awesome-datascience
:memo: An awesome Data Science repository to learn and apply for real world problems.
https://github.com/academic/awesome-datascience
Last synced: 3 days ago
JSON representation
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The Data Science Toolbox
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Miscellaneous Tools
- Codeflash - Fast Python Code — Every Time |
- Hugging Face
- Chinese-Elite - source project that automatically maps relationship networks by parsing public data using LLMs and visualizes it as an interactive graph. |
- RunMat - syntax runtime with automatic CPU/GPU execution and fused array kernels. |
- Python - Pandas - Anaconda - ready Python distribution for large-scale data processing, predictive analytics, and scientific computing |
- Data Science Toolbox
- Turbostream - time data streams, without worrying about streaming infra or backpressure. |
- CorpusExplorer
- Data Science Toolbox
- CorpusExplorer
- Trains - Magical Experiment Manager, Version Control & DevOps for AI |
- Pandas GUI
- DVC - source data science version control system. It helps track, organize and make data science projects reproducible. In its very basic scenario it helps version control and share large data and model files. |
- Chaos Genius
- Aureo.io - code platform that focuses on building artificial intelligence. It provides users with the capability to create pipelines, automations and integrate them with artificial intelligence models – all with their basic data. |
- Hamilton
- SHAP
- skrub
- Desbordante - source data profiler specifically focused on discovery and validation of complex patterns, such as [numerical association rules](https://colab.research.google.com/github/Desbordante/desbordante-core/blob/main/examples/notebooks/Numerical_Association_Rules.ipynb), [differential dependencies](https://colab.research.google.com/github/Desbordante/desbordante-core/blob/main/examples/notebooks/Differential_Dependencies.ipynb), [denial constraints](https://colab.research.google.com/github/Desbordante/desbordante-core/blob/main/examples/notebooks/Denial_Constraints.ipynb), and more. |
- xonsh shell - powered shell that enables integration, management and orchestration of data science libraries mostly written in Python, allowing you to build pipelines, code and command-based workflows. It can also be used as a kernel for Jupyter Notebook. |
- Neptune.ai - friendly platform supporting data scientists in creating and sharing machine learning models. Neptune facilitates teamwork, infrastructure management, models comparison and reproducibility. |
- Polars
- DuckDB - process SQL OLAP database management system |
- Nimblebox - stack MLOps platform designed to help data scientists and machine learning practitioners around the world discover, create, and launch multi-cloud apps from their web browser. |
- dna-claude-analysis - style single-page HTML visualization. |
- WFGY ProblemMap
- Deploybase - time GPU and LLM pricing across all cloud and inference providers. |
- DeepAnalyze
- TabGAN
- FileShot.io - knowledge encrypted file sharing (AES-256-GCM in-browser). No account required, MIT licensed, self-hostable, optional link expiry. |
- Disco - values, effect sizes, and literature citations. Free for public data. |
- Annotation Lab - to-End No-Code platform for text annotation and DL model training/tuning. Out-of-the-box support for Named Entity Recognition, Classification, Relation extraction and Assertion Status Spark NLP models. Unlimited support for users, teams, projects, documents. |
- FunASR - grade speech recognition toolkit supporting 50+ languages with built-in VAD, punctuation, speaker diarization, and emotion detection. OpenAI-compatible API server included. |
- Future AGI - source platform to simulate, evaluate, trace, guardrail, route, and optimize LLM and AI agent apps in one feedback loop, so agents don't just get monitored, they self-improve. Self-hostable. Apache-2.0. |
- NuriStat - source SPSS alternative — menu-driven desktop statistics (t-tests, ANOVA, regression, survival analysis, ROC) with SPSS .sav import/export |
- AI for Database - refreshing dashboards, and trigger automated workflows based on database changes. |
- Hortonworks Sandbox
- RStudio
- Wolfram Data Science Platform - based Wolfram Language. |
- Domino Data Labs
- Data Mechanics - friendly and cost-effective. |
- Valohai
- PyStan
- hmmlearn
- Nimblebox - stack MLOps platform designed to help data scientists and machine learning practitioners around the world discover, create, and launch multi-cloud apps from their web browser. |
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Training Resources
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Colleges
- Data Science Degree @ Berkeley
- Data Science Degree @ UVA
- Data Science Degree @ Wisconsin
- BS in Data Science & Applications
- MS in Computer Information Systems @ Boston University
- MS in Applied Data Science @ Syracuse
- M.S. Management & Data Science @ Leuphana
- Master of Data Science @ Melbourne University
- Msc in Data Science @ The University of Edinburgh
- Master of Management Analytics @ Queen's University
- Master of Data Science @ Illinois Institute of Technology
- Master of Applied Data Science @ The University of Michigan
- Master Data Science and Artificial Intelligence @ Eindhoven University of Technology
- Master's Degree in Data Science and Computer Engineering @ University of Granada
- A list of colleges and universities offering degrees in data science.
