apaas.dev

28 May 2022

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一个很棒的数据科学存储库，用于学习和应用现实世界的问题。

An open source Data Science repository to learn and apply towards solving real world problems.

This is a shortcut path to start studying Data Science. Just follow the steps to answer the questions, "What is Data Science and what should I study to learn Data Science?"

What is Data Science?

Data Science is one of the hottest topics on the Computer and Internet farmland nowadays. People have gathered data from applications and systems until today and now is the time to analyze them. The next steps are producing suggestions from the data and creating predictions about the future. Here you can find the biggest question for Data Science and hundreds of answers from experts.

Link	Preview
What is Data Science @ O'reilly	Data scientists combine entrepreneurship with patience, the willingness to build data products incrementally, the ability to explore, and the ability to iterate over a solution. They are inherently interdisciplinary. They can tackle all aspects of a problem, from initial data collection and data conditioning to drawing conclusions. They can think outside the box to come up with new ways to view the problem, or to work with very broadly defined problems: “here’s a lot of data, what can you make from it?”
What is Data Science @ Quora	Data Science is a combination of a number of aspects of Data such as Technology, Algorithm development, and data interference to study the data, analyse it, and find innovative solutions to difficult problems. Basically Data Science is all about Analysing data and driving for business growth by finding creative ways.
The sexiest job of 21st century	Data scientists today are akin to Wall Street “quants” of the 1980s and 1990s. In those days people with backgrounds in physics and math streamed to investment banks and hedge funds, where they could devise entirely new algorithms and data strategies. Then a variety of universities developed master’s programs in financial engineering, which churned out a second generation of talent that was more accessible to mainstream firms. The pattern was repeated later in the 1990s with search engineers, whose rarefied skills soon came to be taught in computer science programs.
Wikipedia	Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning and big data.
How to Become a Data Scientist	Data scientists are big data wranglers, gathering and analyzing large sets of structured and unstructured data. A data scientist’s role combines computer science, statistics, and mathematics. They analyze, process, and model data then interpret the results to create actionable plans for companies and other organizations.
a very short history of #datascience	The story of how data scientists became sexy is mostly the story of the coupling of the mature discipline of statistics with a very young one--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.

Learn Data Science

Our favorite programming language is Python nowadays for #DataScience. Python's - Pandas library has full functionalities for collecting and analyzing data. We use Anaconda to play with data and to create applications.

Link	Description
The Data Science Lifecycle Process	The Data Science Lifecycle Process is a process for taking data science teams from Idea to Value repeatedly and sustainably. The process is documented in this repo
Data Science Lifecycle Template Repo	Template repository for data science lifecycle project
RexMex	A general purpose recommender metrics library for fair evaluation.
ChemicalX	A PyTorch based deep learning library for drug pair scoring.
PyTorch Geometric Temporal	Representation learning on dynamic graphs.
Little Ball of Fur	A graph sampling library for NetworkX with a Scikit-Learn like API.
Karate Club	An unsupervised machine learning extension library for NetworkX with a Scikit-Learn like API.
ML Workspace	All-in-one web-based IDE for machine learning and data science. The workspace is deployed as a Docker container and is preloaded with a variety of popular data science libraries (e.g., Tensorflow, PyTorch) and dev tools (e.g., Jupyter, VS Code)
Neptune.ai	Community-friendly platform supporting data scientists in creating and sharing machine learning models. Neptune facilitates teamwork, infrastructure management, models comparison and reproducibility.
steppy	Lightweight, Python library for fast and reproducible machine learning experimentation. Introduces very simple interface that enables clean machine learning pipeline design.
steppy-toolkit	Curated collection of the neural networks, transformers and models that make your machine learning work faster and more effective.
Datalab from Google	easily explore, visualize, analyze, and transform data using familiar languages, such as Python and SQL, interactively.
Hortonworks Sandbox	is a personal, portable Hadoop environment that comes with a dozen interactive Hadoop tutorials.
R	is a free software environment for statistical computing and graphics.
RStudio	IDE – powerful user interface for R. It’s free and open source, works on Windows, Mac, and Linux.
Python - Pandas - Anaconda	Completely free enterprise-ready Python distribution for large-scale data processing, predictive analytics, and scientific computing
Pandas GUI	Pandas GUI
Scikit-Learn	Machine Learning in Python
NumPy	NumPy is fundamental for scientific computing with Python. It supports large, multi-dimensional arrays and matrices and includes an assortment of high-level mathematical functions to operate on these arrays.
Vaex	Vaex is a Python library that allows you to visualize large datasets and calculate statistics at high speeds.
SciPy	SciPy works with NumPy arrays and provides efficient routines for numerical integration and optimization.
Data Science Toolbox	Coursera Course
Data Science Toolbox	Blog
Wolfram Data Science Platform	Take numerical, textual, image, GIS or other data and give it the Wolfram treatment, carrying out a full spectrum of data science analysis and visualization and automatically generating rich interactive reports—all powered by the revolutionary knowledge-based Wolfram Language.
Datadog	Solutions, code, and devops for high-scale data science.
Variance	Build powerful data visualizations for the web without writing JavaScript
Kite Development Kit	The Kite Software Development Kit (Apache License, Version 2.0) , or Kite for short, is a set of libraries, tools, examples, and documentation focused on making it easier to build systems on top of the Hadoop ecosystem.
Domino Data Labs	Run, scale, share, and deploy your models — without any infrastructure or setup.
Apache Flink	A platform for efficient, distributed, general-purpose data processing.
Apache Hama	Apache Hama is an Apache Top-Level open source project, allowing you to do advanced analytics beyond MapReduce.
Weka	Weka is a collection of machine learning algorithms for data mining tasks.
Octave	GNU Octave is a high-level interpreted language, primarily intended for numerical computations.(Free Matlab)
Apache Spark	Lightning-fast cluster computing
Hydrosphere Mist	a service for exposing Apache Spark analytics jobs and machine learning models as realtime, batch or reactive web services.
Data Mechanics	A data science and engineering platform making Apache Spark more developer-friendly and cost-effective.
Caffe	Deep Learning Framework
Torch	A SCIENTIFIC COMPUTING FRAMEWORK FOR LUAJIT
Nervana's python based Deep Learning Framework	.
Skale	High performance distributed data processing in NodeJS
Aerosolve	A machine learning package built for humans.

