AI News, Top 50 Data Science Resources: The Best Blogs, Forums, Videos and Tutorials to Learn All about Data Science

Top 50 Data Science Resources: The Best Blogs, Forums, Videos and Tutorials to Learn All about Data Science

The field of data science is constantly evolving and ever-advancing, with new technologies placing more valuable insights in the hands of modern enterprises.

Because the field of data science is so broad and sometimes challenging to navigate, we’ve compiled a list of 50 of the most helpful data science resources on the web.

Whether you’re a student or new professional working in the field of data science, these resources are valuable for discovering the latest employment opportunities, finding tutorials for the processes and systems you’re using on a daily basis, learning hacks and tricks to boost your performance, and connecting with other professionals in your field.

Langford shares his knowledge and personal insights on learning theory, covers conferences and related events, and discusses everything from neuroscience to prediction theory, problems, reduction, and of course, machine learning.

Run by Zygmunt Zając, FastML was born from a frustration with papers and documentation that aren’t easily understood by the average user who lacks both the time and interest in becoming a PhD-level expert in every machine learning topic.

Statistical Modeling, Causal Inference, and Social Science is a blog run by Andrew Gelman, a professor of statistics and political science and director of the Applied Statistics Center at Columbia University.Topics include causal inference, decision theory, multilevel modeling, statistical computing, and statistical graphs, as well as other topics of interest to Gelman such as public health, sociology, and political science.

Data Mining Research, originally started in 2006, covers research and applications in data mining. Sandro Saitta first started the blog as a PhD student at EPFL (Ecole Polytechnique Fédérale de Lausanne), Switzerland, at which time he discussed data mining research issues.

Steve Miller blogs at Information Management, covering data science, predictive analytics, statistical learning, and the impacts of data science on economics and public policy.

Berkeley School of Information runs the datascience@berkeley blog, featuring interviews, data science startups, event coverage, and other insights on data science and information technology.

Three biostatistics professors (Jeff Leek, Roger Peng, and Rafa Irizarry) who are fired up about the new era where data are abundant and statisticians are scientists blog at Simply Statistics, where they post ideas on interesting subject matter, contribute to discussion of science and popular writing, share informative articles, and offer advice to up-and-coming statisticians.

The Insight Data Science blog updates readers on the latest happenings with the program, in addition to offering informative data analyses, industry news, and tips for professionals in the data science field.

The Data Science Report has it all, from case studies and papers to online courses, lectures, and webinars, as well as ongoing news and discussion of happenings in the world of data science.

From courses, education, and meetings, to news, features, and interviews, publications, and webcasts, KDnuggets is a comprehensive resource for anyone with a vested interest in the data science community, whether a student in pursuit of professional goals or a working professional whose role is impacted by data science.

question-and-answer site for statistical topics, machine learning, data analysis, data mining, and data visualization, Cross Validated is a free resource for data scientists and those interested in the field.

An open content portal for self-directed learning in data science, Learn Data Science was developed primarily by Nitin Borwankar, a seasoned database professional with more than two decades of experience.

The Neural Information Processing Systems (NIPS) Foundation, a non-profit corporation, hosts its annual conference to give attendees the opportunity to exchange research on neural information processing systems in the areas of biology, technology, mathematics, and theory.

Featuring tutorials on December 7, conference sessions December 7-10, and workshops December 11-12, NIPS 2015 will be part of the foundation’s continuing series of professional meetings and is a highly regarded data science resource.

Billed as “the industry’s only forum dedicated to discussing successful integration of your organization’s largest pools of data to maximize research and development efforts and minimize cost,”

Presented by Colin White, Presdient of BI Research, this webinar explores the idea that data scientists are invaluable to organizations because they are analysts who play the role of data engineer, statistician, and business analyst.

In this regard, data scientists are the link between simply doing advanced data analysis and actually using the findings to produce business results aligned with the organization’s goals.

The webinar showcases a framework developed to automatically cluster alerts that report on the health of various critical components, making it an informative and useful data science resource.

Considerations of the future of data science and the ethics involved with data analytics and enhanced predictive powers are just two captivating issues that arise in the video, making it an intriguing data science resource.

Most individuals in the data science field are familiar with Booz Allen Hamiltion, the group that provides management and tech consulting to the government, major corporations and institutions, and non-profit organizations.

Their nearly four-and-a-half-minute video focuses on the opportunity their clients have when dealing correctly with their data and serves as a case study for data science professionals.

With a central focus on using data science in sales, the video makes the case for having the necessary data, tools, and resources to use data science productively and efficiently.

Anyone looking for more information about data science is sure to find the Berkeley Institute for Data Science to be a great help, but we think their videos are some of the best choices for data science resources.

Geared toward advanced high school students or college freshmen with high-school level understandings of math, science, word processing, and spreadsheets, Data Science: An Introduction does not require a computer science background, making it an extremely accessible data science resource.

