Essential Statistics for Data Analysis
What you’ll learn
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
Requirements
No math or stats background is required – we’ll start with the absolute basics!
We’ll use Microsoft Excel (Office 365) for course projects and demos
Description
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 workflowUnderstanding Data with Descriptive StatisticsUnderstand data using descriptive statistics, including frequency distributions and measures of central tendency & variabilityPROJECT #1: Maven Pizza ParlorModeling Data with Probability DistributionsModel data with probability distributions, and use the normal distribution to calculate probabilities and make value estimatesPROJECT #2: Maven Medical CenterThe Central Limit TheoremIntroduce the Central Limit Theorem, which leverages the normal distribution to make inferences on populations with any distributionMaking Estimates with Confidence IntervalsMake estimates with confidence intervals, which use sample statistics to define a range where an unknown population parameter likely liesPROJECT #3: Maven PharmaDrawing Conclusions with Hypothesis TestsDraw conclusions with hypothesis tests, which let you evaluate assumptions about population parameters using sample statisticsPROJECT #4: Maven Safety CouncilMaking Predictions with Regression AnalysisMake predictions with regression analysis, and estimate the values of a dependent variable via its relationship with independent variablesPROJECT #5: Maven AirlinesJoin today and get immediate, lifetime access to the following:7.5 hours of high-quality videoStatistics for Data Analysis PDF ebook (150+ pages)Downloadable Excel project files & solutionsExpert support and Q&A forum30-day Udemy satisfaction guaranteeIf 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)
Overview
Section 1: Getting Started
Lecture 1 Course Structure & Outline
Lecture 2 READ ME: Important Notes for New Students
Lecture 3 DOWNLOAD: Course Resources
Lecture 4 Setting Expectations
Lecture 5 The Course Project
Lecture 6 Helpful Resources
Section 2: Why Statistics?
Lecture 7 Section Intro
Lecture 8 Why Statistics?
Lecture 9 Populations & Samples
Lecture 10 The Statistics Workflow
Section 3: Understanding Data with Descriptive Statistics
Lecture 11 Section Intro
Lecture 12 Descriptive Statistics Basics
Lecture 13 Types of Variables
Lecture 14 Types of Descriptive Statistics
Lecture 15 Categorical Frequency Distributions
Lecture 16 Numerical Frequency Distributions
Lecture 17 Histograms
Lecture 18 ASSIGNMENT: Frequency Distributions
Lecture 19 SOLUTION: Frequency Distributions
Lecture 20 Mean, Median, and Mode
Lecture 21 Left & Right Skew
Lecture 22 ASSIGNMENT: Measures of Central Tendency
Lecture 23 SOLUTION: Measures of Central Tendency
Lecture 24 Min, Max & Range
Lecture 25 Interquartile Range
Lecture 26 Box & Whisker Plots
Lecture 27 Variance & Standard Deviation
Lecture 28 PRO TIP: Coefficient of Variation
Lecture 29 ASSIGNMENT: Measures of Variability
Lecture 30 SOLUTION: Measures of Variability
Lecture 31 Key Takeaways
Section 4: PROJECT #1: Maven Pizza Parlor
Lecture 32 PROJECT BRIEF: Maven Pizza Parlor
Lecture 33 SOLUTION: Maven Pizza Parlor
Section 5: Modeling Data with Probability Distributions
Lecture 34 Section Intro
Lecture 35 Probability Distribution Basics
Lecture 36 Types of Probability Distributions
Lecture 37 The Normal Distribution
Lecture 38 Z Scores
Lecture 39 The Empirical Rule
Lecture 40 ASSIGNMENT: Normal Distributions
Lecture 41 SOLUTION: Normal Distributions
Lecture 42 Excel’s Normal Distribution Functions
Lecture 43 Calculating Probabilities with the Normal Distribution
Lecture 44 The NORM.DIST Function
Lecture 45 The NORM.S.DIST Function
Lecture 46 ASSIGNMENT: Calculating Probabilities
Lecture 47 SOLUTION: Calculating Probabilities
Lecture 48 PRO TIP: Plotting the Normal Curve
Lecture 49 Estimating X or Z Values with the Normal Distribution
Lecture 50 The NORM.INV Function
Lecture 51 The NORM.S.INV Function
Lecture 52 ASSIGNMENT: Estimating Values
Lecture 53 SOLUTION: Estimating Values
Lecture 54 Key Takeaways
Section 6: PROJECT #2: Maven Medical Center
Lecture 55 PROJECT BRIEF: Maven Medical Center
Lecture 56 SOLUTION: Maven Medical Center
Section 7: The Central Limit Theorem
Lecture 57 Section Intro
