Data Science Notes

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Notes – Data Science Introduction

Notes – Data Science History

Notes – Data Science Case Studies in Industry

Notes – Data Science Applications in Real World

Data Science Interview Questions – Introduction

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Notes – How to Build a Career in Data Science

Notes – Why Data Science is in Demand

Notes – Career Opportunities in Data Science

Notes – Required Skill Set to become a Data Scientist

Notes – Learning Roadmap of Data Science

Notes – Salary Trends & Career Growth in Data Science

Notes – Future of Data Science

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Notes – Exploratory Data Analysis

Interview Questions – Exploratory Data Analysis

Notes – Numerical vs Categorical Data in Data Science

Notes – Continuous vs. Discrete Data in Data Science

Notes – Feature Engineering in Data Science

Notes – Handling Missing Values in Data Science

Notes – Handling Outliers in Data Science

Notes – Univariate, Bivariate, Multivariate Analysis in Data Science

Notes – Correlation in Data Science

Notes – Data Cleaning Essentials in Data Science

Notes – Business Insights from EDA in Data Science

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Notes – AI Introduction

Data Science Interview Questions – AI Introduction

Notes – ML Introduction

Data Science Interview Questions – ML Introduction

Notes – AI vs ML vs Data Science

Data Science Interview Questions – AI vs ML vs Data Science

Notes – AI & ML Case Studies in Industry

Notes – AI & ML Applications in Real World

Notes – Why Machine Learning?

Notes – Types of Machine Learning

Data Science Interview Questions – Types of Machine Learning

Notes – Key ML Terminologies

Data Science Interview Questions – Key ML Terminologies

Notes – Features and Labels in ML

Notes – Training and Testing in ML

Notes – Overfitting vs. Underfitting

Notes – Machine Learning Workflow

Notes – Data Science Project Architecture

Notes – Data Platform Strategy in Data Science

Notes – Data Science & Engineering Data Flow

Notes – Data Ecosystem Project Architecture

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Notes – Scikit-learn Introduction

Data Science Interview Questions – Scikit-learn Introduction

Notes – Introduction to Regression

Notes – Regression Real-World Use Cases

Data Science Interview Question – Introduction to Regression

Notes – Types of Regression Problem

Notes – Linear Regression

Data Science Interview Question – Linear Regression

Notes – Evaluation Metrics in Data Science

Notes – Common Challenges with Regression

Notes – Linear vs Polynomial vs Ridge vs Lasso Regression

Notes – Regression Industry Applications

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Notes – What is Classification?

Data Science Interview Questions – What is Classification?

Notes – Regression vs. Classification

Data Science Interview Questions – Regression vs. Classification

Notes – Classification Real-World Applications

Notes – Types of Classification Problems

Data Science Interview Questions – Popular Classification Algorithms

Notes – Logistic Regression

Data Science Interview Questions – Logistic Regression

Notes – Data Preparation for Classification

Notes – Label Encoding vs One-Hot Encoding

Notes – Feature Scaling in ML

Notes – Train-Test Split in ML

Notes – Handling Imbalanced Data in Classification

Notes – Model Evaluation Metrics in Classification

Notes – Accuracy in Classification

Notes – Precision, Recall, F1-Score in Classification

Notes – Confusion Matrix in Classification

Notes – ROC Curve and AUC Score in Classification

Notes – Overfitting & Underfitting in ML

Notes – Regularization Techniques in Classification

Notes – Classification Use Cases by Industry

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Notes – What is k-NN?

Data Science Interview Questions – What is k-NN?

Notes – KNN Real-World Applications

Data Science Interview Questions – KNN Real-World Applications

Notes – How k-NN Works

Data Science Interview Questions – How k-NN Works

Notes – KNN Distance Metrics

Notes – Choosing the Right ‘k’ in KNN

Notes – Data Preparation for KNN

Notes – Categorical Features Handling in KNN

Notes – Strengths and Limitations of KNN

Notes – Evaluation Metrics in KNN

Notes – KNN Model Tuning

Notes – Cross-Validation in KNN for Optimal ‘k’

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Notes – Introduction to Decision Tree

Notes – Decision Tree Real-World Use Cases

Notes – Types of Decision Trees

Notes – Components of a Decision Tree

Notes – Decision Tree Splitting Criteria

Notes – Gini Impurity in Decision Tree

Notes – Entropy & Information Gain in Decision Tree

Notes – Advantages and Limitations of Decision Tree

Notes – Pre-Pruning and Post-Pruning in Decision Tree

Notes – Decision Tree Evaluation Metrics

Notes – Decision Tree Hyperparameter Tuning

Notes – Decision Tree Cross-Validation

Data Science Interview Questions – Introduction to Decision Tree

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Notes – Introduction to Random Forest

Notes – Why use Random Forest over a Single Tree?

