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Nilosree Sengupta
This Guided Project "Data Science for Business with R programming "is for the business professionals to assist them in solving the basic questions of Economics which are essential for any business, that is, what to produce and for whom to produce, by...
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This Guided Project "Data Science for Business with R programming "is for the business professionals to assist them in solving the basic questions of Economics which are essential for any business, that is, what to produce and for whom to produce, by...
Read more
This Guided Project "Data Science for Business with R programming "is for the business professionals to assist them in solving the basic questions of Economics which are essential for any business, that is, what to produce and for whom to produce, by analysing the market forces and the trends, with the data science tools. This project will guide the business professionals in Real world strategic decision making, based on data and direct towards effective allocation of resources for business.
This Guided Project was created by a Coursera community member.
Register for this course and see more details by visiting:
OpenCourser.com/course/c4zygw/data
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Business professionals seeking to make real-world data-driven decisions
Focuses on the application of data science techniques to address fundamental economic questions in business
Taught by Nilosree Sengupta, an experienced instructor in the field
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Reviews summary
Hands-on data science for business with r
This highly praised course is a comprehensive blend of Data Science, Economics, and Business Analytics. It imparts advanced skills in handling Data Science tools, enabling learners to gain insights from data and make strategic business decisions using R Programming.
Course is tailored towards business professionals and entrepreneurs.
"This course is titled "Data Science for Business with R programming "is for the business professionals to assist them in solving the basic questions of Economics which are essential for any business, that is, what to produce and for whom to produce, by analysing the market forces and the trends, with the data science tools."
"It is an outstanding wholesome course, combined of several domains. All the domains beautifully organized, flowing with one another."
Delivers a comprehensive course experience within a concise duration.
"Synthesis of mega domains of advanced level into 1 Guided Project under 2 hours indeed makes it a Noteworthy course."
"Quantitative Business Analytics is performed pretty well.Precise in 2 hours, yet elaborate and wholesome."
Provides opportunities for hands-on practice and assessments.
"Excellent course , very helpful . It really helped my knowledge for Business Analytics . self assessments helped big time to build up clarity is the subject ."
"Very impressive experience in working with Data Science packages Dplyr and ggplot2 of R Programming."
Covers a wide range of topics, making it highly informative.
"Top-notch and Well designed Course, covering multiple extensive domains from Data Science to Business Management with Statistical Programming for Strategic decision making, within 2 hours."
"Manifold course with brilliant composition of Data Science, Economics, Statistics, Business Analytics for understanding demand and decision making."
Course is based on real-world datasets and scenarios.
"The course is based on a real world dataset and helps you understand the real world scenarios better than most courses out there."
"Analysis of market forces during a pandemic situation will boost the learners' skills to analyse market forces any situation."
Course covers advanced topics in a comprehensive and accessible manner.
"Advanced subjects linked and explained in a comprehensive way."
"This is an Advanced Level Course."
Instructor is knowledgeable and provides clear explanations.
"The instructor's teaching style is great."
"The instructor Nilosree Sengupta has taught beautifully."
Gains skills in Data Science, Economics, Statistics, and Business Analytics.
"It increased my skills of handling Data Science tools to draw insight from data; And design,model, transform business strategically with R Programming."
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 for Business with R Programming with these
activities:
Review Basic Statistics Concepts
Show steps
Refreshing your basic statistics knowledge will help you better understand the statistical methods used in this course.
Browse courses on
Statistics
Show steps
-
Review your notes or textbooks on basic statistics concepts.
-
Work through some practice problems to test your understanding.
Read 'Data Science for Business'
Show steps
Reviewing this book will provide you with an in-depth understanding of the fundamental concepts and applications of data science in a business context.
View
Data Science for Business: What You Need to...
on Amazon
Show steps
-
Read chapters 1-3 to gain an overview of data science for business.
-
Read chapters 4-6 to learn about data collection and preparation.
-
Read chapters 7-9 to learn about data analysis and modeling.
