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Nilosree Sengupta
This Guided Project “Application of Data Analysis in Business with R Programming” is for the data science learners and enthusiasts of 2 hours long. The learners will learn to discover the underlying patterns and analyse the trends in data with Data Science functions.
Read more
This Guided Project “Application of Data Analysis in Business with R Programming” is for the data science learners and enthusiasts of 2 hours long. The learners will learn to discover the underlying patterns and analyse the trends in data with Data Science functions.
Read more
This Guided Project “Application of Data Analysis in Business with R Programming” is for the data science learners and enthusiasts of 2 hours long. The learners will learn to discover the underlying patterns and analyse the trends in data with Data Science functions.
They will explore a Real world application of Data Analysis in the field of business.They will gain insights that will assist in suggesting recommendations or strategic decision making for optimising business and efficient allocation of resources.
This Guided Project is unique because it is a research study and analysis of data of a pandemic affected period from the year 2020.Hence, learners will study customer purchasing trends of an uncertain period marked by covid-19 where the world economy has been suffering which will also prepare learners for analysing uncertain and uneven trends.
In order to be successful, learners will need prior fundamental knowledge of R programming,Statistics and familiarity with using RStudio.
Register for this course and see more details by visiting:
OpenCourser.com/course/o3r1ip/application
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What's inside
Syllabus
Project Overview
Here you will describe what the project is about...give an overview of what the learner will achieve by completing this project.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Initial introduction explains the importance and analysis functions of data science
Exposes learners to a series of real world business applications of data analysis
The skillset is applicable both for freshers and intermediate level learners in the field of Data Science
Hands-on exercises help in practical implementation of theoretical data science concepts
Emphasizes the core fundamentals of statistical methods and R packages, essential for data analysis
Case study revolves around study of a specific pandemic affected period, providing industry insights
Save this course
Save Application of Data Analysis in Business with R Programming to your list so you can find it easily later:
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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 Application of Data Analysis in Business with R Programming with these
activities:
Review the book "Data Science for Business"
Show steps
Gain broader insights into data science applications in a business context.
View
Data Science for Business: What You Need to...
on Amazon
Show steps
-
Read the book, focusing on chapters relevant to the course
-
Summarize key concepts and practical examples
Review basic R programming and statistics concepts
Show steps
Revisit key R programming and statistical concepts to ensure a solid foundation for this course.
Browse courses on
R Programming
Show steps
-
Review materials from previous coursework or online resources on R programming and statistics
-
Complete practice problems or exercises to reinforce understanding
Practice data analysis exercises
Show steps
Sharpen your data analysis skills by solving practice exercises and challenges.
Browse courses on
Regression Analysis
Show steps
-
Find online resources or textbooks with data analysis exercises
-
Select exercises that cover various aspects of data analysis
-
Solve the exercises, checking your answers against provided solutions or discussing with peers
Four other activities
Expand to see all activities and additional details
Show all seven activities
Create data visualizations to analyze customer trends
Show steps
Apply your data analysis skills to a real-world dataset and create visual representations of customer trends.
Browse courses on
Data Visualization
Show steps
-
Gather and explore the data related to customer purchasing behavior
-
Choose appropriate data visualization techniques to represent the trends
-
Create interactive or static data visualizations using tools like Tableau or Python
-
Analyze the visualizations to identify key insights and patterns
Assist fellow learners in understanding course concepts
Show steps
Strengthen your own understanding by helping others learn and reinforce the concepts covered in the course.
Show steps
-
Identify opportunities to help other learners on discussion forums or online groups
-
Provide clear explanations and examples to clarify concepts
-
Encourage peer discussions and foster a supportive learning environment
Develop a data-driven recommendation report
Show steps
Demonstrate your ability to apply data analysis to generate insights and make recommendations for business optimization.
Browse courses on
Data-Driven Decision Making
Show steps
-
Identify a specific business challenge or opportunity
-
Collect and analyze relevant data
-
Develop data-driven recommendations and support them with evidence
-
Create a clear and concise report presenting your findings and recommendations
Contribute to open-source data analysis projects
Show steps
Enhance your practical skills and immerse yourself in the community by contributing to open-source projects related to data analysis.
Browse courses on
Open Source Software
Show steps
-
Identify open-source data analysis projects on platforms like GitHub
-
Select a project that aligns with your interests and skills
-
Review the project's documentation and contribute in areas such as bug reporting, feature requests, or code improvements
Review the book "Data Science for Business"
Show steps
Gain broader insights into data science applications in a business context.
