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Sharad Borle

The ability to understand and apply Business Statistics is becoming increasingly important in the industry. A good understanding of Business Statistics is a requirement to make correct and relevant interpretations of data. Lack of knowledge could lead to erroneous decisions which could potentially have negative consequences for a firm. This course is designed to introduce you to Business Statistics. We begin with the notion of descriptive statistics, which is summarizing data using a few numbers. Different categories of descriptive measures are introduced and discussed along with the Excel functions to calculate them. The notion of probability or uncertainty is introduced along with the concept of a sample and population data using relevant business examples. This leads us to various statistical distributions along with their Excel functions which are then used to model or approximate business processes. You get to apply these descriptive measures of data and various statistical distributions using easy-to-follow Excel based examples which are demonstrated throughout the course.

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The ability to understand and apply Business Statistics is becoming increasingly important in the industry. A good understanding of Business Statistics is a requirement to make correct and relevant interpretations of data. Lack of knowledge could lead to erroneous decisions which could potentially have negative consequences for a firm. This course is designed to introduce you to Business Statistics. We begin with the notion of descriptive statistics, which is summarizing data using a few numbers. Different categories of descriptive measures are introduced and discussed along with the Excel functions to calculate them. The notion of probability or uncertainty is introduced along with the concept of a sample and population data using relevant business examples. This leads us to various statistical distributions along with their Excel functions which are then used to model or approximate business processes. You get to apply these descriptive measures of data and various statistical distributions using easy-to-follow Excel based examples which are demonstrated throughout the course.

To successfully complete course assignments, students must have access to Microsoft Excel.

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WEEK 1

Module 1: Basic Data Descriptors

In this module you will get to understand, calculate and interpret various descriptive or summary measures of data. These descriptive measures summarize and present data using a few numbers. Appropriate Excel functions to do these calculations are introduced and demonstrated.

Topics covered include:

• Categories of descriptive data

• Measures of central tendency, the mean, median, mode, and their interpretations and calculations

• Measures of spread-in-data, the range, interquartile-range, standard deviation and variance

• Box plots

• Interpreting the standard deviation measure using the rule-of-thumb and Chebyshev’s theorem

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WEEK 2

Module 2: Descriptive Measures of Association, Probability, and Statistical Distributions

This module presents the covariance and correlation measures and their respective Excel functions. You get to understand the notion of causation versus correlation. The module then introduces the notion of probability and random variables and starts introducing statistical distributions.

Topics covered include:

• Measures of association, the covariance and correlation measures; causation versus correlation

• Probability and random variables; discrete versus continuous data

• Introduction to statistical distributions

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WEEK 3

Module 3: The Normal Distribution

This module introduces the Normal distribution and the Excel function to calculate probabilities and various outcomes from the distribution.

Topics covered include:

• Probability density function and area under the curve as a measure of probability

• The Normal distribution (bell curve), NORM.DIST, NORM.INV functions in Excel

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WEEK 4

Module 4: Working with Distributions, Normal, Binomial, Poisson

In this module, you'll see various applications of the Normal distribution. You will also get introduced to the Binomial and Poisson distributions. The Central Limit Theorem is introduced and explained in the context of understanding sample data versus population data and the link between the two.

Topics covered include:

• Various applications of the Normal distribution

• The Binomial and Poisson distributions

• Sample versus population data; the Central Limit Theorem

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What's inside

Syllabus

Basic Data Descriptors
Descriptive Measures of Association, Probability, and Statistical Distributions
The Normal Distribution
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Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Explores descriptive statistics, which is standard in industry
Introduces to probability and statistical distributions, which are key data science concepts
Instructs students on how to work with the Normal, Binomial, and Poisson distributions, which are common in practical applications
Teaches students to make correct and data-driven decisions, which is a valuable skill in any industry
Course content is delivered through Excel, industry-standard software
Requires learners to have access to Microsoft Excel, a paid software, which may be a barrier to some students