Participate in a Data Science Study Group
Show steps
Participating in a study group will provide opportunities to discuss course concepts, work on projects together, and learn from your peers.
Show steps
-
Find or create a study group with other students in the course.
-
Meet regularly to discuss course material, share ideas, and work on assignments.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Create a Resource List on Data Science Tools and Techniques
Show steps
Creating a resource list will help you organize and expand your knowledge of data science tools and techniques, ensuring you have a valuable reference point for your future work.
Browse courses on
Data Science Tools
Show steps
-
Identify and research different data science tools and techniques.
-
Create a document or spreadsheet to organize your findings.
-
Include information such as tool descriptions, use cases, and links to resources.
Practice Market Analysis Exercises
Show steps
Solving real-world market analysis exercises will reinforce your understanding of the concepts and techniques covered in this course, helping you develop strong analytical skills.
Browse courses on
Market Analysis
Show steps
-
Identify a specific market or industry.
-
Gather and analyze relevant data on market size, competition, and customer demographics.
-
Identify trends and patterns in the data.
-
Develop and test hypotheses about market behavior.
Complete the Coursera Tutorial on Data Visualization
Show steps
Completing this tutorial will enhance your skills in visualizing data effectively, which is crucial for communicating your findings and making data-driven decisions.
Browse courses on
Data Visualization
Show steps
-
Sign up for the Coursera tutorial on Data Visualization.
-
Watch the video lectures and complete the interactive exercises.
-
Create your own data visualizations using the techniques you learned.
Create a Data-Driven Business Case
Show steps
Creating a data-driven business case will allow you to apply the skills learned in this course to a real-world problem. You will learn how to communicate your findings and recommendations effectively.
Browse courses on
Data Analysis
Show steps
-
Identify a business problem or opportunity.
-
Collect and analyze relevant data.
-
Develop insights and recommendations based on your analysis.
-
Create a compelling presentation or report outlining your findings.
Contribute to an Open-Source Data Science Project
Show steps
Contributing to an open-source project will allow you to apply your skills on a real-world project, collaborate with others, and learn from the community.
Browse courses on
Open Source
Show steps
-
Find an open-source data science project on GitHub or other platforms.
-
Identify an area where you can contribute your skills.
-
Submit a pull request with your contributions.
Volunteer as a Data Analyst at a Non-Profit Organization
Show steps
Volunteering as a data analyst will provide you with practical experience applying your skills to make a positive impact on society.
Browse courses on
Data Analysis
Show steps
-
Identify non-profit organizations that are seeking data analysts.
-
Inquire about volunteer opportunities and discuss how you can contribute your skills.
-
Work with the organization to define a project or task that meets their needs.
Review Basic Statistics Concepts
Show steps
Refreshing your basic statistics knowledge will help you better understand the statistical methods used in this course.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Review your notes or textbooks on basic statistics concepts.
- Work through some practice problems to test your understanding.
Read 'Data Science for Business'
Show steps
Reviewing this book will provide you with an in-depth understanding of the fundamental concepts and applications of data science in a business context.
View
Data Science for Business: What You Need to...
on Amazon
Show steps
Show steps
Show steps
- Read chapters 1-3 to gain an overview of data science for business.
- Read chapters 4-6 to learn about data collection and preparation.
- Read chapters 7-9 to learn about data analysis and modeling.
Participate in a Data Science Study Group
Show steps
Participating in a study group will provide opportunities to discuss course concepts, work on projects together, and learn from your peers.
Show steps
Show steps
Show steps
- Find or create a study group with other students in the course.
- Meet regularly to discuss course material, share ideas, and work on assignments.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Create a Resource List on Data Science Tools and Techniques
Show steps
Creating a resource list will help you organize and expand your knowledge of data science tools and techniques, ensuring you have a valuable reference point for your future work.
Browse courses on
Data Science Tools
Show steps
Show steps
Show steps
- Identify and research different data science tools and techniques.
