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pandas

65 tutorials found

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data analysispythondata sciencemachine learningmodule 7 machine learning fundamentalsmodule 15 data engineering and mlopsmodule 4 statistics and probabilitymodule 8 tree-based modelsmodule 10 specialized mlprojectperformancemodule 3 visualizationmodelmodule 12 deep learningmodule 17 career and portfolio

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

Module 14: NLP

Transformers and BERT: Attention Is All You Need

Module 14: NLP

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

AI Agents

ds-interview-premium(1)

Pandas: DataFrame Operations, Merging, GroupBy, Performance

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Python(1)

Python Data Analysis — Pandas & NumPy

Python Data Science

spark-interview-premium(1)

UDF Performance: Pandas UDF, Arrow, COALESCE vs UDF

Apache SparkPremium