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Fataneh Taghaboni-Dutta, Ph.D., PMP, CSM, CSPO

This course provides an analytical framework to help you evaluate key problems in a structured fashion and will equip you with tools to better manage the uncertainties that pervade and complicate business processes. Specifically, you will learn how to summarize data and learn concepts of frequency, normal distribution, statistical studies, sampling, and confidence intervals.

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This course provides an analytical framework to help you evaluate key problems in a structured fashion and will equip you with tools to better manage the uncertainties that pervade and complicate business processes. Specifically, you will learn how to summarize data and learn concepts of frequency, normal distribution, statistical studies, sampling, and confidence intervals.

While you will be introduced to some of the science of what is being taught, the focus will be on applying the methodologies. This will be accomplished through the use of Excel and data sets from different disciplines, allowing you to see the use of statistics in a range of settings. The course will focus not only on explaining these concepts, but also understanding and interpreting the results obtained.

You will be able to:

• Summarize large data sets in graphical, tabular, and numerical forms

• Understand the significance of proper sampling and why one can rely on sample information

• Understand why normal distribution can be used in a wide range of settings

• Use sample information to make inferences about the population with a certain level of confidence about the accuracy of the estimations

• Use Excel for statistical analysis

This course is part of Gies College of Business’ suite of online programs, including the iMBA and iMSM. Learn more about admission into these programs and explore how your Coursera work can be leveraged if accepted into a degree program at https://degrees.giesbusiness.illinois.edu/idegrees/.

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What's inside

Syllabus

Course Orientation
You will become familiar with the course, your classmates, and our learning environment. The orientation will also help you obtain the technical skills required for the course.
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Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Teaches complex ideas in a clear and structured way, making it suitable for beginners seeking a solid foundation
Develops employable data analysis skills and provides hands-on experience using industry-standard tools
Covers a comprehensive range of statistical concepts, providing a well-rounded understanding
Brings statistical analysis to life with real-world examples and applications
Taught by experienced instructors with industry expertise
Requires students to purchase a textbook separately

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Reviews summary

Data and statistics for business

According to learners, this course provides a strong foundation in using statistics for business decision making. Many found the content, covering topics like summarizing data, sampling, and inference, to be highly relevant and directly applicable to their work. The use of Excel for practical application was also frequently highlighted as a positive aspect, making complex concepts more accessible. While the course is generally well-received and the instructor's explanations are often described as clear, some students, particularly those new to statistics, found certain modules or concepts to be challenging and requiring significant effort or supplementary resources to fully grasp.
Instructor explains topics effectively
"The instructor is very clear in explaining complex statistical concepts, breaking them down into understandable parts."
"I found the explanations easy to follow, which is crucial when learning a subject like statistics."
"While generally clear, some difficult concepts still required rewatching lectures or seeking external help."
Learn to apply statistics using Excel
"I really liked how the course integrated the use of Excel throughout the modules. The practical exercises made the concepts much easier to apply."
"Using Excel for the statistical analysis was incredibly helpful and immediately applicable to tasks I encounter in my job."
"The Excel demonstrations were useful, though sometimes the application felt a bit basic if you have prior Excel experience."
Directly applicable to business roles
"The skills learned in this course are highly relevant to my current job and have already helped me make better data-driven decisions."
"I can immediately apply the concepts of summarizing data and interpreting results in my business reports."
"This course provides useful tools and strategies for anyone involved in business decision making."
Provides a solid base in essential statistics
"This course provides a great introduction to statistics, focusing on the concepts needed for business decision making. I finally understand sampling and inference."
"The modules on descriptive statistics and probability distributions were very clear and helped me build a strong foundation in these core concepts."
"I gained a solid understanding of fundamental statistical ideas and how they relate to real-world business problems."
Can be difficult for beginners in stats
"As someone new to statistics, I found certain parts of the course, particularly the inference module, quite challenging."
"This course required a significant amount of effort and practice to fully grasp the concepts, especially the mathematical underpinnings."
"While the pace is generally good, if you don't have a quantitative background, be prepared to dedicate extra study time."

