Statistics with SAS from Coursera

What's inside

Syllabus

Course Overview and Data Setup

In this module you learn about the course and the data you analyze in this course. Then you set up the data you need to do the practices in the course.

Introduction and Review of Concepts

In this module you learn about the models required to analyze different types of data and the difference between explanatory vs predictive modeling. Then you review fundamental statistical concepts, such as the sampling distribution of a mean, hypothesis testing, p-values, and confidence intervals. After reviewing these concepts, you apply one-sample and two-sample t tests to data to confirm or reject preconceived hypotheses.

ANOVA and Regression

In this module you learn to use graphical tools that can help determine which predictors are likely or unlikely to be useful. Then you learn to augment these graphical explorations with correlation analyses that describe linear relationships between potential predictors and our response variable. After you determine potential predictors, tools like ANOVA and regression help you assess the quality of the relationship between the response and predictors.

More Complex Linear Models

In this module you expand the one-way ANOVA model to a two-factor analysis of variance and then extend simple linear regression to multiple regression with two predictors. After you understand the concepts of two-way ANOVA and multiple linear regression with two predictors, you'll have the skills to fit and interpret models with many variables.

Model Building and Effect Selection

In this module you explore several tools for model selection. These tools help limit the number of candidate models so that you can choose an appropriate model that's based on your expertise and research priorities.

Model Post-Fitting for Inference

In this module you learn to verify the assumptions of the model and diagnose problems that you encounter in linear regression. You learn to examine residuals, identify outliers that are numerically distant from the bulk of the data, and identify influential observations that unduly affect the regression model. Finally, you learn to diagnose collinearity to avoid inflated standard errors and parameter instability in the model.

Model Building for Scoring and Prediction

In this module you learn how to transition from inferential statistics to predictive modeling. Instead of using p-values, you learn about assessing models using honest assessment. After you choose the best performing model, you learn about ways to deploy the model to predict new data.

Categorical Data Analysis

In this module you look for associations between predictors and a binary response using hypothesis tests. Then you build a logistic regression model and learn about how to characterize the relationship between the response and predictors. Finally, you learn how to use logistic regression to build a model, or classifier, to predict unknown cases.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Provides a strong foundation for beginners

Builds skills in t tests, ANOVA, and linear regression

Introduces logistic regression

Offers hands-on labs and interactive materials

Requires knowledge of SAS/STAT software

Teaches slightly older versions of software

Reviews summary

Excellent sas and statistics coverage

Learners say that Statistics with SAS is an excellent course that effectively teaches statistics and SAS programming. The course is praised for its clear explanations, engaging examples, and well-structured content. Students with a strong foundation in statistics will find this course particularly valuable for its focus on SAS applications.

Emphasizes practical applications of statistics and SAS

"The technical parts i.e coding applications complement the theoretical parts 100%."

"This course provided me a clear way of strengthening my statistical knowledge. It wasn't limited to it, further this course added SAS skills to my basket."

"If you are changing your career path to statistics and want to learn SAS programming with statistics , changing theory into practical knowledge this is the course I will recommend ."

Suitable for beginners and advanced learners

"A good course that helped me with SAS coding and commands. "

"This course is a great way to introduce yourself to higher level SAS programming."

"Absolutely top notch course for introductory as well as some more advanced statistics."

Numerous examples and real-life applications

"The real life examples to drive the concepts were very good and easy to understand"

"A Guided lesson even for a beginner. It gives you a general overview of statistics with great emphasis on SAS programming and statistical interpretations of your analyses."

"The best part about this course was how well he explained the different types of statistical studies in general and also with the examples. Very very useful."

Knowledgeable and engaging instructor

"He knows statistical concepts very well and is able to explain in a clear and concise manner."

"The teaching style of the instructor is excellent and one of best I found across platforms."

"A really informative course. The instructor explains the concepts very well ( Stats, as well as SAS)."

Thorough coverage of both statistics and SAS

"It gives you a general overview of statistics with great emphasis on SAS programming and statistical interpretations of your analyses."

"The theoretical parts on introductory statistical analysis...are well covered. The technical parts i.e coding applications complement the theoretical parts 100%."

"A really informative course. The instructor explains the concepts very well ( Stats, as well as SAS)."

Could provide more helpful documentation for SAS

"Nothing would benefit more from AI than SAS's documentation/help/assist features since they aren't more user friendly than they were 25 years ago."

"However, my knowledge of statistics and ability to pick up statistical concepts got me through the tougher parts of this course. Looking back, I wish I had waited to take this course until I had more experience with SAS."

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 Statistics with SAS with these activities:

Review statistical concepts

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Strengthen your foundational understanding of statistical concepts to enhance your ability to apply them effectively in this course.

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Review your notes or textbooks from previous statistics courses.
Focus on the key concepts mentioned in the course syllabus, such as sampling distribution of a mean, hypothesis testing, p-values, and confidence intervals.
Consider using online resources or videos to supplement your review.

Answer questions and support fellow learners

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Deepen your understanding of the course material by assisting others, clarifying concepts, and sharing your knowledge with fellow learners.

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Participate in online discussion forums or study groups.
Offer help to classmates who may have questions or need support.
Provide thoughtful and constructive feedback to others' posts and responses.

Colt Steele's CSS Course

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Colt Steele's CSS course is a beginner-friendly tutorial that covers all the fundamentals of CSS and how to use it to create beautiful and responsive web pages. This will help students master the basics of CSS, a crucial skill for any web developer.

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Watch the introduction and overview videos.
Complete the practice exercises.
Build a simple webpage using the skills learned.

Six other activities

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Practice t-tests and ANOVA

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Reinforce your understanding of t-tests and ANOVA through hands-on practice, building your confidence in applying these techniques to real-world data.

