# Python으로 배우는 Bayesian 데이터 분석
This is a DataCamp course: Bayesian 데이터 분석의 장점을 이해하고, 다양한 실제 사례에 직접 적용해 보세요!
## Course Details
- **Duration:** ~4h
- **Level:** Intermediate
- **Instructor:** Michał Oleszak
- **Students:** ~19,440,000 learners
- **Subjects:** Python, Probability & Statistics, Data Science and Analytics
- **Content brand:** DataCamp
- **Practice:** Hands-on practice included
- **Prerequisites:** Introduction to Statistics in Python
## Learning Outcomes
- Python
- Probability & Statistics
- Data Science and Analytics
- Python으로 배우는 Bayesian 데이터 분석
## Traditional Course Outline
1. The Bayesian way - Take your first steps in the Bayesian world. In this chapter, you’ll be introduced to the basic concepts of probability and statistical distributions, as well as to the famous Bayes' Theorem, the cornerstone of Bayesian methods. Finally, you’ll build your first Bayesian model to draw conclusions from randomized coin tosses.
2. Bayesian estimation - It’s time to look under the Bayesian hood. You’ll learn how to apply Bayes' Theorem to drug-effectiveness data to estimate the parameters of probability distributions using the grid approximation technique, and update these estimates as new data become available. Next, you’ll learn how to incorporate prior knowledge into the model before finally practicing the important skill of reporting results to a non-technical audience.
3. Bayesian inference - Apply your newly acquired Bayesian data analysis skills to solve real-world business challenges. You’ll work with online sales marketing data to conduct A/B tests, decision analysis, and forecasting with linear regression models.
4. Bayesian linear regression with pyMC3 - In this final chapter, you’ll take advantage of the powerful PyMC3 package to easily fit Bayesian regression models, conduct sanity checks on a model's convergence, select between competing models, and generate predictions for new data. To wrap up, you’ll apply what you’ve learned to find the optimal price for avocados in a Bayesian data analysis case study. Good luck!
## Resources and Related Learning
**Resources:** Ads Data (dataset), Bikes Data (dataset)
**Related tracks:** 응용 통계학 파이썬에서
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Introduction to Statistics in Python1
The Bayesian way
Take your first steps in the Bayesian world. In this chapter, you’ll be introduced to the basic concepts of probability and statistical distributions, as well as to the famous Bayes' Theorem, the cornerstone of Bayesian methods. Finally, you’ll build your first Bayesian model to draw conclusions from randomized coin tosses.
2
Bayesian estimation
It’s time to look under the Bayesian hood. You’ll learn how to apply Bayes' Theorem to drug-effectiveness data to estimate the parameters of probability distributions using the grid approximation technique, and update these estimates as new data become available. Next, you’ll learn how to incorporate prior knowledge into the model before finally practicing the important skill of reporting results to a non-technical audience.
3
Bayesian inference
Apply your newly acquired Bayesian data analysis skills to solve real-world business challenges. You’ll work with online sales marketing data to conduct A/B tests, decision analysis, and forecasting with linear regression models.
4
Bayesian linear regression with pyMC3
In this final chapter, you’ll take advantage of the powerful PyMC3 package to easily fit Bayesian regression models, conduct sanity checks on a model's convergence, select between competing models, and generate predictions for new data. To wrap up, you’ll apply what you’ve learned to find the optimal price for avocados in a Bayesian data analysis case study. Good luck!
Python으로 배우는 Bayesian 데이터 분석
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