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Top 23 Jupyter Notebook Statistic Projects
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Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)
Also this is quite nice practical introduction which might help with finding answers to your questions: https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
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Project mention: How often do you see Bayesian Statistics or Stan in the DS world? Essential skill or a nice to have? | /r/datascience | 2023-06-17
TensorFlow-Probability
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SonarLint
Clean code begins in your IDE with SonarLint. Up your coding game and discover issues early. SonarLint is a free plugin that helps you find & fix bugs and security issues from the moment you start writing code. Install from your favorite IDE marketplace today.
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cracking-the-data-science-interview
A Collection of Cheatsheets, Books, Questions, and Portfolio For DS/ML Interview Prep
Project mention: Can someone recommend some website for data science interview preparation | /r/datascience | 2023-06-02 -
ML-foundations
Machine Learning Foundations: Linear Algebra, Calculus, Statistics & Computer Science
As others have said, you won't need calculus immediately, but it's important that you make a good attempt at learning up to Calc3. I also didn't have a math heavy undergrad so it took a lot of self-study for me, but it's possible. Simulation has a great math boot camp at the beginning to review everything but you'll want to be prepped with Calc before that because that class is all calculus based probability. Some other good resources are the 3Blue1Brown videos on YouTube. They have a great series for both calc & linear algebra to talk through all the intuition with visuals. I also really like John Krohns series because you code through the math which is very applicable for us in this program. I only did his linear Algebra, but he has a whole series with Calc and probability, too. https://github.com/jonkrohn/ML-foundations
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imodels
Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
Project mention: [D] Have researchers given up on traditional machine learning methods? | /r/MachineLearning | 2023-01-31- all domains requiring high interpretability absolutely ignore deep learning at all, and put all their research into traditional ML; see e.g. counterfactual examples, important interpretability methods in finance, or rule-based learning, important in medical or law applications
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Mergify
Updating dependencies is time-consuming.. Solutions like Dependabot or Renovate update but don't merge dependencies. You need to do it manually while it could be fully automated! Add a Merge Queue to your workflow and stop caring about PR management & merging. Try Mergify for free.
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fecon235
Notebooks for financial economics. Keywords: Jupyter notebook pandas Federal Reserve FRED Ferbus GDP CPI PCE inflation unemployment wage income debt Case-Shiller housing asset portfolio equities SPX bonds TIPS rates currency FX euro EUR USD JPY yen XAU gold Brent WTI oil Holt-Winters time-series forecasting statistics econometrics
Project mention: Financial Economics: Financial Economics Models. Extended Research - star count:915.0 | /r/algoprojects | 2023-06-10 -
Project mention: What is the most performant way to visualize pytorch tensor that stores an image in an interactive manner? | /r/learnmachinelearning | 2023-01-29
Maybe this: https://github.com/xl0/lovely-tensors
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DataScienceProjects
The code repository for projects and tutorials in R and Python that covers a variety of topics in data visualization, statistics sports analytics and general application of probability theory.
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Basic-Mathematics-for-Machine-Learning
The motive behind Creating this repo is to feel the fear of mathematics and do what ever you want to do in Machine Learning , Deep Learning and other fields of AI
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data-science-learning
Repository of code and resources related to different data science and machine learning topics. For learning, practice and teaching purposes.
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the-elements-of-statistical-learning
My notes and codes (jupyter notebooks) for the "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani and Jerome Friedman
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stanford-CS229
Python solutions to the problem sets of Stanford's graduate course on Machine Learning, taught by Prof. Andrew Ng
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covid19-severity-prediction
Extensive and accessible COVID-19 data + forecasting for counties and hospitals. 📈
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conformal_classification
Wrapper for a PyTorch classifier which allows it to output prediction sets. The sets are theoretically guaranteed to contain the true class with high probability (via conformal prediction).
Project mention: [P] 🚀 AWS launches Fortuna, an open-source library for Uncertainty Quantification | /r/MachineLearning | 2023-01-04What is the best end-to-end example showing it? https://github.com/awslabs/fortuna/blob/main/examples/mnist_classification.ipynb ? It would be nice to have some visual explainer, as in https://github.com/aangelopoulos/conformal_classification .
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Project mention: Andrew Ng's Machine Learning Specialization or Introduction to Statistical Learning? For someone who's comfortable with mathematics. | /r/learnmachinelearning | 2023-05-28
https://github.com/emredjan/ISL-python this GitHub has the exercises in python but I am so pumped the python version is coming out this summer.
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Introduction_to_statistical_learning_summary_python
Summary of each chapter of the book- Introduction of Statistical Learning (ISL), along with Python code & data.
Project mention: Can you recommend a Python textbook to replace "An Introduction to Statistical Learning with Applications in R", Witten, J. et. al. [E] | /r/statistics | 2022-12-12There is Python code available for that book.
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InfluxDB
Collect and Analyze Billions of Data Points in Real Time. Manage all types of time series data in a single, purpose-built database. Run at any scale in any environment in the cloud, on-premises, or at the edge.
NOTE:
The open source projects on this list are ordered by number of github stars.
The number of mentions indicates repo mentiontions in the last 12 Months or
since we started tracking (Dec 2020).
The latest post mention was on 2023-09-06.
Jupyter Notebook Statistics related posts
- Apple Maps Gradually Winning over Google Maps Users, Report Suggests
- An Introduction to Statistical Learning with Applications in Python
- Andrew Ng's Machine Learning Specialization or Introduction to Statistical Learning? For someone who's comfortable with mathematics.
- Statistics about how many edits are from users from which year? (e.g. in 2022, a little less than 50% of all edits are from people who had their first edit between 2018-2022)
- What is the most performant way to visualize pytorch tensor that stores an image in an interactive manner?
- datacamp: NEW Courses - star count:200.0
- datacamp: NEW Courses - star count:200.0
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A note from our sponsor - InfluxDB
www.influxdata.com | 27 Sep 2023
Index
What are some of the best open-source Statistic projects in Jupyter Notebook? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | Probabilistic-Programming-and-Bayesian-Methods-for-Hackers | 25,865 |
| 2 | probability | 3,979 |
| 3 | cracking-the-data-science-interview | 2,921 |
| 4 | ML-foundations | 2,418 |
| 5 | hyperlearn | 1,434 |
| 6 | ppd599 | 1,232 |
| 7 | imodels | 1,173 |
| 8 | fecon235 | 1,011 |
| 9 | lovely-tensors | 1,002 |
| 10 | geomstats | 995 |
| 11 | edward2 | 653 |
| 12 | DataScienceProjects | 558 |
| 13 | Basic-Mathematics-for-Machine-Learning | 535 |
| 14 | data-science-learning | 393 |
| 15 | the-elements-of-statistical-learning | 382 |
| 16 | monad-bayes | 379 |
| 17 | stanford-CS229 | 375 |
| 18 | datacamp | 264 |
| 19 | covid19-severity-prediction | 226 |
| 20 | conformal_classification | 184 |
| 21 | ISL-python | 178 |
| 22 | Introduction_to_statistical_learning_summary_python | 157 |
| 23 | gan-vae-pretrained-pytorch | 153 |
Collect and Analyze Billions of Data Points in Real Time
Manage all types of time series data in a single, purpose-built database. Run at any scale in any environment in the cloud, on-premises, or at the edge.
www.influxdata.com