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Sharad Borle
The Business Statistics and Analysis Capstone is an opportunity to apply various skills developed across the four courses in the specialization to a real life data. The Capstone, in collaboration with an industry partner uses publicly available ‘Housing Data’ to pose various questions typically a client would pose to a data analyst.
Read more
The Business Statistics and Analysis Capstone is an opportunity to apply various skills developed across the four courses in the specialization to a real life data. The Capstone, in collaboration with an industry partner uses publicly available ‘Housing Data’ to pose various questions typically a client would pose to a data analyst.
Read more
The Business Statistics and Analysis Capstone is an opportunity to apply various skills developed across the four courses in the specialization to a real life data. The Capstone, in collaboration with an industry partner uses publicly available ‘Housing Data’ to pose various questions typically a client would pose to a data analyst.
Your job is to do the relevant statistical analysis and report your findings in response to the questions in a way that anyone can understand.
Please remember that this is a Capstone, and has a degree of difficulty/ambiguity higher than the previous four courses. The aim being to mimic a real life application as close as possible.
Register for this course and see more details by visiting:
OpenCourser.com/course/60hewn/business
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What's inside
Syllabus
Business Statistics and Analysis Capstone: An Introduction
Business Statistics and Analysis Capstone: Assessments 1 & 2
Business Statistics and Analysis Capstone: Assessment 3
Read more
Syllabus
Business Statistics and Analysis Capstone: An Introduction
Business Statistics and Analysis Capstone: Assessments 1 & 2
Business Statistics and Analysis Capstone: Assessment 3
Read more
Business Statistics and Analysis Capstone: Assessment 4
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Taught by Sharad Borle, who are recognized for their work in statistics and data analysis
Develops proficiency in statistical analysis, which is integral for decision-making in various industries
Examines business statistics and analysis through the lens of real-life data, enhancing its relevance and applicability
Involves hands-on analysis using publicly available 'Housing Data', mirroring real-life scenarios and industry practices
Employs a multi-modal approach incorporating videos, readings, and discussions, fostering a comprehensive learning experience
Assumes basic knowledge of statistics, potentially requiring additional preparation for learners with limited grounding in the subject
Save this course
Save Business Statistics and Analysis Capstone to your list so you can find it easily later:
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Reviews summary
In-depth business statistics capstone
Learners say this well-received Business Statistics and Analysis Capstone course is packed with useful information, leading to a better understanding of the subject. Students report being particularly engaged with the lectures, readings, and assignments. Overall, learners report that the course has helped them develop a deeper understanding of business statistics and analysis.
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 Business Statistics and Analysis Capstone with these
activities:
Compile a list of statistical resources
Show steps
Enhance your learning experience by compiling a comprehensive list of statistical resources, including books, articles, and online tools.
Browse courses on
Data Science Tools
Show steps
-
Research and identify valuable statistical resources.
-
Organize the resources into categories for easy access.
Review probability
Show steps
Review probability to refresh your knowledge and ensure you are ready to tackle more complex statistical concepts.
Browse courses on
Probability
Show steps
-
Revisit basic concepts such as sample space, events, and probability distributions.
-
Practice solving probability problems involving conditional probability and independence.
Volunteer with a data-driven organization
Show steps
Strengthen your practical skills and make a meaningful contribution by volunteering with organizations that leverage data for positive change.
Browse courses on
Volunteering
Show steps
-
Identify organizations that align with your interests and skills.
-
Offer your services to assist with data analysis or visualization projects.
-
Gain hands-on experience and contribute to impactful initiatives.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice statistical hypothesis testing
Show steps
Practice statistical hypothesis testing to improve your understanding of the different types of tests and how to apply them to real-world scenarios.
Browse courses on
Hypothesis Testing
Show steps
-
Understand the concepts of null and alternative hypotheses.
-
Practice performing hypothesis tests for different types of data (e.g., means, proportions).
-
Interpret the results of hypothesis tests and make appropriate conclusions.
Mentor junior data analysts
Show steps
Sharpen your communication and analytical skills by mentoring junior data analysts, helping them navigate the field and develop their abilities.
Browse courses on
Mentoring
Show steps
-
Identify opportunities to mentor junior data analysts.
-
Provide guidance and support on technical and career-related matters.
