Essential Statistics for Data Analysis from Udemy

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)

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

Setting Expectations

The Course Project

Helpful Resources

Why Statistics?

Section Intro

Populations & Samples

The Statistics Workflow

QUIZ: Why Statistics?

Understanding Data with Descriptive Statistics

Descriptive Statistics Basics

Types of Variables

Types of Descriptive Statistics

Categorical Frequency Distributions

Numerical Frequency Distributions

Histograms

ASSIGNMENT: Frequency Distributions

KNOWLEDGE CHECK: Frequency Distributions

SOLUTION: Frequency Distributions

Mean, Median, and Mode

Left & Right Skew

ASSIGNMENT: Measures of Central Tendency

KNOWLEDGE CHECK: Measures of Central Tendency

SOLUTION: Measures of Central Tendency

Min, Max & Range

Interquartile Range

Box & Whisker Plots

Variance & Standard Deviation

PRO TIP: Coefficient of Variation

ASSIGNMENT: Measures of Variability

KNOWLEDGE CHECK: Measures of Variability

SOLUTION: Measures of Variability

Key Takeaways

QUIZ: Descriptive Statistics

PROJECT #1: Maven Pizza Parlor

PROJECT BRIEF: Maven Pizza Parlor

SOLUTION: Maven Pizza Parlor

Modeling Data with Probability Distributions

Probability Distribution Basics

Types of Probability Distributions

The Normal Distribution

Z Scores

The Empirical Rule

ASSIGNMENT: Normal Distributions

KNOWLEDGE CHECK: Normal Distributions

SOLUTION: Normal Distributions

Excel's Normal Distribution Functions

Calculating Probabilities with the Normal Distribution

The NORM.DIST Function

The NORM.S.DIST Function

ASSIGNMENT: Calculating Probabilities

KNOWLEDGE CHECK: Calculating Probabilities

SOLUTION: Calculating Probabilities

PRO TIP: Plotting the Normal Curve

Estimating X or Z Values with the Normal Distribution

The NORM.INV Function

The NORM.S.INV Function

ASSIGNMENT: Estimating Values

KNOWLEDGE CHECK: Estimating Values

SOLUTION: Estimating Values

QUIZ: Probability Distributions

PROJECT #2: Maven Medical Center

PROJECT BRIEF: Maven Medical Center

SOLUTION: Maven Medical Center

The Central Limit Theorem

DEMO: Proving the Central Limit Theorem

Standard Error

Implications of the Central Limit Theorem

Applications of the Central Limit Theorem

QUIZ: The Central Limit Theorem

Making Estimates with Confidence Intervals

Confidence Intervals Basics

Confidence Level

Margin of Error

DEMO: Calculating Confidence Intervals

The CONFIDENCE.NORM Function

ASSIGNMENT: Confidence Intervals

KNOWLEDGE CHECK: Confidence Intervals

SOLUTION: Confidence Intervals

Types of Confidence Intervals

T Distribution

Excel's T Distribution Functions

Confidence Intervals with the T Distribution

ASSIGNMENT: Confidence Intervals (T Distribution)

KNOWLEDGE CHECK: Confidence Intervals (T Distribution)

SOLUTION: Confidence Intervals (T Distribution)

Confidence Intervals for Proportions

ASSIGNMENT: Confidence Intervals (Proportions)

KNOWLEDGE CHECK: Confidence Intervals (Proportions)

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Explores probability distributions, including the normal distribution, which are widely used in data analysis

Teaches students how to estimate and model data using confidence intervals, which is a critical skill for statisticians and data analysts

Demonstrates how to hypothesis testing, which enables students to draw conclusions about data

Emphasizes practical application by using examples from various industries, such as healthcare and business, making the course relevant to real-world scenarios

Provides step-by-step instructions and hands-on exercises, ensuring that students gain a thorough understanding of statistical concepts

Taught by Enrique Ruiz, a recognized expert in the field of statistics and data analysis

Career center

Learners who complete Essential Statistics for Data Analysis will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data scientists use statistical methods to analyze data and extract meaningful insights. This course provides a comprehensive introduction to essential statistics concepts, which are essential for success in this role. The course also covers topics such as machine learning and artificial intelligence, which are increasingly important for data scientists.

