Data Science Beginner Course in 27 Hours (2025)

Data Science FULL Course for Beginners in 27 HOURS - 2025 Edition

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00:00:00 - Data Science Full Course Intro
00:01:31 Data Science Maths in-depth
1 Introduction to Statistics
• Population & Sample
• Descriptive vs Inferential Statistics
• Basic Statistical Measures
2 Measure of Central Tendency (Median, Mean, Mode)
• Measures of Variability
• Percentage, Percentiles, and Quartiles
3 Probability
• Probability Distribution
• Normal Distribution
• Advanced Statistical Concepts
4 Covariance and Correlation
• Central Limit Theorem
• Hypothesis Testing

05:52:03 - Machine Learning Complete
1 Introduction to Machine Learning (ML)
2 Roadmap to Learning Machine Learning
3 Types of Data and Variables in ML
2 Data Cleaning:
• Identifying and Handling Missing Values
• One Hot Encoding & Dummy Variables
• Label Encoding
• Ordinal Encoding
• Outlier Detection and Removal
• Feature Scaling (Standardization and Normalization)
• Handling Duplicate Data
• Data Type Transformation
5 Feature Selection Techniques:
• Backward Elimination (using mixed)
• Forward Elimination (using mixed)

09:33:17 - Supervised Learning in ML
1 Train Test Split in Dataset
2 Regression Analysis:
• Linear Regression Algorithm (Simple Linear)
• Multiple Linear Regression
• Polynomial Regression
3 Cost Function in Regression
4 R Squared Score & Adjusted R Squared in Regression Analysis

12:15:30 - Classification in ML
1 Classification
2 Logistic Regression:
• Binary Classification (Practical)
• Binary Classification with Multiple Inputs (Practical)
• Binary Classification with Polynomial Inputs (Practical)
• Multiclass Classification (Practical)
3 Confusion Matrix
4 Imbalanced Dataset Handling
5 Naive Bayes Algorithm

15:18:11 - Non-Linear Supervised Algorithm in ML
1 Non-Linear Supervised Algorithms:
• Decision Tree (Classification)
• Decision Tree (Regression)
• K-Nearest Neighbors (Classification)
2 Hyperparameter Tuning
3 Cross-Validation
4 Unsupervised Learning

19:01:23 - Clustering in ML
1 Clustering
2 K-means Clustering
3 Hierarchical Clustering
4 DBSCAN Clustering Algorithm
5 Silhouette Score

20:52:57 - Association in ML
1 Association
2 Association Rule Learning
3 Apriori Algorithm
4 Frequent Pattern Growth Algorithm

22:15:34 - Ensemble Learning in ML
1 Ensemble Learning
2 Max Voting, Averaging & Weighted Average Voting
• Practical Implementation for Regression
• Practical Implementation for Classification
3 Bagging (with Bagging meta-estimator and Random forest)

23:25:50 - Deep Learning & AI Complete
1 Deep Learning Overview:
• Introduction to Deep Learning
• Neurons, Neural Networks, and Types of Deep Learning Networks
3 Perceptrons:
• Single Layer Perceptron
• Multilayer Perceptron (Artificial Neural Networks)
3 Training Process:
• Forward Propagation and Backpropagation
• Activation Functions
• Loss Functions
• Optimizers
4 Practical Applications

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