Statistics for Data Science Certification

Module 1: Introduction to Statistics for Data Science

• Statistics fundamentals
• Role of statistics in data science
• Descriptive statistics
• Inferential statistics
• Statistical analysis workflow
• Data-driven decision-making

Module 2: Understanding Data & Variables

• Population and samples
• Numerical data
• Categorical data
• Discrete variables
• Continuous variables
• Levels of measurement

Module 3: Descriptive Statistics

• Mean
• Median
• Mode
• Range
• Percentiles
• Quartiles

Module 4: Measures of Dispersion

• Variance
• Standard deviation
• Interquartile range
• Mean absolute deviation
• Coefficient of variation
• Interpreting variability

Module 5: Data Distributions

• Frequency distributions
• Histograms
• Distribution shapes
• Skewness
• Kurtosis
• Distribution interpretation

Module 6: Probability Fundamentals

• Probability concepts
• Sample spaces
• Events
• Probability rules
• Conditional probability
• Independence

Module 7: Random Variables

• Random variable concepts
• Discrete random variables
• Continuous random variables
• Probability mass functions
• Probability density functions
• Expected values

Module 8: Probability Distributions

• Normal distribution
• Binomial distribution
• Poisson distribution
• Uniform distribution
• Exponential distribution
• Distribution selection

Module 9: Sampling Techniques

• Random sampling
• Stratified sampling
• Systematic sampling
• Cluster sampling
• Sampling bias
• Representative samples

Module 10: Sampling Distributions

• Sampling distribution concepts
• Sample means
• Standard error
• Central Limit Theorem
• Sample size
• Distribution of estimators

Module 11: Statistical Estimation

• Point estimation
• Interval estimation
• Confidence intervals
• Confidence levels
• Margin of error
• Estimation interpretation

Module 12: Hypothesis Testing Fundamentals

• Null hypothesis
• Alternative hypothesis
• Significance levels
• P-values
• Type I errors
• Type II errors

Module 13: Parametric Statistical Tests

• One-sample t-test
• Independent t-test
• Paired t-test
• Z-test concepts
• Test assumptions
• Result interpretation

Module 14: Non-Parametric Testing

• Non-parametric concepts
• Mann-Whitney test
• Wilcoxon test
• Kruskal-Wallis concepts
• When to use non-parametric tests
• Results interpretation

Module 15: Chi-Square Analysis

• Chi-square concepts
• Goodness-of-fit
• Test of independence
• Contingency tables
• Expected frequencies
• Interpretation

Module 16: Correlation Analysis

• Covariance
• Pearson correlation
• Spearman correlation
• Correlation coefficients
• Correlation matrices
• Correlation vs causation

Module 17: Regression Analysis

• Regression fundamentals
• Simple linear regression
• Multiple regression
• Regression coefficients
• R-squared
• Residual analysis

Module 18: Analysis of Variance

• ANOVA fundamentals
• Between-group variation
• Within-group variation
• F-statistic
• One-way ANOVA
• Post-hoc analysis concepts

Module 19: Experimental Design & A/B Testing

• Experimental design
• Control groups
• Treatment groups
• Randomization
• A/B testing
• Experiment interpretation

Module 20: Bayesian Statistics Fundamentals

• Bayesian thinking
• Prior probability
• Likelihood
• Posterior probability
• Bayes' theorem
• Bayesian applications

Module 21: Statistics with Python

• NumPy statistical functions
• Pandas statistics
• SciPy concepts
• Statistical calculations
• Hypothesis testing workflows
• Statistical visualization

Module 22: Practical Statistical Analysis

• Problem formulation
• Dataset exploration
• Statistical test selection
• Hypothesis testing
• Relationship analysis
• Results interpretation