80. How to Break Into Data Science (and Land a High-Paying Job) with Dr. Gene Ray скачать в хорошем качестве

80. How to Break Into Data Science (and Land a High-Paying Job) with Dr. Gene Ray

Want to break into data science and land a high-paying job? In this episode of The Tech Guide Podcast, we sit down with Dr. Gene Ray, a professor at Kennesaw State University and an industry expert who has helped countless students launch their careers in data science. Dr. Ray shares insider tips on: ✅ The most important skills to learn (Hint: It’s not just coding!) ✅ The best programming languages for data science ✅ How to get hands-on experience while still in school ✅ The role of AI in data science (and whether it’s coming for your job) ✅ How to stand out in interviews and impress top employers Whether you're a student, career switcher, or just curious about data science, this episode is packed with actionable advice to help you turn classroom knowledge into a successful career. Timestamps: 0:00 - Welcome to The Tech Guide Podcast 0:30 - From Student to Data Science Pro: What It Takes 2:00 - Meet Dr. Gene Ray: His Journey & Expertise 4:00 - The #1 Skill You Need to Succeed in Data Science 5:20 - Python vs. R vs. SAS: Which Language Should You Learn? 6:45 - Do You Need to Be Good at Math for Data Science? 8:30 - Data Science Career Paths: Which One is Right for You? 10:15 - How to Get Hands-On Experience in College (Even Without an Internship) 11:45 - Best Side Projects to Build Your Data Science Portfolio 13:00 - Real Student Success Stories: How They Landed Jobs 14:30 - Should You Do Kaggle Competitions? Are They Worth It? 16:00 - Master’s vs. PhD in Data Science: Do You Really Need One? 17:50 - Can You Get a Data Science Job with Just a Bachelor’s Degree? 19:30 - How AI & ChatGPT Are Changing Data Science Careers 21:00 - Soft Skills You Need to Stand Out in Data Science 22:30 - How to Land Your First Data Science Job 23:30 - Dr. Gene Ray’s #1 Advice for Aspiring Data Scientists 24:30 - Where to Connect with Dr. Gene Ray #DataScience #techcareers #MachineLearning