Notes – Random Forest Real-World Applications

Notes – Concept Behind Random Forest

Notes – How Random Forest Works

Notes – Random Forest Hyperparameters

Notes – Random Forest Advantages and Limitations

Notes – Random Forest Use Cases

Data Science Interview Questions – Introduction to Random Forest

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Notes – What is Unsupervised Learning?

Data Science Interview Questions – What is Unsupervised Learning?

Notes – Supervised vs Unsupervised Learning

Data Science Interview Questions – Supervised vs Unsupervised Learning

Notes – Where to use Unsupervised Learning

Notes – Application of Unsupervised Learning

Notes – Real world use cases of Unsupervised learnering

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Notes – Clustering

Data Science Interview Questions – Clustering

Notes – Clustering Real-world industry use cases

Notes – Classification vs Clustering

Data Science Interview Questions – Classification vs Clustering

Notes – K-means Clustering

Data Science Interview Questions – K-means Clustering

Notes – K-means Clustering Real-world industry use cases

Notes – Elbow Method in K-means Clustering

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Notes – Hierarchical Clustering

Notes – Agglomerative vs Divisive Approach in Hierarchical Clustering

Notes – Hierarchical Clustering Real-world Industry Use Cases

Data Science Interview Questions – Hierarchical Clustering

Notes – Dimensionality Reduction in Data Science

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Notes – Deep Learning vs Machine Learning

Data Science Interview Questions – Deep Learning vs Machine Learning

Notes – Deep Learning Introduction

Data Science Interview Questions – Deep Learning Introduction

Notes – Deep Learning Case Studies in Industry

Notes – Why Deep Learning?

Notes – Need for Deep Learning in Industry

Notes – Why Deep Learning is in Demand?

Notes – Key Deep Learning Terminologies

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Notes – CNN (Convolutional Neural Network)

Data Science Interview Questions – CNN

Notes – Activation Functions in Deep Learning

Data Science Interview Questions – Activation Functions in Deep Learning

Notes – RELU Activation Function in Deep Learning

Data Science Interview Questions – RELU Activation Function in Deep Learning

Notes – Optimizers in Deep Learning

Data Science Interview Questions – Optimizers in Deep Learning

Notes – Adam Optimizers in Deep Learning

Data Science Interview Questions – Adam Optimizers in Deep Learning

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Notes – ResNet50 in Data Science

Notes – Vanishing Gradients in Deep Learning

Notes – Transfer Learning

Data Science Interview Questions – Transfer Learning

Notes – TensorFlow Introduction

Data Science Interview Questions – TensorFlow Introduction

Notes – DenseNet121

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Notes – RNN in Data Science

Data Science Interview Questions – RNN

Notes – ANN vs CNN vs RNN

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Notes – Long Short-Term Memory

Data Science Interview Questions – LSTM

Notes – RNN vs LSTM

Notes – LSTM in Deep Learning

Notes – RNN vs LST

Notes – LSTM Applications and Use Cases

Data Science Interview Questions – LSTM in Deep Learning

Notes – Natural Language Processing

Data Science Interview Questions – NLP

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Notes – PyTorch

Data Science Interview Questions – PyTorch

Notes – PyTorch vs TensorFlow

Notes – PyTorch Features

Notes – PyTorch Use Cases

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Notes – Big Data in Data Science

Data Science Interview Questions – Big Data

Notes – Data Engineering in Data Science

Data Science Interview Questions – Data Engineering

Notes – Hadoop in Data Science

Data Science Interview Questions – Hadoop

Notes – HDFS in Data Science

Data Science Interview Questions – HDFS

Notes – MapReduce in Data Science

Data Science Interview Questions – MapReduce

Notes – Hadoop Ecosystem in Data Science

Data Science Interview Questions – Hadoop Ecosystem

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Notes – Spark vs Hadoop

Notes – What is Spark?

Notes – Spark for Data Science

Notes – Data Lake

Notes – ETL vs ELT

Notes – Schema on read vs Schema on write

Notes – Batch vs Micro-batch vs Stream Data Collection

Notes – Batch vs Micro-batch vs Stream Processing

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Notes – Gen AI

Notes – Prompt Engineering

Notes – LLM

Notes – Agentic AI

Data Science Notes

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