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Data Science
1. Foundations of Data Science
2. Mathematical and Statistical Foundations
3. Computational Foundations and Tools
4. Data Acquisition and Management
5. Exploratory Data Analysis
6. Feature Engineering and Selection
7. Machine Learning Fundamentals
8. Advanced Machine Learning Topics
9. Big Data and Distributed Computing
10. Data Visualization and Communication
11. Model Deployment and MLOps
12. Ethics and Responsible AI
Mathematical and Statistical Foundations
Linear Algebra
Vectors and Vector Operations
Vector Definition
Vector Addition and Subtraction
Scalar Multiplication
Dot Product
Cross Product
Vector Norms
Vector Spaces
Vector Space Definition
Subspaces
Linear Independence
Basis and Dimension
Span
Matrices and Matrix Operations
Matrix Definition
Matrix Addition and Subtraction
Matrix Multiplication
Transpose
Inverse
Determinants
Rank
Systems of Linear Equations
Gaussian Elimination
Matrix Form of Linear Systems
Solution Methods
Eigenvalues and Eigenvectors
Definitions
Characteristic Equation
Eigendecomposition
Diagonalization
Applications in Data Science
Singular Value Decomposition
SVD Definition
Computing SVD
Applications in Dimensionality Reduction
Applications in Recommendation Systems
Calculus
Limits and Continuity
Limit Definition
Continuity
Applications to Optimization
Derivatives
Derivative Definition
Differentiation Rules
Chain Rule
Applications in Machine Learning
Partial Derivatives
Multivariable Functions
Partial Derivative Definition
Gradient Vectors
Directional Derivatives
Optimization
Critical Points
Local and Global Extrema
Lagrange Multipliers
Gradient Descent
Stochastic Gradient Descent
Learning Rate and Convergence
Multivariable Calculus
Jacobian Matrices
Hessian Matrices
Taylor Series Expansion
Probability Theory
Basic Probability Concepts
Sample Spaces and Events
Probability Axioms
Probability Rules
Combinatorics
Conditional Probability
Independence
Multiplication Rule
Law of Total Probability
Bayes' Theorem
Statement and Proof
Applications in Machine Learning
Naive Bayes Classifier Foundation
Random Variables
Discrete Random Variables
Continuous Random Variables
Probability Mass Functions
Probability Density Functions
Cumulative Distribution Functions
Expected Value and Variance
Expected Value Definition
Properties of Expected Value
Variance Definition
Properties of Variance
Covariance
Correlation
Common Probability Distributions
Discrete Distributions
Bernoulli Distribution
Binomial Distribution
Poisson Distribution
Geometric Distribution
Negative Binomial Distribution
Continuous Distributions
Uniform Distribution
Normal Distribution
Exponential Distribution
Gamma Distribution
Beta Distribution
Chi-Square Distribution
t-Distribution
F-Distribution
Joint Distributions
Joint Probability Mass Functions
Joint Probability Density Functions
Marginal Distributions
Independence of Random Variables
Central Limit Theorem
Statement
Sampling Distributions
Descriptive Statistics
Measures of Central Tendency
Mean
Median
Mode
Geometric Mean
Harmonic Mean
Measures of Dispersion
Range
Variance
Standard Deviation
Interquartile Range
Mean Absolute Deviation
Coefficient of Variation
Measures of Shape
Skewness
Kurtosis
Measures of Position
Percentiles
Quartiles
Z-scores
Correlation and Association
Pearson Correlation Coefficient
Spearman Rank Correlation
Kendall's Tau
Covariance
Correlation vs Causation
Inferential Statistics
Sampling Theory
Population vs Sample
Sampling Methods
Simple Random Sampling
Stratified Sampling
Cluster Sampling
Systematic Sampling
Sampling Bias
Sampling Distributions
Estimation
Point Estimation
Properties of Estimators
Method of Moments
Maximum Likelihood Estimation
Interval Estimation
Confidence Intervals
Construction
Interpretation
Confidence Intervals for Means
Confidence Intervals for Proportions
Confidence Intervals for Differences
Hypothesis Testing
Null and Alternative Hypotheses
Test Statistics
p-values
Significance Levels
Type I and Type II Errors
Statistical Power
One-tailed vs Two-tailed Tests
Common Statistical Tests
t-tests
One-sample t-test
Two-sample t-test
Paired t-test
Chi-square Tests
Goodness of Fit
Test of Independence
ANOVA
One-way ANOVA
Two-way ANOVA
Non-parametric Tests
Mann-Whitney U Test
Wilcoxon Signed-rank Test
Kruskal-Wallis Test
Experimental Design
Principles of Experimental Design
Randomization
Blocking
Factorial Designs
A/B Testing
Design Principles
Sample Size Calculation
Statistical Analysis
Interpreting Results
Multiple Testing Corrections
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