- Create a document or spreadsheet to organize your findings.
- Include information such as tool descriptions, use cases, and links to resources.
Practice Market Analysis Exercises
Show steps
Solving real-world market analysis exercises will reinforce your understanding of the concepts and techniques covered in this course, helping you develop strong analytical skills.
Browse courses on
Market Analysis
Show steps
Show steps
Show steps
- Identify a specific market or industry.
- Gather and analyze relevant data on market size, competition, and customer demographics.
- Identify trends and patterns in the data.
- Develop and test hypotheses about market behavior.
Complete the Coursera Tutorial on Data Visualization
Show steps
Completing this tutorial will enhance your skills in visualizing data effectively, which is crucial for communicating your findings and making data-driven decisions.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Sign up for the Coursera tutorial on Data Visualization.
- Watch the video lectures and complete the interactive exercises.
- Create your own data visualizations using the techniques you learned.
Create a Data-Driven Business Case
Show steps
Creating a data-driven business case will allow you to apply the skills learned in this course to a real-world problem. You will learn how to communicate your findings and recommendations effectively.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Identify a business problem or opportunity.
- Collect and analyze relevant data.
- Develop insights and recommendations based on your analysis.
- Create a compelling presentation or report outlining your findings.
Contribute to an Open-Source Data Science Project
Show steps
Contributing to an open-source project will allow you to apply your skills on a real-world project, collaborate with others, and learn from the community.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Find an open-source data science project on GitHub or other platforms.
- Identify an area where you can contribute your skills.
- Submit a pull request with your contributions.
Volunteer as a Data Analyst at a Non-Profit Organization
Show steps
Volunteering as a data analyst will provide you with practical experience applying your skills to make a positive impact on society.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Identify non-profit organizations that are seeking data analysts.
- Inquire about volunteer opportunities and discuss how you can contribute your skills.
- Work with the organization to define a project or task that meets their needs.
Career center
Learners who complete Data Science for Business with R Programming will develop knowledge and skills
that may be useful to these careers:
Business Intelligence Analyst
Business Intelligence Analyst
A Business Intelligence Analyst uses data analysis and visualization techniques to help businesses make informed decisions. The Guided Project Data Science for Business with R Programming may be useful for this career by providing skills in data analysis, visualization, and reporting that can be applied to business intelligence tasks.
Business Analyst
Business Analyst
A Business Analyst studies the business needs to determine objectives, identify and verify solutions, and analyze business processes. The Guided Project Data Science for Business with R Programming can be useful for this career by helping to develop analytical skills and an understanding of data science tools that can be used to solve business problems.
Statistician
Statistician
A Statistician collects, analyzes, interprets, and presents data to help businesses and organizations make informed decisions. The Guided Project Data Science for Business with R Programming may be useful for this career by providing a foundation in statistical methods and data analysis techniques that can be applied to a variety of business problems.
Risk Analyst
Risk Analyst
A Risk Analyst identifies, assesses, and manages risks for businesses and organizations. The Guided Project Data Science for Business with R Programming may be useful for this career by providing skills in data analysis and risk modeling that can be applied to risk management.
Market Research Analyst
Market Research Analyst
A Market Research Analyst conducts research on market conditions, customer demographics, and buying habits to help businesses make informed decisions. The Guided Project Data Science for Business with R Programming may be useful for this career by providing skills in data analysis and interpretation that can be applied to market research.
Product Analyst
Product Analyst
A Product Analyst analyzes product data to identify trends, improve product development, and optimize product marketing. The Guided Project Data Science for Business with R Programming may be useful for this career by providing skills in data analysis and product domain knowledge that can be applied to product analytics tasks.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses analytical methods to solve complex problems in business and industry. The Guided Project Data Science for Business with R Programming may be useful for this career by providing a foundation in data analysis and optimization techniques that can be applied to operations research problems.