See salaries and explore the career path for Data Scientist

Data Analyst

Data analysts use statistical methods to analyze data and extract meaningful insights. This course provides a comprehensive introduction to essential statistics concepts, which are essential for success in this role. The course also covers topics such as data mining and data visualization, which are increasingly important for data analysts.

See salaries and explore the career path for Data Analyst

Business Analyst

Business analysts use statistical methods to analyze data and identify business trends. This course provides a strong foundation in essential statistics concepts, such as data mining and forecasting, which are essential for success in this field.

See salaries and explore the career path for Business Analyst

Quantitative Researcher

Quantitative researchers use statistical methods to analyze financial data and make investment recommendations. This course provides a strong foundation in essential statistics concepts, such as time series analysis and risk management, which are essential for success in this field.

See salaries and explore the career path for Quantitative Researcher

Machine Learning Engineer

Machine learning engineers use statistical methods to develop and implement machine learning models. This course provides a strong foundation in essential statistics concepts, such as supervised and unsupervised learning, which are essential for success in this field.

See salaries and explore the career path for Machine Learning Engineer

Quantitative Analyst

Quantitative analysts use statistical methods to develop and implement trading strategies. This course provides a strong foundation in essential statistics concepts, such as time series analysis and risk management, which are essential for success in this field.

See salaries and explore the career path for Quantitative Analyst

Market Researcher

Market researchers use statistical methods to collect and analyze data about consumers and markets. This course provides a strong foundation in essential statistics concepts, such as sampling, hypothesis testing, and regression analysis, which are essential for success in this field.

See salaries and explore the career path for Market Researcher

Statistician

Statisticians design, collect, analyze, and interpret data to answer questions and solve problems. This course can help build a foundation for a career as a statistician by providing a strong understanding of essential statistics concepts, such as descriptive statistics, probability distributions, and hypothesis testing. Additionally, the course's focus on real-world business applications will help you develop the skills needed to succeed in this field.

See salaries and explore the career path for Statistician

Operations Research Analyst

Operations research analysts use statistical methods to improve the efficiency of operations. This course provides a strong foundation in essential statistics concepts, such as optimization and simulation, which are essential for success in this field.

See salaries and explore the career path for Operations Research Analyst

Actuary

Actuaries use statistical methods to assess risk and develop insurance products. This course provides a strong foundation in essential statistics concepts, such as life tables and risk assessment, which are essential for success in this field.

See salaries and explore the career path for Actuary

Risk Manager

Risk managers use statistical methods to assess and manage risk. This course provides a strong foundation in essential statistics concepts, such as risk assessment and mitigation, which are essential for success in this field.

See salaries and explore the career path for Risk Manager

Epidemiologist

Epidemiologists use statistical methods to study the distribution and causes of disease. This course provides a strong foundation in essential statistics concepts, such as sampling and hypothesis testing, which are essential for success in this field.

See salaries and explore the career path for Epidemiologist

Biostatistician

Biostatisticians use statistical methods to analyze data from biological and medical studies. This course provides a strong foundation in essential statistics concepts, such as clinical trials and survival analysis, which are essential for success in this field.

See salaries and explore the career path for Biostatistician

Financial Analyst

Financial analysts use statistical methods to analyze financial data and make investment recommendations. This course provides a solid foundation in essential statistics concepts, such as time series analysis and risk management, which are essential for success in this field.

See salaries and explore the career path for Financial Analyst

Software Engineer

Software engineers use statistical methods to develop and improve software. This course provides a strong foundation in essential statistics concepts, such as testing and quality assurance, which are essential for success in this field.

See salaries and explore the career path for Software Engineer

Essential Statistics for Data Analysis

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