Data Science : Begineer to Advance

Chapters

1Data Science Foundations and Workflow

What is Data Science Roles in a Data Team Data Science Lifecycle CRISP DM and OSEMN Problem Framing and Hypotheses Data Types and Formats Structured vs Unstructured Data Reproducibility and Version Control Basics Notebooks vs Scripts Environments and Package Management Data Ethics and Bias Overview Experiment Tracking Concepts Documentation and Reporting Basics Project Scoping and KPIs Essential Tools Overview

2Python Programming Essentials for Data Science

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/Data Science Foundations and Workflow

Data Science Foundations and Workflow

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Understand the data science landscape, roles, workflows, and tools. Learn problem framing, reproducibility, and ethical principles that guide successful projects from idea to impact.

Content

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Data Ethics and Bias Overview

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