Statistics with R Capstone from Coursera

The capstone project will be an analysis using R that answers a specific scientific/business question provided by the course team. A large and complex dataset will be provided to learners and the analysis will require the application of a variety of methods and techniques introduced in the previous courses, including exploratory data analysis through data visualization and numerical summaries, statistical inference, and modeling as well as interpretations of these results in the context of the data and the research question. The analysis will implement both frequentist and Bayesian techniques and discuss in context of the data how these two approaches are similar and different, and what these differences mean for conclusions that can be drawn from the data. A sampling of the final projects will be featured on the Duke Statistical Science department website. Note: Only learners who have passed the four previous courses in the specialization are eligible to take the Capstone.

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Analyzes real-world data to answer scientific/business questions

Develops strong analytical abilities

Requires previous knowledge in the specialization

Needs independent access to software, such as R

Reviews summary

Practical end-to-end data analysis project in r

This course teaches learners to conduct data analysis and model construction using R. It focuses on building a step-by-step process for data handling and Bayesian concepts through a series of quizzes and culminates in a final project. Although it is considered challenging, learners often find the project to be rewarding, especially when real-life application is utilized. Many students also remark that they feel very confident at taking on regression projects after completing this course.

Emphasizes Bayesian statistics and modeling.

Requires effort and dedication.

"The capstone is difficult, especially the quizzes..."

Teaches real-world data analysis skills.

"I especially liked this last course of the specialization Statistics with R, because it was semi guided."

"The Capstone project focusses on this process extensively."

May require self-teaching or external resources.

"I had to teach myself a lot, particularly R code as it's simply not taught or not explained well enough."

Limited assistance from instructors or moderators.

"There is also no real support from moderators or course leaders"

"No one seems to have looked at the forums for 3-4 years"

Career center

Learners who complete Statistics with R Capstone will develop knowledge and skills that may be useful to these careers:

Statistician

Statisticians collect, analyze, interpret, and present data. They work in a variety of fields, including healthcare, finance, and education. This course can help you prepare for a career as a Statistician through its focus on `exploratory data analysis through data visualization and numerical summaries, statistical inference, and modeling`. Additionally, the course's emphasis on `frequentist and Bayesian techniques` will give you a strong foundation in the statistical methods used in this field.

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Market Research Analyst

Market Research Analysts conduct research to understand consumer behavior and trends. They work with businesses to help them develop new products and services, and to improve their marketing campaigns. This course can help you prepare for a career as a Market Research Analyst through its focus on `exploratory data analysis through data visualization and numerical summaries, statistical inference, and modeling`. Additionally, the course's emphasis on `frequentist and Bayesian techniques` will give you a strong foundation in the statistical methods used in this field.

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Research Analyst

Research Analysts help businesses make decisions by providing them with data and insights. Their findings have a strong impact on the company's strategy, business operations, and marketing. Statistical modeling, data mining, and econometrics are common tools used by Research Analysts. This course will help you prepare for this role through its focus on `exploratory data analysis through data visualization and numerical summaries, statistical inference, and modeling`. Additionally, the course's requirement of a `large and complex dataset` will help you develop skills in analyzing real-world business data like Research Analysts do.

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Data Analyst

Data Analysts clean, analyze, and interpret data to help businesses make better decisions. They work in a variety of industries, including healthcare, finance, and retail. This course can help you prepare for a career as a Data Analyst through its focus on `exploratory data analysis through data visualization and numerical summaries, statistical inference, and modeling`. Additionally, the course's emphasis on `programming in R` and working with a `large and complex dataset` will be valuable experience for this role.

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Quantitative Analyst

Quantitative Analysts, or Quants, use mathematics, statistics, econometrics, and computational methods to analyze complex financial data. They build and validate models to predict future trends and inform investment decisions. This course will help you develop a foundation for a Quant career through its focus on `statistical inference, and modeling` as well as its requirement to work with `a large and complex dataset`.

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Machine Learning Engineer

Machine Learning Engineers build and deploy machine learning models. They work with data scientists and engineers to design, develop, and test models. This course can help you prepare for a Machine Learning Engineering role through its focus on `statistical inference, and modeling` as well as its requirement to work with `a large and complex dataset`.

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Business Analyst

Business Analysts help businesses understand their data and make better decisions. They analyze data from a variety of sources, including financial statements, sales figures, and customer surveys. This course can help you develop the skills needed for this role through its focus on `exploratory data analysis through data visualization and numerical summaries, statistical inference, and modeling`.

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Financial Analyst

Financial Analysts analyze financial data to make recommendations about investments. They work with individuals and institutions to help them make informed investment decisions. This course can help you prepare for a career as a Financial Analyst through its focus on `statistical inference, and modeling` as well as its requirement to work with `a large and complex dataset`.

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Actuary

Actuaries use mathematics, statistics, and financial theory to assess risk and uncertainty. They work in a variety of fields, including insurance, pensions, and healthcare. This course can help you prepare for a career as an Actuary through its focus on `statistical inference, and modeling` as well as its requirement to work with `a large and complex dataset`.

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Operations Research Analyst

Operations Research Analysts use mathematical and analytical techniques to solve business problems. They work in a variety of industries, including manufacturing, transportation, and healthcare. This course can help you prepare for a career as an Operations Research Analyst through its focus on `exploratory data analysis through data visualization and numerical summaries, statistical inference, and modeling`. Additionally, the course's emphasis on `programming in R` and working with a `large and complex dataset` will be valuable experience for this role.

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Data Engineer

Data Engineers build and maintain the infrastructure that stores and processes data. They work with data scientists and analysts to ensure that data is reliable, secure, and accessible. This course can help you prepare for a Data Engineering role by focusing on `exploratory data analysis through data visualization and numerical summaries, statistical inference, and modeling`. Additionally, the course's emphasis on `programming in R` and working with a `large and complex dataset` will be valuable experience for this role.

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Data Scientist

Data Scientists analyze data to solve business problems. They may do so by identifying and addressing biases, leveraging traditional statistical techniques, or using more modern machine learning approaches. With the provided course's focus on `analysis using R` and `exploring data through data visualization, numerical summaries, statistical inference, and modeling`, this course may help individuals build a foundation for a career as a Data Scientist.

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Software Engineer

Software Engineers design, develop, and test software applications. They work in a variety of industries, including technology, finance, and healthcare. While this course is not specifically focused on software engineering, the emphasis on `programming in R` may be beneficial for individuals interested in a career in this field.

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Web Developer

Web Developers design and develop websites. While this course is not specifically focused on web development, the emphasis on `programming in R` may be beneficial for individuals interested in a career in this field.

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Practical end-to-end data analysis project in r

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