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Janani Ravi
The tools of machine learning - algorithms, solution techniques, and even neural network architectures, are becoming commoditized. Everyone is using the same tools these days, so your edge needs to come from how well you adapt those tools to your data. Today,...
Read more
The tools of machine learning - algorithms, solution techniques, and even neural network architectures, are becoming commoditized. Everyone is using the same tools these days, so your edge needs to come from how well you adapt those tools to your data. Today,...
Read more
The tools of machine learning - algorithms, solution techniques, and even neural network architectures, are becoming commoditized. Everyone is using the same tools these days, so your edge needs to come from how well you adapt those tools to your data. Today, more than ever, it is important that you really know your data well. In this course, Building Statistical Summaries with R, you will gain the ability to harness the full power of inferential statistics, which are truly richly supported in R. First, you will learn how hypothesis testing, which is the foundation of inferential statistics, helps posit and test assumptions about data. Next, you will discover how the classic t-test can be used in a variety of common scenarios around estimating means. You will also learn about related tests such as the Z-test, the Pearson’s Chi-squared test, Levene’s test and Welch’s t-test for dealing with populations that have unequal variances. Finally, you will round out your knowledge by using ANOVA, a powerful statistical technique used to measure statistical properties across different categories of data. Along the way, you will explore several variants of ANOVA, including one-way, two-way, Kruskal-Wallis, and Welch’s ANOVA. You will build predictive models using linear regression and classification and finally, you will understand A/B testing, and implement both the frequentist and the Bayesian approaches to implement this incredibly powerful technique. When you’re finished with this course, you will have the skills and knowledge to use powerful techniques from hypothesis testing, including t-tests, ANOVA and Bayesian A/B testing in order to measure the strength of statistical relationships within your data.
Register for this course and see more details by visiting:
OpenCourser.com/course/mq13rb/building
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Suitable for individuals looking to enhance their statistical knowledge and apply it to data analysis
Ideal for those seeking a deeper understanding of statistical techniques and their application in data exploration
Assumes a foundational understanding of statistics
Requires proficiency in programming with R
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Career center
Learners who complete Building Statistical Summaries with R will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data analysts are responsible for collecting, cleaning, and analyzing data to identify trends and patterns. They use statistical methods to extract meaningful insights and translate them into actionable recommendations for businesses, organizations, and government agencies. The Building Statistical Summaries with R course will provide you with a comprehensive understanding of hypothesis testing, ANOVA, and other statistical techniques essential for success in this field.
Data Scientist
Data Scientist
Data scientists are responsible for extracting knowledge and insights from data using statistical techniques, machine learning, and data mining. They use these insights to solve business problems and drive innovation. The Building Statistical Summaries with R course will provide you with a strong foundation in statistical principles and techniques that are essential for a successful career as a data scientist.
Statistician
Statistician
Statisticians apply mathematical and statistical principles to the collection, analysis, interpretation, and presentation of data. They analyze data to provide insights, draw conclusions, and inform decision-making, and are often employed in scientific research, business, government, and healthcare. The Building Statistical Summaries with R course will help you build a solid foundation in data analysis principles and statistical modeling techniques. You will learn how to test hypotheses, estimate means, and conduct ANOVA, which are essential skills for statisticians.
Econometrician
Econometrician
Econometricians analyze economic data to develop and test economic models. They use statistical techniques to forecast economic trends, evaluate the impact of policies, and make recommendations for economic decision-making. The Building Statistical Summaries with R course will provide you with the statistical skills and knowledge necessary for a successful career in econometrics.
Machine Learning Engineer
Machine Learning Engineer
Machine learning engineers design, develop, and deploy machine learning models to solve complex business problems. They build and maintain algorithms and models that enable computers to learn from data and make predictions or decisions. The Building Statistical Summaries with R course will help you build a strong foundation in statistical concepts and techniques that are essential for understanding and applying machine learning algorithms.
Operations Research Analyst
Operations Research Analyst
Operations research analysts develop and apply mathematical and statistical techniques to solve complex problems in business and industry. They analyze data, build models, and design solutions to improve efficiency, productivity, and decision-making. The Building Statistical Summaries with R course will provide you with the statistical expertise necessary for a successful career in operations research.
Epidemiologist
Epidemiologist
Epidemiologists investigate the causes and distribution of diseases in populations. They use statistical techniques to analyze data, identify risk factors, and develop prevention and control measures. The Building Statistical Summaries with R course will provide you with the statistical skills and knowledge necessary for a successful career in epidemiology.
Biostatistician
Biostatistician
Biostatisticians apply statistical principles to the design, analysis, and interpretation of biomedical research studies. They ensure the validity and reliability of research findings and contribute to the development of new treatments and therapies. The Building Statistical Summaries with R course will provide you with the statistical expertise necessary for a successful career in biostatistics.
Business Analyst
Business Analyst
Business analysts bridge the gap between business and technology. They analyze business processes, identify areas for improvement, and recommend solutions. Building Statistical Summaries with R will teach you how to use statistical techniques to analyze data, identify trends, and make recommendations that can drive business success.