View
Data Science for Business: What You Need to...
on Amazon
Show steps
Show steps
Show steps
- Read the book, focusing on chapters relevant to the course
- Summarize key concepts and practical examples
Review basic R programming and statistics concepts
Show steps
Revisit key R programming and statistical concepts to ensure a solid foundation for this course.
Browse courses on
R Programming
Show steps
Show steps
Show steps
- Review materials from previous coursework or online resources on R programming and statistics
- Complete practice problems or exercises to reinforce understanding
Practice data analysis exercises
Show steps
Sharpen your data analysis skills by solving practice exercises and challenges.
Browse courses on
Regression Analysis
Show steps
Show steps
Show steps
- Find online resources or textbooks with data analysis exercises
- Select exercises that cover various aspects of data analysis
- Solve the exercises, checking your answers against provided solutions or discussing with peers
Four other activities
Expand to see all activities and additional details
Show all seven activities
Create data visualizations to analyze customer trends
Show steps
Apply your data analysis skills to a real-world dataset and create visual representations of customer trends.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Gather and explore the data related to customer purchasing behavior
- Choose appropriate data visualization techniques to represent the trends
- Create interactive or static data visualizations using tools like Tableau or Python
- Analyze the visualizations to identify key insights and patterns
Assist fellow learners in understanding course concepts
Show steps
Strengthen your own understanding by helping others learn and reinforce the concepts covered in the course.
Show steps
Show steps
Show steps
- Identify opportunities to help other learners on discussion forums or online groups
- Provide clear explanations and examples to clarify concepts
- Encourage peer discussions and foster a supportive learning environment
Develop a data-driven recommendation report
Show steps
Demonstrate your ability to apply data analysis to generate insights and make recommendations for business optimization.
Browse courses on
Data-Driven Decision Making
Show steps
Show steps
Show steps
- Identify a specific business challenge or opportunity
- Collect and analyze relevant data
- Develop data-driven recommendations and support them with evidence
- Create a clear and concise report presenting your findings and recommendations
Contribute to open-source data analysis projects
Show steps
Enhance your practical skills and immerse yourself in the community by contributing to open-source projects related to data analysis.
Browse courses on
Open Source Software
Show steps
Show steps
Show steps
- Identify open-source data analysis projects on platforms like GitHub
- Select a project that aligns with your interests and skills
- Review the project's documentation and contribute in areas such as bug reporting, feature requests, or code improvements
Career center
Learners who complete Application of Data Analysis in Business with R Programming will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data scientists use data analysis techniques to develop predictive models and solve complex business problems. They work in a variety of industries, including finance, healthcare, and retail. This course can be useful in developing the skills needed for a career as a data scientist.
Statistician
Statistician
Statisticians use data analysis techniques to collect, analyze, and interpret data. They work in a variety of industries, including government, academia, and healthcare. This course can be useful in developing the skills needed for a career as a statistician, which may be a good job fit for someone with R programming experience.
Quantitative Analyst
Quantitative Analyst
Quantitative analysts use data analysis techniques to develop and implement trading strategies. They work in investment banks, hedge funds, and asset management firms. This course can be useful in developing the skills needed to succeed as a quantitative analyst, which may be a good job fit for those with experience in R programming.
Data Analyst
Data Analyst
Data analysts use data analysis techniques to extract insights from data. They work in a variety of industries, including finance, healthcare, and retail. This course can be useful in developing the skills needed to succeed as a data analyst.
Data Engineer
Data Engineer
Data engineers design and build systems to store, process, and analyze data. They work in a variety of industries, including tech, finance, and healthcare. This course can be useful in developing the skills needed to succeed as a data engineer, which may be a good job fit for someone with experience in R programming.
Operations Research Analyst
Operations Research Analyst
Operations research analysts use data analysis techniques to improve the efficiency and effectiveness of business processes. They work in a variety of industries, including manufacturing, healthcare, and transportation. This course can be useful in developing the skills needed to succeed as an operations research analyst.
Web Analyst
Web Analyst
Web analysts use data analysis techniques to track website traffic and user behavior. They work in a variety of industries, including marketing, advertising, and e-commerce. This course can be useful in developing the skills needed to succeed as a web analyst, which may be a good job fit for someone with experience in R programming.
Business Analyst
Business Analyst
Business analysts use data analysis techniques to identify and solve business problems. They work in a variety of industries, including finance, healthcare, and retail. This course can be useful in developing the skills needed to be a successful business analyst.
Market Researcher
Market Researcher
Market researchers use data analysis techniques to understand consumer behavior and market trends. They work in a variety of industries, including marketing, advertising, and product development. This course can be useful in developing the skills needed to succeed as a market researcher.
