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data analysis

334 tutorials found

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statistics probability math foundations of statistics sql database query python pandas machine learning data science advanced statistical methods descriptive statistics hypothesis testing regression analysis

Statistics(200)

What is Statistics? — A Complete Introduction

Foundations of Statistics

Types of Data in Statistics — Quantitative vs Qualitative

Foundations of Statistics

Levels of Measurement — Nominal, Ordinal, Interval, Ratio

Foundations of Statistics

Population vs Sample — The Foundation of Statistical Inference

Foundations of Statistics

Data Collection Methods — Surveys, Experiments, Observations

Foundations of Statistics

Sampling Techniques — Simple Random, Stratified, Cluster, Systematic

Foundations of Statistics

Sampling Bias and Errors — Types, Detection, and Prevention

Foundations of Statistics

Frequency Distributions — Tables, Relative Frequency, Cumulative

Foundations of Statistics

Histograms — Construction, Interpretation, and Common Shapes

Foundations of Statistics

Stem-and-Leaf Plots — Construction and Interpretation

Foundations of Statistics

Permutation Tests — Distribution-Free Hypothesis Testing

Hypothesis Testing

Wilcoxon Signed-Rank Test — Nonparametric Paired Test

Nonparametric Tests

Mann-Whitney U Test — Nonparametric Two-Sample Test

Nonparametric Tests

Kruskal-Wallis Test — Nonparametric One-Way ANOVA

Nonparametric Tests

Friedman Test — Nonparametric Repeated Measures ANOVA

Nonparametric Tests

Runs Test for Randomness

Nonparametric Tests

Simple Linear Regression — Theory, Assumptions, and Python

Regression Analysis

OLS Estimation — Deriving Regression Coefficients from Scratch

Regression Analysis

Regression Assumptions — LINE Framework and Diagnostics

Regression Analysis

Residual Analysis — Diagnosing Regression Problems

Regression Analysis

Bar Charts and Pie Charts — When and How to Use Each

Foundations of Statistics

R-Squared and Adjusted R-Squared — Measuring Model Fit

Regression Analysis

Multiple Linear Regression — Theory and Python

Regression Analysis

Multicollinearity — Detection and Solutions

Regression Analysis

Heteroscedasticity — Detection, Consequences, Solutions

Regression Analysis

Autocorrelation in Regression — Detection and Corrections

Regression Analysis

Polynomial Regression — Fitting Nonlinear Relationships

Regression Analysis

Logistic Regression — Binary Classification with Statistics

Regression Analysis

Odds Ratios — Understanding and Interpreting ORs

Regression Analysis

Ridge Regression (L2 Regularization) — Complete Guide

Regression Analysis

Lasso Regression (L1 Regularization) — Feature Selection

Regression Analysis

Box Plots — Five-Number Summary, IQR, and Outlier Detection

Foundations of Statistics

Elastic Net — Combining Ridge and Lasso

Regression Analysis

Quantile Regression — Beyond the Mean

Foundations of Statistics

One-Way ANOVA — Comparing Multiple Group Means

Foundations of Statistics

Complete Regression Diagnostics Toolkit

Foundations of Statistics

Survival Analysis — Time-to-Event Data

Foundations of Statistics

Principal Component Analysis (PCA) — Dimensionality Reduction

Foundations of Statistics

Factor Analysis — Latent Variable Models

Cluster Analysis — k-Means, Hierarchical, DBSCAN

Stationarity in Time Series — Tests and Transformations

ACF and PACF — Identifying ARIMA Orders

Scatter Plots: Correlation, Patterns, and Relationships

Foundations of Statistics

ARIMA Models — Complete Guide

Seasonal Decomposition — STL and Classical

Exponential Smoothing — Simple, Holt-Winters

Granger Causality — Time Series Causality Testing

Kaplan-Meier Estimator — Survival Function

Cox Proportional Hazards Model

Mediation and Moderation Analysis

Multilevel (Hierarchical) Linear Models

Panel Data Analysis — Fixed and Random Effects

Causal Inference — Potential Outcomes Framework

Measures of Central Tendency — Mean, Median, Mode Compared

Foundations of Statistics

Randomized Controlled Trials — Design and Analysis

Instrumental Variables — IV Estimation

Regression Discontinuity Design

Difference-in-Differences Estimation

Propensity Score Matching

Missing Data — MCAR, MAR, MNAR, Imputation

Multiple Imputation for Missing Data

Bootstrap Methods — Resampling for Inference

Cross-Validation in Statistics

AIC and BIC — Information Criteria for Model Selection

Arithmetic Mean — Formula, Properties, Computation, Limitations

Foundations of Statistics

ROC Curves and AUC � Model Discrimination

Calibration and Model Checking

Advanced Statistical Methods

Robust Statistics — Resistant to Outliers

Advanced Statistical Methods

Nonparametric Density Estimation

Advanced Statistical Methods

Structural Equation Modeling (SEM)

