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The course will begin with what is familiar to many business managers and those who have taken the first two courses in this specialization. The first set of tools will explore data description, statistical inference, and regression. We will extend these...
Read more
The course will begin with what is familiar to many business managers and those who have taken the first two courses in this specialization. The first set of tools will explore data description, statistical inference, and regression. We will extend these...
Read more
The course will begin with what is familiar to many business managers and those who have taken the first two courses in this specialization. The first set of tools will explore data description, statistical inference, and regression. We will extend these concepts to other statistical methods used for prediction when the response variable is categorical such as win-don’t win an auction. In the next segment, students will learn about tools used for identifying important features in the dataset that can either reduce the complexity or help identify important features of the data or further help explain behavior.
Register for this course and see more details by visiting:
OpenCourser.com/course/8sa4i5/data
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possible dealbreakers
Teaches a variety of statistical methods for categorical variables
Focuses on practical applications of statistical concepts
Requires knowledge of basic statistical concepts
Relevant for business managers and professionals involved in data analysis
Covers topics that are foundational in many data-related roles
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Reviews summary
Data-driven analytics
This course takes you on a journey into data analysis and modeling. You will cover everything from descriptive statistics to predictive modeling. With 5-star reviews taking up the majority of the feedback, this course gets high marks for the instructor and the material.
The instructor presents the material well.
"Well teaching"
Course provides valuable learning material.
"This course is very valuable to me."
The course relies on out-of-date software.
"The software packages the course relies on are no longer supported. ..."
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 Modeling and Regression Analysis in Business with these
activities:
Follow online tutorials
Show steps
Supplement course material by exploring guided tutorials covering specific statistical techniques
Browse courses on
Regression Analysis
Show steps
-
Watch videos on how to conduct hypothesis testing
-
Follow tutorials on using regression analysis software
Review introductory statistics
Show steps
Review fundamental concepts of statistical inference and regression to prepare for the course
Browse courses on
Hypothesis Testing
Show steps
-
Review Probability and Distributions
-
Practice basic hypothesis testing
-
Interpret results of regression analysis
Gather additional resources
Show steps
Enhance learning by exploring additional articles, videos, and online resources related to the course material
Show steps
-
Find articles on recent advancements in statistical methods
-
Identify videos explaining complex statistical concepts
Six other activities
Expand to see all activities and additional details
Show all nine activities
Develop a cheat sheet
Show steps
Create a concise reference guide for key formulas and concepts, fostering memorization and quick recall
Browse courses on
Regression Analysis
Show steps
-
Compile formulas for hypothesis testing and regression
-
Create a glossary of statistical terms
Solve statistical problems
Show steps
Enhance understanding by solving practice problems covering key statistical concepts
Browse courses on
Hypothesis Testing
Show steps
-
Answer quiz questions on statistical inference
-
Apply regression techniques to solve real-world problems
Join a study group
Show steps
Engage in discussions and problem-solving with peers, fostering understanding and retention
Show steps
-
Find a group of classmates to study with
-
Take turns leading discussions on different topics
-
Collaborate on practice problems and assignments
Contribute to open-source projects
Show steps
Gain practical experience by working on open-source statistical software or projects
Show steps
-
Identify open-source projects related to statistical analysis
-
Contribute code or documentation to the project
Attend industry conferences
Show steps
Connect with professionals in the field, learn about industry trends, and discover potential career opportunities
Show steps
-
Identify industry conferences related to statistical analysis
-
Attend workshops and presentations on statistical methods
-
Network with professionals in the field
Participate in statistical workshops
Show steps
Enhance skills by participating in hands-on workshops covering advanced statistical techniques
Show steps
-
Identify workshops on topics of interest within statistical analysis
-
Attend workshops and actively participate in exercises
-
Implement newly learned techniques in personal projects
Follow online tutorials
Show steps
Supplement course material by exploring guided tutorials covering specific statistical techniques
Browse courses on
Regression Analysis
Show steps
Show steps
Show steps
- Watch videos on how to conduct hypothesis testing
- Follow tutorials on using regression analysis software