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 Exploring and Producing Data for Business Decision Making with these activities:
Refresh Statistics
Help refresh your statistics foundation ahead of taking this course to strengthen foundational knowledge.
Browse courses on Statistics
Show steps
  • Review the basic concepts of data analysis.
  • Practice with descriptive statistics.
  • Apply statistical concepts to real-world problems.
Read 'Statistical Analysis with Excel'
Supplement your understanding of statistical analysis techniques by reading a comprehensive book that covers the topic in depth.
Show steps
  • Read the book thoroughly.
  • Take notes and highlight important concepts.
Follow Excel Tutorials
Enhance your Excel skills by following guided tutorials to become proficient using Excel for statistical analysis.
Browse courses on Excel
Show steps
  • Complete beginner-friendly Excel tutorials.
  • Practice using Excel for data analysis.
Three other activities
Expand to see all activities and additional details
Show all six activities
Practice Data Summarization
Solidify your understanding of data summarization by engaging in repetitive exercises and practice drills.
Browse courses on Data Summarization
Show steps
  • Summarize data using graphical, tabular, and numerical methods.
  • Interpret the results of data summarization.
Create Data Analysis Presentation
Showcase your understanding of data analysis by creating a comprehensive presentation that effectively communicates your findings.
Browse courses on Data Analysis
Show steps
  • Gather and analyze data relevant to a specific topic.
  • Develop a clear and engaging presentation.
  • Deliver the presentation to an audience.
Teach a Statistical Concept
Master a statistical concept by teaching it to others, whether through a presentation, tutorial, or written article.
Show steps
  • Choose a statistical concept to focus on.
  • Develop a lesson plan or teaching materials.
  • Teach the concept to others.

Career center

Learners who complete Exploring and Producing Data for Business Decision Making will develop knowledge and skills that may be useful to these careers:
Data Analyst
Data Analysts collect, transform, and analyze data from various sources, including structured and unstructured data. They use this data to help businesses understand their customers, make better decisions, and improve their operations. This course can help you build a foundation in data analysis, providing you with the skills to collect, clean, and analyze data, and to communicate your findings effectively. If you are interested in a career as a Data Analyst, this course can provide you with the knowledge and skills you need to get started.
Statistician
Statisticians collect, analyze, and interpret data to help solve problems in a wide range of fields, including medicine, business, and government. This course can help you develop the skills you need to become a successful Statistician, including data analysis, probability, and inference.
Market Researcher
Market Researchers collect and analyze data about markets, customers, and competitors. They use this data to help businesses make informed decisions about their products, services, and marketing campaigns. This course can help you develop the skills you need to become a successful Market Researcher, including data analysis, survey design, and statistical analysis.
Data Scientist
Data Scientists use data to build models that can predict future events or outcomes. They work with businesses to help them make better decisions, improve their operations, and increase profits. This course can help you build a foundation in data science, providing you with the skills to collect, clean, and analyze data, and to build and evaluate models.
Business Analyst
Business Analysts work with businesses to improve their operations and achieve their goals. They analyze data, identify problems, and develop solutions that can help businesses save money, improve efficiency, and increase profits. This course can help you develop the skills you need to become a successful Business Analyst, including data analysis, problem-solving, and communication skills.
Machine Learning Engineer
Machine Learning Engineers build and maintain machine learning models. They work with data scientists to develop and deploy models that can be used to solve a variety of problems, such as fraud detection, spam filtering, and image recognition. This course can help you develop the skills you need to become a successful Machine Learning Engineer, including data analysis, machine learning, and software engineering.
Information Security Analyst
Information Security Analysts protect computer systems and networks from unauthorized access, use, disclosure, disruption, modification, or destruction. They work with organizations to develop and implement security measures that can help protect their data and systems from cyber attacks. This course can help you develop the skills you need to become a successful Information Security Analyst, including data analysis, security risk assessment, and incident response.
Data Engineer
Data Engineers build and maintain data pipelines and infrastructure. They work with data scientists and other data professionals to ensure that data is available, reliable, and secure. This course can help you develop the skills you need to become a successful Data Engineer, including data engineering, cloud computing, and data security.
Risk Analyst
Risk Analysts identify, assess, and manage risks for organizations. They work with organizations to develop and implement risk management strategies that can help them avoid or mitigate risks. This course can help you develop the skills you need to become a successful Risk Analyst, including data analysis, risk assessment, and risk management.
Operations Research Analyst
Operations Research Analysts use data to improve the efficiency and effectiveness of organizations. They use mathematical models and statistical analysis to identify problems and develop solutions that can help organizations improve their operations, reduce costs, and increase profits. This course can help you develop the skills you need to become a successful Operations Research Analyst, including data analysis, modeling, and optimization.
Financial Analyst
Financial Analysts use data to make recommendations on investments. They analyze financial data, such as company earnings and stock prices, to identify opportunities and risks. This course can help you develop the skills you need to become a successful Financial Analyst, including data analysis, financial modeling, and valuation.
Actuary
Actuaries use data to assess risk and uncertainty. They work with insurance companies, pension funds, and other financial institutions to develop products and services that help people manage their financial risks. This course can help you develop the skills you need to become a successful Actuary, including data analysis, probability, and statistics.
Compliance Analyst
Compliance Analysts ensure that organizations comply with laws and regulations. They work with organizations to develop and implement compliance programs that can help them avoid legal penalties and reputational damage. This course can help you develop the skills you need to become a successful Compliance Analyst, including data analysis, regulatory compliance, and corporate governance.
Database Administrator
Database Administrators manage and maintain databases. They are responsible for ensuring that data is stored securely and efficiently, and that it is available to users when they need it. This course can help you develop the skills you need to become a successful Database Administrator, including database management, SQL, and data security.
Forensic Analyst
Forensic Analysts investigate computer crimes and digital evidence. They work with law enforcement and other organizations to collect, analyze, and interpret digital evidence that can be used to solve crimes and prevent future attacks. This course can help you develop the skills you need to become a successful Forensic Analyst, including data analysis, digital forensics, and law enforcement.