Advanced Statistical Methods

Path Analysis

Advanced Statistical Methods

Bayesian Linear Regression

Advanced Statistical Methods

Hierarchical Bayesian Models

Advanced Statistical Methods

MCMC Diagnostics and Convergence

Advanced Statistical Methods

Statistical Decision Theory

Advanced Statistical Methods

Median — Calculation, Robustness, and When to Use It

Foundations of Statistics

Statistical Process Control — Control Charts

Advanced Statistical Methods

Six Sigma — DMAIC and Process Improvement

Advanced Statistical Methods

Design of Experiments (DOE)

Advanced Statistical Methods

Response Surface Methods

Advanced Statistical Methods

Multivariate Analysis of Variance (MANOVA)

Advanced Statistical Methods

Analysis of Covariance (ANCOVA)

Advanced Statistical Methods

Discriminant Analysis

Advanced Statistical Methods

Canonical Correlation Analysis

Advanced Statistical Methods

Multidimensional Scaling (MDS)

Advanced Statistical Methods

Correspondence Analysis

Advanced Statistical Methods

Mode — Most Frequent Value, Multimodality, Limitations

Foundations of Statistics

Finite Mixture Models

Advanced Statistical Methods

Hidden Markov Models (HMM)

Advanced Statistical Methods

Bayesian Networks

Advanced Statistical Methods

Monte Carlo Simulation

Advanced Statistical Methods

Extreme Value Theory

Advanced Statistical Methods

Copulas — Modeling Dependence

Advanced Statistical Methods

Spatial Statistics

Advanced Statistical Methods

Geostatistics

Advanced Statistical Methods

Functional Data Analysis (FDA)

