Data Science : Begineer to Advance

Chapters

1Data Science Foundations and Workflow

2Python Programming Essentials for Data Science

Python Syntax and Data Types Control Flow and Functions Comprehensions and Generators Modules and Packages Virtual Environments Error Handling and Logging Object Oriented Basics Working with Files and Paths Itertools and Functional Tools Type Hints and Docstrings Performance Basics and Profiling Using Jupyter and IPython Testing with pytest Code Style and Linting Packaging and Dependency Management

3Working with Data Sources and SQL

4Data Wrangling with NumPy and Pandas

5Data Cleaning and Preprocessing

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/Python Programming Essentials for Data Science

Python Programming Essentials for Data Science

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Gain fluency in Python as the primary language for data work. Write clean, maintainable code and leverage core libraries to build reliable data science workflows.

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Control Flow and Functions

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