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Jeff Grossman
and
Yan Luo
In this capstone course, you will apply various data science skills and techniques that you have learned as part of the previous courses in the IBM Data Science with R Specialization or IBM Data Analytics with Excel and R Professional Certificate.
Read more
In this capstone course, you will apply various data science skills and techniques that you have learned as part of the previous courses in the IBM Data Science with R Specialization or IBM Data Analytics with Excel and R Professional Certificate.
Read more
In this capstone course, you will apply various data science skills and techniques that you have learned as part of the previous courses in the IBM Data Science with R Specialization or IBM Data Analytics with Excel and R Professional Certificate.
For this project, you will assume the role of a Data Scientist who has recently joined an organization and be presented with a challenge that requires data collection, analysis, basic hypothesis testing, visualization, and modeling to be performed on real-world datasets. You will collect and understand data from multiple sources, conduct data wrangling and preparation with Tidyverse, perform exploratory data analysis with SQL, Tidyverse and ggplot2, model data with linear regression, create charts and plots to visualize the data, and build an interactive dashboard.
The project will culminate with a presentation of your data analysis report, with an executive summary for the various stakeholders in the organization.
Register for this course and see more details by visiting:
OpenCourser.com/course/msj6by/data
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What's inside
Syllabus
Module 1 - Capstone Overview and Data Collection
Module 2 - Data Wrangling
Module 3: Performing Exploratory Data Analysis with SQL, Tidyverse & ggplot2
Read more
Syllabus
Module 1 - Capstone Overview and Data Collection
Module 2 - Data Wrangling
Module 3: Performing Exploratory Data Analysis with SQL, Tidyverse & ggplot2
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Develops data science skills and techniques, which are core skills in industry
Taught by recognized instructors Yan Luo and Jeff Grossman
Emphasizes hands-on labs and interactive materials
Focuses on real-world datasets and projects, which is highly relevant to industry
Culminates in a presentation and report, developing professional skills
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Reviews summary
Practical r data science capstone project
According to learners, this course is a highly valuable capstone experience for anyone looking to apply their R data science skills in a real-world project context. Many find the course to be challenging yet incredibly rewarding, serving as an excellent culmination of prior learning. The emphasis on hands-on activities, particularly the development of an R Shiny dashboard and preparing a data analysis report for stakeholders, is frequently highlighted as a major strength. While largely positive, some students mention that the course assumes significant prior knowledge of R, making it potentially difficult for absolute beginners.
Provides a tangible project for a data science portfolio.
"I feel confident presenting this on my resume. Highly recommend for those who completed the prior specialization courses."
"This is exactly what I needed to bridge the gap between theory and practical application."
"A solid course for portfolio building, especially with the R Shiny component."
Teaches practical skills like R Shiny and stakeholder reports.
"Loved the R Shiny dashboard creation, felt very practical and useful for my portfolio."
"The emphasis on presenting insights to stakeholders is a crucial real-world skill that's often overlooked."
"The final dashboard was a great takeaway. It definitely prepares you for real data science work."
Reinforces skills through a practical, real-world project.
"The capstone project really tied everything together from the specialization. It was challenging but immensely rewarding."
"Applying all the skills to a real-world scenario and building that R Shiny dashboard was incredibly valuable."
"I found the data wrangling and EDA modules to be well-structured. The practical application is excellent."
Some learners report occasional issues with lab environments.
"My only minor gripe was occasional issues with the lab environment setup, but usually, support threads helped resolve them quickly."
"Lab environments were constantly buggy, making it hard to focus on the project."
"I encountered several small issues that slowed me down, which were mostly technical."
Requires strong R background; challenging for beginners.
"I found this project extremely challenging without a very strong background in R. Felt like a huge jump."
"The course assumes too much prior knowledge and doesn't adequately support beginners."
"It's definitely a capstone, so you need strong prior knowledge. If you don't have a solid R background, it can be quite daunting."
