کتاب ریاضیات ضروری برای علوم داده

مرور ریاضیات پایه و حساب دیفرانسیل و انتگرال

Number Theory • Order of Operations • Variables • Functions • Summations • Exponents • Logarithms • Euler’s Number and Natural Logarithms • Euler’s Number • Natural Logarithms • Limits • Derivatives • Partial Derivatives • The Chain Rule • Integrals • Conclusion • Exercises

احتمالات

Understanding Probability • Probability Versus Statistics • Probability Math • Joint Probabilities • Union Probabilities • Conditional Probability and Bayes’ Theorem • Joint and Union Conditional Probabilities • Binomial Distribution • Beta Distribution • Conclusion • Exercises

آمار توصیفی و استنباطی

What Is Data? • Descriptive Versus Inferential Statistics • Populations, Samples, and Bias • Descriptive Statistics • Mean and Weighted Mean • Median • Mode • Variance and Standard Deviation • The Normal Distribution • The Inverse CDF • Z-Scores • Inferential Statistics • The Central Limit Theorem • Confidence Intervals • Understanding P-Values • Hypothesis Testing • The T-Distribution: Dealing with Small Samples • Big Data Considerations and the Texas Sharpshooter Fallacy • Conclusion • Exercises

جبر خطی

What Is a Vector? • Adding and Combining Vectors • Scaling Vectors • Span and Linear Dependence • Linear Transformations • Basis Vectors • Matrix Vector Multiplication • Matrix Multiplication • Determinants • Special Types of Matrices • Square Matrix • Identity Matrix • Inverse Matrix • Diagonal Matrix • Triangular Matrix • Sparse Matrix • Systems of Equations and Inverse Matrices • Eigenvectors and Eigenvalues • Conclusion • Exercises

رگرسیون خطی

A Basic Linear Regression • Residuals and Squared Errors • Finding the Best Fit Line • Closed Form Equation • Inverse Matrix Techniques • Matrix Decomposition • Gradient Descent • Overfitting and Variance • Stochastic Gradient Descent • The Correlation Coefficient • Statistical Significance • Coefficient of Determination • Standard Error of the Estimate • Prediction Intervals • Train/Test Splits • Multiple Linear Regression • Conclusion • Exercises

رگرسیون لجستیک و طبقه‌بندی

Understanding Logistic Regression • Performing a Logistic Regression • Logistic Function • Fitting the Logistic Curve • Multivariable Logistic Regression • Understanding the Log-Odds • R-Squared • P-Values • Train/Test Splits • Confusion Matrices • Bayes’ Theorem and Classification • Receiver Operator Characteristics/Area Under Curve • Class Imbalance • Conclusion • Exercises

شبکه‌های عصبی

When to Use Neural Networks and Deep Learning • A Simple Neural Network • Activation Functions • Forward Propagation • Backpropagation • Calculating the Weight and Bias Derivatives • Stochastic Gradient Descent • Using scikit-learn • Limitations of Neural Networks and Deep Learning • Conclusion • Exercise

مشاوره شغلی و مسیر پیش رو

Redefining Data Science • A Brief History of Data Science • Finding Your Edge • SQL Proficiency • Programming Proficiency • Data Visualization • Knowing Your Industry • Productive Learning • Practitioner Versus Advisor • What to Watch Out For in Data Science Jobs • Role Definition • Organizational Focus and Buy-In • Adequate Resources • Reasonable Objectives • Competing with Existing Systems • A Role Is Not What You Expected • Does Your Dream Job Not Exist? • Where Do I Go Now? • Conclusion