Consult Kaggle’s Wiki for answers to all your frequently asked questions about data science and Kaggle’s competitions, look for professional opportunities on the job board, and participate in discussions with other users in the forum.

free weekly newsletter that features curated news, articles, and data science job openings, Data Science Weekly is a must-receive news source for data scientists and related professionals delivered to your inbox every Thursday.

Top 50 Data Science Resources: The Best Blogs, Forums, Videos and Tutorials to Learn All about Data Science

The field of data science is constantly evolving and ever-advancing, with new technologies placing more valuable insights in the hands of modern enterprises.

Because the field of data science is so broad and sometimes challenging to navigate, we’ve compiled a list of 50 of the most helpful data science resources on the web.

Whether you’re a student or new professional working in the field of data science, these resources are valuable for discovering the latest employment opportunities, finding tutorials for the processes and systems you’re using on a daily basis, learning hacks and tricks to boost your performance, and connecting with other professionals in your field.

Langford shares his knowledge and personal insights on learning theory, covers conferences and related events, and discusses everything from neuroscience to prediction theory, problems, reduction, and of course, machine learning.

Run by Zygmunt Zając, FastML was born from a frustration with papers and documentation that aren’t easily understood by the average user who lacks both the time and interest in becoming a PhD-level expert in every machine learning topic.

Statistical Modeling, Causal Inference, and Social Science is a blog run by Andrew Gelman, a professor of statistics and political science and director of the Applied Statistics Center at Columbia University.Topics include causal inference, decision theory, multilevel modeling, statistical computing, and statistical graphs, as well as other topics of interest to Gelman such as public health, sociology, and political science.

Data Mining Research, originally started in 2006, covers research and applications in data mining. Sandro Saitta first started the blog as a PhD student at EPFL (Ecole Polytechnique Fédérale de Lausanne), Switzerland, at which time he discussed data mining research issues.

Steve Miller blogs at Information Management, covering data science, predictive analytics, statistical learning, and the impacts of data science on economics and public policy.

Berkeley School of Information runs the datascience@berkeley blog, featuring interviews, data science startups, event coverage, and other insights on data science and information technology.

Three biostatistics professors (Jeff Leek, Roger Peng, and Rafa Irizarry) who are fired up about the new era where data are abundant and statisticians are scientists blog at Simply Statistics, where they post ideas on interesting subject matter, contribute to discussion of science and popular writing, share informative articles, and offer advice to up-and-coming statisticians.

The Insight Data Science blog updates readers on the latest happenings with the program, in addition to offering informative data analyses, industry news, and tips for professionals in the data science field.

The Data Science Report has it all, from case studies and papers to online courses, lectures, and webinars, as well as ongoing news and discussion of happenings in the world of data science.

From courses, education, and meetings, to news, features, and interviews, publications, and webcasts, KDnuggets is a comprehensive resource for anyone with a vested interest in the data science community, whether a student in pursuit of professional goals or a working professional whose role is impacted by data science.

question-and-answer site for statistical topics, machine learning, data analysis, data mining, and data visualization, Cross Validated is a free resource for data scientists and those interested in the field.

An open content portal for self-directed learning in data science, Learn Data Science was developed primarily by Nitin Borwankar, a seasoned database professional with more than two decades of experience.

The Neural Information Processing Systems (NIPS) Foundation, a non-profit corporation, hosts its annual conference to give attendees the opportunity to exchange research on neural information processing systems in the areas of biology, technology, mathematics, and theory.

Featuring tutorials on December 7, conference sessions December 7-10, and workshops December 11-12, NIPS 2015 will be part of the foundation’s continuing series of professional meetings and is a highly regarded data science resource.

Billed as “the industry’s only forum dedicated to discussing successful integration of your organization’s largest pools of data to maximize research and development efforts and minimize cost,”

Presented by Colin White, Presdient of BI Research, this webinar explores the idea that data scientists are invaluable to organizations because they are analysts who play the role of data engineer, statistician, and business analyst.

In this regard, data scientists are the link between simply doing advanced data analysis and actually using the findings to produce business results aligned with the organization’s goals.

The webinar showcases a framework developed to automatically cluster alerts that report on the health of various critical components, making it an informative and useful data science resource.

Considerations of the future of data science and the ethics involved with data analytics and enhanced predictive powers are just two captivating issues that arise in the video, making it an intriguing data science resource.

Most individuals in the data science field are familiar with Booz Allen Hamiltion, the group that provides management and tech consulting to the government, major corporations and institutions, and non-profit organizations.

Their nearly four-and-a-half-minute video focuses on the opportunity their clients have when dealing correctly with their data and serves as a case study for data science professionals.

With a central focus on using data science in sales, the video makes the case for having the necessary data, tools, and resources to use data science productively and efficiently.

Anyone looking for more information about data science is sure to find the Berkeley Institute for Data Science to be a great help, but we think their videos are some of the best choices for data science resources.

Geared toward advanced high school students or college freshmen with high-school level understandings of math, science, word processing, and spreadsheets, Data Science: An Introduction does not require a computer science background, making it an extremely accessible data science resource.

