Mastering Data Analysis in Excel from Coursera

Important: The focus of this course is on math - specifically, data-analysis concepts and methods - not on Excel for its own sake. We use Excel to do our calculations, and all math formulas are given as Excel Spreadsheets, but we do not attempt to cover Excel Macros, Visual Basic, Pivot Tables, or other intermediate-to-advanced Excel functionality.

The second big idea this course seeks to demonstrate is that your data-analysis results cannot and should not aim to eliminate all uncertainty. Your role as a data-analyst is to reduce uncertainty for decision-makers by a financially valuable increment, while quantifying how much uncertainty remains. You will learn to calculate and apply to real-world examples the most important uncertainty measures used in business, including classification error rates, entropy of information, and confidence intervals for linear regression.

All the data you need is provided within the course, all assignments are designed to be done in MS Excel, and you will learn enough Excel to complete all assignments. The course will give you enough practice with Excel to become fluent in its most commonly used business functions, and you’ll be ready to learn any other Excel functionality you might need in the future (module 1).

The course does not cover Visual Basic or Pivot Tables and you will not need them to complete the assignments. All advanced concepts are demonstrated in individual Excel spreadsheet templates that you can use to answer relevant questions. You will emerge with substantial vocabulary and practical knowledge of how to apply business data analysis methods based on binary classification (module 2), information theory and entropy measures (module 3), and linear regression (module 4 and 5), all using no software tools more complex than Excel.

What's inside

Syllabus

About This Course

This course will prepare you to design and implement realistic predictive models based on data. In the Final Project (module 6) you will assume the role of a business data analyst for a bank, and develop two different predictive models to determine which applicants for credit cards should be accepted and which rejected. Your first model will focus on minimizing default risk, and your second on maximizing bank profits. The two models should demonstrate to you in a practical, hands-on way the idea that your choice of business metric drives your choice of an optimal model.The second big idea this course seeks to demonstrate is that your data-analysis results cannot and should not aim to eliminate all uncertainty. Your role as a data-analyst is to reduce uncertainty for decision-makers by a financially valuable increment, while quantifying how much uncertainty remains. You will learn to calculate and apply to real-world examples the most important uncertainty measures used in business, including classification error rates, entropy of information, and confidence intervals for linear regression. All the data you need is provided within the course, and all assignments are designed to be done in MS Excel. The course will give you enough practice with Excel to become fluent in its most commonly used business functions, and you’ll be ready to learn any other Excel functionality you might need in future (module 1). The course does not cover Visual Basic or Pivot Tables and you will not need them to complete the assignments. All advanced concepts are demonstrated in individual Excel spreadsheet templates that you can use to answer relevant questions. You will emerge with substantial vocabulary and practical knowledge of how to apply business data analysis methods based on binary classification (module 2), information theory and entropy measures (module 3), and linear regression (module 4 and 5), all using no software tools more complex than Excel.

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Develops advanced business data analysis methods, which are necessary for business data analysts

Taught by highly experienced instructors, ensuring quality of instruction

Builds beginner-friendly Excel skills, allowing for accessible and practical learning

Emphasizes hands-on learning through practical assignments to enhance practical skills

Provides valuable data science concepts and techniques for real-world application

Reviews summary

Data analysis concepts using excel

According to students, this course is a strong introduction focused on the mathematical and conceptual foundations of data analysis, rather than advanced Excel features. Learners appreciated the clear explanations of complex topics like binary classification, entropy, and linear regression. The practical application of concepts and the hands-on approach using Excel for calculations were frequently highlighted as major strengths, allowing them to apply skills directly. While generally well-received, some noted the pace can be quick for those without prior math or statistics background.

Avoids advanced Excel features.

"As advertised, the course strictly avoids advanced Excel features like Pivot Tables or VBA."

"Don't expect to become an Excel guru here; the focus is solely on using it for the math."

"This course is not for you if you're looking to master complex Excel functions beyond basic calculations."

"The course outline makes it clear what Excel features are covered, aligning with the description."

Project applies skills to a business case.

"The final project was a highlight, allowing me to apply everything learned to a realistic business scenario."

"Analyzing the credit card data for the final project felt like a genuine data analyst task."

"It was very valuable to build two different predictive models based on different business objectives."

"The project effectively integrated all the concepts from the previous modules."

Complex topics made understandable.

"The instructor did a fantastic job explaining difficult concepts like entropy and AUC in a way that was easy to follow."

"I appreciated the step-by-step breakdown of the linear regression calculations; it was very clear."

"Even without a strong math background, the explanations helped me grasp the core ideas."

