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Yan Luo
and
Jeff Grossman
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 or IBM Data Analytics with Excel and R Professional Certificate Programs.
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 or IBM Data Analytics with Excel and R Professional Certificate Programs.
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 or IBM Data Analytics with Excel and R Professional Certificate Programs.
In this capstone project, you will take on the role of a data scientist who has recently joined an organization and is 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/yna6j5/r
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What's inside
Learning objectives
- Prepare data for modelling by handling missing values, formatting and normalizing data, binning, and turning categorical values into numeric values.
- Do exploratory data analysis using descriptive statistics, data grouping, data analysis and correlation statistics.
- Prepare data for modelling by handling missing values, formatting and normalizing data, binning, and turning categorical values into numeric values.
- Do exploratory data analysis using descriptive statistics, data grouping, data analysis and correlation statistics.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Introduces you to industry standard data analytics tools and skills like Tidyverse and ggplot2
Provides hands-on experience with real-world datasets, which strengthens foundational techniques
Guides you on organizing and analyzing data, a process that is applicable across industries
Empowers you with problem-solving skills in data science by guiding you to understand and execute on complex datasets
You will learn to format, normalize, and turn categorical values into numeric values, which are essential skills for data wrangling
You will strengthen your data analysis fundamentals through summarizing data and analyzing correlations using descriptive statistics and grouping
Save this course
Save R Data Science Capstone Project to your list so you can find it easily later:
Save
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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 R Data Science Capstone Project with these
activities:
Review Data Wrangling techniques with Tidyverse
Show steps
Refresh your understanding of data wrangling techniques using Tidyverse to prepare for the course's data analysis projects.
Browse courses on
Data Preprocessing
Show steps
-
Review the Tidyverse documentation or online tutorials on data wrangling.
-
Practice using Tidyverse functions for data cleaning, transformation, and aggregation.
-
Complete short coding exercises or quizzes on data wrangling with Tidyverse.
Participate in a study group with other students
Show steps
Study groups provide a supportive environment to discuss course material, ask questions, and work through problems together, reinforcing your understanding.
Browse courses on
Collaboration
Show steps
-
Find or create a study group with other students
-
Meet regularly to discuss the course material
Explore Exploratory Data Analysis with SQL and Tidyverse
Show steps
Enhance your understanding of exploratory data analysis techniques using SQL and Tidyverse to prepare for the course's data visualization and modeling projects.
Browse courses on
Exploratory Data Analysis
Show steps
-
Follow guided tutorials on performing EDA using SQL and Tidyverse.
-
Practice writing SQL queries to extract and summarize data.
-
Create interactive visualizations using ggplot2 and Tidyverse functions.
12 other activities
Expand to see all activities and additional details
Show all 15 activities
Read and summarize a book on data science
Show steps
This will introduce you to core concepts and tools, familiarizing you with the vocabulary and understanding how they are utilized in this field.
View
Data Science for Business: What You Need to...
on Amazon
Show steps
-
Read the book and take notes
-
Summarize the main concepts
Follow online tutorials on data modeling
Show steps
Tutorials will provide a structured way to learn about data modeling techniques and best practices.
Browse courses on
Data Modeling
Show steps
-
Find a tutorial on a specific data modeling technique
-
Follow the tutorial and implement the technique
Complete online coding exercises
Show steps
Coding exercises will give you hands-on experience applying the techniques you learn in the course, deepening your understanding.
Browse courses on
Coding
Show steps
-
Find a platform with coding exercises
-
Complete several exercises on data manipulation and visualization
Data wrangling exercises
Show steps
Sharpen your data wrangling skills by working through a series of guided exercises.
Browse courses on
Data Wrangling
Show steps
-
Clean and prepare real-world datasets using Tidyverse tools.
-
Handle missing values, outliers, and data inconsistencies.
-
Transform and reshape data to suit analysis needs.
Practice Linear Regression Modeling
Show steps
Strengthen your skills in linear regression modeling to enhance your ability to perform modeling tasks in the course's capstone project.
Browse courses on
Linear Regression
Show steps
-
Solve practice problems on fitting and evaluating linear regression models.
-
Implement linear regression algorithms using R or Python.
-
Participate in online challenges or hackathons focused on linear regression.
Exploratory data analysis report
Show steps
Conduct an in-depth exploratory data analysis and create a comprehensive report to gain insights into the project dataset.
Browse courses on
Exploratory Data Analysis
Show steps
-
Use SQL, Tidyverse, and ggplot2 to explore and visualize data distributions, relationships, and trends.