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Reviews summary

Basic business statistics with excel

According to learners, this course provides a clear and accessible introduction to business statistics. Many found the explanations easy to follow, especially if they were new to the subject. The Excel demonstrations were frequently highlighted as a major strength, making the concepts practical and applicable to real-world business scenarios. While many felt it was good for beginners, a few reviewers noted that it might be too basic or slow for those with prior statistics knowledge. Overall, students felt the course successfully covered basic data descriptors and statistical distributions with a relevant focus on business applications.
Directly applicable to business.
"The business examples used throughout the course were very relevant and helped me see the practical use."
"I can see how these statistics concepts apply directly to my work in marketing."
"It's great to learn stats with a clear business context in mind, rather than just abstract theory."
Accessible intro to statistics.
"This course is perfect for someone starting out in business statistics and needs a solid foundation."
"It assumes no prior knowledge of statistics and builds the foundation well, which was great for me as a beginner."
"As a complete beginner, I found the pace and content manageable and not overwhelming."
Demonstrations make concepts concrete.
"The Excel demonstrations were incredibly helpful in understanding how to apply the concepts learned."
"Learning the specific Excel functions for calculating statistics made this course very practical for my work."
"I can immediately use what I learned about using Excel for data analysis at my job, which is fantastic."
Concepts are explained simply.
"The lectures are very clear and easy to follow, even for someone new to statistics."
"I appreciated the step-by-step explanations of potentially complex topics, making them understandable."
"Everything was explained in a way that built logically upon previous lessons, making it easy to grasp."
May be slow if you have background.
"If you have any prior statistics background, this course might feel a bit slow and repetitive at times."
"I was hoping for more advanced topics than what was covered here; it really only scratched the surface."
"For someone with some experience, the initial modules might feel overly simplistic and drawn out."

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 Basic Data Descriptors, Statistical Distributions, and Application to Business Decisions with these activities:
Business Statistics for Dummies
Expand knowledge and reinforce understanding of business statistics concepts.
Show steps
  • Read the relevant chapters in the book.
  • Make notes and highlight key concepts.
  • Complete the practice exercises at the end of each chapter.
Peer Mentoring in Business Statistics
巩固知识 by helping fellow students understand concepts and solve problems.
Browse courses on Business Statistics
Show steps
  • Identify a peer or group who needs assistance with Business Statistics.
  • Review the course materials and refresh your understanding of the relevant concepts.
  • Meet with your mentees regularly to discuss their questions and provide guidance.
  • Encourage your mentees to ask questions and engage actively in learning.
Solve Excel-Based Data Analysis Exercises
Reinforce your understanding of Excel functions and statistical techniques by solving practice problems.
Browse courses on Excel Functions
Show steps
  • Download the practice dataset
  • Use Excel formulas and functions
  • Interpret the results and draw conclusions
Eight other activities
Expand to see all activities and additional details
Show all 11 activities
Exercises in Descriptive Statistics
Practice calculations for basic descriptive statistics to reinforce understanding of concepts covered in Week 1.
Browse courses on Descriptive Statistics
Show steps
  • Review the lecture notes for Week 1.
  • Solve the practice problems at the end of each section.
  • Complete the Excel exercises provided in the course materials.
Excel Functions for Business Statistics
Enhance practical knowledge of Excel functions used in business statistics.
Browse courses on Excel Functions
Show steps
  • Review the Excel functions covered in the course materials.
  • Follow video or online tutorials on how to use these functions.
  • Apply the functions to practice exercises or real-world data.
Analyze Real-World Business Cases
Apply the concepts and techniques learned in the course to analyze real-world business scenarios and make data-driven recommendations.
Show steps
  • Identify a suitable business scenario
  • Collect and preprocess relevant data
  • Apply statistical analyses to extract insights
  • Formulate data-driven recommendations
Attend Business Statistics Conference
Expand professional network and stay updated on latest trends and applications.
Browse courses on Business Statistics
Show steps
  • Research and identify relevant industry conferences.
  • Register for the conference and prepare your elevator pitch.
  • Attend keynote speeches and breakout sessions on Business Statistics.
  • Network with professionals and exchange insights.
Explore Advanced Statistical Distributions
Expand your knowledge of statistical distributions by studying advanced tutorials and applying them to business scenarios.
Browse courses on Statistical Distributions
Show steps
  • Identify the appropriate statistical distribution
  • Estimate the parameters of the distribution
  • Apply the distribution to model business processes
Statistical Distribution Analysis Project
Apply knowledge of statistical distributions to analyze real-world data and make predictions.
Browse courses on Statistical Distributions
Show steps
  • Choose a dataset from the course materials or a credible external source.
  • Identify the appropriate statistical distribution(s) for the data.
  • Use Excel functions to fit the distribution(s) to the data.
  • Analyze the results and make predictions based on the fitted distribution(s).
  • Create a report or presentation summarizing your findings.
Explanatory Video on Statistical Concepts
Reinforce understanding by creating visual content that illustrates statistical concepts.
Browse courses on Descriptive Statistics
Show steps
  • Choose a statistical concept to explain.
  • Create a script or storyboard for a short video.
  • Record and edit the video, using visuals, animations, and clear explanations.
  • Share the video with peers or instructors for feedback.
Data Analysis Volunteer at Non-Profit Organization
Apply statistical skills to real-world problems and make a meaningful contribution.
Browse courses on Business Statistics
Show steps
  • Identify local non-profit organizations that need assistance with data analysis.
  • Contact the organizations and offer your services.
  • Collaborate with the organization to define the problem and scope of work.
  • Collect, clean, and analyze data to provide insights and recommendations.
  • Present your findings to the organization and discuss potential solutions.

