Data Science : Begineer to Advance

Chapters

1Data Science Foundations and Workflow

2Python Programming Essentials for Data Science

3Working with Data Sources and SQL

4Data Wrangling with NumPy and Pandas

5Data Cleaning and Preprocessing

Data Quality Dimensions Detecting Outliers Handling Missing Data Strategies Encoding Categorical Variables Scaling and Normalization Feature Transformation Date and Time Handling Text Normalization Basics Imbalanced Data Techniques Dealing with Duplicates Data Leakage Prevention Target Leakage Detection Data Splitting Strategies Skewness and Box Cox Data Validation Checks

6Exploratory Data Analysis and Visualization

7Probability and Statistics for Data Science

8Machine Learning Foundations

9Supervised Learning Algorithms

10Unsupervised Learning and Dimensionality Reduction

11Model Evaluation, Validation, and Tuning

12Feature Engineering and ML Pipelines

13Time Series Analysis and Forecasting

14Natural Language Processing

15Deep Learning, Deployment, and MLOps

Courses/Data Science : Begineer to Advance/Data Cleaning and Preprocessing

Data Cleaning and Preprocessing

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Assess data quality and apply systematic cleaning, encoding, and transformation techniques to prepare robust training and analysis datasets.

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Dealing with Duplicates

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