Consult Kaggle’s Wiki for answers to all your frequently asked questions about data science and Kaggle’s competitions, look for professional opportunities on the job board, and participate in discussions with other users in the forum.

free weekly newsletter that features curated news, articles, and data science job openings, Data Science Weekly is a must-receive news source for data scientists and related professionals delivered to your inbox every Thursday.

This can be daunting if you’re new to data science, but keep in mind that different roles and companies will emphasize some skills over others, so you don’t have to be an expert at everything.

This will enable you to apply to jobs you’re already qualified for, or develop specific data skill sets to match the roles you want to pursue.

The Data EngineerSome companies get to the point where they have a lot of traffic (and an increasingly large amount of data), and they start looking for someone to set up a lot of the data infrastructure that the company will need moving forward.

As a result, you’ll have great opportunities to shine and grow via trial by fire, but there will be less guidance and you may face a greater risk of flopping or stagnating.

Generally, these companies are either looking for generalists or they’re looking to fill a specific niche where they feel their team is lacking, such as data visualization or machine learning.

As you look for your ideal data scientist job, make sure to look closely at the job descriptions, to find the role and company that best match your skills and experience.

No matter what skills you possess or how much experience you have, Udacity has the perfect data program for you, to ensure you learn the skills you’ll need, to build a career you’ll love!

How to Find Your Dream Data Science Job

Data science is one of the hottest new fields to emerge in the last few years, resulting in a huge burst of interest as a career option.

The data science interview process is the complex beast that stands before you if you’re going to break into a career with lucrative pay, great social impact, and great upwards mobility.

This path will help you find data science interviews -- and ace them.Springboard also offers a mentored data science career track bootcamp where you can benefit from personalized career coaching and a job guarantee -- get a data science job or your money back. 

The best data science learning resources out there and my journey into data science-

So, in this post I will be sharing some of my tips and the best resources out there which I used to get started when I was a complete stranger to this buzz word around 2 years back.

So this is literally the best time for anyone who is into this field or who aspires to become a data professional and wants to learn and get started to increase his/her employability chances, or say wants to learn it to experience something new or to stay updated and with the present trends, or who is deciding to change his professional field etc , this post will give you some of the handpicked, best possible resources out there to learn and get started with data science or simply improve and hone your data science, statistics and machine learning skills.

When I started my journey and got interested in this subject during the early days of my sophomore year at collage, I approached a senior who was a good friend of mine from my collage who was recently placed in a reputed Fintech firm as a Data scientist.

I too started with doing courses on descriptive and inferential statistics in R from DataCamp , joining various data science related groups on facebook, linkedIn, following and connecting with the data science professionals, influencers on github etc.

Exploratory data analysis in R -Next thing after learning statistics and probability theory is to get started with exploring the data and gathering information and extracting knowledge out of it with the help of the statistical and probabilistic techniques learned by doing the above courses.

7.Pre-processing data for Machine learning in Python, is also very important task for any data scientist as a data scientist spends more than 70% of his time pre-processing and cleaning data.

Now for further list of courses you guys can search for amazing courses here courses on statistics and probability This is an amazing cheat sheet on probability and statistics formulas which I keep handy and use very often.

Next what I did was learned how to visualize data and make plots, which is really really important for any data analyst or scientist for exploring the data nicely and sharing their results.

Now to learn data visualization I didn’t do any course specifically rather, explored Youtube, google, downloaded various cheatsteets, used Kaggle to search for examples etc.

It can create versatile, data-driven graphics, and connect the full power of the entire Python data-science stack to rich, interactive visualizations.

I now knew how to extract knowledge and insights from data using statistical concepts, data visualization, uni-variate, bi-variate and multi-variate data analysis etc.

I believe you cannot find any other best course to get started with understanding the core mathematical concepts behind major supervised and unsupervised learning techniques.

2.Machine learning with R is a fantastic course track, which is basically all the machine learning fundamentals at one place, if you opt this amazing course then you don’t need to wander at different places and waste you time and energy being confused about what to do.

You can do projects, start doing some research work, compete on Kaggle competitions, start build applications, start blogging, learn uses of machine learning and data science for some specific fields such Deep learning, natural language processing ,computer vision, economics and finance, financial econometrics, time series forecasting etc.

The courses which are the best to get started with Deep learning and other applications of data science and machine learning are— Now if you are willing to spend some extra time and energy to learn the compete fundamentals in a single track course which teaches you the complete fundamentals from the very basics to the advanced level of data science, then you can definitely opt for these DataCamp track courses- 1.

I actually completed the Data scientist with R track course which included all the different courses which I have included in the different sections of this post into 1 single track course.

So it’s basically your choice , if you want to learn a specific topic then go for individual courses, or otherwise if you are a beginner and want to learn from the scratch and basics, then do go for these track courses which will include all the relevant and important topics and courses into 1 single track course and will help you build the right set of skills in a good incremental manner.

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