Advanced Statistical Methods

High-Dimensional Statistics

Advanced Statistical Methods

Weighted Mean — Formula, Applications, and Python

Foundations of Statistics

False Discovery Rate — Advanced Methods

Advanced Statistical Methods

Network Analysis and Graph Statistics

Advanced Statistical Methods

Text Mining and Statistical NLP

Advanced Statistical Methods

Survey Sampling and Weighting

Advanced Statistical Methods

Record Linkage and Data Matching

Advanced Statistical Methods

Optimal Experimental Design

Advanced Statistical Methods

Adaptive Trial Design

Advanced Statistical Methods

Equivalence Testing

Advanced Statistical Methods

Meta-Analysis

Advanced Statistical Methods

Systematic Review Methodology

Advanced Statistical Methods

Geometric Mean — Formula, Applications in Finance and Growth

Foundations of Statistics

The Replication Crisis in Statistics

Advanced Statistical Methods

Open Science Practices

Advanced Statistical Methods

Pre-registration of Studies

Advanced Statistical Methods

Statistical Reporting Standards

Advanced Statistical Methods

Statistical Software Comparison

Advanced Statistical Methods

Big Data and Statistics

Advanced Statistical Methods

Streaming Statistics and Online Learning

Advanced Statistical Methods

Statistics Meets Machine Learning

Advanced Statistical Methods

Ethics in Statistics

Advanced Statistical Methods

Statistics Career Guide

Advanced Statistical Methods

Harmonic Mean — When Average Rate Requires Harmonic Mean

Foundations of Statistics

Statistics Review and Roadmap

Advanced Statistical Methods

Range and IQR — Measures of Spread Explained

Foundations of Statistics

Variance — Population vs Sample Formula and Interpretation

Foundations of Statistics

Standard Deviation — Formula, Empirical Rule, and Coefficient of Variation

Foundations of Statistics

Coefficient of Variation — Relative Dispersion Across Different Scales

Foundations of Statistics

Skewness — Measuring Asymmetry of Distributions

Foundations of Statistics

Kurtosis — Fat Tails and Extreme Events

Foundations of Statistics

Z-Scores — Standardization and the Normal Table

Foundations of Statistics

Percentiles and Quartiles — Calculation and Interpretation

Foundations of Statistics

Five-Number Summary — Box Plot Foundation

Foundations of Statistics

Covariance — Measuring Joint Variation of Two Variables

Foundations of Statistics

Pearson Correlation — r Coefficient Formula and Testing

Foundations of Statistics

Spearman Rank Correlation — Non-Parametric Association

Foundations of Statistics

Kendall's Tau — Concordance-Based Correlation

Foundations of Statistics

Point-Biserial Correlation — Binary and Continuous Variables

Foundations of Statistics

Phi Coefficient — Correlation Between Two Binary Variables

Foundations of Statistics

Cramér's V — Effect Size for Chi-Square Tests

Foundations of Statistics

Cross-Tabulation — Analyzing Relationships Between Categorical Variables

Foundations of Statistics

Contingency Tables — Construction, Analysis, and Chi-Square

Foundations of Statistics

Relative Frequency — Proportions and Probability Estimation

Foundations of Statistics

Cumulative Frequency — Ogives and Percentile Estimation

Foundations of Statistics

Introduction to Probability — Foundations and Definitions

Foundations of Statistics

Sample Space and Events — Set Theory for Probability

Foundations of Statistics

The Addition Rule — P(A or B) for Probability

Foundations of Statistics

The Multiplication Rule — P(A and B) for Probability

Foundations of Statistics

Conditional Probability — P(A|B) and Its Applications

Foundations of Statistics

Bayes' Theorem — Updating Beliefs with Evidence

Foundations of Statistics

Independence vs Mutual Exclusivity — Key Distinctions

Foundations of Statistics

Counting Principles — Permutations and Combinations

Foundations of Statistics

Discrete Random Variables — PMF, CDF, and Properties

Foundations of Statistics

Expected Value — The Mean of a Random Variable

Foundations of Statistics

Variance of a Random Variable — Formula and Properties

Foundations of Statistics

Bernoulli Distribution — Binary Outcomes

Foundations of Statistics

Binomial Distribution — Count of Successes in n Trials

Foundations of Statistics

Poisson Distribution — Modeling Rare Events

Foundations of Statistics

Geometric Distribution — Waiting Time for First Success

Foundations of Statistics

Hypergeometric Distribution — Sampling Without Replacement

Foundations of Statistics

Negative Binomial Distribution — Waiting for r-th Success

Foundations of Statistics

Continuous Random Variables — PDF and CDF

Foundations of Statistics

Uniform Distribution — Equal Probability Across a Range

Foundations of Statistics

Normal Distribution — The Bell Curve and Its Properties

Foundations of Statistics

Standard Normal Distribution and Z-Table

Foundations of Statistics

The Empirical Rule — 68-95-99.7 for Normal Distributions

Foundations of Statistics

Exponential Distribution — Time Between Events

Foundations of Statistics

Gamma Distribution — Sum of Exponential Variables

Foundations of Statistics

Beta Distribution — Modeling Probabilities and Proportions

Foundations of Statistics

Sampling Distribution of the Mean — Foundation of Inference

Foundations of Statistics

Central Limit Theorem — The Most Important Theorem in Statistics

Foundations of Statistics

Standard Error — Precision of Sample Statistics

Foundations of Statistics

t-Distribution — When σ is Unknown

Foundations of Statistics

Chi-Square Distribution — Sum of Squared Normals

Foundations of Statistics

F-Distribution — Ratio of Variances

Foundations of Statistics

Confidence Intervals for the Mean — z and t Intervals

Foundations of Statistics

Confidence Intervals for Proportions — Estimating p

Foundations of Statistics

Confidence Intervals for Variance — Chi-Square Interval

Foundations of Statistics

Confidence Intervals for Two Samples — Comparing Groups

Foundations of Statistics

Margin of Error — Precision and Sample Size

Foundations of Statistics

Sample Size Determination — How Many Observations Do You Need?