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 Science with R - Capstone Project with these
activities:
Review and Organize Course Materials
Show steps
Enhance retention by organizing and reviewing course materials
Show steps
-
Gather notes, assignments, and quizzes from the course
-
Review and highlight important concepts
-
Create summaries and cheat sheets
R Programming
Show steps
This course requires a solid foundation in R programming
Browse courses on
R Programming
Show steps
-
Review R syntax and data structures
-
Practice data manipulation and cleaning
-
Explore R functions and packages
Introduction to Statistical Learning
Show steps
Provides a comprehensive foundation in statistical learning methods
View
Predictive Analytics for Toxicology:...
on Amazon
Show steps
-
Read chapters relevant to the course material
-
Review key concepts and equations
-
Apply concepts to real-world examples
Six other activities
Expand to see all activities and additional details
Show all nine activities
IBM Data Science with R Specialization
Show steps
This course will reinforce concepts learned throughout the IBM Data Science with R Specialization
Browse courses on
Data Analysis
Show steps
-
Complete the R Programming Fundamentals course
-
Explore the Data Science Tools course
-
Review the Machine Learning with R course
Exploratory Data Analysis Project
Show steps
Develop skills in data exploration, data visualization, and hypothesis testing
Browse courses on
Exploratory Data Analysis
Show steps
-
Perform data cleaning and preparation
-
Identify a dataset for analysis
-
Explore data using SQL, Tidyverse, and ggplot2
-
Create visualizations to present findings
-
Write a report summarizing results
LeetCode Problems
Show steps
Sharpen problem-solving skills and reinforce coding concepts
Show steps
-
Select problems based on difficulty and relevance
-
Solve problems using the R programming language
-
Review solutions and optimize code
Kaggle Competitions
Show steps
Gain practical experience in real-world data science challenges
Browse courses on
Kaggle
Show steps
-
Identify a competition that aligns with interests
-
Download the dataset and familiarize with the problem
-
Develop and implement a model
-
Submit results and analyze performance
-
Read and discuss solutions from others
R Shiny Dashboard App
Show steps
Develop skills in creating interactive dashboards for data visualization
Browse courses on
R Shiny
Show steps
-
Design the dashboard layout and functionality
-
Write R code for data manipulation and visualization
-
Deploy the dashboard and share with others
Peer Mentoring
Show steps
Reinforce understanding by helping others learn
Show steps
-
Identify a peer who needs assistance
-
Provide guidance and support on specific topics
-
Facilitate discussions and Q&A sessions
Review and Organize Course Materials
Show steps
Enhance retention by organizing and reviewing course materials
Show steps
Show steps
Show steps
- Gather notes, assignments, and quizzes from the course
- Review and highlight important concepts
- Create summaries and cheat sheets
R Programming
Show steps
This course requires a solid foundation in R programming
Browse courses on
R Programming
Show steps
Show steps
Show steps
- Review R syntax and data structures
- Practice data manipulation and cleaning
- Explore R functions and packages
Introduction to Statistical Learning
Show steps
Provides a comprehensive foundation in statistical learning methods
View
Predictive Analytics for Toxicology:...