-
Share your knowledge and experience to accelerate their growth.
Explore advanced statistical techniques
Show steps
Expand your statistical knowledge by exploring advanced techniques that can enhance your problem-solving abilities.
Browse courses on
Regression Analysis
Show steps
-
Identify areas where advanced statistical techniques can be applied.
-
Research and learn about specific techniques (e.g., regression analysis, time series analysis).
-
Apply these techniques to real-world datasets to gain practical experience.
Contribute to open-source statistical projects
Show steps
Expand your technical skills and contribute to the data science community by actively participating in open-source statistical projects.
Browse courses on
Open-Source
Show steps
-
Identify open-source projects that align with your interests.
-
Contribute to code development, documentation, or testing.
-
Interact with other contributors and learn from their expertise.
Analyze a dataset and generate insights
Show steps
Apply your statistical knowledge to analyze a real-world dataset, generate insights, and present your findings effectively.
Browse courses on
Data Analysis
Show steps
-
Choose a dataset that aligns with your interests or career goals.
-
Clean and explore the data to identify patterns and trends.
-
Develop and apply appropriate statistical models to analyze the data.
-
Interpret the results and generate actionable insights.
-
Prepare a presentation or report to communicate your findings.
Compile a list of statistical resources
Show steps
Enhance your learning experience by compiling a comprehensive list of statistical resources, including books, articles, and online tools.
Browse courses on
Data Science Tools
Show steps
Show steps
Show steps
- Research and identify valuable statistical resources.
- Organize the resources into categories for easy access.
Review probability
Show steps
Review probability to refresh your knowledge and ensure you are ready to tackle more complex statistical concepts.
Browse courses on
Probability
Show steps
Show steps
Show steps
- Revisit basic concepts such as sample space, events, and probability distributions.
- Practice solving probability problems involving conditional probability and independence.
Volunteer with a data-driven organization
Show steps
Strengthen your practical skills and make a meaningful contribution by volunteering with organizations that leverage data for positive change.
Browse courses on
Volunteering
Show steps
Show steps
Show steps
- Identify organizations that align with your interests and skills.
- Offer your services to assist with data analysis or visualization projects.
- Gain hands-on experience and contribute to impactful initiatives.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice statistical hypothesis testing
Show steps
Practice statistical hypothesis testing to improve your understanding of the different types of tests and how to apply them to real-world scenarios.
Browse courses on
Hypothesis Testing
Show steps
Show steps
Show steps
- Understand the concepts of null and alternative hypotheses.
- Practice performing hypothesis tests for different types of data (e.g., means, proportions).
- Interpret the results of hypothesis tests and make appropriate conclusions.
Mentor junior data analysts
Show steps
Sharpen your communication and analytical skills by mentoring junior data analysts, helping them navigate the field and develop their abilities.
Browse courses on
Mentoring
Show steps
Show steps
Show steps
- Identify opportunities to mentor junior data analysts.
- Provide guidance and support on technical and career-related matters.
- Share your knowledge and experience to accelerate their growth.
Explore advanced statistical techniques
Show steps
Expand your statistical knowledge by exploring advanced techniques that can enhance your problem-solving abilities.
Browse courses on
Regression Analysis
Show steps
Show steps
Show steps
- Identify areas where advanced statistical techniques can be applied.
- Research and learn about specific techniques (e.g., regression analysis, time series analysis).
- Apply these techniques to real-world datasets to gain practical experience.
Contribute to open-source statistical projects
Show steps
Expand your technical skills and contribute to the data science community by actively participating in open-source statistical projects.
Browse courses on
Open-Source
Show steps
Show steps
Show steps
- Identify open-source projects that align with your interests.
- Contribute to code development, documentation, or testing.
- Interact with other contributors and learn from their expertise.
Analyze a dataset and generate insights
Show steps
Apply your statistical knowledge to analyze a real-world dataset, generate insights, and present your findings effectively.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Choose a dataset that aligns with your interests or career goals.
- Clean and explore the data to identify patterns and trends.
- Develop and apply appropriate statistical models to analyze the data.
- Interpret the results and generate actionable insights.
- Prepare a presentation or report to communicate your findings.