Systems Analyst
Systems Analyst
Systems analysts use data analysis techniques to design and implement computer systems. They work in a variety of industries, including healthcare, finance, and manufacturing. This course can be useful in developing the skills needed to succeed as a systems analyst, which may be a good job fit for someone with experience in R programming.
User Experience Researcher
User Experience Researcher
User experience researchers use data analysis techniques to understand how users interact with products and services. They work in a variety of industries, including tech, retail, and healthcare. This course can be useful in developing the skills needed to succeed as a user experience researcher, which may be a good job fit for someone with experience in R programming.
Financial Analyst
Financial Analyst
Financial analysts use data analysis techniques to evaluate investments and make financial recommendations. They work in a variety of industries, including investment banking, asset management, and insurance. This course can be useful in understanding the statistical concepts and techniques used by financial analysts.
Risk Manager
Risk Manager
Risk managers use data analysis techniques to identify and manage risk. They work in a variety of industries, including finance, healthcare, and energy. This course can be useful in understanding the statistical concepts and techniques used by risk managers.
Teacher
Teacher
Teachers use data analysis techniques to assess student learning and improve teaching methods. They work in a variety of educational settings, including K-12 schools, colleges, and universities. This course can be useful in developing the skills needed to succeed as a teacher, which may be a good job fit for someone with a passion for education and R programming experience.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risk and uncertainty. They are employed by insurance companies, consulting firms, and government agencies. This course can be useful in understanding the statistical concepts and techniques used by actuaries.
For more career information including salaries, visit:
OpenCourser.com/course/o3r1ip/application
Reading list
We've selected 14 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
Application of Data Analysis in Business with R Programming.
Provides a comprehensive overview of data science techniques and their applications in business. It valuable resource for learners who want to gain a deeper understanding of the concepts covered in the course.
Save
Practical guide to data analysis and visualization using R, with a focus on real-world business applications. It provides step-by-step instructions and examples, making it a valuable resource for learners of all levels.
Save
Provides a comprehensive overview of deep learning algorithms. It covers a wide range of topics, from convolutional neural networks to recurrent neural networks. It valuable resource for learners who want to gain a deeper understanding of the theoretical foundations of deep learning.
Provides a practical introduction to machine learning for business users. It covers a wide range of topics, from supervised learning to unsupervised learning. It valuable resource for learners who want to learn how to use machine learning to solve business problems.
Provides a practical introduction to machine learning using TensorFlow. It covers a wide range of topics, from data wrangling to machine learning. It valuable resource for learners who want to learn how to use TensorFlow for data analysis.
Save
Provides a practical guide to using data visualization to communicate data insights to others. It covers a wide range of topics, from chart types to data storytelling. It valuable resource for learners who want to learn how to use data visualization to make their presentations more effective.
Save
Provides a practical guide to using data storytelling to communicate data insights to others. It covers a wide range of topics, from data visualization to storytelling techniques. It valuable resource for learners who want to learn how to use data storytelling to make their presentations more effective.
Provides a practical introduction to data science using Python. It covers a wide range of topics, from data wrangling to machine learning. It valuable resource for learners who want to learn how to use Python for data analysis.
Provides a practical introduction to machine learning using R. It covers a wide range of topics, from data wrangling to machine learning. It valuable resource for learners who want to learn how to use R for data analysis.
Provides a comprehensive overview of business intelligence and data mining techniques. It covers a wide range of topics, from data warehousing to data visualization. It valuable resource for learners who want to gain a deeper understanding of the concepts covered in the course.
Save
Provides a solid foundation in statistical data analysis using R, making it a useful reference for learners of all levels. It covers topics such as data exploration, hypothesis testing, and regression analysis.
Provides a comprehensive overview of business statistics using Excel, with a focus on practical applications. It useful supplement to this course for learners who want to enhance their understanding of statistical concepts.
Save
Provides a practical guide to data analytics for business users, covering topics such as data visualization, data mining, and statistical analysis. It valuable resource for learners who want to learn how to use data to make better business decisions.
Save
Provides a comprehensive overview of predictive analytics, covering topics such as data mining, machine learning, and statistical modeling. It valuable resource for learners who want to learn how to use these tools to predict future events.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/o3r1ip/application
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Nilosree Sengupta
Language
English
Good to know
Initial introduction explains the importance and analysis functions of data science
Exposes learners to a series of real world business applications of data analysis
The skillset is applicable both for freshers and intermediate level learners in the field of Data Science
Hands-on exercises help in practical implementation of theoretical data science concepts
Emphasizes the core fundamentals of statistical methods and R packages, essential for data analysis
Case study revolves around study of a specific pandemic affected period, providing industry insights
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