"The video lectures were well-structured and the explanations were precise."

Learn concepts through hands-on Excel use.

"Using Excel to work through the calculations really solidified my understanding of the formulas and concepts."

"I found the assignments in Excel incredibly practical and directly applicable to my work."

"The course provided enough Excel practice to feel comfortable performing the required data analysis tasks."

"Working hands-on with the data in Excel was crucial for making the theoretical concepts concrete."

Highlights core data analysis principles.

"This course was a great way to understand the concepts behind data analysis techniques like classification and regression..."

"What I liked most was the emphasis on the math and theory, not just button-pushing in Excel."

"I feel like I really grasp the 'why' behind these methods now, which is much more valuable than just knowing the 'how'."

"The course clearly prioritizes understanding data analysis methods over specific Excel tricks."

May be challenging without prior knowledge.

"While the explanations are good, I struggled with the pace because my math background isn't strong."

"Some parts felt very challenging and required reviewing external statistics resources to keep up."

"I think having a basic understanding of statistics or algebra would make this course much easier."

"The jump in complexity between modules was sometimes significant, especially if you're new to the math involved."

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 Mastering Data Analysis in Excel with these activities:

Read 'Data Science for Dummies'

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Enhance your foundational understanding of data science concepts by reading this introductory book, which provides a comprehensive overview of the field.

View Artificial Intelligence For Dummies (For... on Amazon

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Read selected chapters relevant to the course topics.
Take notes and highlight key concepts for future reference.

Refresh Excel Basics

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Start by recapping the basics of Excel and its most common functions to ensure a smooth start to the course.

Browse courses on Excel

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Review basic Excel functions, such as SUM, AVERAGE, and COUNTIF.
Create a simple spreadsheet to practice data entry and formatting.

Compile a Glossary of Key Terms

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Create a comprehensive glossary of important terms and concepts covered in the course, ensuring a clear understanding of the terminology used.

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Review course materials and identify key terms.
Define each term concisely and accurately.

Five other activities

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Participate in a Study Group

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Connect with fellow learners to discuss course concepts, share insights, and provide support, fostering a collaborative learning environment.

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Join or form a study group with other students in the course.
Meet regularly to review materials, solve problems, and prepare for assessments.

Practice Binary Classification Metrics

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Complete exercises to strengthen your understanding of binary classification metrics, ensuring proficiency in evaluating model performance.

Browse courses on Binary Classification

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Calculate AUC, TPR, FPR, and Accuracy for given binary classification scenarios.
Interpret ROC curves to assess model performance and identify optimal thresholds.

Explore Information Theory and Entropy

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Follow online tutorials or read articles to deepen your understanding of information theory and entropy, which are crucial concepts for quantifying uncertainty in data analysis.

Browse courses on Information Theory

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Research and understand the concepts of entropy, information gain, and cross-entropy.
Apply entropy calculations to real-world scenarios to measure uncertainty.

Develop a Linear Regression Model

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Apply your knowledge by creating a linear regression model using real-world data. This hands-on project will solidify your understanding of model building and evaluation.

Browse courses on Linear Regression

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Collect and prepare data relevant to the chosen business problem.
Build a linear regression model and evaluate its performance using metrics such as R-squared and MSE.
Interpret the model's coefficients and make predictions based on the model.

Work on the Final Course Project

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Engage in the comprehensive final project, which challenges you to develop predictive models for credit card applications. This project will test your ability to apply the concepts learned throughout the course.

Browse courses on Predictive Modeling

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Analyze the provided credit card application data.
Develop two predictive models, one for minimizing default risk and one for maximizing bank profits.
Evaluate the performance of both models using relevant metrics.

Career center

Learners who complete Mastering Data Analysis in Excel will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists are highly trained professionals who use their knowledge of math, statistics, and computer science to extract insights from data. This course may be useful for Data Scientists who want to learn more about data analysis techniques, such as binary classification, information theory, and linear regression. These techniques can be used to build predictive models that can help businesses make better decisions.

See salaries and explore the career path for Data Scientist

Business Analyst

Business Analysts use data to solve business problems. They work with stakeholders to understand their needs and then use data to develop solutions. This course may be useful for Business Analysts who want to learn more about data analysis techniques, such as binary classification, information theory, and linear regression. These techniques can be used to build predictive models that can help businesses make better decisions.

See salaries and explore the career path for Business Analyst

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data. They use this information to make investment recommendations. This course may be useful for Quantitative Analysts who want to learn more about data analysis techniques, such as binary classification, information theory, and linear regression. These techniques can be used to build predictive models that can help investors make better decisions.

