To explain Data Science in simple words: It is a study of where information is extracted from, what it represents and how it can be converted into a valuable source in the building of IT and Business approaches.
Data science is a field which is composed of statistics, mathematics, and computer science disciplines efficiently and incorporates techniques like cluster analysis, machine learning, Data visualization, and data mining.
In the past few years, Data Science has calmly spread to include organizations and businesses worldwide. It is widely used by astronomers, analytics, and research, geneticists, entrepreneurs as well as engineers.
A data scientist aims to transform data into knowledge, knowledge which is used to make rational decisions. They possess all three set of skills - Mathematics and Statistics, Machine Learning and Subject Matter Expertise. Patrons who are masters in all three skills are less in number. The three skills required for data science mastery are explained below:
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Machine learning is a subfield of artificial intelligence based on statistics. It involves machines learning how to complete tasks without being explicitly programmed to do so. This part is explained in details further.
While everyone knows what math is, statistics is the study of data: how to collect, summarize and present it. The statistics part will be covered in details later on.
In general, a domain expert or subject-matter expert (SME) is an individual who is a specialist in a specific area or topic. An SME should also have basic knowledge of other technical subjects too. In Data Science an SME Provides industry/process-specific context for what the patterns identified by the algorithms and models mean.
Such Individuals who master all three skills are also called as Unicorn Data Scientist. Despite how rare unicorn data scientists are they are rapidly growing in demand. Also, there doesn't appear to be any end in sight for the growth of this demand. As a result, in the very near future, this specific set of skills will be in high demand, whether you're a data scientist or applying data science practices to your current job role. The rarity of data scientists combined with their high demand leads to much higher salaries for data scientists and IT professionals with similar skills.
Data Science website is a platform for data scientists who can explore varied sources of data, build algorithms and models, and deploy work seamlessly. It also manages a blog, in which the articles are written by professionals who are currently working as data scientists.
Kaggle is a platform for analytics and predictive modeling, which is turning data science into a sport. A Data Science blog and kaggle’s competition, No Free Hunch, discusses tutorials, news and expert interviews exclusively related to data science.
Data Science [at] Berkeley blog which is an information hub for data science followers, featuring events coverage, interviews, data science startups and other insights on information technology.
FlowingData finds different ways in which statisticians, designers, and scientists use visualization, analysis, and exploration to enhance their knowledge of data. The Flowing Data maintains a blog which presents concepts on data which support readers to understand the trends in a relationship, transportation and more.
Insight Data Science conducts a six-week fellowship program, which is a postdoctoral program for connecting academia with data science. This website runs a blog which gives readers updates on industry news, descriptive data analyses, latest happenings and engages professionals with tips in the areas of data science.
KDnuggets is the best resource for data science where we gain knowledge through news, publications, webcasts, courses, etc. This is a community for patrons who want to convey their thoughts and gain the understanding of data related areas like Data Science, Data Mining, Business Analytics, and Machine Learning.
Galvanize community is a network of entrepreneurs, data scientists, and developers who collaborate under one roof. Through this community, you can get guidance from experts, enroll in G School programs, attend workshops and also connect with community members.
Data Science central is the widely preferred community for those who are highly immersed in the culture of data science. Participating in the forum discussions, read blog posts from peers and stay updated with the latest research on Data Science Central.
Quora is a global platform for gaining knowledge through question and answers, Where people engage in discussions, ask questions and find resources on any topic virtually. This community aims at “the technical approach to extraction of knowledge from data.”
There exists a lot of articles on O’Reilly, which seem to be very interesting. They cover most of the articles related to artificial intelligence and data science written by experts and influencers in data science. Therefore, they sound technically strong and also share concepts which are advanced. O'Reilly is notable for putting on events, for example, Strata that help characterize the data science group.
Data Science Association is a non-profit group of professionals offering professional certification, education, meetups and conferences, and even “Code of Professional Conduct on Data Science,” this is a highly recommended community for professionals of data science.
Mindmajix is a platform to educate people, who want to become professionals in the respective technologies. It provides an online course on Data Science and also spreads most of the information through blogs.
The Open Source Data Science Masters
Harvard CS109 Data Science
Introduction to Data Science @coursera
Data Science Course @ColumbiaUni notes by @mathbabe
Data Science Fundamentals course by Cognitive Class
Ravindra Savaram is a Content Lead at Mindmajix.com. His passion lies in writing articles on the most popular IT platforms including Machine learning, DevOps, Data Science, Artificial Intelligence, RPA, Deep Learning, and so on. You can stay up to date on all these technologies by following him on LinkedIn and Twitter.