Kaggle, the online data science competition platform, will be hosting a free online conference for people interested in landing their first data science job.
Kaggle CareerCon 2018 on March 20, 2018. There are a bunch of great speakers lined up, and all the talks are focused on preparing someone to get a data science job (resume tips, interview questions, portfolio examples, …).
Data Science Society is organizing the first ONLINE #Datathon2018 – a 48-hours challenge for all people passionate about data, willing to experiment with new types of data, and expand their network of connections in the field globally.
The Datathon is one of the initiatives of Data Science Society, happening for the third time, this time fully digital!
The participants will have the chance to work on real- world cases of top companies such as Telenor, Receipt Bank, Ontotext, Kaufland, VMWare, ZenCodeo, and А Data Pro, while working and communicating on an internal platform, supported by the services of the best cloud providers – IBM, Microsoft and Amazon.
NLP,Computer Vision and AI
At the #Datathon2018 are expected many data passionates coming from a variety of backgrounds and interests. Academics and practitioners will have the chance to bring their knowledge in action in three categories of cases – NLP, Artificial Intelligence and Computer Vision. Go out of the theory and see the data from a different perspective while collaborating in a team of like-minded people and learning to deal with unexpected issues regarding the real-world data.
All data scientists, mathematicians, data analytics experts, software engineers and data enthusiasts will have the chance to dive deep in the data and be mentored by internationally renowned experts.
The #Datathon2018 is happening between 9th and 11th of February and the registration is open
Google has recently released a Jupyter Notebook platform called Google Colaboratory. You can run Python code in a browser, share results, and save your code for later. It currently does not support R code.
Andrew Ng, co-founder of Coursera and Deep Learning Expert, is launching a new specialization on Coursera. Details can be found at DeepLearning.ai or the Deep Learning Specialization Page. The specialization consists of 5 courses. They are free to audit and watch the videos. There is a fee to get graded assignments and receive a certificate of completion. The first course just started this week, so it is great time to start learning some deep learning.
Renowned data scientist, Kirk Borne will take viewers on a journey through his career in science and technology explaining how the industry-and himself have evolved over the last 4 decades. Starting with skipping lunches in high school to a systematic twitter obsession, Kirk will shed light on his road to success in the data science industry.
Kirk is universally considered one of the most (if not the most) influential voices in data science. If you are interested in a career in data science, this is a webinar you will not want to miss.
The webinar is 5:30 Eastern Time on August 29, 2017, and registrations are currently being accepted. It is free.
The Data Incubator, a data science fellowship program, is currently running a Data Science in 30 minutes webinar series. Next week features a free webinar with Dr. Becky Tucker of Netflix. Dr. Tucker is a Senior Data Scientist at Netflix where she specializes in predictive modeling for content demand (think what do people want to watch). The full abstract of the webinar is below. The webinar is free; all you need to do is register.
Abstract: Netflix is well-known for its data-driven recommendations that seek to customize the user experience for every subscriber. But data science at Netflix extends far beyond that – from optimizing streaming and content caching to informing decisions about the TV shows and films available on the service. The talk will cover work done by Becky and the Content Data Science team at Netflix, which seeks to evaluate where Netflix should spend their next content dollar using machine learning and predictive models.
Deep Learning Summer School, Montreal 2016 is aimed at graduate students and industrial engineers and researchers who already have some basic knowledge of machine learning (and possibly but not necessarily of deep learning) and wish to learn more about this rapidly growing field of research. If that is you, there are plenty of videos to help you learn more.
This is just a short list of a few books that I have have recently discovered online.
Model-Based Machine Learning – Chapters of this book become available as they are being written. It introduces machine learning via case studies instead of just focusing on the algorithms.
Foundations of Data Science – This is a much more academic-focused book which could be used at the undergraduate or graduate level. It covers many of the topics one would expect: machine learning, streaming, clustering and more.