How To Become A Data Scientist In 2024 ?| Data Science Roadmap 2024 | Simplilearn
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 Published On Premiered Apr 23, 2024

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Welcome to this video on "How To Become A Data Scientist In 2024?"! Join us as we embark on a journey through the dynamic world of data science, where every number tells a story waiting to be discovered. From the swinging '60s to the digital age, we uncover the origins and evolution of data science, showcasing its transformative impact on industries worldwide. Through practical insights and expert guidance, we outline a comprehensive learning path for aspiring data scientists, equipping you with the skills and knowledge needed to thrive in this exciting field. Get ready to unlock the secrets of data science and shape the future with us!

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➡️ About Caltech Post Graduate Program In Data Science
This Post Graduation in Data Science leverages the superiority of Caltech's academic eminence. The Data Science program covers critical Data Science topics like Python programming, R programming, Machine Learning, Deep Learning, and Data Visualization tools through an interactive learning model with live sessions by global practitioners and practical labs.

âś… Key Features
- Simplilearn's JobAssist helps you get noticed by top hiring companies
- Caltech PG program in Data Science completion certificate
- Earn up to 14 CEUs from Caltech CTME
- Masterclasses delivered by distinguished Caltech faculty and IBM experts
- Caltech CTME Circle membership
- Online convocation by Caltech CTME Program Director
- IBM certificates for IBM courses
- Access to hackathons and Ask Me Anything sessions from IBM
- 25+ hands-on projects from the likes of Amazon, Walmart, Uber, and many more
- Seamless access to integrated labs
- Capstone projects in 3 domains
- Simplilearn’s Career Assistance to help you get noticed by top hiring companies
- 8X higher interaction in live online classes by industry experts

âś… Skills Covered
- Exploratory Data Analysis
- Descriptive Statistics
- Inferential Statistics
- Model Building and Fine Tuning
- Supervised and Unsupervised Learning
- Ensemble Learning
- Deep Learning
- Data Visualization

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