MAZHER KHAN

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Mazher Khan (Senior Data Analyst) - IIT Graduate

Topics Covered

Descriptive Statistics:

• Measures of central tendency (mean, median, mode)
• Measures of dispersion (range, variance, standard deviation, interquartile range)
• Skewness and kurtosis
• Data visualization (histograms, box plots, scatter plots)

Probability Theory:

• Basic probability concepts
• Probability distributions (normal, binomial, Poisson, etc.)
• Theoretical vs. empirical probability
• Conditional probability and Bayes’ theorem

Inferential Statistics:

• Sampling methods (random, stratified, cluster)
• Sampling distributions
• Hypothesis testing (null and alternative hypotheses)
• p-values and significance levels
• Confidence intervals
• t-tests (one-sample, independent, paired)
• Chi-square tests
• ANOVA (Analysis of Variance)

Regression Analysis:

• Simple linear regression
• Multiple linear regression
• Assumptions of regression analysis
• Model evaluation metrics (R-squared, Adjusted R-squared, residual plots)
• Multicollinearity

Correlation Analysis:

• Pearson and Spearman correlation coefficients
• Covariance
• Correlation vs. causation

Time Series Analysis:

• Time series components (trend, seasonality, cyclicality)
• Forecasting methods (moving averages, exponential smoothing)
• Autocorrelation and partial autocorrelation

Non-parametric Methods:

• Mann-Whitney U test
• Wilcoxon signed-rank test
• Kruskal-Wallis test
• Friedman test

Data Transformation and Cleaning:

• Handling missing data
• Data normalization and standardization
• Outlier detection and treatment

Bayesian Statistics:

• Bayesian inference
• Prior, likelihood, posterior distributions

Advanced Topics:

• Logistic regression
• Principal Component Analysis (PCA)
• Cluster analysis (K-means, hierarchical clustering)
• Decision trees and random forests

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