This is a hands-on, project-based course designed to help you learn and apply essential statistics concepts for data analysis & business intelligence. Our goal is to simplify and demystify the world of statistics using familiar tools like Microsoft Excel, and empower everyday people to understand and apply these tools and techniques – even if you have absolutely no background in math or stats.
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This is a hands-on, project-based course designed to help you learn and apply essential statistics concepts for data analysis & business intelligence. Our goal is to simplify and demystify the world of statistics using familiar tools like Microsoft Excel, and empower everyday people to understand and apply these tools and techniques – even if you have absolutely no background in math or stats.
This is a hands-on, project-based course designed to help you learn and apply essential statistics concepts for data analysis & business intelligence. Our goal is to simplify and demystify the world of statistics using familiar tools like Microsoft Excel, and empower everyday people to understand and apply these tools and techniques – even if you have absolutely no background in math or stats.
We'll start by discussing the role of statistics in business intelligence, the difference between sample and population data, and the importance of using statistical techniques to make smart predictions and data-driven decisions.
Next we'll explore our data using descriptive statistics and probability distributions, introduce the normal distribution and empirical rule, and learn how to apply the central limit theorem to make inferences about populations of any type.
From there we'll practice making estimates with confidence intervals, and using hypothesis tests to evaluate assumptions about unknown population parameters. We'll introduce the basic hypothesis testing framework, then dive into concepts like null and alternative hypotheses, t-scores, p-values, type I vs. type II errors, and more.
Last but not least, we'll introduce the fundamentals of regression analysis, explore the difference between correlation and causation, and practice using basic linear regression models to make predictions using Excel's Analysis Toolpak.
Throughout the course, you'll play the role of a Recruitment Analyst for Maven Business School. Your goal is to use the statistical techniques you've learned to explore student data, predict the performance of future classes, and propose changes to help improve graduate outcomes.
You'll also practice applying your skills to 5 real-world BONUS PROJECTS, and use statistics to explore data from restaurants, medical centers, pharmaceutical companys, safety teams, airlines, and more.
COURSE OUTLINE:
Why Statistics?
Discuss the role of statistics in the context of business intelligence and decision-making, and introduce the statistics workflow
Understanding Data with Descriptive Statistics
Understand data using descriptive statistics, including frequency distributions and measures of central tendency & variability
PROJECT #1: Maven Pizza Parlor
Modeling Data with Probability Distributions
Model data with probability distributions, and use the normal distribution to calculate probabilities and make value estimates
PROJECT #2: Maven Medical Center
The Central Limit Theorem
Introduce the Central Limit Theorem, which leverages the normal distribution to make inferences on populations with any distribution
Making Estimates with Confidence Intervals
Make estimates with confidence intervals, which use sample statistics to define a range where an unknown population parameter likely lies
PROJECT #3: Maven Pharma
Drawing Conclusions with Hypothesis Tests
Draw conclusions with hypothesis tests, which let you evaluate assumptions about population parameters using sample statistics
PROJECT #4: Maven Safety Council
Making Predictions with Regression Analysis
Make predictions with regression analysis, and estimate the values of a dependent variable via its relationship with independent variables
PROJECT #5: Maven Airlines
Join today and get immediate, lifetime access to the following:
7.5 hours of high-quality video
Statistics for Data Analysis PDF ebook (150+ pages)
Downloadable Excel project files & solutions
Expert support and Q&A forum
30-day Udemy satisfaction guarantee
If you're an analyst, data scientist, business intelligence professional, or anyone looking to use statistics to make smart, data-driven decisions, this course is for you.
Happy learning.