Review introductory statistics
Show steps
Review fundamental concepts of statistical inference and regression to prepare for the course
Browse courses on
Hypothesis Testing
Show steps
Show steps
Show steps
- Review Probability and Distributions
- Practice basic hypothesis testing
- Interpret results of regression analysis
Gather additional resources
Show steps
Enhance learning by exploring additional articles, videos, and online resources related to the course material
Show steps
Show steps
Show steps
- Find articles on recent advancements in statistical methods
- Identify videos explaining complex statistical concepts
Six other activities
Expand to see all activities and additional details
Show all nine activities
Develop a cheat sheet
Show steps
Create a concise reference guide for key formulas and concepts, fostering memorization and quick recall
Browse courses on
Regression Analysis
Show steps
Show steps
Show steps
- Compile formulas for hypothesis testing and regression
- Create a glossary of statistical terms
Solve statistical problems
Show steps
Enhance understanding by solving practice problems covering key statistical concepts
Browse courses on
Hypothesis Testing
Show steps
Show steps
Show steps
- Answer quiz questions on statistical inference
- Apply regression techniques to solve real-world problems
Join a study group
Show steps
Engage in discussions and problem-solving with peers, fostering understanding and retention
Show steps
Show steps
Show steps
- Find a group of classmates to study with
- Take turns leading discussions on different topics
- Collaborate on practice problems and assignments
Contribute to open-source projects
Show steps
Gain practical experience by working on open-source statistical software or projects
Show steps
Show steps
Show steps
- Identify open-source projects related to statistical analysis
- Contribute code or documentation to the project
Attend industry conferences
Show steps
Connect with professionals in the field, learn about industry trends, and discover potential career opportunities
Show steps
Show steps
Show steps
- Identify industry conferences related to statistical analysis
- Attend workshops and presentations on statistical methods
- Network with professionals in the field
Participate in statistical workshops
Show steps
Enhance skills by participating in hands-on workshops covering advanced statistical techniques
Show steps
Show steps
Show steps
- Identify workshops on topics of interest within statistical analysis
- Attend workshops and actively participate in exercises
- Implement newly learned techniques in personal projects
Career center
Learners who complete Data Modeling and Regression Analysis in Business will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts translate raw data into actionable insights that drive business decisions. They design and implement data collection systems, perform statistical analysis, and build data models to identify trends and patterns. This course can help aspiring Data Analysts develop the foundational skills necessary to succeed in this role, including data description, statistical inference, regression analysis, and identifying important data features.
Data Scientist
Data Scientist
Data Scientists combine programming skills with statistical and analytical expertise to solve complex business problems. They use data to build predictive models, develop algorithms, and create data visualizations to support decision-making. This course can provide Data Scientists with essential knowledge in data modeling, regression analysis, and feature selection, which are key components of the data science process.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models to automate tasks and improve business outcomes. They collaborate with Data Scientists and other technical professionals to build and maintain machine learning systems. This course can help Machine Learning Engineers gain a strong foundation in data modeling, regression analysis, and statistical methods, which are essential for understanding and working with machine learning algorithms.
Business Analyst
Business Analyst
Business Analysts bridge the gap between business and technology teams, using their analytical and problem-solving skills to translate business requirements into technical solutions. They develop data models, conduct market research, and analyze business processes to identify opportunities for improvement. This course can help Business Analysts enhance their data analysis and modeling skills, equipping them to make more informed decisions and drive business value.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts (Quants) use mathematical and statistical models to assess risk and make investment decisions in the financial industry. They build complex data models to analyze market data, price financial instruments, and manage risk. This course can provide Quants with the necessary skills in data modeling, regression analysis, and statistical inference to develop and evaluate financial models.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to optimize business processes and systems. They develop models to simulate and analyze complex systems, identify bottlenecks, and design solutions to improve efficiency. This course can provide Operations Research Analysts with the foundational knowledge in data modeling, regression analysis, and statistical methods required for building and evaluating optimization models.