Marketing Analyst
Marketing Analyst
A Marketing Analyst analyzes marketing data to identify trends, improve campaign performance, and optimize marketing strategies. The Guided Project Data Science for Business with R Programming may be useful for this career by providing skills in data analysis and marketing domain knowledge that can be applied to marketing analytics tasks.
Data Engineer
Data Engineer
A Data Engineer designs, builds, and maintains data systems and infrastructure to support data science and analytics. The Guided Project Data Science for Business with R Programming may be useful for this career by providing a foundation in data management and engineering principles that can be applied to data engineering tasks.
Healthcare Analyst
Healthcare Analyst
A Healthcare Analyst analyzes healthcare data to identify trends, improve patient care, and optimize healthcare delivery. The Guided Project Data Science for Business with R Programming may be useful for this career by providing skills in data analysis and healthcare domain knowledge that can be applied to healthcare analytics tasks.
Sales Analyst
Sales Analyst
A Sales Analyst analyzes sales data to identify trends, improve sales performance, and optimize sales strategies. The Guided Project Data Science for Business with R Programming may be useful for this career by providing skills in data analysis and sales domain knowledge that can be applied to sales analytics tasks.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer develops and deploys machine learning models to solve business problems. The Guided Project Data Science for Business with R Programming may be useful for this career by providing a foundation in machine learning concepts and techniques that can be applied to machine learning engineering tasks.
Data Scientist
Data Scientist
A Data Scientist uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured. The Guided Project Data Science for Business with R Programming may be useful for this career by providing a foundation in data science concepts and techniques.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses mathematical and statistical models to analyze financial data and make investment decisions. The Guided Project Data Science for Business with R Programming may be useful for this career by providing a foundation in data analysis and modeling techniques that can be applied to financial analysis.
Financial Analyst
Financial Analyst
A Financial Analyst analyzes financial data to make investment recommendations and provide financial advice to businesses and individuals. The Guided Project Data Science for Business with R Programming may be useful for this career by providing skills in data analysis and financial modeling that can be applied to financial analysis tasks.
For more career information including salaries, visit:
OpenCourser.com/course/c4zygw/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 for Business with R Programming.
Save
Serves as a comprehensive resource for data science with R, covering essential concepts, techniques, and tools, making it highly relevant to the course's focus on R programming.
Provides a comprehensive overview of statistical learning methods, serving as a valuable resource for those looking to deepen their understanding of the statistical foundations of data science.
Adopts a hands-on approach to data science, providing a practical understanding of data manipulation, analysis, and visualization techniques, complementing the course's emphasis on real-world applications.
Combines economic principles with data science techniques, providing business professionals with a holistic understanding of data-driven decision-making and optimization.
Save
Offers a clear and concise introduction to causal inference, providing a valuable foundation for those interested in understanding the challenges and techniques involved in drawing causal conclusions from data.
Provides insights into the art and science of prediction, offering valuable lessons for those seeking to make informed decisions based on data.
Save
Delves into the practicalities of developing and deploying predictive models, offering valuable insights for business professionals seeking to leverage data for forecasting and decision-making.
Provides a critical examination of econometric methods, offering insights into the assumptions and limitations of these techniques, which is valuable for those seeking a deeper understanding of the statistical foundations of data science.
As a beginner-friendly guide, this book provides a clear and accessible introduction to the fundamentals of data science, making it suitable for those looking to build a foundation in the field.
As a practical guide to data analysis using Python, this book complements the course's focus on R programming by providing insights into another widely used data science language.
Save
While not directly covered in the course, this book offers a comprehensive introduction to deep learning, providing a valuable resource for those interested in exploring advanced data science techniques.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/c4zygw/data
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Time
2 hours
Via
Coursera
Institution
Coursera Community Project Network
Instructor
Nilosree Sengupta
Good to know
Business professionals seeking to make real-world data-driven decisions
Focuses on the application of data science techniques to address fundamental economic questions in business
Taught by Nilosree Sengupta, an experienced instructor in the field
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