See salaries and explore the career path for Quantitative Analyst

Market Researcher

Market Researchers collect and analyze data about consumers. They use this information to help businesses develop products and services that meet the needs of their customers. This course may be useful for Market Researchers who want to learn more about data analysis techniques, such as binary classification, information theory, and linear regression. These techniques can be used to build predictive models that can help businesses make better decisions about product development and marketing.

See salaries and explore the career path for Market Researcher

Operations Research Analyst

Operations Research Analysts use mathematical and statistical models to improve the efficiency of business operations. They work with businesses to identify and solve problems, such as how to optimize production schedules or how to improve customer service. This course may be useful for Operations Research Analysts who want to learn more about data analysis techniques, such as binary classification, information theory, and linear regression. These techniques can be used to build predictive models that can help businesses make better decisions about how to operate their businesses.

See salaries and explore the career path for Operations Research Analyst

Risk Analyst

Risk Analysts assess and manage financial risks. They work with businesses to identify, measure, and mitigate risks. This course may be useful for Risk Analysts who want to learn more about data analysis techniques, such as binary classification, information theory, and linear regression. These techniques can be used to build predictive models that can help businesses make better decisions about how to manage their risks.

See salaries and explore the career path for Risk Analyst

Data Engineer

Data Engineers build and maintain the infrastructure that is used to store and process data. They work with data scientists and business analysts to ensure that data is available and accessible for analysis. This course may be useful for Data Engineers who want to learn more about data analysis techniques, such as binary classification, information theory, and linear regression. These techniques can be used to build predictive models that can help businesses make better decisions about how to use their data.

See salaries and explore the career path for Data Engineer

Machine Learning Engineer

Machine Learning Engineers build and maintain machine learning models. They work with data scientists and business analysts to develop and deploy machine learning models that can be used to automate tasks and make predictions. This course may be useful for Machine Learning Engineers who want to learn more about data analysis techniques, such as binary classification, information theory, and linear regression. These techniques can be used to build predictive models that can help businesses make better decisions about how to use their data.

See salaries and explore the career path for Machine Learning Engineer

Statistician

Statisticians collect, analyze, and interpret data. They work with businesses and organizations to help them make informed decisions. This course may be useful for Statisticians who want to learn more about data analysis techniques, such as binary classification, information theory, and linear regression. These techniques can be used to build predictive models that can help businesses make better decisions about how to use their data.

See salaries and explore the career path for Statistician

Financial Analyst

Financial Analysts analyze financial data to make investment recommendations. They work with businesses and individuals to help them make informed decisions about how to invest their money. This course may be useful for Financial Analysts who want to learn more about data analysis techniques, such as binary classification, information theory, and linear regression. These techniques can be used to build predictive models that can help businesses make better decisions about how to invest their money.

See salaries and explore the career path for Financial Analyst

Actuary

Actuaries use mathematical and statistical models to assess and manage financial risks. They work with insurance companies and other financial institutions to help them make informed decisions about how to price insurance policies and manage their investments. This course may be useful for Actuaries who want to learn more about data analysis techniques, such as binary classification, information theory, and linear regression. These techniques can be used to build predictive models that can help businesses make better decisions about how to manage their risks.

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Economist

Economists study the production, distribution, and consumption of goods and services. They use data to analyze economic trends and make predictions about the future. This course may be useful for Economists who want to learn more about data analysis techniques, such as binary classification, information theory, and linear regression. These techniques can be used to build predictive models that can help businesses make better decisions about how to operate their businesses.

See salaries and explore the career path for Economist

Data Analyst

Data Analysts collect, clean, and analyze data. They work with businesses and organizations to help them make informed decisions. This course may be useful for Data Analysts who want to learn more about data analysis techniques, such as binary classification, information theory, and linear regression. These techniques can be used to build predictive models that can help businesses make better decisions about how to use their data.

See salaries and explore the career path for Data Analyst

Software Engineer

Software Engineers design, develop, and maintain software applications. They work with businesses and organizations to help them solve problems and improve their operations. This course may be useful for Software Engineers who want to learn more about data analysis techniques, such as binary classification, information theory, and linear regression. These techniques can be used to build predictive models that can help businesses make better decisions about how to use their data.

See salaries and explore the career path for Software Engineer

Computer Scientist

Computer Scientists study the theory and practice of computing. They work with businesses and organizations to help them solve problems and improve their operations. This course may be useful for Computer Scientists who want to learn more about data analysis techniques, such as binary classification, information theory, and linear regression. These techniques can be used to build predictive models that can help businesses make better decisions about how to use their data.

See salaries and explore the career path for Computer Scientist