What are the best ways to stay current in data engineering and science?

One counter-intuitive advice to stay current in Data Engineering and science is to stick with the basics. No matter how many new tools and technologies will come, the basics will remain the same. If your basics are strong grasping new tools and current trends will be easier. Data modeling, Data processing patterns, algorithms, and understanding of ETL are some starting points. #staybasic #dataengineering

Blogs to follow: - Data Science Central - KDnuggets - Towards Data Science - Netflix Tech Blog - Airbnb Engineering & Data Science - Uber Engineering Blog - LinkedIn Engineering Blog - Team Data Science Blog Podcasts: - Data Engineering Podcast - Data Futurology - Software Engineering Daily - Data Brew by Databricks - DataAware by Ascend - The Data Engineering Show - IDataengineer - The Engineering Side of Data - The Datanation Podcast - Data Skeptic - DataFramed - Banana Data Podcast

To stay current in data engineering and data science is very hard, but following this approach definitely put you on top. Continuous Learning Industry Forums Hands-On Projects Stay Inquisitive Read and Research Join Communities Follow Thought Leaders Build a Portfolio Experiment and Innovate Collaborate

I'm definitely in agreement with the other suggestions that have been offered. I'd also like to add in YouTube channels like Two Minute Papers, which go into a high level new state of the art research.

Data Science has been an ever evolving field with many sub-fields. I will list out some resources that have helped me a lot DS /ML/statistics: 1.Josh Stammer’s youtube ( amazing intuitive explanations) 2.Towards data science - blogs 3. Introduction to statistical learning (textbook) 4. Doing data science (textbook) Deep learning 1. Stanford course CS230 by Andrew Ng 2. Advanced NLP course - CS 685 by Mohit iyyer, textbook - Jurafsky, 3.Jay Alamar’s blogs - mostly NLP 4. Lang chain to work with LLMs Research papers can also be a good way to learn new developments. If you don’t have enough time, DAIR.ai provides good summaries

Some of the ways to stay current in data engineering and science includes being connected to known trend setters in the subject matter. Participate in Conferences, Webinars and pilot/testing programs.

In my neck of the wood, Telegram is very popular. I'm a member of multiple chats, channels and groups on either a specific technology (Greenplum), or a domain (data engineering). I try to read through these instead of doom-scrolling or Instagram, because reading through other people's struggles and experiences is very stimulating. Being able to participate in problem-solving or discussions of use-cases, tools and business problems is invaluable, because you can bounce your ideas off many experts in real time. Even if topics of discussions are not very relatable to you now, you never know whey they can become such. Such online communities over Slack, Telegram and Discord are great source of knowledge, but also just as a networking tool.

Joining online communities is one of the effective ways to stay connected with folks having common interest and get comprehensive knowledge around the topic of interest. One trick which I play to get more attention and get the accurate knowledge is comment with wrong or inaccurate information this grabs attention and members engage with immense knowledge with supporting resources this helps to seek assistance and also gain insight through shared cases studies and error resolving techniques.

The collective wisdom of online communities is a wellspring of knowledge. From rapid problem-solving to exploring avant-garde ideas, these forums are the modern-day agoras for data discussions. Communities to Explore: Data Engineering Learners on Slack: A nurturing space for learners and mentors. Data Science Stack Exchange: Q&A haven for data science professionals. Kaggle: A battleground for data science competitions and collaborative projects. Reddit Subreddits: Sub-communities dedicated to every data niche imaginable. These platforms enable you to engage in meaningful dialogue, exchanging experiences and staying abreast of industry shifts.

In the recent times, a lot of companies are shifting to Azure Cloud platform for their data related work. This trend is expected to continue growing, given Microsoft’s comprehensive suite of services. Acquiring practical experience with at least one cloud platform is crucial for staying current in data engineering and science roles.

For Deep Learning Practioners, Andrew Ng's Deep Learning Specialisation course on Coursera is very good and was recently updated for Python (the most used programming language for AI).

To the best of my knowledge, DE does not have all encompassing certifications or learning paths due to the vastness of technologies involved and abstractness of subject. Unless you aim to be a DE with a specific stack, i.e. DataBricks, Snowflake, or DE with clouds - AWS, GCP, Azure, there are no ultimate courses or certificates to signal a potential employer how good you are. Books are where it's at! Learning fundamentals and good practices will carry over with whatever technology stack you use. You won't be able to learn how to wrangle petabytes of data and build robust data models to business requirements on your laptop with Docker. But you can test out new tools occasionally, document your experience and share Github code, do it!

It will also help you know more about general development across the globe. Also, try to do cloud certifications and use that knowledge as much as possible in practical life. Once you do self-study, you mostly study what you like most and when you are doing certification, you are bound to go through the entire course and that's the best part where you learn beyond your comfort zone.

Learn what’s new but stay on top of what you know. That’s the best advice I’ve received. Sometimes leveraging Gen AI is the way to create excellent, tailored learning programs.

The dynamic realm of online events and webinars is akin to a virtual campus where the curriculum is the future of data science. Events to Bookmark: Data + AI Summit: An international confluence for data aficionados. Data Science Salon: A cross-industry nexus for data science leaders. Data Engineering Weekly: Your weekly digest of data engineering novelties. DataCamp Live: Interactive webinars with a focus on skill-building. Such events serve as a compass to navigate the evolving landscapes of data technology, offering a front-row seat to the future.

Always keep an open mind on new tech or methods. Consume opinions and insights from webinars to keep aware of what others are seeing and thinking. There is always something new to learn so embrace it.

There are several great weekly newsletters, such as: - The Batch from DeepLearning.AI / Andrew Ng - TheSequence - The Algorithm from MIT - The Download from MIT (good for business and less technical audience) - The Machine Learning Engineer from Alejandro Saucedo. Moreover, there are several great YouTube channels like: - Two Minute Papers - This Day in AI (also podcast) - Jeff Heaton ML/AI Moreover, I have found Google Alerts to be effective in keeping up with specific AI topics, news and trends in my industry. Finally, LLMs like ChatGPT are great for summarising, synthesising and extracting key insights from lots of research papers!

Pick one or two projects to work on personally that provide opportunities for hands on exploration of the latest in data science (e.g., LLMs) or data engineering. This could be part of your job or field, or be something outside of it. Have well defined goals for these projects and commit to a timeline.

I would suggest reading science publications from select journals. This approach guarantees a competitive advantage - by the time information reaches blogs / mass media, it's already too late, if you're looking to beat your competition.

Although I agree, Getting inspired is one way of appreciating new tech and staying updated but being said that it should always be second to original ideas of individuals. Have an open mind, read about things you like and also ones which doesn’t suit your taste.