-
Summarize key findings and insights in a written report.
-
Create interactive visualizations to showcase data patterns.
Create a visualization using real-world data
Show steps
Creating visualizations using real-world data will reinforce your ability to analyze and present insights effectively, a crucial skill for data scientists.
Browse courses on
Data Visualization
Show steps
-
Find a dataset of interest
-
Clean and explore the data
-
Create a visualization that communicates the key insights
Develop an Interactive Dashboard
Show steps
Demonstrate your ability to create interactive dashboards that communicate insights from real-world datasets, as required in the course's capstone project.
Browse courses on
Data Visualization
Show steps
-
Gather data from multiple sources and clean it using wrangling techniques.
-
Design and develop interactive visualizations using appropriate tools.
-
Create a user-friendly and visually appealing dashboard.
-
Present the dashboard and its insights to a hypothetical audience.
Build a simple data science project
Show steps
Working on a project will give you a chance to apply everything you've learned in the course and build a tangible artifact that demonstrates your skills, boosting your confidence and knowledge retention.
Browse courses on
Data Science Project
Show steps
-
Brainstorm project ideas
-
Choose a project and gather data
-
Build and test your model
-
Deploy your project
Interactive data dashboard
Show steps
Create an interactive dashboard that presents key data insights and allows users to explore the project dataset.
Browse courses on
Data Visualization
Show steps
-
Design and implement an interactive dashboard using visualization tools.
-
Allow users to filter, sort, and drill down into data.
-
Showcase data patterns, trends, and insights through visualizations.
Write a blog post or article on a data science topic
Show steps
Writing about data science concepts will help you synthesize and internalize your learning, while also contributing to the wider data science community.
Browse courses on
Content Creation
Show steps
-
Choose a topic and research it
-
Write a draft of your blog post or article
-
Get feedback and revise your writing
-
Publish your blog post or article
Data modeling project
Show steps
Build and evaluate a predictive model using linear regression techniques to address the problem posed in the capstone project.
Browse courses on
Data Modeling
Show steps
-
Select and prepare relevant features for model training.
-
Train and evaluate a linear regression model.
-
Interpret model results and evaluate its performance.
-
Communicate findings and insights to stakeholders.
Review Data Wrangling techniques with Tidyverse
Show steps
Refresh your understanding of data wrangling techniques using Tidyverse to prepare for the course's data analysis projects.
Browse courses on
Data Preprocessing
Show steps
Show steps
Show steps
- Review the Tidyverse documentation or online tutorials on data wrangling.
- Practice using Tidyverse functions for data cleaning, transformation, and aggregation.
- Complete short coding exercises or quizzes on data wrangling with Tidyverse.
Participate in a study group with other students
Show steps
Study groups provide a supportive environment to discuss course material, ask questions, and work through problems together, reinforcing your understanding.
Browse courses on
Collaboration
Show steps
Show steps
Show steps
- Find or create a study group with other students
- Meet regularly to discuss the course material
Explore Exploratory Data Analysis with SQL and Tidyverse
Show steps
Enhance your understanding of exploratory data analysis techniques using SQL and Tidyverse to prepare for the course's data visualization and modeling projects.
Browse courses on
Exploratory Data Analysis
Show steps
Show steps
Show steps
- Follow guided tutorials on performing EDA using SQL and Tidyverse.
- Practice writing SQL queries to extract and summarize data.
- Create interactive visualizations using ggplot2 and Tidyverse functions.
12 other activities
Expand to see all activities and additional details
Show all 15 activities
Read and summarize a book on data science
Show steps
This will introduce you to core concepts and tools, familiarizing you with the vocabulary and understanding how they are utilized in this field.
View
Data Science for Business: What You Need to...
on Amazon
Show steps
Show steps
Show steps
- Read the book and take notes
- Summarize the main concepts
Follow online tutorials on data modeling
Show steps
Tutorials will provide a structured way to learn about data modeling techniques and best practices.
Browse courses on
Data Modeling
Show steps
Show steps
Show steps
- Find a tutorial on a specific data modeling technique
- Follow the tutorial and implement the technique
Complete online coding exercises
Show steps
Coding exercises will give you hands-on experience applying the techniques you learn in the course, deepening your understanding.
Browse courses on
Coding
Show steps
Show steps
Show steps
- Find a platform with coding exercises
- Complete several exercises on data manipulation and visualization
Data wrangling exercises
Show steps
Sharpen your data wrangling skills by working through a series of guided exercises.
Browse courses on
Data Wrangling
Show steps
Show steps
Show steps
- Clean and prepare real-world datasets using Tidyverse tools.