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Find online exercises or practice problems related to t-tests and ANOVA.
Work through the problems step-by-step, ensuring you understand the underlying concepts.
Check your answers against provided solutions or consult with classmates or online forums for support.

Seek guidance from experienced statisticians

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Accelerate your learning by connecting with experienced statisticians who can provide valuable insights and support throughout your journey.

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Attend industry events or workshops to network with professionals in the field.
Reach out to professors or researchers in your university or online communities.
Seek guidance on specific statistical concepts or project ideas.

Compile a resource library on statistical software

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Enhance your proficiency in statistical software by creating a comprehensive resource library that includes tutorials, documentation, and best practices.

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Identify the most useful resources for SAS, R, Python, or other relevant software.
Organize the resources into a central location, such as a shared document or online repository.
Include documentation, tutorials, code snippets, and links to helpful online communities.

Explore more advanced regression techniques

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Expand your knowledge beyond the course material by delving into more advanced regression techniques, enhancing your ability to analyze complex data sets.

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Identify online tutorials or resources that cover multiple regression, model building, and model selection.
Follow the tutorials carefully, taking notes and practicing the techniques.
Apply what you learn to analyze a small data set of your own.

Participate in data analysis competitions

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Challenge yourself and showcase your skills by participating in data analysis competitions, fostering a growth mindset and honing your problem-solving abilities.

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Identify a data analysis competition that aligns with your interests.
Form a team or work individually to analyze the data and develop solutions.
Submit your findings and compete against other participants.
Reflect on your performance and areas for improvement.

Develop a data visualization dashboard

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Enhance your ability to communicate insights effectively by creating an interactive data visualization dashboard that showcases your findings from the course.

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Choose a data set that aligns with your interests or a specific business problem.
Use data visualization tools to create charts, graphs, and other visual elements that effectively represent your data.
Organize the visualizations into a cohesive dashboard that tells a clear and compelling story.
Consider sharing your dashboard with others for feedback and insights.

Career center

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

Machine Learning Engineer

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Statistician

Statisticians use statistical methods to collect, analyze, interpret, and present data. They work in a variety of industries, including healthcare, finance, and manufacturing. This course can help you prepare for a career in statistics by providing you with the skills you need to design and conduct statistical studies, analyze data, and draw conclusions. You'll also learn about the different types of statistical methods and how to apply them to real-world problems.

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Data Scientist

Data Scientists use a variety of techniques to extract insights from data. They work in a variety of industries, including technology, finance, and healthcare. This course can help you prepare for a career in data science by providing you with the skills you need to clean and prepare data, perform statistical analysis, and build machine learning models. You'll also learn about the different types of data science techniques and how to apply them to real-world problems.

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Biostatistician

Biostatisticians use statistical methods to analyze biological and health data. They work in a variety of settings, including hospitals, universities, and government agencies. This course can help you prepare for a career in biostatistics by providing you with the skills you need to design and conduct research studies, analyze data, and interpret results. You'll also learn about the different types of statistical methods used in biostatistics and how to apply them to real-world problems.

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Data Analyst

Data Analysts identify and interpret patterns in data to drive business decisions. They often work with large datasets to uncover insights that can improve operations, increase efficiency, and boost revenue. This course can help prepare you for a career in data analysis by providing you with the skills you need to clean and prepare data, perform statistical analysis, and interpret results. You'll also learn about the different types of data analysis techniques and how to apply them to real-world problems.

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Data Engineer

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Quantitative Analyst

Quantitative Analysts use mathematical and statistical methods to analyze financial data. They work with investment firms, banks, and hedge funds to help them make informed decisions about how to invest their money. This course can help you prepare for a career in quantitative analysis by providing you with the skills you need to analyze financial data, build financial models, and make investment recommendations. You'll also learn about the different types of quantitative analysis techniques and how to apply them to real-world problems.

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Market Researcher

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Business Analyst

Business Analysts use data to make better business decisions. They work in a variety of industries, including technology, finance, and healthcare. This course can help you prepare for a career in business analysis by providing you with the skills you need to analyze data, identify trends, and make recommendations for improvement. You'll also learn about the different types of business analysis techniques and how to apply them to real-world problems.

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Operations Research Analyst

Operations Research Analysts use mathematical and statistical methods to improve the efficiency of business operations. They work in a variety of industries, including manufacturing, healthcare, and transportation. This course can help you prepare for a career in operations research by providing you with the skills you need to analyze data, build mathematical models, and make recommendations for improvement. You'll also learn about the different types of operations research techniques and how to apply them to real-world problems.

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Actuary

Actuaries use mathematical and statistical methods to assess risk and uncertainty. They work in a variety of industries, including insurance, finance, and healthcare. This course can help you prepare for a career in actuarial science by providing you with the skills you need to analyze data, build financial models, and make risk assessments. You'll also learn about the different types of actuarial methods and how to apply them to real-world problems.

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Risk Manager

Risk Managers identify, assess, and manage risks. They work in a variety of industries, including finance, healthcare, and manufacturing. This course can help you prepare for a career in risk management by providing you with the skills you need to identify and assess risks, develop risk management plans, and implement risk management strategies. You'll also learn about the different types of risk management techniques and how to apply them to real-world problems.

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Financial Analyst

Financial Analysts use financial data to make investment decisions. They work with companies, governments, and individuals to help them make informed decisions about how to invest their money. This course can help you prepare for a career in financial analysis by providing you with the skills you need to analyze financial data, build financial models, and make investment recommendations. You'll also learn about the different types of financial analysis techniques and how to apply them to real-world problems.

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Software Engineer

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