Twitter	Description
Big Data Combine	Rapid-fire, live tryouts for data scientists seeking to monetize their models as trading strategies
Big Data Mania	Data Viz Wiz , Data Journalist , Growth Hacker , Author of Data Science for Dummies (2015)
Big Data Science	Big Data, Data Science, Predictive Modeling, Business Analytics, Hadoop, Decision and Operations Research.
Charlie Greenbacker	Director of Data Science at @ExploreAltamira
Chris Said	Data scientist at Twitter
Clare Corthell	Dev, Design, Data Science @mattermark #hackerei
DADI Charles-Abner	#datascientist @Ekimetrics. , #machinelearning #dataviz #DynamicCharts #Hadoop #R #Python #NLP #Bitcoin #dataenthousiast
Data Science Central	Data Science Central is the industry's single resource for Big Data practitioners.
Data Science London	Data Science. Big Data. Data Hacks. Data Junkies. Data Startups. Open Data
Data Science Renee	Documenting my path from SQL Data Analyst pursuing an Engineering Master's Degree to Data Scientist
Data Science Report	Mission is to help guide & advance careers in Data Science & Analytics
Data Science Tips	Tips and Tricks for Data Scientists around the world! #datascience #bigdata
Data Vizzard	DataViz, Security, Military
DataScienceX
deeplearning4j
DJ Patil	White House Data Chief, VP @ RelateIQ.
Domino Data Lab
Drew Conway	Data nerd, hacker, student of conflict.
Emilio Ferrara	#Networks, #MachineLearning and #DataScience. I work on #Social Media. Postdoc at @IndianaUniv
Erin Bartolo	Running with #BigData--enjoying a love/hate relationship with its hype. @iSchoolSU #DataScience Program Mgr.
Greg Reda	Working @ GrubHub about data and pandas
Gregory Piatetsky	KDnuggets President, Analytics/Big Data/Data Mining/Data Science expert, KDD & SIGKDD co-founder, was Chief Scientist at 2 startups, part-time philosopher.
Hadley Wickham	Chief Scientist at RStudio, and an Adjunct Professor of Statistics at the University of Auckland, Stanford University, and Rice University.
Hakan Kardas	Data Scientist
Hilary Mason	Data Scientist in Residence at @accel.
Jeff Hammerbacher	ReTweeting about data science
John Myles White	Scientist at Facebook and Julia developer. Author of Machine Learning for Hackers and Bandit Algorithms for Website Optimization. Tweets reflect my views only.
Juan Miguel Lavista	Principal Data Scientist @ Microsoft Data Science Team
Julia Evans	Hacker - Pandas - Data Analyze
Kenneth Cukier	The Economist's Data Editor and co-author of Big Data (http://www.big-data-book.com/).
Kevin Davenport	Organizer of https://www.meetup.com/San-Diego-Data-Science-R-Users-Group/
Kevin Markham	Data science instructor, and founder of Data School
Kim Rees	Interactive data visualization and tools. Data flaneur.
Kirk Borne	DataScientist, PhD Astrophysicist, Top #BigData Influencer.
Linda Regber	Data story teller, visualizations.
Luis Rei	PhD Student. Programming, Mobile, Web. Artificial Intelligence, Intelligent Robotics Machine Learning, Data Mining, Natural Language Processing, Data Science.
Mark Stevenson	Data Analytics Recruitment Specialist at Salt (@SaltJobs) Analytics - Insight - Big Data - Datascience
Matt Harrison	Opinions of full-stack Python guy, author, instructor, currently playing Data Scientist. Occasional fathering, husbanding, organic gardening.
Matthew Russell	Mining the Social Web.
Mert Nuhoğlu	Data Scientist at BizQualify, Developer

Preview	Description
	Key differences of a data scientist vs. data engineer
	A visual guide to Becoming a Data Scientist in 8 Steps by DataCamp (img)
	Mindmap on required skills (img)
	Swami Chandrasekaran made a Curriculum via Metro map.
	by @kzawadz via twitter
	By Data Science Central
	Data Science Wars: R vs Python
	How to select statistical or machine learning techniques
	Choosing the Right Estimator
	The Data Science Industry: Who Does What
	Data Science ~~Venn~~ Euler Diagram
	Different Data Science Skills and Roles from this article by Springboard
	A simple and friendly way of teaching your non-data scientist/non-statistician colleagues how to avoid mistakes with data. From Geckoboard's Data Literacy Lessons.

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