Career center

Learners who complete Basic Data Descriptors, Statistical Distributions, and Application to Business Decisions will develop knowledge and skills that may be useful to these careers:
Statistician
A Statistician is responsible for collecting, analyzing, and interpreting data. They use their skills to help businesses make better decisions. This course would be a valuable asset to anyone looking to enter this field. It provides a strong foundation in the basics of data description and statistical distributions, which are essential for understanding and working with data.
Machine Learning Engineer
A Machine Learning Engineer is responsible for developing and deploying machine learning models. They use their skills to automate tasks and make predictions. This course would be a valuable asset to anyone looking to enter this field. It provides a strong foundation in the basics of data description and statistical distributions, which are essential for understanding and working with machine learning data.
Data Scientist
A Data Scientist is responsible for using data to solve business problems. They use their skills to develop machine learning models that can automate tasks and make predictions. This course would be a valuable asset to anyone looking to enter this field. It provides a strong foundation in the basics of data description and statistical distributions, which are essential for understanding and working with data.
Data Analyst
A Data Analyst specializes in extracting and interpreting large amounts of data. They use their skills to identify trends and patterns that can help businesses make better decisions. This course would be a valuable asset to anyone looking to enter this field. It covers the basics of data description, which is essential for understanding and working with data sets. Additionally, the course introduces learners to statistical distributions, which are used in a variety of data analysis applications.
Operations Research Analyst
An Operations Research Analyst is responsible for using mathematical and statistical techniques to solve business problems. They use their skills to develop solutions that can help businesses improve their efficiency and productivity. This course would be a valuable asset to anyone looking to enter this field. It provides a strong foundation in the basics of data description and statistical distributions, which are essential for understanding and working with operations research data.
Quantitative Analyst
A Quantitative Analyst is responsible for using mathematical and statistical techniques to analyze financial data and make investment recommendations. They use their skills to develop trading strategies that can help investors generate alpha. This course would be a valuable asset to anyone looking to enter this field. It provides a strong foundation in the basics of data description and statistical distributions, which are essential for understanding and working with financial data.
Risk Analyst
A Risk Analyst is responsible for identifying and assessing risks for an organization. They use their skills to develop strategies that can help the organization mitigate these risks. This course would be a valuable asset to anyone looking to enter this field. It provides a strong foundation in the basics of data description and statistical distributions, which are essential for understanding and working with risk data.
Financial Analyst
A Financial Analyst is responsible for analyzing financial data to make investment recommendations. They use their skills to identify undervalued stocks and bonds that can help investors grow their wealth. This course would be a valuable asset to anyone looking to enter this field. It provides a strong foundation in the basics of data description and statistical distributions, which are essential for understanding and working with financial data.
Market Research Analyst
A Market Research Analyst is responsible for conducting research to understand consumer behavior and trends. They use their findings to help businesses develop and market their products and services. This course would be a valuable asset to anyone looking to enter this field. It provides a strong foundation in the basics of data description and statistical distributions, which are essential for understanding and working with market research data.
Business Analyst
A Business Analyst is responsible for analyzing an organization's business processes and identifying areas for improvement. They use their skills to develop solutions that can help the organization achieve its goals. This course would be a valuable asset to anyone looking to enter this field. It provides a strong foundation in the basics of data description and statistical distributions, which are essential for understanding and working with business data.
Software Engineer
A Software Engineer is responsible for designing, developing, and testing software applications. They use their skills to create software that meets the needs of users. This course may be useful to someone looking to enter this field as it provides a foundation in the basics of data description and statistical distributions. These concepts are used in various software applications, such as data analysis and visualization tools.
Database Administrator
A Database Administrator is responsible for managing and maintaining databases. They use their skills to ensure that data is stored and retrieved efficiently. This course may be useful to someone looking to enter this field as it provides a foundation in the basics of data description and statistical distributions. These concepts are used in database management systems to optimize data storage and retrieval.
IT Manager
An IT Manager is responsible for planning, implementing, and managing an organization's IT infrastructure. They use their skills to ensure that IT systems are running smoothly and efficiently. This course may be useful to someone looking to enter this field as it provides a foundation in the basics of data description and statistical distributions. These concepts are used in IT management to analyze system performance and identify areas for improvement.
Network Engineer
A Network Engineer is responsible for designing, implementing, and maintaining computer networks. They use their skills to ensure that networks are running smoothly and efficiently. This course may be useful to someone looking to enter this field as it provides a foundation in the basics of data description and statistical distributions. These concepts are used in network engineering to analyze network traffic and identify areas for improvement.
Computer Scientist
A Computer Scientist is responsible for studying the theoretical foundations of computing. They use their skills to develop new algorithms and data structures that can be used to solve problems in a variety of fields. This course may be useful to someone looking to enter this field as it provides a foundation in the basics of data description and statistical distributions. These concepts are used in computer science to analyze the performance of algorithms and data structures.

