Data science is skyrocketing as one of the most sought-after fields in the tech industry. A data scientist has been in the top three positions on Glassdoors. It is going to be one of the most vital elements for enterprises and businesses in the close to future. This has created an abundance of opportunities for professionals well-versed in data science and offers them a very fruitful career. The field is developing surprisingly that it can be hard to even comply with all the new algorithms, techniques, and approaches. So working in data science, likewise to software engineering, frequently requires consistent learning and development.
Depending on your training and prior experience, you can decide the role that suits you best to urge your data science career. Either you are searching to learn data science from scratch, want to brush up on your skills, or prefer to study more advanced topics, these resources can assist and give you a personalized roadmap to becoming an outstanding Data Science expert.
- PyData
PyData is the educational program of NumFOCUS, are a nonprofit charity advertising open practices in research, data, and scientific computing. They arrange conferences all over the world encouraging researchers and practitioners to share their insights from their work. In the talks, you can locate a mix of well-known Python fine practices, examples of real-life cases the data scientists worked on, how they model churn or what tools they use to generate an uplift in their marketing campaigns, and introductions to some new libraries. It is a lot of exciting to certainly attend the conference in person, as you can actively take part in the presentations, ask questions, and network with people who share your interests. However, as this is not usually possible and there are definitely too many conferences to attend, you can find all the recordings on their YouTube channel. Normally, the recordings are posted a few months after every conference.
- arXiv
arXiv is Cornell University’s open-access repository of digital preprints of scientific papers in fields including computer science, machine learning, and many more. Basically, this is the place to focus on the latest research and state-of-the-art algorithms. Besides, presently there are so many new articles brought every day that it is primarily not possible to comply with everything. That is why Andrej Karpathy created the ArXiv Sanity Preserver to strive to filter out the most important and relevant papers. Additionally, you can follow arXiv daily on Twitter to receive an everyday curated list of the most important research articles.
- Papers With Code
Papers with Code is a remarkable initiative to create a free and open useful resource pool containing ML papers, together with the code and comparison tables. You can easily browse the accessible papers and search by topics, for example, image colorization within the computer vision domain. This website is definitely accessible when you choose to scan with some method or practice it to your dataset, besides actually writing all the code yourself. While such an exercise is certainly useful and you will learn a lot, often you just want to hack together an MVP to exhibit that something really works for your use case and generates value-added. After getting the required approval, you can calmly dive into the code to understand all the nuances of a specific model or architecture.
- Open Source Data Science Masters
This site offers a free list of online classes and resources. The resources are organized as a self-paced curriculum, with the assumption that you have a basic understanding of programming. The curriculum includes theoretical and foundational classes’ moreover tactical, hands-on classes in computer science, programming, and design so that you move through the curriculum with a strong understanding of data science.
- Data Science Weekly
A database of Data Science resources and information updates, this website gives readers the chance to choose from weekly newsletters which feature jobs, articles, and news. The web page additionally provides a listing of the most valuable books, data sets, and blogs alongside interviews with influential Data Scientists.
- R-bloggers
It is a blog aggregator and covers a wide range of topics. While most of them are R-related, you can still learn quite a lot by reading about general approaches to data science tasks. One should not restrict themselves to just one programming language and ignore everything else. Maybe you will read about an interesting project in R and will decide to port it to Python Alternatively, you can use rpy2 to access R packages from Python. While Python is currently the number 1 language in data science, there are still many packages and tools that have not been ported to Python from R. Thoroughly, R-bloggers is a very valuable resource and might be a source of inspiration for porting some R functionalities to Python.
- Machine Learning Mastery
Jason Brownlee’s website/blog is a gold mine of content for data scientists, especially the more junior ones. You can find a plethora of tutorials, from classic statistical modeling approaches linear regression, ARIMA, to the latest and greatest machine learning solutions. The articles are always very hands-on and contain Python code applying the particular concept to a toy dataset. The great purpose of the website is that Jason clearly explains the concepts and also refers to further reading for those who want to dive extra deep into the theoretical background. You can also filter all the articles by topic, in case you are interested only in imbalanced learning or how to code your first LSTM network.
The Latest Data Science news:
Data Science Weekly: Each Thursday, Data Science Weekly sends an e-mail with the latest information and developments in data science. You can additionally search their website for interviews, job opportunities, and resources on how to build a career in data science.
To Follow Intellectual Resources:
These expert data scientists tweet about the latest news and trends in the field.
Hillary Mason (@hmason): Data Scientist in Residence at Accel and Scientist Emeritus at bitly.
Dj Patil (@dpatil): VP of Product at RelateIQ.
Jeff Hammerbacher (@hackingdata): Founder and Chief Scientist at Cloudera and Assistant Professor at the Icahn School of Medicine at Mount Sinai.
Peter Skomoroch (@peteskomoroch): Equity Partner at Data Collective, former Principal Data Scientist at LinkedIn.
Drew Conway (@drewconway): Head of Data at Project Florida.
Nathan Yau (@flowingdata): Statistician and Author of Flowing Data.