on Amazon
Show steps
Show steps
Show steps
- Read chapters relevant to the course material
- Review key concepts and equations
- Apply concepts to real-world examples
Six other activities
Expand to see all activities and additional details
Show all nine activities
IBM Data Science with R Specialization
Show steps
This course will reinforce concepts learned throughout the IBM Data Science with R Specialization
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Complete the R Programming Fundamentals course
- Explore the Data Science Tools course
- Review the Machine Learning with R course
Exploratory Data Analysis Project
Show steps
Develop skills in data exploration, data visualization, and hypothesis testing
Browse courses on
Exploratory Data Analysis
Show steps
Show steps
Show steps
- Perform data cleaning and preparation
- Identify a dataset for analysis
- Explore data using SQL, Tidyverse, and ggplot2
- Create visualizations to present findings
- Write a report summarizing results
LeetCode Problems
Show steps
Sharpen problem-solving skills and reinforce coding concepts
Show steps
Show steps
Show steps
- Select problems based on difficulty and relevance
- Solve problems using the R programming language
- Review solutions and optimize code
Kaggle Competitions
Show steps
Gain practical experience in real-world data science challenges
Browse courses on
Kaggle
Show steps
Show steps
Show steps
- Identify a competition that aligns with interests
- Download the dataset and familiarize with the problem
- Develop and implement a model
- Submit results and analyze performance
- Read and discuss solutions from others
R Shiny Dashboard App
Show steps
Develop skills in creating interactive dashboards for data visualization
Browse courses on
R Shiny
Show steps
Show steps
Show steps
- Design the dashboard layout and functionality
- Write R code for data manipulation and visualization
- Deploy the dashboard and share with others
Peer Mentoring
Show steps
Reinforce understanding by helping others learn
Show steps
Show steps
Show steps
- Identify a peer who needs assistance
- Provide guidance and support on specific topics
- Facilitate discussions and Q&A sessions
Career center
Learners who complete Data Science with R - Capstone Project will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
As a Data Scientist, you will be responsible for using data to solve business problems. This course will help you build a solid foundation in data science, including data collection, wrangling, and analysis. You will also learn how to use R to build predictive models and create data visualizations. These skills are essential for success in data science, and this course will give you the knowledge and experience you need to succeed in this field.
Data Analyst
Data Analyst
As a Data Analyst, you will be responsible for collecting, cleaning, and analyzing data to identify trends and patterns. This course will help you build a strong foundation in data analysis, including data wrangling, exploratory data analysis, and visualization. You will also learn how to use R to perform statistical analysis and build predictive models. These skills are essential for success in data analysis, and this course will give you the knowledge and experience you need to succeed in this field.
Business Analyst
Business Analyst
As a Business Analyst, you will be responsible for using data to improve business processes. This course will help you build a strong foundation in business analysis, including data collection, wrangling, and analysis. You will also learn how to use R to build predictive models and create data visualizations. These skills are essential for success in business analysis, and this course will give you the knowledge and experience you need to succeed in this field.
Statistician
Statistician
As a Statistician, you will be responsible for collecting, analyzing, and interpreting data. This course will help you build a strong foundation in statistics, including data collection, wrangling, and analysis. You will also learn how to use R to perform statistical analysis and build predictive models. These skills are essential for success in statistics, and this course will give you the knowledge and experience you need to succeed in this field.
Machine Learning Engineer
Machine Learning Engineer
As a Machine Learning Engineer, you will be responsible for building and deploying machine learning models. This course will help you build a strong foundation in machine learning, including data wrangling, feature engineering, and model building. You will also learn how to use R to build and deploy machine learning models. These skills are essential for success in machine learning engineering, and this course will give you the knowledge and experience you need to succeed in this field.
Data Engineer
Data Engineer
As a Data Engineer, you will be responsible for building and maintaining data pipelines. This course will help you build a strong foundation in data engineering, including data wrangling, data transformation, and data storage. You will also learn how to use R to build and maintain data pipelines. These skills are essential for success in data engineering, and this course will give you the knowledge and experience you need to succeed in this field.
Software Engineer
Software Engineer
As a Software Engineer, you will be responsible for designing, developing, and maintaining software applications. This course will help you build a strong foundation in software engineering, including data structures, algorithms, and design patterns. You will also learn how to use R to develop software applications. These skills are essential for success in software engineering, and this course will give you the knowledge and experience you need to succeed in this field.
Web Developer
Web Developer
As a Web Developer, you will be responsible for designing, developing, and maintaining websites. This course will help you build a strong foundation in web development, including HTML, CSS, and JavaScript. You will also learn how to use R to develop web applications. These skills are essential for success in web development, and this course will give you the knowledge and experience you need to succeed in this field.