- Msc in Data Science @ The University of Edinburgh
- Master of Applied Data Science @ The University of Michigan
- Msc in Data Science @ The University of Edinburgh
- Msc in Data Science @ The University of Edinburgh
- Master Data Science and Artificial Intelligence @ Eindhoven University of Technology
- Data Science Degree @ Wisconsin
- MS in Business Analytics @ ASU Online
- MS in Applied Data Science @ Syracuse
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Free Courses
- Data Scientist with R
- Data Scientist with Python
- Genetic Algorithms OCW Course
- Convex Optimization - Convex Optimization (basics of convex analysis; least-squares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems; optimality conditions, duality theory...)
- Skillcombo - Data Science - 1000+ free online Data Science courses
- Learning from Data - Introduction to machine learning covering basic theory, algorithms and applications
- Kaggle - Learn about Data Science, Machine Learning, Python etc
- ML Observability Fundamentals - Learn how to monitor and root-cause production ML issues.
- Weights & Biases Effective MLOps: Model Development - Free Course and Certification for building an end-to-end machine using W&B
- Python for Data Science by Scaler - This course is designed to empower beginners with the essential skills to excel in today's data-driven world. The comprehensive curriculum will give you a solid foundation in statistics, programming, data visualization, and machine learning.
- MLSys-NYU-2022 - Slides, scripts and materials for the Machine Learning in Finance course at NYU Tandon, 2022.
- LLMOps: Building Real-World Applications With Large Language Models - Learn to build modern software with LLMs using the newest tools and techniques in the field.
- Prompt Engineering for Vision Models - Learn to prompt cutting-edge computer vision models with natural language, coordinate points, bounding boxes, segmentation masks, and even other images in this free course from DeepLearning.AI.
- AI Expert Roadmap - Roadmap to becoming an Artificial Intelligence Expert
- Hands-on Train and Deploy ML - A hands-on course to train and deploy a serverless API that predicts crypto prices.
- Data Science Course By IBM - Free resources and learn what data science is and how it’s used in different industries.
- Python for Machine Learning - Start your journey to machine learning with Python, one of the most powerful programming languages.
- Data Scientist with R
- Data Scientist with Python
- Data Science - Open Source Society University
- Genetic Algorithms OCW Course
- Neural Networks: Zero to Hero - A free video series by Andrej Karpathy covering neural networks from scratch — backpropagation, makemore, GPT, and more.
- Genetic Algorithms OCW Course
- Convex Optimization - Convex Optimization (basics of convex analysis; least-squares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems; optimality conditions, duality theory...)