Foundations of Statistics

Bootstrap Confidence Intervals — Resampling-Based Inference

Foundations of Statistics

Point Estimation — Estimating Population Parameters

Foundations of Statistics

Properties of Estimators — Unbiasedness, Efficiency, Consistency

Foundations of Statistics

Null and Alternative Hypothesis — How to Formulate Statistical Tests

Hypothesis Testing

Type I and Type II Errors — False Positives, False Negatives, Power

Hypothesis Testing

P-Values — What They Mean, What They Don't, and Common Misconceptions

Hypothesis Testing

Significance Levels (α) — Choosing Alpha and What It Means

Hypothesis Testing

One-Sample Z-Test — When and How to Use It

Hypothesis Testing

One-Sample T-Test — Complete Guide with Python

Hypothesis Testing

Two-Sample T-Test — Independent Groups Comparison

Hypothesis Testing

Paired T-Test — Before/After and Matched Pairs Analysis

Hypothesis Testing

One-Proportion Z-Test — Testing Population Proportions

Hypothesis Testing

Two-Proportion Z-Test — Comparing Two Proportions

Hypothesis Testing

Chi-Square Goodness-of-Fit Test

Hypothesis Testing

Chi-Square Test of Independence

Hypothesis Testing

F-Test for Equality of Variances

Hypothesis Testing

Levene's Test for Homogeneity of Variances

Hypothesis Testing

Statistical Power — Definition, Factors, and Power Analysis

Hypothesis Testing

Effect Size — Cohen's d, r, η², ω² and When They Matter

Hypothesis Testing

Multiple Testing Problem — FWER and False Discovery Rate

Hypothesis Testing

Bonferroni Correction — When and How to Apply It

Hypothesis Testing

FDR — Benjamini-Hochberg Procedure for Multiple Testing

Hypothesis Testing

SQL(70)

SQL Introduction

SQL Fundamentals

Database Concepts

SQL Fundamentals

SQL Data Types

SQL Fundamentals

CREATE TABLE Statement

SQL Fundamentals

INSERT INTO Statement

SQL Fundamentals

SELECT Statement

SQL Fundamentals

WHERE Clause

SQL Fundamentals

SELECT DISTINCT

SQL Fundamentals

ORDER BY Clause

SQL Fundamentals

LIMIT and OFFSET

SQL Fundamentals

Aliases (AS)

SQL Fundamentals

UPDATE Statement

SQL Fundamentals

DELETE Statement

SQL Fundamentals

AND, OR, NOT Operators

SQL Fundamentals

IN and BETWEEN Operators

SQL Fundamentals

LIKE Wildcards for Pattern Matching

Handling NULL Values with IS NULL

INNER JOIN for Combining Tables

LEFT JOIN for Left Outer Queries

RIGHT JOIN for Right Outer Queries

FULL OUTER JOIN

CROSS JOIN

Self JOIN

UNION, INTERSECT, EXCEPT

GROUP BY Clause

SQL Aggregation

HAVING Clause

SQL Aggregation

Aggregate Functions Overview

SQL Aggregation

COUNT, SUM, AVG

SQL Aggregation

MIN and MAX

SQL Aggregation

String Functions

SQL Aggregation

Date and Time Functions

Numeric Functions

Subqueries

Correlated Subqueries

CASE Expression

COALESCE and NULLIF

SQL Database Objects

SQL Views

SQL Database Objects

SQL Indexes

SQL Database Objects

SQL Constraints

SQL Database Objects

ALTER TABLE Statement

SQL Database Objects

DROP TABLE and TRUNCATE

Transactions (COMMIT, ROLLBACK)

Stored Procedures

User-Defined Functions

Database Triggers

Introduction to Window Functions

Window Functions

ROW_NUMBER, RANK, and DENSE_RANK

Window Functions

LEAD and LAG Functions

Window Functions

Introduction to Common Table Expressions

Common Table Expressions

Recursive CTEs

Common Table Expressions

Database Normalization

SQL Performance

Entity-Relationship Diagrams

SQL Performance

Query Performance

SQL Performance

EXPLAIN and ANALYZE

SQL Best Practices

SQL Joins Masterclass

Practicing SQL

SQL Glossary

SQL Cheatsheet

Advanced Window Functions

Window Functions

PIVOT and UNPIVOT

Working with JSON in SQL

Full-Text Search

PostgreSQL vs MySQL

SQL for Data Analysis

CTE Deep Dive

Table Partitioning

SQL ETL Pipelines

SQL Security

SQL A to Z

Data Science(61)

What is Data Science?