- Handle missing values, outliers, and data inconsistencies.
- Transform and reshape data to suit analysis needs.
Practice Linear Regression Modeling
Show steps
Strengthen your skills in linear regression modeling to enhance your ability to perform modeling tasks in the course's capstone project.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Solve practice problems on fitting and evaluating linear regression models.
- Implement linear regression algorithms using R or Python.
- Participate in online challenges or hackathons focused on linear regression.
Exploratory data analysis report
Show steps
Conduct an in-depth exploratory data analysis and create a comprehensive report to gain insights into the project dataset.
Browse courses on
Exploratory Data Analysis
Show steps
Show steps
Show steps
- Use SQL, Tidyverse, and ggplot2 to explore and visualize data distributions, relationships, and trends.
- Summarize key findings and insights in a written report.
- Create interactive visualizations to showcase data patterns.
Create a visualization using real-world data
Show steps
Creating visualizations using real-world data will reinforce your ability to analyze and present insights effectively, a crucial skill for data scientists.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Find a dataset of interest
- Clean and explore the data
- Create a visualization that communicates the key insights
Develop an Interactive Dashboard
Show steps
Demonstrate your ability to create interactive dashboards that communicate insights from real-world datasets, as required in the course's capstone project.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Gather data from multiple sources and clean it using wrangling techniques.
- Design and develop interactive visualizations using appropriate tools.
- Create a user-friendly and visually appealing dashboard.
- Present the dashboard and its insights to a hypothetical audience.
Build a simple data science project
Show steps
Working on a project will give you a chance to apply everything you've learned in the course and build a tangible artifact that demonstrates your skills, boosting your confidence and knowledge retention.
Browse courses on
Data Science Project
Show steps
Show steps
Show steps
- Brainstorm project ideas
- Choose a project and gather data
- Build and test your model
- Deploy your project
Interactive data dashboard
Show steps
Create an interactive dashboard that presents key data insights and allows users to explore the project dataset.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Design and implement an interactive dashboard using visualization tools.
- Allow users to filter, sort, and drill down into data.
- Showcase data patterns, trends, and insights through visualizations.
Write a blog post or article on a data science topic
Show steps
Writing about data science concepts will help you synthesize and internalize your learning, while also contributing to the wider data science community.
Browse courses on
Content Creation
Show steps
Show steps
Show steps
- Choose a topic and research it
- Write a draft of your blog post or article
- Get feedback and revise your writing
- Publish your blog post or article
Data modeling project
Show steps
Build and evaluate a predictive model using linear regression techniques to address the problem posed in the capstone project.
Browse courses on
Data Modeling
Show steps
Show steps
Show steps
- Select and prepare relevant features for model training.
- Train and evaluate a linear regression model.
- Interpret model results and evaluate its performance.
- Communicate findings and insights to stakeholders.
Career center
Learners who complete R Data Science Capstone Project will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
As a Data Scientist, you will use your skills in data wrangling, preparation, analysis, and modeling to solve complex business problems. This course will help you build a strong foundation in these skills, making you a more competitive candidate for Data Scientist positions.
Data Analyst
Data Analyst
As a Data Analyst, you will use your skills in data analysis to identify trends and patterns in data. This course will help you build a strong foundation in data analysis techniques, making you a more competitive candidate for Data Analyst positions.
Business Analyst
Business Analyst
As a Business Analyst, you will use your skills in data analysis to identify opportunities and solve problems for businesses. This course will help you build a strong foundation in data analysis techniques, making you a more competitive candidate for Business Analyst positions.
Business Intelligence Analyst
Business Intelligence Analyst
As a Business Intelligence Analyst, you will use your skills in data analysis to identify trends and patterns in data. This course will help you build a strong foundation in data analysis techniques, making you a more competitive candidate for Business Intelligence Analyst positions.
Financial Analyst
Financial Analyst
As a Financial Analyst, you will use your skills in data analysis to identify trends and patterns in financial data. This course will help you build a strong foundation in data analysis techniques, making you a more competitive candidate for Financial Analyst positions.
Statistician
Statistician
As a Statistician, you will use your skills in data analysis to design and conduct statistical studies. This course will help you build a strong foundation in statistical methods, making you a more competitive candidate for Statistician positions.
Market Researcher
Market Researcher
As a Market Researcher, you will use your skills in data analysis to identify trends and patterns in market data. This course will help you build a strong foundation in data analysis techniques, making you a more competitive candidate for Market Researcher positions.
