Becoming a Data Head. Alex J. Gutman
PROBABILITIES ARE CONDITIONAL ENSURE THE PROBABILITIES HAVE MEANING CHAPTER SUMMARY NOTES CHAPTER 7: Challenge the Statistics QUICK LESSONS ON INFERENCE THE PROCESS OF STATISTICAL INFERENCE THE QUESTIONS YOU SHOULD ASK TO CHALLENGE THE STATISTICS CHAPTER SUMMARY NOTES
12 PART III: Understanding the Data Scientist's Toolbox CHAPTER 8: Search for Hidden Groups UNSUPERVISED LEARNING DIMENSIONALITY REDUCTION PRINCIPAL COMPONENT ANALYSIS CLUSTERING K-MEANS CLUSTERING CHAPTER SUMMARY NOTES CHAPTER 9: Understand the Regression Model SUPERVISED LEARNING LINEAR REGRESSION: WHAT IT DOES LINEAR REGRESSION: WHAT IT GIVES YOU LINEAR REGRESSION: WHAT CONFUSION IT CAUSES OTHER REGRESSION MODELS CHAPTER SUMMARY NOTES CHAPTER 10: Understand the Classification Model INTRODUCTION TO CLASSIFICATION LOGISTIC REGRESSION DECISION TREES ENSEMBLE METHODS WATCH OUT FOR PITFALLS MISUNDERSTANDING ACCURACY CHAPTER SUMMARY NOTES CHAPTER 11: Understand Text Analytics EXPECTATIONS OF TEXT ANALYTICS HOW TEXT BECOMES NUMBERS TOPIC MODELING TEXT CLASSIFICATION PRACTICAL CONSIDERATIONS WHEN WORKING WITH TEXT CHAPTER SUMMARY NOTES CHAPTER 12: Conceptualize Deep Learning NEURAL NETWORKS APPLICATIONS OF DEEP LEARNING DEEP LEARNING IN PRACTICE ARTIFICIAL INTELLIGENCE AND YOU CHAPTER SUMMARY NOTES
13 PART IV: Ensuring Success CHAPTER 13: Watch Out for Pitfalls BIASES AND WEIRD PHENOMENA IN DATA THE BIG LIST OF PITFALLS CHAPTER SUMMARY NOTES CHAPTER 14: Know the People and Personalities SEVEN SCENES OF COMMUNICATION BREAKDOWNS DATA PERSONALITIES CHAPTER SUMMARY NOTES CHAPTER 15: What's Next?
14 Index
List of Tables
1 Chapter 2TABLE 2.1 Example Dataset on Advertisement Spending and Revenue
2 Chapter 3TABLE 3.1 Probability Dentists Agree to an Advertising ClaimTABLE 3.2 Possible Combinations of 4 out of 5 Dentists Agreeing
3 Chapter 6TABLE 6.1 Probabilities Scenarios with Associated NotationTABLE 6.2 Cumulative Probability of a Die Roll Less than 7
4 Chapter 7TABLE 7.1 Questions, Null Hypotheses (H 0 ), and Alternative Hypotheses (H a )TABLE 7.2 False Positive vs. False Negative Decision Errors
5 Chapter 8TABLE 8.1 Which of These Two Athletes are “Closest” to Each Other?TABLE 8.2 Clustering Algorithms Get Confused If Your Data Isn't Scaled.TABLE 8.3 Summarizing Unsupervised Learning and the Supervision Required
6 Chapter 9TABLE 9.1 Applications of Supervised LearningTABLE 9.2 Multiple Linear Regression Model Fit to Housing Data. All correspon...TABLE 9.3 Sample Housing Data
7 Chapter 10TABLE 10.1 Simple Dataset for Logistic Regression: Using GPA to Predict Inter...TABLE 10.2 Snapshot of the Intern Dataset from HR. The majors are CS = Comput...TABLE 10.3 Confusion Matrix for Predictions from a Classification Model with ...TABLE 10.4 Confusion Matrix for Predictions from a Classification Model with ...
8 Chapter