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 Basic Data Descriptors, Statistical Distributions, and Application to Business Decisions.
Classic textbook that has been used by business students for decades. It is known for its clear and concise explanations, and it includes numerous real-world examples to help readers understand the concepts.
Provides an introduction to causal inference, which field of statistics that is concerned with determining the causal relationships between variables. It covers topics such as graphical models, structural equation modeling, and counterfactual reasoning.
Provides an introduction to data science, which rapidly growing field that is becoming increasingly important in business. It covers topics such as data mining, machine learning, and statistical modeling.
This textbook that provides a more in-depth coverage of business statistics than the previous book. It is written in a clear and engaging style, and it includes numerous examples and exercises to help readers understand the concepts.
Provides an introduction to econometrics, which field of statistics that is used to analyze economic data. It covers topics such as multiple regression, time series analysis, and panel data analysis.
Provides an introduction to Bayesian statistics, which different approach to statistics that is gaining popularity in business. It covers topics such as Bayesian inference, Bayesian modeling, and Bayesian decision theory.
Provides an introduction to data mining, which field of statistics that is used to extract knowledge from data. It covers topics such as data preprocessing, feature selection, and model evaluation.
Provides an introduction to machine learning, which subset of data science that is used to build models that can learn from data. It covers topics such as supervised learning, unsupervised learning, and deep learning.
Provides an introduction to time series analysis, which field of statistics that is used to analyze data that is collected over time. It covers topics such as ARIMA models, GARCH models, and forecasting.
Provides an introduction to statistical methods that are used in finance. It covers topics such as asset pricing, portfolio optimization, and risk management.
This is another classic textbook that has been used by engineering students for decades. It is known for its clear and concise explanations, and it includes numerous real-world examples to help readers understand the concepts.

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