Course curriculum

  • 1

    1 Data

    • 1.1 Understanding and Using Data

    • 1.2 Descriptive, Predictive and Prescriptive Analytics

    • 1.3 Data Innovators

    • 1.4 Applications

    • 1.5 Summary

    • Quiz

  • 2

    2 Descriptive Analytics

    • 2.1 Measuring Central Tendency

    • 2.2 Measuring Variation

    • 2.3 Standard Deviation

    • 2.4 Types of Variables

    • 2.5 Summary

    • Quiz

  • 3

    3 Data Visualization

    • 3.1 Making Sense of Numerical Data

    • 3.2 A Picture is Worth a Thousand Words

    • 3.3 Line Charts, Bar Charts and Pie Charts

    • 3.4 Geospatial Charts, Bubble Charts and Dashboards

    • 3.5 Boston Heart Diagnostics

    • 3.6 Summary

    • Quiz

  • 4

    4 Data Visualization with Tableau

    • 4.1 Downloading Tableau

    • 4.2 Starting with a Spreadsheet

    • 4.3 Bar Charts

    • 4.4 Chart Titles, Labels and Order

    • 4.5 Geospatial Maps

    • 4.6 Dashboards

    • 4.7 Adding Additional Dashboard Charts

    • 4.8 Summary

    • Quiz

  • 5

    5 Probability Distributions

    • 5.1 Classes of Distributions

    • 5.2 Special Properties of the Normal Distribution

    • 5.3 Z Distribution and Z Value

    • 5.4 Summary

    • Quiz

  • 6

    6 Sampling and Process Control

    • 6.1 Sampling

    • 6.2 Sampling Techniques

    • 6.3 Process Control

    • 6.4 Covid-19

    • 6.5 Summary

    • Quiz

  • 7

    7 Hypothesis Testing

    • 7.1 The Basics of Hypothesis Testing

    • 7.2 One Sample t Test

    • 7.3 Two Sample t Test

    • 7.4 ANOVA

    • 7.5 Pfizer

    • 7.6 Summary

    • Quiz

  • 8

    8 Non-Parametric Hypothesis Tests

    • 8.1 Assumptions

    • 8.2 One-Sample Sign Test

    • 8.3 Mann Whitney Test

    • 8.4 Kruskal-Wallis Test

    • 8.5 Chi Square Test

    • 8.6 Friedman Test

    • 8.7 Summary

    • Quiz

  • 9

    9 A/B Testing

    • 9.1 Testing Alternative Strategies

    • 9.2 Lyyti.com

    • 9.3 Exact Target

    • 9.4 SixPackAbsExercises.com

    • 9.5 Summary

    • Quiz

  • 10

    10 Confidence Interval Estimation

    • 10.1 From Sample to Population

    • 10.2 Constructing the Confidence Interval for Means

    • 10.3 Sample Sizes

    • 10.4 Constructing the Confidence Interval for Proportions

    • 10.5 Summary

    • Quiz

  • 11

    11 Simple Linear Regression

    • 11.1 Understanding the Relationship Between Variables

    • 11.2 The Regression Line

    • 11.3 Evaluating the Significance of the Regression Line and the Strength of the Relationship

    • 11.4 Summary

    • Quiz

  • 12

    12 Multiple Linear Regression

    • 12.1 Multiple Regression

    • 12.2 Significance

    • 12.3 Residuals

    • 12.4 Multicollinearity

    • 12.5 Prediction Interval

    • 12.6 Summary

    • Quiz

  • 13

    13 Data Mining

    • 13.1 Datasets, Structure, Supervised and Unsupervised Models

    • 13.2 Scriptbook

    • 13.3 Summary

    • Quiz

  • 14

    14 Classification

    • 14.1 Predefined Target Categories

    • 14.2 Decision Trees

    • 14.3 Defining Similarity

    • 14.4 Nearest Neighbor Algorithm

    • 14.5 Target

    • 14.6 Netflix

    • 14.7 Summary

    • Quiz

  • 15

    15 Clustering

    • 15.1 The Data Does the Talking

    • 15.2 Clustering Algorithm

    • 15.3 Macy's

    • 15.4 Battlefield 2: Bad Company 2

    • 15.5 Dallas Museum of Art

    • 15.6 Summary

    • Quiz

  • 16

    16 Co-Occurrence

    • 16.1 Finding Relationships Between Entities

    • 16.2 Co-Occurrence Algorithm

    • 16.3 Summary

    • Quiz

  • 17

    17 Big Data

    • 17.1 Characteristics of Big Data

    • 17.2 Caesar's Entertainment

    • 17.3 Centers for Disease Control

    • 17.4 Opower

    • 17.5 Summary

    • Quiz

  • 18

    18 Database Technology

    • 18.1 The Software Technology of Data Analytics

    • 18.2 Understanding the Logical Relationships Among Data

    • 18.3 Why Combining Data into One Table is Not an Alternative

    • 18.4 How is the Problem Solved?

    • 18.5 Combining Tables to Create Reports

    • 18.6 Summary

    • Quiz

  • 19

    19 SQL

    • 19.1 Accessing Data from an SQL Database

    • 19.2 Filtering Data

    • 19.3 Conditional Operators

    • 19.4 Brigham and Women's Hospital

    • 19.5 Summary

    • Quiz

  • 20

    20 Statistical Analysis Using r

    • 20.1 Downloading r

    • 20.2 Expressions, Variables and Functions

    • 20.3 Vectors

    • 20.4 Descriptive Anaytics

    • 20.5 Graphics

    • 20.6 Regression Correlation

    • 20.7 Summary

    • Quiz

  • 21

    21 Summary and Conclusions

    • Summary and Conclusions