, visit:
OpenCourser.com/course/q7b3nh/introduction
Reading list
We've selected 16 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
Introduction to Business Analytics with R.
Save
A practical guide to using R for data science, including data cleaning, transformation, and visualization.
Save
A comprehensive introduction to the R language, covering data types, data structures, and programming techniques.
A widely-used textbook on statistical learning methods, covering supervised and unsupervised learning, regression, classification, and more.
Save
A collection of recipes for solving common data analysis problems using the Tidyverse, a set of R packages for data science.
Save
A comprehensive guide to using R Markdown to create reproducible documents, reports, and presentations.
A more advanced textbook on statistical learning methods, covering topics such as boosting, bagging, and support vector machines.
Save
Provides a collection of recipes for solving common problems with R. It covers a wide range of topics, from data manipulation to statistical analysis to machine learning.
Provides a practical introduction to data manipulation with R. It covers the basics of data manipulation, as well as more advanced topics such as reshaping data, joining data sets, and working with missing data.
Provides a comprehensive introduction to data visualization with R. It covers the basics of data visualization, as well as more advanced topics such as interactive graphics, geospatial visualization, and statistical graphics.
Provides a comprehensive introduction to business analytics with R. It covers the basics of business analytics, as well as more advanced topics such as data mining, predictive modeling, and optimization.
Provides a comprehensive introduction to predictive modeling with R. It covers the basics of predictive modeling, as well as more advanced topics such as regression, classification, and time series analysis.
Provides a practical introduction to R for data analysis. It covers the basics of R, as well as more advanced topics such as data manipulation, visualization, and modeling.
Save
Provides a comprehensive introduction to R for business. It covers the basics of R, as well as more advanced topics such as data analysis, financial analysis, and marketing analytics.
Save
Provides a gentle introduction to R for beginners. It covers the basics of R, as well as more advanced topics such as data manipulation, visualization, and modeling.
Provides a comprehensive introduction to machine learning with R. It covers the basics of machine learning, as well as more advanced topics such as supervised learning, unsupervised learning, and ensemble methods.
Save
Provides a comprehensive introduction to optimization with R. It covers the basics of optimization, as well as more advanced topics such as linear programming, nonlinear programming, and integer programming.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/q7b3nh/introduction
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Effort
4 weeks of study, 4-6 hours/week
Level
Introductory
Via
Coursera
Institution
University of Illinois at Urbana-Champaign
Instructors
Ronald Guymon
Ashish Khandelwal
Language
English
Transcripts
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آذربایجان دیلی
This course belongs to a
collection
called
Business Analytics. It's comprised of six courses:
Good to know
Covers data analytics workflow, which is a skill required almost in every business area
Develops data processing skills and knowledge applicable to many business settings
Teaches how to use R, a data analytic language, and RStudio, an integrated development environment (IDE)
Focuses on data preparation and workflow principles to help learners understand the interplay between business and analytics
Involves hands-on experience in cleaning, transforming, aggregating, and reshaping data using R
May require additional software or hardware, which could pose financial barriers for some learners
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