Data Science Roadmap

Chapter 1: Introduction to Data Science

• Data Scientist vs Data Analyst vs Data Engineer
• Using Google Colab
• Introduction to Command Line (Unix/Linux)
• Version Control: GitHub
• Visual Studio Code Setup
• Getting Started with LinkedIn
• Building Your Online Presence: GitHub and Kaggle Portfolio
• Visual Studio Code Editor
• The Multidisciplinary Journey of Data Science

Chapter 2: Build a Strong Foundation

• Comprehensive Python Programming for Data Science
• Probability Basics
• Bayesian vs. Frequentist Statistics
• Mastering Machine Learning: A 2025 Guide for Aspiring Practitioners
• Transition from a non-technical background to machine learning scientist
• Essential Skills for Aspiring Machine Learning Engineers

Chapter 3: Data Manipulation and Visualization

• Exploratory Data Analysis for Machine Learning
• Handling Imbalanced Data
• Dimensionality Reduction
• Mastering Data Cleaning: A Five-Step Framework for Analysts

Chapter 4: Machine Learning Fundamentals

• Reinforcement Learning

Chapter 5: SQL and Databases

• Mastering SQL in 2025: Top Resources and Strategies for Data Professionals

Chapter 6: Deep Learning

• Phase Transitions in Recurrent Neural Networks (RNN): A Statistical Physics Perspective

Chapter 7: Specialized Topics

Chapter 8: Real-World Projects

Chapter 9: Develop Industry-Specific Knowledge

Chapter 10: Build Your Portfolio and Online Presence

• Crafting a Winning Data Science Portfolio: Key Steps to Stand Out
• How to Write and Structure a Project's ReadMe File

Chapter 11: Prepare for Job Applications

• Strategies for Success in Data Science
• How to Become a Data Scientist in 2025
• Tips on Data Science Interviews
• Avoid These Job Search Pitfalls
• Data Scientist Salaries
• Key Questions in Data Science Interviews
• Data Science Behavioral Interviews
• Navigating the Data Science Job Market: Strategies for Success
• 8 Essential Steps to Crafting a Winning Data Science Resume
• Optimal Timing and Strategies for Applying to Data Science Jobs