Data Scientist Career Checklist Prompt: Skills, Salaries, Roles & Opportunities Guide

You are an expert career coach specializing in data science. Create a comprehensive, actionable CHECKLIST for someone aspiring to become a data scientist. Structure your response as a detailed checklist with the following sections, using bullet points, sub-bullets, and actionable steps for each item:

### 1. Core Roles and Responsibilities
- [ ] Define the primary daily tasks of a data scientist (e.g., data cleaning, modeling, visualization)
- [ ] List key industries hiring data scientists (e.g., tech, finance, healthcare)
- [ ] Explain how data scientists collaborate with teams like engineers and analysts

### 2. Essential Skills and Qualifications
- [ ] Technical skills: Programming languages (Python, R, SQL) with learning resources
- [ ] Math/Stats foundations: Key concepts (linear algebra, probability) and practice tips
- [ ] Tools mastery: Libraries (Pandas, Scikit-learn, TensorFlow) and certification paths
- [ ] Soft skills: Communication, problem-solving with real-world examples

### 3. Education and Entry Requirements
- [ ] Recommended degrees (Bachelor's/Master's in CS, Stats, etc.) vs. bootcamps
- [ ] Online courses/platforms (Coursera, edX, DataCamp) with top-rated ones
- [ ] Building a portfolio: Project ideas and GitHub best practices

### 4. Job Market Demand and Salaries
- [ ] Current demand stats by region (US, EU, Asia) and growth projections
- [ ] Average salaries by experience level (entry, mid, senior) and location
- [ ] High-paying niches (AI/ML, Big Data) with salary ranges

### 5. Career Advancement Paths
- [ ] Progression ladder: Junior → Senior → Lead → Manager
- [ ] Networking tips: Conferences, LinkedIn strategies, communities
- [ ] Continuous learning: Certifications (Google Data Analytics, AWS ML) and trends (GenAI, ethics)

### 6. Success Stories and Action Plan
- [ ] 3 real-world success stories with key takeaways
- [ ] Personalized 6-month action plan: Weekly milestones to land first job
- [ ] Common pitfalls to avoid and motivation boosters

Make it engaging, data-backed (cite recent sources like BLS, Glassdoor), and tailored for beginners to intermediates. End with motivational advice to pursue a data science career.