Learn Data Science Tutorial - Full Course for Beginners
Key Takeaways
Covers data science principles, practices, and tools for beginners
Original Description
Learn Data Science is this full tutorial course for absolute beginners. Data science is considered the "sexiest job of the 21st century." You'll learn the important elements of data science. You'll be introduced to the principles, practices, and tools that make data science the powerful medium for critical insight in business and research. You'll have a solid foundation for future learning and applications in your work. With data science, you can do what you want to do, and do it better. This course covers the foundations of data science, data sourcing, coding, mathematics, and statistics.
💻 Course created by Barton Poulson from datalab.cc.
🔗 Check out the datalab.cc YouTube channel: https://www.youtube.com/user/datalabcc
🔗 Watch more free data science courses at http://datalab.cc/
❤️ Try interactive Algorithms courses we love, right in your browser: https://scrimba.com/freeCodeCamp-Algorithms (Made possible by a grant from our friends at Scrimba)
⭐️ Course Contents ⭐️
⌨️ Part 1: Data Science: An Introduction: Foundations of Data Science
- Welcome (1.1)
- Demand for Data Science (2.1)
- The Data Science Venn Diagram (2.2)
- The Data Science Pathway (2.3)
- Roles in Data Science (2.4)
- Teams in Data Science (2.5)
- Big Data (3.1)
- Coding (3.2)
- Statistics (3.3)
- Business Intelligence (3.4)
- Do No Harm (4.1)
- Methods Overview (5.1)
- Sourcing Overview (5.2)
- Coding Overview (5.3)
- Math Overview (5.4)
- Statistics Overview (5.5)
- Machine Learning Overview (5.6)
- Interpretability (6.1)
- Actionable Insights (6.2)
- Presentation Graphics (6.3)
- Reproducible Research (6.4)
- Next Steps (7.1)
⌨️ Part 2: Data Sourcing: Foundations of Data Science (1:39:46)
- Welcome (1.1)
- Metrics (2.1)
- Accuracy (2.2)
- Social Context of Measurement (2.3)
- Existing Data (3.1)
- APIs (3.2)
- Scraping (3.3)
- New Data (4.1)
- Interviews (4.2)
- Surveys (4.3)
- Card Sorting (4.4)
- Lab Experiments (4
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