Machine Learning Engineer
Machine Learning Engineer
As a Machine Learning Engineer, you will use your skills in data analysis and machine learning to build and deploy machine learning models. This course will help you build a strong foundation in machine learning techniques, making you a more competitive candidate for Machine Learning Engineer positions.
Data Engineer
Data Engineer
As a Data Engineer, you will use your skills in data wrangling, preparation, and storage to build and maintain data pipelines. This course will help you build a strong foundation in data engineering techniques, making you a more competitive candidate for Data Engineer positions.
Operations Research Analyst
Operations Research Analyst
As an Operations Research Analyst, you will use your skills in data analysis to identify trends and patterns in operational data. This course will help you build a strong foundation in data analysis techniques, making you a more competitive candidate for Operations Research Analyst positions.
Risk Analyst
Risk Analyst
As a Risk Analyst, you will use your skills in data analysis to identify trends and patterns in risk data. This course will help you build a strong foundation in data analysis techniques, making you a more competitive candidate for Risk Analyst positions.
Database Administrator
Database Administrator
As a Database Administrator, you will use your skills in data management to design, build, and maintain databases. This course will help you build a strong foundation in database management techniques, making you a more competitive candidate for Database Administrator positions.
Web Developer
Web Developer
As a Web Developer, you will use your skills in programming to design, build, and maintain websites. This course may be useful for Web Developers who want to develop data-driven websites.
Actuary
Actuary
As an Actuary, you will use your skills in data analysis to identify trends and patterns in actuarial data. This course will help you build a strong foundation in data analysis techniques, making you a more competitive candidate for Actuary positions.
Software Engineer
Software Engineer
As a Software Engineer, you will use your skills in programming to design, build, and test software applications. This course may be useful for Software Engineers who want to develop data-driven applications.
For more career information including salaries, visit:
OpenCourser.com/course/yna6j5/r
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
R Data Science Capstone Project.
Classic in the field of data science, providing a comprehensive overview of statistical learning methods. It is particularly useful for readers who are interested in learning about the theoretical foundations of data science.
Save
Comprehensive introduction to deep learning, covering the fundamentals of neural networks and their applications. It is particularly useful for readers who are interested in learning about the latest advances in deep learning.
Widely-used textbook for introductory courses in statistical learning. It covers the fundamentals of supervised and unsupervised learning, and provides a solid foundation for understanding the theoretical foundations of data science.
Classic in the field of machine learning, providing a comprehensive overview of the theory and practice of pattern recognition. It is particularly useful for readers who are interested in understanding the mathematical foundations of machine learning.
Save
Comprehensive introduction to machine learning, covering a wide range of topics from linear regression to deep learning. It is particularly useful for readers who are looking for a theoretical foundation in machine learning.
Practical guide to natural language processing with Python, covering a wide range of topics from text preprocessing to natural language understanding. It is particularly useful for readers who are interested in learning how to apply natural language processing techniques to real-world problems.
Practical guide to machine learning with R, covering a wide range of topics from data preprocessing to model evaluation. It is particularly useful for readers who are interested in learning how to apply machine learning techniques to real-world problems.
Practical guide to machine learning with R, covering a wide range of topics from data preprocessing to model evaluation. It is particularly useful for readers who are interested in learning how to apply machine learning techniques to real-world problems.
Practical guide to deep learning with Python, covering a wide range of topics from neural networks to deep learning architectures. It is particularly useful for readers who are interested in learning how to apply deep learning techniques to real-world problems.
Provides a gentle introduction to data science, covering the fundamentals of data collection, analysis, and visualization. It is particularly useful for readers who are new to data science and are looking for a hands-on approach to learning.
Gentle introduction to data science, covering the fundamentals of data collection, analysis, and visualization. It is particularly useful for readers who are new to data science and are looking for a non-technical approach to learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/yna6j5/r
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Effort
1 to 2 hours per week for 4 weeks
Level
Intermediate
Via
edX
Institution
IBM
Instructors
Yan Luo
Jeff Grossman
Language
English
Transcript
English
Good to know
Introduces you to industry standard data analytics tools and skills like Tidyverse and ggplot2
Provides hands-on experience with real-world datasets, which strengthens foundational techniques
Guides you on organizing and analyzing data, a process that is applicable across industries
Empowers you with problem-solving skills in data science by guiding you to understand and execute on complex datasets
You will learn to format, normalize, and turn categorical values into numeric values, which are essential skills for data wrangling
You will strengthen your data analysis fundamentals through summarizing data and analyzing correlations using descriptive statistics and grouping
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