-Enrique Ruiz (Lead Statistics & Excel Instructor, Maven Analytics)
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What's inside
Learning objectives
- Learn powerful statistics tools and techniques for data analysis & business intelligence
- Understand how to apply foundational statistics concepts like the central limit theorem and empirical rule
- Explore data with descriptive statistics, including probability distributions and measures of variability & central tendency
- Model data and make estimates using probability distributions and confidence intervals
- Make data-driven decisions and draw conclusions with hypothesis testing
- Use linear regression models to explore variable relationships and make predictions
- Learn powerful statistics tools and techniques for data analysis & business intelligence
- Understand how to apply foundational statistics concepts like the central limit theorem and empirical rule
- Explore data with descriptive statistics, including probability distributions and measures of variability & central tendency
- Model data and make estimates using probability distributions and confidence intervals
- Make data-driven decisions and draw conclusions with hypothesis testing
- Use linear regression models to explore variable relationships and make predictions
Syllabus
Getting Started
Course Structure & Outline
READ ME: Important Notes for New Students
DOWNLOAD: Course Resources
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Syllabus
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Activities
Practice Probability Distributions concepts
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Refine your understanding of probability distributions by reviewing key concepts and solving practice problems
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Probability Distributions
Show steps
-
Review the types of probability distributions and their properties
-
Solve practice problems to calculate probabilities using the normal distribution
-
Apply the binomial distribution to solve problems involving discrete events
Solve Confidence Intervals exercises
Show steps
Strengthen your ability to calculate confidence intervals and make inferences about population parameters
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Confidence Intervals
Show steps
-
Review the concept of confidence intervals and their interpretation
-
Solve practice exercises involving calculating confidence intervals for means and proportions
-
Apply confidence intervals to real-world scenarios
Explore Hypothesis Testing with Online Tutorials
Show steps
Enhance your knowledge of hypothesis testing by following guided tutorials that provide step-by-step explanations and exercises
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Hypothesis Testing
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-
Identify the null and alternative hypotheses
-
Calculate the test statistic and p-value
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Interpret the results and make a decision
Two other activities
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Build a Regression Model for a Business Case
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Apply your skills by creating a regression model that can be used to make predictions and improve business performance
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Regression Analysis
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-
Define the business problem and collect relevant data
-
Select appropriate independent and dependent variables
-
Build and evaluate the regression model
-
Interpret the results and make recommendations
Develop a Data Analysis Plan for a Real-World Dataset
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Create a comprehensive data analysis plan that outlines the steps and methods you would use to analyze a real-world dataset
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Data Analysis
Show steps
-
State the research question or business objective
-
Identify the data sources and variables to be analyzed
-
Describe the statistical methods you will use
-
Outline the expected outcomes and how they will be communicated
Practice Probability Distributions concepts
Show steps
Refine your understanding of probability distributions by reviewing key concepts and solving practice problems
Browse courses on
Probability Distributions
Show steps
- Review the types of probability distributions and their properties
- Solve practice problems to calculate probabilities using the normal distribution
- Apply the binomial distribution to solve problems involving discrete events
Solve Confidence Intervals exercises
Show steps
Strengthen your ability to calculate confidence intervals and make inferences about population parameters
Browse courses on
Confidence Intervals
Show steps
- Review the concept of confidence intervals and their interpretation
- Solve practice exercises involving calculating confidence intervals for means and proportions
- Apply confidence intervals to real-world scenarios
Explore Hypothesis Testing with Online Tutorials
Show steps
Enhance your knowledge of hypothesis testing by following guided tutorials that provide step-by-step explanations and exercises
Browse courses on
Hypothesis Testing
Show steps
- Identify the null and alternative hypotheses
- Calculate the test statistic and p-value
- Interpret the results and make a decision
Build a Regression Model for a Business Case
Show steps
Apply your skills by creating a regression model that can be used to make predictions and improve business performance
Browse courses on
Regression Analysis
Show steps
- Define the business problem and collect relevant data
- Select appropriate independent and dependent variables
- Build and evaluate the regression model
- Interpret the results and make recommendations
Develop a Data Analysis Plan for a Real-World Dataset
Show steps
Create a comprehensive data analysis plan that outlines the steps and methods you would use to analyze a real-world dataset
Browse courses on
Data Analysis
Show steps
- State the research question or business objective
- Identify the data sources and variables to be analyzed
- Describe the statistical methods you will use
- Outline the expected outcomes and how they will be communicated
Career center
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Data Analyst
Business Analyst
Quantitative Researcher
Machine Learning Engineer
Quantitative Analyst
Market Researcher
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Actuary
Risk Manager
Epidemiologist
Biostatistician
Financial Analyst
Software Engineer
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