Database Administrator
Database Administrator
As a Database Administrator, you will be responsible for managing and maintaining databases. This course will help you build a strong foundation in database administration, including database design, data storage, and data security. You will also learn how to use R to manage and maintain databases. These skills are essential for success in database administration, and this course will give you the knowledge and experience you need to succeed in this field.
Financial Analyst
Financial Analyst
As a Financial Analyst, you will be responsible for analyzing financial data to make investment decisions. This course will help you build a strong foundation in financial analysis, including financial modeling, data analysis, and investment strategies. You will also learn how to use R to analyze financial data. These skills are essential for success in financial analysis, and this course will give you the knowledge and experience you need to succeed in this field.
Marketing Analyst
Marketing Analyst
As a Marketing Analyst, you will be responsible for analyzing marketing data to improve marketing campaigns. This course will help you build a strong foundation in marketing analytics, including data collection, data analysis, and data visualization. You will also learn how to use R to analyze marketing data. These skills are essential for success in marketing analytics, and this course will give you the knowledge and experience you need to succeed in this field.
Operations Research Analyst
Operations Research Analyst
As an Operations Research Analyst, you will be responsible for using data to improve business operations. This course will help you build a strong foundation in operations research, including data analysis, optimization, and simulation. You will also learn how to use R to solve operations research problems. These skills are essential for success in operations research, and this course will give you the knowledge and experience you need to succeed in this field.
Risk Analyst
Risk Analyst
As a Risk Analyst, you will be responsible for identifying and assessing risks. This course will help you build a strong foundation in risk analysis, including data analysis, risk modeling, and risk management. You will also learn how to use R to perform risk analysis. These skills are essential for success in risk analysis, and this course will give you the knowledge and experience you need to succeed in this field.
Insurance Analyst
Insurance Analyst
As an Insurance Analyst, you will be responsible for analyzing insurance data to make insurance decisions. This course will help you build a strong foundation in insurance analysis, including data analysis, insurance modeling, and insurance regulations. You will also learn how to use R to analyze insurance data. These skills are essential for success in insurance analysis, and this course will give you the knowledge and experience you need to succeed in this field.
Actuary
Actuary
As an Actuary, you will be responsible for analyzing financial data to assess risk and make financial decisions. This course will help you build a strong foundation in actuarial science, including data analysis, financial modeling, and risk management. You will also learn how to use R to analyze financial data. These skills are essential for success in actuarial science, and this course will give you the knowledge and experience you need to succeed in this field.
For more career information including salaries, visit:
OpenCourser.com/course/msj6by/data
Reading list
We've selected 11 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
Data Science with R - Capstone Project.
Classic text on statistical learning. It would be a valuable resource for students who want to learn more about the theoretical foundations of data science.
Save
Provides a comprehensive overview of R, a popular programming language for data science. It would be a valuable resource for students who want to learn more about data science with R.
Provides a comprehensive overview of statistical learning methods. It would be a valuable resource for students who want to learn more about data modeling and machine learning.
Provides a comprehensive overview of data science, covering topics such as data collection, wrangling, analysis, and visualization. It would be a valuable resource for students who want to learn more about the fundamentals of data science.
Save
Focuses on deep learning with R. It would be a useful resource for students who want to learn more about deep learning algorithms and their applications.
Save
Focuses on natural language processing with R. It would be a useful resource for students who want to learn more about NLP techniques and their applications.
Save
Focuses on time series analysis with R. It would be a useful resource for students who want to learn more about time series analysis techniques and their applications.
Focuses on spatial data analysis with R. It would be a useful resource for students who want to learn more about spatial data analysis techniques and their applications.
Focuses on ggplot2, a popular R library for data visualization. It would be a useful resource for students who want to learn more about data visualization with R.
Save
Focuses on data mining with R. It would be a useful resource for students who want to learn more about data mining techniques and their applications.
Focuses on machine learning with R. It would be a useful resource for students who want to learn more about machine learning algorithms and their applications.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/msj6by/data
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Level
Intermediate
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Institution
IBM
Instructors
Jeff Grossman
Yan Luo
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