Python Data Types and Structures

String and Text Processing

Functions, Lambda, and Comprehensions

File I/O and Data Import

OOP for Data Science

NumPy Essentials

Pandas Series and DataFrames

GroupBy, Merge, and Pivot Tables

Data Cleaning Essentials

Data Cleaning: Missing Values, Outliers and Types

Module 2: NumPy and Pandas

Advanced Pandas: Performance and Patterns

Module 2: NumPy and Pandas

Matplotlib and Seaborn: Data Visualization

Module 3: Visualization

Advanced Visualization: Plotly and Interactive Charts

Module 3: Visualization

Data Storytelling and Communication

Module 3: Visualization

Statistics 101: Mean, Median, Variance and Distributions

Module 4: Statistics and Probability

Probability, Bayes' Theorem and PDF/CDF

Module 4: Statistics and Probability

Statistical Testing: Hypothesis, t-tests and Chi-square

Module 4: Statistics and Probability

A/B Testing and Experimentation

Module 4: Statistics and Probability

Causal Inference: Beyond Correlation

Module 4: Statistics and Probability

SQL for Data Scientists

Module 5: Data Collection and SQL

Web Scraping and API Data Collection

Module 5: Data Collection and SQL

Project 1: EDA on Real Dataset

Module 6: EDA Project

Capstone Foundation Project

Module 6: EDA Project

Introduction to Machine Learning: Supervised, Unsupervised and Reinforcement

Module 7: Machine Learning Fundamentals

Linear Regression: Math, Code and Assumptions

Module 7: Machine Learning Fundamentals

Logistic Regression: Sigmoid, Decision Boundary and Multi-class

Module 7: Machine Learning Fundamentals

Model Evaluation: ROC, AUC, Precision, Recall and F1

Module 7: Machine Learning Fundamentals

Cross-Validation and Bias-Variance Tradeoff

Module 7: Machine Learning Fundamentals

Regularization: Ridge, Lasso and ElasticNet

Module 7: Machine Learning Fundamentals

Decision Trees: Gini, Entropy and Pruning

Module 8: Tree-Based Models

Random Forest: Bootstrap Aggregating and Feature Randomness

Module 8: Tree-Based Models

Gradient Boosting: XGBoost, LightGBM, CatBoost

Module 8: Tree-Based Models

Hyperparameter Tuning: Grid Search, Random Search and Optuna

Module 8: Tree-Based Models

Model Interpretability: SHAP, LIME and Feature Importance

Module 8: Tree-Based Models

K-Means Clustering

Module 2: Machine Learning

PCA: Dimensionality Reduction and Feature Extraction

Module 9: Unsupervised Learning

Anomaly Detection: Isolation Forest, LOF and Autoencoders

Module 9: Unsupervised Learning

Imbalanced Data: SMOTE, Class Weights and Sampling

Module 10: Specialized ML

Time Series Basics: Trend, Seasonality and Stationarity

Module 10: Specialized ML

ARIMA and Prophet: Time Series Forecasting

Module 10: Specialized ML

Recommendation Systems: Collaborative and Content-Based

Module 10: Specialized ML

Graph Data Science: Networks and Graph Analytics

Module 10: Specialized ML

Project 2: End-to-End ML Pipeline

Module 11: End-to-End ML

Neural Networks: Perceptron to MLP — Full Mathematical Foundation

Module 12: Deep Learning

PyTorch Fundamentals: Tensors, Autograd and GPU Computing

Module 12: Deep Learning

Training Loops: Loss Functions, Optimizers and Learning Rate Schedules

Module 12: Deep Learning

CNNs for Image Data: Convolution, Pooling and Architectures

Module 13: Computer Vision

Transfer Learning: Fine-tuning Pre-trained Models

Module 13: Computer Vision

NLP Basics: Tokenization, Embeddings and Word Vectors

Transformers and BERT: Attention Is All You Need

Spark Fundamentals for Big Data

Module 15: Data Engineering and MLOps

Analytics Engineering with dbt

Module 15: Data Engineering and MLOps

MLOps: Experiment Tracking and MLflow

Module 15: Data Engineering and MLOps

Data Versioning with DVC

Module 15: Data Engineering and MLOps

Cloud ML: AWS SageMaker and GCP Vertex AI

Module 15: Data Engineering and MLOps

Model Deployment with FastAPI

Module 15: Data Engineering and MLOps

Project 3: Deploy a Deep Learning Model

Module 16: Deployment Project

Data Science Case Study Prep

Module 17: Career and Portfolio

Building a Data Science Portfolio and GitHub

Module 17: Career and Portfolio

Capstone: End-to-End Data Science Project

Module 17: Career and Portfolio

AI Agents(1)

Data Analysis Agent with Pandas and Visualization

PySpark(1)

Advanced Aggregations in PySpark

Python(1)

Python Data Analysis — Pandas & NumPy

Python Data Science