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Intensive Programs
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MOOC's
- Coursera Introduction to Data Science
- Data Science - 9 Steps Courses, A Specialization on Coursera
- Data Mining - 5 Steps Courses, A Specialization on Coursera
- Machine Learning – 5 Steps Courses, A Specialization on Coursera
- OpenIntro
- CS 171 Visualization
- Process Mining: Data science in Action
- Oxford Deep Learning
- Oxford Machine Learning
- UBC Machine Learning - video
- Coursera Big Data Specialization
- Statistical Thinking for Data Science and Analytics by Edx
- Cognitive Class AI by IBM
- Udacity - Deep Learning
- Keras in Motion
- Microsoft Professional Program for Data Science
- COMP3222/COMP6246 - Machine Learning Technologies
- CS 231 - Convolutional Neural Networks for Visual Recognition
- Coursera Tensorflow in practice
- Coursera Deep Learning Specialization
- 365 Data Science Course
- Coursera Natural Language Processing Specialization
- Coursera GAN Specialization
- Codecademy's Data Science
- Linear Algebra - Linear Algebra course by Gilbert Strang
- A 2020 Vision of Linear Algebra (G. Strang)
- Data Science: Statistics & Machine Learning
- Machine Learning Engineering for Production (MLOps)
- Recommender Systems Specialization from University of Minnesota
- Stanford Artificial Intelligence Professional Program
- Data Scientist with Python
- Programming with Julia
- Scaler Data Science & Machine Learning Program
- Data Scientist with Python
- CS 109 Data Science
- Data Science Specialization
- Python for Data Science Foundation Course
- Data Science Skill Tree
- Data Science for Beginners - Learn with AI tutor
- Machine Learning for Beginners - Learn with AI tutor
- Linear Algebra - Linear Algebra course by Gilbert Strang
- Introduction to Data Science
- Getting Started with Python for Data Science
- Maschinelle Sprachgebrauchsanalyse - Grundlagen der Korpuslinguistik - course material on text-mining / corpus-linguistics *in German* funded by the federal state of North Rhine-Westphalia
- Programmieren für Germanist*innen - course material: programming in python *in German* for digital humanities - funded by the federal state of North Rhine-Westphalia
- Oxford Deep Learning - video
- Statistical Thinking for Data Science and Analytics by Edx
- A 2020 Vision of Linear Algebra (G. Strang)
- Python for Data Science Foundation Course
- Google Advanced Data Analytics Certificate
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Tutorials
- 1000 Data Science Projects
- Machine Learning, Data Science and Deep Learning with Python
- How To Label Data
- Your Guide to Latent Dirichlet Allocation
- Over 1000 Data Science Online Courses at Classpert Online Search Engine
- Python for Data Science: A Beginner’s Guide
- 12 free Data Science projects to practice Python and Pandas
- #tidytuesday
- Data science your way
- PySpark Cheatsheet
- Tutorials of source code from the book Genetic Algorithms with Python by Clinton Sheppard
- Tutorials to get started on signal processing for machine learning
- Minimum Viable Study Plan for Machine Learning Interviews
- Best CV/Resume for Data Science Freshers
- Understand Data Science Course in Java
- Data Analytics Interview Questions (Beginner to Advanced)
- Top 100+ Data Science Interview Questions and Answers
- DataCamp Cheatsheets
- Realtime deployment - series model deployment.
- 1000 Data Science Projects
- DataDriven - SQL, Python, and Data Modeling Interview Questions
- TutorialSearch - Free cross-platform search engine indexing 50,000+ tutorials from Udemy, Skillshare, Pluralsight, and other major learning platforms across 45+ categories.
- StepByStepML - Interactive calculator that visualizes the step-by-step manual math behind machine learning algorithms for exam prep.
- How to Build Optimal AI Agents That Actually Work - A developer handbook on designing and building effective AI agents.
- Train LLM From Scratch - A straightforward method for training your LLM, from downloading data to generating text.
- Understand and Know Machine Learning Engineering by Building Solid Projects
- DataDriven - SQL, Python, and Data Modeling Interview Questions
- StepByStepML - Interactive calculator that visualizes the step-by-step manual math behind machine learning algorithms for exam prep.
- How to Build Optimal AI Agents That Actually Work - A developer handbook on designing and building effective AI agents.
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What is Data Science?
