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Data Science Notes
Data Science and ML with AI
Getting Started with Data Science
Sample Lesson
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Getting Started with Data Science
5 Topics
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
Career in Data Science
Sample Lesson
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Career in Data Science
7 Topics
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
Exploratory Data Analysis Unlocking Insights
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Exploratory Data Analysis Unlocking Insights
11 Topics
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
The Foundation of Artificial Intelligence
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The Foundation of Artificial Intelligence
21 Topics
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
Supervised Learning
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Supervised Learning
12 Topics
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
Logistic Regression
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Logistic Regression
23 Topics
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
Notes – Popular Classification Algorithms
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
Dive into KNN
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Dive into KNN
14 Topics
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’
Decision Tree
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Decision Tree
13 Topics
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
Random Forest Algorithm
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Random Forest Algorithm
9 Topics
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
Unsupervised Learning
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Unsupervised Learning
8 Topics
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 – Popular Algorithms in Unsupervised Learning
Notes – Real world use cases of Unsupervised learnering
K-means Clustering
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K-means Clustering
9 Topics
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
Hierarchical Clustering
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Hierarchical Clustering
5 Topics
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
Deep Learning
Start with Deep Learning
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Start with Deep Learning
9 Topics
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
Convolutional Neural Network
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Convolutional Neural Network
10 Topics
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
TensorFlow
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TensorFlow
7 Topics
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
Recurrent Neural Networks
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Recurrent Neural Networks
3 Topics
Notes – RNN in Data Science
Data Science Interview Questions – RNN
Notes – ANN vs CNN vs RNN
Long Short-Term Memory
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Long Short-Term Memory
9 Topics
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
Let’s Start PyTorch
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Let’s Start PyTorch
5 Topics
Notes – PyTorch
Data Science Interview Questions – PyTorch
Notes – PyTorch vs TensorFlow
Notes – PyTorch Features
Notes – PyTorch Use Cases
Data Engineering
Start with Data Engineering
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Start with Data Engineering
12 Topics
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
Spark for Data Science
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Spark for Data Science
8 Topics
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
Gen AI
Start with Gen AI
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Start with Gen AI
4 Topics
Notes – Gen AI
Notes – Prompt Engineering
Notes – LLM
Notes – Agentic AI
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Getting Started with Data Science
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Data Science Notes
Getting Started with Data Science
Lesson Content
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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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