## Book description

Master the math needed to excel in data science, machine learning, and statistics. In this book author Thomas Nield guides you through areas like calculus, probability, linear algebra, and statistics and how they apply to techniques like linear regression, logistic regression, and neural networks. Along the way you'll also gain practical insights into the state of data science and how to use those insights to maximize your career.

Learn how to:

• Use Python code and libraries like SymPy, NumPy, and scikit-learn to explore essential mathematical concepts like calculus, linear algebra, statistics, and machine learning
• Understand techniques like linear regression, logistic regression, and neural networks in plain English, with minimal mathematical notation and jargon
• Perform descriptive statistics and hypothesis testing on a dataset to interpret p-values and statistical significance
• Manipulate vectors and matrices and perform matrix decomposition
• Integrate and build upon incremental knowledge of calculus, probability, statistics, and linear algebra, and apply it to regression models including neural networks
• Navigate practically through a data science career and avoid common pitfalls, assumptions, and biases while tuning your skill set to stand out in the job market

## Publisher resources

View/Submit Errata

1. Preface
2. 1. Basic Math and Calculus Review
1. Number Theory
2. Order of Operations
3. Variables
4. Functions
5. Summations
6. Exponents
7. Logarithms
8. Euler’s Number and Natural Logarithms
9. Limits
10. Derivatives
11. Integrals
12. Conclusion
13. Exercises
3. 2. Probability
1. Understanding Probability
2. Probability Math
3. Binomial Distribution
4. Beta Distribution
5. Conclusion
6. Exercises
4. 3. Descriptive and Inferential Statistics
1. What Is Data?
2. Descriptive Versus Inferential Statistics
3. Populations, Samples, and Bias
4. Descriptive Statistics
5. Inferential Statistics
6. The T-Distribution: Dealing with Small Samples
7. Big Data Considerations and the Texas Sharpshooter Fallacy
8. Conclusion
9. Exercises
5. 4. Linear Algebra
1. What Is a Vector?
2. Linear Transformations
3. Matrix Multiplication
4. Determinants
5. Special Types of Matrices
6. Systems of Equations and Inverse Matrices
7. Eigenvectors and Eigenvalues
8. Conclusion
9. Exercises
6. 5. Linear Regression
1. A Basic Linear Regression
2. Residuals and Squared Errors
3. Finding the Best Fit Line
4. Overfitting and Variance
6. The Correlation Coefficient
7. Statistical Significance
8. Coefficient of Determination
9. Standard Error of the Estimate
10. Prediction Intervals
11. Train/Test Splits
12. Multiple Linear Regression
13. Conclusion
14. Exercises
7. 6. Logistic Regression and Classification
1. Understanding Logistic Regression
2. Performing a Logistic Regression
3. Multivariable Logistic Regression
4. Understanding the Log-Odds
5. R-Squared
6. P-Values
7. Train/Test Splits
8. Confusion Matrices
9. Bayes’ Theorem and Classification
10. Receiver Operator Characteristics/Area Under Curve
11. Class Imbalance
12. Conclusion
13. Exercises
8. 7. Neural Networks
1. When to Use Neural Networks and Deep Learning
2. A Simple Neural Network
3. Backpropagation
4. Using scikit-learn
5. Limitations of Neural Networks and Deep Learning
6. Conclusion
7. Exercise
9. 8. Career Advice and the Path Forward
1. Redefining Data Science
2. A Brief History of Data Science
4. What to Watch Out For in Data Science Jobs
5. Does Your Dream Job Not Exist?
6. Where Do I Go Now?
7. Conclusion
10. A. Supplemental Topics