- What is Data Science @ Quora
- What is Data Science @ O'reilly
- The sexiest job of 21st century
- Wikipedia
- How to Become a Data Scientist
- a very short history of #datascience - -computer science. The term “Data Science” has emerged only recently to specifically designate a new profession that is expected to make sense of the vast stores of big data. But making sense of data has a long history and has been discussed by scientists, statisticians, librarians, computer scientists and others for years. The following timeline traces the evolution of the term “Data Science” and its use, attempts to define it, and related terms._ |
- Software Development Resources for Data Scientists - ready code and tools._|
- Data Scientist Roadmap - driven world where approx 328.77 million terabytes of data are generated daily. And this number is only increasing day by day, which in turn increases the demand for skilled data scientists who can utilize this data to drive business growth._|
- Navigating Your Path to Becoming a Data Scientist - demand careers today. With businesses increasingly relying on data to make decisions, the need for skilled data scientists has grown rapidly. Whether it’s tech companies, healthcare organizations, or even government institutions, data scientists play a crucial role in turning raw data into valuable insights. But how do you become a data scientist, especially if you’re just starting out? _|
- Data Science For Beginners - week, 20-lesson curriculum all about Data Science. |
- Software Development Resources for Data Scientists - ready code and tools._|
- Software Development Resources for Data Scientists - ready code and tools._|
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Where do I Start?
- Scikit-Learn - purpose data science package which implements the most popular algorithms - it also includes rich documentation, tutorials, and examples of the models it implements. Even if you prefer to write your own implementations, Scikit-Learn is a valuable reference to the nuts-and-bolts behind many of the common algorithms you'll find. With [Pandas](https://pandas.pydata.org/), one can collect and analyze their data into a convenient table format. [Numpy](https://numpy.org/) provides very fast tooling for mathematical operations, with a focus on vectors and matrices. [Seaborn](https://seaborn.pydata.org/), itself based on the [Matplotlib](https://matplotlib.org/) package, is a quick way to generate beautiful visualizations of your data, with many good defaults available out of the box, as well as a gallery showing how to produce many common visualizations of your data.
- Python - generated packages. To install packages, there are two main methods: Pip (invoked as `pip install`), the package manager that comes bundled with Python, and [Anaconda](https://www.anaconda.com) (invoked as `conda install`), a powerful package manager that can install packages for Python, R, and can download executables like Git.
Programming Languages
Categories
Sub Categories
Miscellaneous Tools
144
Bloggers
125
Books
97
Deep Learning Packages
90
Datasets
88
Comparison
78
Twitter Accounts
70
YouTube Videos & Channels
59
MOOC's
50
Comics
50
Journals, Publications and Magazines
42
Facebook Accounts
41
General Machine Learning Packages
40
Podcasts
36
Tutorials
29
Algorithms
28
Free Courses
24
Colleges
23
Infographics
15
Presentations
11
Data Science Competitions
5
Tools
5
Newsletters
4
Research & Knowledge Retrieval
4
Telegram Channels
3
Intensive Programs
2
Frameworks
2
Workflow
2
Mailing lists
1
Hobby
1
Slack Communities
1
GitHub Groups
1
Disaster
1
Keywords
machine-learning
86
python
60
deep-learning
58
data-science
50
pytorch
26
tensorflow
21
scikit-learn
13
data-analysis
13
keras
13
ml
12
neural-network
11
reinforcement-learning
11
mlops
10
artificial-intelligence
10
data-visualization
9
ai
9
computer-vision
8
numpy
8
llm
7
hyperparameter-optimization
7
neural-networks
7
gradient-boosting
7
awesome-list
7
object-detection
7
jupyter-notebook
6
dataset
6
data-mining
6
r
6
jupyter
6
pandas
6
explainable-ml
5
image-processing
5
explainable-ai
5
data
5
spark
5
big-data
5
awesome
5
workflow
5
nlp
5
statistics
5
pipeline
5
reproducibility
4
data-engineering
4
feature-engineering
4
scientific-computing
4
gpu
4
machine-learning-algorithms
4
cli
4
optimization
4
open-source
4