Career center
Learners who complete Business Statistics and Analysis Capstone will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
A Data Analyst collects, analyzes, interprets, and presents data to support decision making within an organization. This course's thorough examination of business statistics, data analysis techniques, and their application to real-life business scenarios will help equip you with the skills necessary to extract meaningful insights from data, enabling you to succeed in this role.
Data Scientist
Data Scientist
A Data Scientist is responsible for developing and deploying machine learning models to solve complex business problems. The course's focus on statistical modeling, data visualization, and real-life case studies will provide you with a comprehensive foundation for understanding and applying data science principles in this role.
Business Analyst
Business Analyst
A Business Analyst is responsible for analyzing business processes, gathering and interpreting data, and making recommendations to improve efficiency and solve problems. This course's focus on data analysis, statistical inference, and business modeling will provide you with the skills to excel in this analytical role.
Market Research Analyst
Market Research Analyst
A Market Research Analyst is responsible for collecting, analyzing, and interpreting market data to provide insights into customer behavior, market trends, and competitive landscapes. This course's focus on data collection, statistical analysis, and presenting actionable insights will equip you with the necessary skills for this role.
Financial Analyst
Financial Analyst
A Financial Analyst is responsible for analyzing financial data to make recommendations on investments and financial decisions. This course's focus on statistical modeling, risk analysis, and financial data analysis will provide you with the analytical skills required for this role.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst is responsible for applying quantitative methods to solve complex business problems. This course's focus on statistical modeling, optimization techniques, and real-life case studies will provide you with the analytical skills necessary for this role.
Econometrician
Econometrician
An Econometrician is responsible for developing and applying statistical models to analyze economic data. This course's focus on statistical modeling, regression analysis, and economic data analysis will provide you with the necessary skills for this role.
Actuary
Actuary
An Actuary is responsible for assessing risk and uncertainty in financial and insurance settings. This course's focus on statistical modeling, probability theory, and data analysis will help you develop the analytical skills required for this role.
Biostatistician
Biostatistician
A Biostatistician is responsible for applying statistical methods to design and analyze biomedical research studies. This course's focus on statistical modeling, survival analysis, and clinical trial data analysis will provide you with the analytical skills required for this role.
Statistician
Statistician
A Statistician is responsible for collecting, analyzing, interpreting, and presenting data to solve real-world problems. This course's comprehensive coverage of statistical methods, data analysis techniques, and real-life case studies will provide you with the necessary skills for this role.
Data Engineer
Data Engineer
A Data Engineer is responsible for designing, building, and maintaining data infrastructure to support data analysis and data science initiatives. This course's focus on data management, data warehousing, and data engineering principles may provide you with a foundational understanding of the data infrastructure that supports data analysis and data science.
Software Engineer
Software Engineer
A Software Engineer is responsible for designing, developing, and maintaining software applications. This course's focus on data analysis techniques and their application in real-life business scenarios may provide you with a foundational understanding of the role of data and analytics in software development.
Product Manager
Product Manager
A Product Manager is responsible for overseeing the development and launch of new products or features. This course's focus on data analysis and its application in real-life business scenarios may provide you with a foundational understanding of the role of data and analytics in product development and management.
Marketing Manager
Marketing Manager
A Marketing Manager is responsible for developing and executing marketing campaigns to promote products or services. This course's focus on data analysis and its application in real-life business scenarios may provide you with a foundational understanding of the role of data and analytics in marketing and advertising.
Project Manager
Project Manager
A Project Manager is responsible for planning, executing, and monitoring projects to ensure their successful completion. This course's focus on data analysis and its application in real-life business scenarios may provide you with a foundational understanding of the role of data and analytics in project management.
For more career information including salaries, visit:
OpenCourser.com/course/60hewn/business
Reading list
We've selected 27 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
Business Statistics and Analysis Capstone.
Save
Covers a wide range of topics in business statistics and data analysis, including data collection, preparation, analysis, and interpretation.
Focuses on the application of statistical methods to business problems, with a strong emphasis on using data to make informed decisions.
Provides a thorough introduction to business analytics, including the use of data visualization, statistical modeling, and machine learning to solve business problems.
A classic textbook on statistical inference that covers topics such as sampling distributions, hypothesis testing, and confidence intervals. Provides a solid foundation in the statistical concepts used in the Business Statistics and Analysis Capstone.