Reading list

We've selected 14 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 Exploring and Producing Data for Business Decision Making.
This textbook provides a comprehensive overview of data science, with a focus on business applications. It covers topics such as data mining, machine learning, and statistical modeling.
This popular textbook provides a comprehensive overview of statistics, with a focus on real-world applications. It covers similar topics to the course, but in greater depth, making it a valuable resource for students who want to delve deeper into the subject.
This open-source textbook covers a wide range of statistical concepts, including those covered in the course. It is written in a clear and engaging style, making it accessible to students with diverse backgrounds.
This practical guide to data analysis in Python covers a wide range of topics, including data preprocessing, data exploration, and data visualization. It is suitable for students with some programming experience.
This practical guide to data science in R covers a wide range of topics, including data preprocessing, data exploration, and data visualization. It is suitable for students with some programming experience.
This practical guide to machine learning is written for programmers with little or no statistical background. It covers a wide range of topics, including data preprocessing, feature engineering, and model evaluation.
This textbook provides a comprehensive overview of statistical quality control techniques, including topics such as process monitoring, control charts, and acceptance sampling. It is suitable for students interested in quality management.
This practical textbook provides an overview of data mining techniques and their applications in business. It covers topics such as data preprocessing, feature selection, and model evaluation.
This classic textbook provides a comprehensive overview of time series analysis, a statistical technique used to analyze time-dependent data. It covers topics such as forecasting, smoothing, and seasonal adjustment.
Provides a comprehensive overview of causal inference, a statistical technique used to determine the causal effects of interventions. It is suitable for students interested in causal modeling.
This textbook introduces Bayesian statistics, a powerful approach to statistical modeling. It is written in a clear and engaging style, making it accessible to students with diverse backgrounds.
This advanced textbook provides a comprehensive overview of statistical learning methods, including topics such as regression, classification, and clustering. It is suitable for students with a strong foundation in statistics.
This comprehensive textbook provides a deep dive into deep learning, a state-of-the-art machine learning technique. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks.

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