Actuary
Actuary
Actuaries assess risk and uncertainty in financial and insurance industries. They develop mathematical models to calculate probabilities, premiums, and reserves. This course can help Actuaries gain a solid understanding of data modeling, regression analysis, and statistical inference, which are essential for analyzing and managing financial risks.
Market Researcher
Market Researcher
Market Researchers collect, analyze, and interpret data to understand market trends, consumer behavior, and industry dynamics. They use data modeling and regression analysis to forecast demand, identify target markets, and develop marketing strategies. This course can help Market Researchers enhance their data analysis and modeling skills, enabling them to make more informed decisions and provide actionable insights to businesses.
Econometrician
Econometrician
Econometricians apply statistical and econometric methods to study economic phenomena. They develop models to analyze economic data, test economic theories, and forecast economic trends. This course can provide Econometricians with a solid foundation in data modeling, regression analysis, and statistical inference, which are crucial for building and evaluating econometric models.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines and infrastructure to manage and process large volumes of data. They use data modeling techniques to design data schemas and ensure data quality. This course can help Data Engineers gain a foundational understanding of data modeling, which is essential for designing and implementing data management systems.
Financial Analyst
Financial Analyst
Financial Analysts evaluate and recommend investment opportunities for individuals and institutions. They use data analysis and modeling to assess financial statements, analyze market trends, and make investment decisions. This course can help Financial Analysts enhance their data analysis and modeling skills, enabling them to make more informed investment recommendations and manage financial portfolios.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data to provide insights into various fields. They develop statistical models to analyze data, test hypotheses, and make predictions. This course can provide Statisticians with a strong foundation in data modeling, regression analysis, and statistical inference, which are essential for conducting statistical research and drawing meaningful conclusions from data.
Data Journalist
Data Journalist
Data Journalists use data analysis and visualization techniques to tell stories and communicate complex information to the public. They use data models and regression analysis to analyze data, identify trends, and present findings in a clear and engaging way. This course can help Data Journalists develop the skills necessary to effectively use data in their storytelling and reporting.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They use data modeling techniques to design and implement data structures and algorithms. This course may be helpful for Software Engineers who want to gain a better understanding of data modeling, which can enhance their ability to design and develop efficient and scalable software solutions.
Marketing Manager
Marketing Manager
Marketing Managers develop and execute marketing strategies to promote products or services and build brand awareness. They use data analysis and modeling to understand consumer behavior, target audiences, and measure marketing campaign effectiveness. This course may be helpful for Marketing Managers who want to enhance their data analysis skills, enabling them to make more informed decisions and optimize their marketing campaigns.
For more career information including salaries, visit:
OpenCourser.com/course/8sa4i5/data
Reading list
We've selected ten 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 Modeling and Regression Analysis in Business.
Provides a comprehensive treatment of regression analysis, covering advanced topics relevant to the course.
Provides an in-depth treatment of regression analysis techniques, covering both classical and modern methods.
Save
Provides a theoretical foundation for causal inference, helping students understand the limitations and challenges of drawing causal conclusions.
Provides a comprehensive overview of data science techniques and their applications in business.
A classic textbook covering a wide range of statistical learning methods, including regression, classification, and clustering.
Focuses on hierarchical models and their applications in social science research, providing insights into multilevel data analysis.
Covers statistical methods commonly used in business and economics, providing a solid foundation for understanding the course topics.
A more accessible introduction to statistical learning, providing a gentle entry point for students new to the field.
Covers Bayesian statistical methods, providing an alternative perspective on statistical inference and parameter estimation.
Offers a more technical perspective on machine learning, providing insights into the underlying algorithms and theory.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/8sa4i5/data
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Time
25 hours
Via
Coursera
Institution
University of Illinois at Urbana-Champaign
Good to know
Teaches a variety of statistical methods for categorical variables
Focuses on practical applications of statistical concepts
Requires knowledge of basic statistical concepts
Relevant for business managers and professionals involved in data analysis
Covers topics that are foundational in many data-related roles
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