Save
Was described by The New York Times as 'the best business book of the year,' and provides accessible explanations of the predictive modeling techniques that have shaped the digital economy.
Covers the fundamentals of data science, including data mining, machine learning, and statistical modeling, with a focus on applications in business.
Save
This widely-cited text examines how the availability of 'big data' is redefining the nature of business, as well as the opportunities and challenges that data present.
Save
Provides a practical guide to using predictive analytics to solve business problems, with a focus on using data to predict customer behavior.
Save
This user-friendly guide is designed to make fundamental statistics accessible to readers from non-technical backgrounds.
Covers the fundamentals of big data analytics, including data collection, preparation, analysis, and interpretation, with a focus on using data to solve business problems.
Save
This popular textbook bridges the gap between theory and practice, providing a comprehensive overview of statistical methods and applications in a variety of fields.
A comprehensive guide to using Python for data analysis tasks, including data manipulation, visualization, and statistical modeling. Provides a solid foundation in Python for those who want to explore data analysis beyond the R language used in the Business Statistics and Analysis Capstone.
Save
Provides a comprehensive overview of probability and statistics, with a focus on applications in engineering and science.
A beginner-friendly introduction to machine learning concepts and algorithms, with a focus on business applications. Provides a high-level overview of machine learning techniques that complements the more in-depth coverage in the Business Statistics and Analysis Capstone.
This introductory text provides a gentle introduction to statistical learning, with a focus on practical applications and implementation in the R programming language.
Save
A practical guide to data visualization techniques that covers topics such as chart types, color palettes, and dashboard design. Helps learners develop effective data visualizations to communicate insights from the Business Statistics and Analysis Capstone.
Provides a comprehensive overview of statistical learning, with a focus on using data to solve business problems.
A comprehensive textbook on statistics for business and economics that covers essential topics such as descriptive statistics, probability, hypothesis testing, and regression analysis. Provides a solid foundation in the statistical concepts used in the Business Statistics and Analysis Capstone.
Provides a comprehensive overview of Bayesian data analysis, with a focus on using data to solve business problems.
A practical guide to using statistics in data science applications. Covers topics such as data cleaning, feature engineering, and statistical modeling. Provides insights into the challenges and opportunities of using statistics in real-world data science projects.
Provides a comprehensive overview of Bayesian statistics, with a focus on using data to solve business problems.
Save
Differs from the others in the list in that it focuses not just on the technical aspects of data science but also on the ethical and social implications of data.
Save
Provides a comprehensive overview of pattern recognition and machine learning, with a focus on using data to solve business problems.
Nate Silver, the founder of FiveThirtyEight, examines the challenges of prediction, drawing on his experience in politics, sports, and finance.
Save
While not directly related to the subject matter of this course, this book explores the potential long-term implications of advances in artificial intelligence, and thought-provoking read for anyone interested in the future of technology.
Save
Provides a comprehensive overview of deep learning, with a focus on using data to solve business problems.
Save
A comprehensive guide to data mining techniques, including clustering, classification, and association analysis. Provides a deeper dive into data mining algorithms that may be of interest to learners who want to explore more advanced data analysis methods.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/60hewn/business
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Effort
4 weeks of study
Via
Coursera
Institution
Rice University
Instructor
Sharad Borle
Language
English
Transcripts
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This course belongs to a
collection
called
Business Statistics and Analysis. It's comprised of five courses:
- Linear Regression for Business Statistics
- Business Applications of Hypothesis Testing and Confidence Interval Estimation
- Business Statistics and Analysis Capstone (viewing)
- Introduction to Data Analysis Using Excel
- Basic Data Descriptors, Statistical Distributions, and Application to Business Decisions
Good to know
Taught by Sharad Borle, who are recognized for their work in statistics and data analysis
Develops proficiency in statistical analysis, which is integral for decision-making in various industries
Examines business statistics and analysis through the lens of real-life data, enhancing its relevance and applicability
Involves hands-on analysis using publicly available 'Housing Data', mirroring real-life scenarios and industry practices
Employs a multi-modal approach incorporating videos, readings, and discussions, fostering a comprehensive learning experience
Assumes basic knowledge of statistics, potentially requiring additional preparation for learners with limited grounding in the subject
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