Lecture 58 The Central Limit Theorem
Lecture 59 DEMO: Proving the Central Limit Theorem
Lecture 60 Standard Error
Lecture 61 Implications of the Central Limit Theorem
Lecture 62 Applications of the Central Limit Theorem
Lecture 63 Key Takeaways
Section 8: Making Estimates with Confidence Intervals
Lecture 64 Section Intro
Lecture 65 Confidence Intervals Basics
Lecture 66 Confidence Level
Lecture 67 Margin of Error
Lecture 68 DEMO: Calculating Confidence Intervals
Lecture 69 The CONFIDENCE.NORM Function
Lecture 70 ASSIGNMENT: Confidence Intervals
Lecture 71 SOLUTION: Confidence Intervals
Lecture 72 Types of Confidence Intervals
Lecture 73 T Distribution
Lecture 74 Excel’s T Distribution Functions
Lecture 75 Confidence Intervals with the T Distribution
Lecture 76 ASSIGNMENT: Confidence Intervals (T Distribution)
Lecture 77 SOLUTION: Confidence Intervals (T Distribution)
Lecture 78 Confidence Intervals for Proportions
Lecture 79 ASSIGNMENT: Confidence Intervals (Proportions)
Lecture 80 SOLUTION: Confidence Intervals (Proportions)
Lecture 81 Confidence Intervals for Two Populations
Lecture 82 Dependent Samples
Lecture 83 ASSIGNMENT: Confidence Intervals (Dependent Samples)
Lecture 84 SOLUTION: Confidence Intervals (Dependent Samples)
Lecture 85 Independent Samples
Lecture 86 ASSIGNMENT: Confidence Intervals (Independent Samples)
Lecture 87 SOLUTION: Confidence Intervals (Independent Samples)
Lecture 88 PRO TIP: Difference Between Proportions
Lecture 89 Key Takeaways
Section 9: PROJECT #3: Maven Pharma
Lecture 90 PROJECT BRIEF: Maven Pharma
Lecture 91 SOLUTION: Maven Pharma
Section 10: Drawing Conclusions with Hypothesis Tests
Lecture 92 Section Intro
Lecture 93 Hypothesis Testing Basics
Lecture 94 Null & Alternative Hypothesis
Lecture 95 Significance Level
Lecture 96 Test Statistic (T-score)
Lecture 97 P-Value
Lecture 98 Drawing Conclusions from Hypothesis Tests
Lecture 99 ASSIGNMENT: Hypothesis Tests
Lecture 100 SOLUTION: Hypothesis Tests
Lecture 101 Relationship between Confidence Intervals & Hypothesis Tests
Lecture 102 Type I & Type II Errors
Lecture 103 One Tail & Two Tail Hypothesis Tests
Lecture 104 DEMO: One Tail Hypothesis Test
Lecture 105 Hypothesis Tests for Proportions
Lecture 106 ASSIGNMENT: Hypothesis Tests (Proportions)
Lecture 107 SOLUTION: Hypothesis Tests (Proportions)
Lecture 108 Hypothesis Tests for Dependent Samples
Lecture 109 ASSIGNMENT: Hypothesis Tests (Dependent Samples)
Lecture 110 SOLUTION: Hypothesis Tests (Dependent Samples)
Lecture 111 Hypothesis Tests for Independent Samples
Lecture 112 ASSIGNMENT: Hypothesis Tests (Independent Samples)
Lecture 113 SOLUTION: Hypothesis Tests (Independent Samples)
Lecture 114 Key Takeaways
Section 11: PROJECT #4: Maven Safety Council
Lecture 115 PROJECT BRIEF: Maven Safety Council
Lecture 116 SOLUTION: Maven Safety Council
Section 12: Making Predictions with Regression Analysis
Lecture 117 Section Intro
Lecture 118 Linear Relationships
Lecture 119 Correlation (R)
Lecture 120 ASSIGNMENT: Linear Relationships
Lecture 121 SOLUTION: Linear Relationships
Lecture 122 Linear Regression & Least Squared Error
Lecture 123 Excel’s Linear Regression Functions
Lecture 124 ASSIGNMENT: Simple Linear Regression
Lecture 125 SOLUTION: Simple Linear Regression
Lecture 126 Determination (R-Squared)
Lecture 127 Standard Error
Lecture 128 Homoskedasticity & Heteroskedasticity
Lecture 129 Hypothesis Testing with Regression
Lecture 130 ASSIGNMENT: Model Evaluation
Lecture 131 SOLUTION: Model Evaluation
Lecture 132 Excel’s Regression Tool (Analysis ToolPak)
Lecture 133 PRO TIP: Multiple Linear Regression
Lecture 134 Key Takeaways
Section 13: PROJECT #5: Maven Airlines
Lecture 135 PROJECT BRIEF: Maven Airlines
Lecture 136 SOLUTION: Maven Airlines
Section 14: BONUS LESSON
Lecture 137 BONUS LESSON
Aspiring data professionals who want an intuitive, beginner-friendly introduction to the world of statistics,Business intelligence professionals who want to make confident, data-driven decisions,Anyone using data to make assumptions, estimates or predictions on the job,Students looking to learn powerful, practical skills with unique, hands-on projects and demos
Course Information:
Udemy | English | 7h 51m | 2.84 GB
Created by: Maven Analytics
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