Public Health Analyst
Public Health Analyst
Public health analysts collect, analyze, and interpret data to assess and improve the health of populations. They use statistical techniques to identify health trends, evaluate the effectiveness of public health programs, and develop policies to improve public health. The Building Statistical Summaries with R course will provide you with the statistical skills and knowledge necessary for a successful career in public health analysis.
Quantitative Analyst
Quantitative Analyst
Quantitative analysts develop and use mathematical and statistical models to analyze financial data and make investment decisions. They use statistical techniques to evaluate risk, identify trading opportunities, and develop trading strategies. Building Statistical Summaries with R will teach you the statistical skills and knowledge necessary for a successful career in quantitative analysis.
Risk Manager
Risk Manager
Risk managers identify, assess, and manage risks to organizations. They use statistical techniques to evaluate the likelihood and impact of risks, and develop strategies to mitigate them. The Building Statistical Summaries with R course will provide you with the statistical expertise necessary for a successful career in risk management.
Market Researcher
Market Researcher
Market researchers collect, analyze, and interpret data to understand consumer behavior and market trends. They use statistical techniques to design surveys, analyze data, and make recommendations for marketing strategies. The Building Statistical Summaries with R course will provide you with the statistical skills and knowledge necessary for a successful career in market research.
Financial Analyst
Financial Analyst
Financial analysts analyze financial data to make investment recommendations and provide guidance to businesses and investors. They use statistical techniques to evaluate financial performance, identify trends, and forecast future performance. The Building Statistical Summaries with R course will provide you with a strong foundation in statistical methods and techniques that are essential for success in financial analysis.
Biomedical Engineer
Biomedical Engineer
Biomedical engineers apply engineering principles to solve problems in biology and medicine. They design and develop medical devices, diagnostic tools, and treatments. The Building Statistical Summaries with R course will provide you with the statistical skills and knowledge necessary to analyze and interpret biomedical data, and to design and evaluate medical devices and treatments.
For more career information including salaries, visit:
OpenCourser.com/course/mq13rb/building
Reading list
We've selected 19 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
Building Statistical Summaries with R.
This textbook provides a comprehensive introduction to statistical learning, covering topics such as supervised and unsupervised learning, regression, and classification. It valuable resource for students and practitioners who want to learn about the latest advances in statistical learning.
Provides a comprehensive introduction to deep learning with R, covering topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks. It valuable resource for students and practitioners who want to learn about the latest advances in deep learning.
This textbook provides a comprehensive introduction to statistical methods for machine learning, covering topics such as supervised and unsupervised learning, regression, and classification. It valuable resource for students and practitioners who want to learn about the latest advances in statistical learning.
This textbook provides a comprehensive overview of statistical learning methods, including supervised and unsupervised learning, regression, and classification. It valuable resource for students and practitioners who want to learn about the latest advances in statistical learning.
Provides a comprehensive introduction to machine learning with R, covering topics such as supervised and unsupervised learning, regression, and classification. It valuable resource for students and practitioners who want to learn about the latest advances in machine learning.
This textbook provides a comprehensive introduction to statistical inference, covering topics such as hypothesis testing, confidence intervals, and regression analysis. It valuable reference for students and practitioners alike.
Save
Provides a comprehensive overview of statistical inference, covering a wide range of topics in a clear and concise manner.
Provides a hands-on introduction to machine learning with R, covering topics such as supervised and unsupervised learning, regression, and classification. It valuable resource for students and practitioners who want to learn about the latest advances in machine learning.
Provides a practical introduction to machine learning, covering topics such as supervised and unsupervised learning, regression, and classification. It valuable resource for students and practitioners who want to learn about the latest advances in machine learning.
Provides a rigorous treatment of statistical theory, with a focus on applications in the social and natural sciences.
Provides a practical introduction to statistics, with a focus on real-world applications.
Provides a comprehensive overview of Bayesian data analysis, with a focus on practical applications.
Provides a clear and concise introduction to causal inference, with a focus on statistical methods.
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Provides a practical guide to econometrics, with a focus on real-world applications.
Provides a practical introduction to Bayesian statistics, with a focus on real-world applications.
Save
Provides a free and open-source introduction to statistics, with a focus on real-world applications.
Provides a comprehensive overview of statistical methods used in the atmospheric sciences.
Provides a practical introduction to statistical thinking, with a focus on data science and analytics.
Provides a comprehensive overview of statistical methods used in agricultural research.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/mq13rb/building
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Time
3 hours
Via
Pluralsight
Instructor
Janani Ravi
This course belongs to a
collection
called
Building Statistical and Mathematical Models with R. It's comprised of nine courses:
- Understanding Statistical Models and Mathematical Models
- Solving Problems with Numerical Methods
- Applying the Mathematical MASS Model with R
- Applying Differential Equations and Inverse Models with R
- Performing Dimension Analysis with R
- Applying Linear Algebra with R
- Building Statistical Summaries with R (viewing)
- Implementing Bootstrap Methods in R
- Implementing Monte Carlo Method in R
Good to know
Suitable for individuals looking to enhance their statistical knowledge and apply it to data analysis
Ideal for those seeking a deeper understanding of statistical techniques and their application in data exploration
Assumes a foundational understanding of statistics
Requires proficiency in programming with R
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