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?"
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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. | |Software Development Resources for Data Scientists|Data scientists concentrate on making sense of data through exploratory analysis, statistics, and models. Software developers apply a separate set of knowledge with different tools. Although their focus may seem unrelated, data science teams can benefit from adopting software development best practices. Version control, automated testing, and other dev skills help create reproducible, production-ready code and tools.|
While not strictly necessary, having a programming language is a crucial skill to be effective as a data scientist. Currently, the most popular language is Python, closely followed by R. Python is a general-purpose scripting language which sees applications in a wide variety of fields. R is a domain-specific language for statistics, which contains a lot of common statistics tools out of the box.
Python is by far the most popular language in science, due in no small part to the ease at which it can be used and the vibrant ecosystem of user-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 (invoked as
conda install), a powerful package manager that can install packages for Python, R, and can download executables like Git.
Unlike R, Python was not built from the ground up with data science in mind, but there are plenty of third party libraries to make up for this. A much more exhaustive list of packages can be found later in this document, but these four packages are a good set of choices to start your data science journey with: Scikit-Learn is a general-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, one can collect and analyze their data into a convenient table format. Numpy provides very fast tooling for mathematical operations, with a focus on vectors and matrices. Seaborn, itself based on the Matplotlib 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.
When embarking on your journey to becoming a data scientist, the choice of language isn't particularly important, and both Python and R have their pros and cons. Pick a language you like, and check out one of the Free courses we've listed below!
Data science is a powerful tool that is utilized in various fields to solve real-world problems by extracting insights and patterns from complex data.
How do you learn data science? By doing data science, of course! Okay, okay - that might not be particularly helpful when you're first starting out. In this section, we've listed some learning resources, in a rough order from least to greatest commitment - Tutorials, Massively Open Online Courses (MOOCs), Intensive Programs, and Colleges.
This section is a collection of packages, tools, algorithms, and other useful items in the data science world.
These are some Machine Learning and Data Mining algorithms and models help you to understand your data and derive meaning from it.
This section includes some additional reading material, channels to watch, and talks to listen to.
Below are some Social Media links. Connect with other data scientists!
| 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 | | Monica Rogati | Data @ Jawbone. Turned data into stories & products at LinkedIn. Text mining, applied machine learning, recommender systems. Ex-gamer, ex-machine coder; namer. | | Noah Iliinsky | Visualization & interaction designer. Practical cyclist. Author of vis books: https://www.oreilly.com/pub/au/4419 | | Paul Miller | Cloud Computing/ Big Data/ Open Data Analyst & Consultant. Writer, Speaker & Moderator. Gigaom Research Analyst. | | Peter Skomoroch | Creating intelligent systems to automate tasks & improve decisions. Entrepreneur, ex Principal Data Scientist @LinkedIn. Machine Learning, ProductRei, Networks | | Prash Chan | Solution Architect @ IBM, Master Data Management, Data Quality & Data Governance Blogger. Data Science, Hadoop, Big Data & Cloud. | | Quora Data Science | Quora's data science topic | | R-Bloggers | Tweet blog posts from the R blogosphere, data science conferences and (!) open jobs for data scientists. | | Rand Hindi | | | Randy Olson | Computer scientist researching artificial intelligence. Data tinkerer. Community leader for @DataIsBeautiful. #OpenScience advocate. | | Recep Erol | Data Science geek @ UALR | | Ryan Orban | Data scientist, genetic origamist, hardware aficionado | | Sean J. Taylor | Social Scientist. Hacker. Facebook Data Science Team. Keywords: Experiments, Causal Inference, Statistics, Machine Learning, Economics. | | Silvia K. Spiva | #DataScience at Cisco | | Harsh B. Gupta | Data Scientist at BBVA Compass | | Spencer Nelson | Data nerd | | Talha Oz | Enjoys ABM, SNA, DM, ML, NLP, HI, Python, Java. Top percentile kaggler/data scientist | | Tasos Skarlatidis | Complex Event Processing, Big Data, Artificial Intelligence and Machine Learning. Passionate about programming and open-source. | | Terry Timko | InfoGov; Bigdata; Data as a Service; Data Science; Open, Social & Business Data Convergence | | Tony Baer | IT analyst with Ovum covering Big Data & data management with some systems engineering thrown in. | | Tony Ojeda | Data Scientist , Author , Entrepreneur. Co-founder @DataCommunityDC. Founder @DistrictDataLab. #DataScience #BigData #DataDC | | Vamshi Ambati | Data Science @ PayPal. #NLP, #machinelearning; PhD, Carnegie Mellon alumni (Blog: https://allthingsds.wordpress.com ) | | Wes McKinney | Pandas (Python Data Analysis library). | | WileyEd | Senior Manager - @Seagate Big Data Analytics @McKinsey Alum #BigData + #Analytics Evangelist #Hadoop, #Cloud, #Digital, & #R Enthusiast | | WNYC Data News Team | The data news crew at @WNYC. Practicing data-driven journalism, making it visual and showing our work. | | Alexey Grigorev | Data science author | | İlker Arslan | Data science author. Shares mostly about Julia programming |
Some data mining competition platforms
| 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. |