8 Ways to Stay on Top of the Latest Trends in Data Science

Data science is constantly evolving, with new papers and technologies coming out frequently. As such, data scientists may feel overwhelmed when trying to keep up with the latest innovations.

However, with the right tips, you can stay current and remain relevant in this competitive field. Thus, here are eight ways to stay on top of the latest trends in data science.

1. Follow Data Science Blogs and Newsletters

Data science blogs are a great way to brush up on the basics while learning about new ideas and technologies. Several tech conglomerates produce high-quality blog content where you can learn about their latest experiments, research, and projects. Great examples are Google, Facebook, and Netflix blogs, so waste no time checking them out.

Alternatively, you can look into online publications and individual newsletters. Depending on your experience level and advancement in the field, these blogs may address topics you’d find more relatable. For example, Version Control for Jupyter Notebook is easier for a beginner to digest than Google’s Preference learning for cache eviction.

You can find newsletters by doing a simple search, but we’d recommend Data Elixir, Data Science Weekly, and KDnuggets News, as these are some of the best.

2. Listen to Data Science Podcasts and Watch YouTube Videos

Podcasts are easily accessible and a great option when you’re pressed for time and want to get knowledge on the go. Listening to podcasts exposes you to new data science concepts while letting you carry out other activities simultaneously. Also, using interviews with experts in the field, some podcasts offer a window into the industry and let you learn from professionals’ experiences.

On the other hand, YouTube is a better alternative for audio-visual learners and has several videos at your disposal. Channels like Data School and StatQuest with Josh Starmer cover a wide range of topics for both aspiring and experienced data scientists. They also touch on new trends and methods, so following these channels is a good idea to keep current.

It’s easy to get lost in a sea of podcasts and videos, so carefully select detailed videos and the best podcasts for data science. This way, you can acquire accurate knowledge from the best creators and channels.

3. Learn Data Science Skills and Concepts From Courses and Books

Online courses allow learning from data science academics and experts, who condense their years of experience into digestible content. Recent courses cover several data science necessities, from hard-core machine learning to starting a career in data science without a degree. They may not be cheap, but they are well worth their cost in the value they give.

Additionally, books play an important role as well. Reading current data science books can help you learn new techniques, understand real-world data science applications, and develop critical thinking and problem-solving skills. These books explain in-depth data science concepts you may not find elsewhere.

Such books include The Data Science Handbook, Data Science on the Google Cloud Platform, and Think Bayes. You should also check out a few data science courses on sites like Coursera and Udemy.

4. Meet Industry Experts and Enthusiasts From Events and Communities

Attending conferences ushers you into an environment of like-minded individuals you can connect with. Although talking to strangers may feel uncomfortable, you will learn so much from the people at these events. By staying home, you will likely miss out on networking, job opportunities, and modern techniques like deep learning methods.

Furthermore, presentations allow you to observe other projects and familiarize yourself with the latest trends. Seeing what big tech companies are up to is encouraging and educative, and you can always take away something from them to apply in your work.

Data science events can be physical or virtual. Some good data science events to consider are the Open Data Science Conference (ODSC), Data Science Salon, and the Big Data and Analytics Summit.

5. Participate in Data Science Competitions and Hackathons

Data science hackathons unite data scientists to develop models that solve real-world problems within a specified time frame. They can be hosted by various platforms, such as Kaggle, DataHack, or UN Big Data Hackathon.

Participating in hackathons enhances your mastery and accuracy and exposes you to the latest data science tools and popular techniques for building models. Regardless of your results, competing with other data scientists in hackathons offers valuable insights into the latest advancements in data science.

Consider participating in the NERSC Open Hackathon, BNL Open Hackathon, and other virtual hackathons. Also, don’t forget to register for physical hackathons that may be happening near your location.

6. Contribute to Data Science Open Source or Social Good Projects

Contributing to open-source data science projects lets you work with other data scientists in development. From them, you’ll learn new tools and frameworks used by the data science community, and you can study project codes to implement in your work.

Furthermore, you can collaborate with other data scientists with different perspectives in an environment where exchanging ideas, feedback, and insights is encouraged. You can discover the latest techniques data science professionals use, industry standards, best practices, and how they keep up with data science trends.

First, search for repositories tagged with the data science topic on GitHub or Kaggle. Once you discover a project, consider how to contribute, regardless of your skill level, and start collaborating with other data scientists.

Following data science thought leaders and influencers on social media keep you informed about the latest data science trends. This way, you can learn about their views on existing subject matters and up-to-date news on data science trends. Additionally, it allows you to inquire about complicated subjects and get their reply.

You can take it a step further and follow Google, Facebook, Apple, and other big tech companies on Twitter. This gives you the privilege of knowing tech trends to expect, not only limited to data science.

Kirk Borne, Ronald van Loon, and Ian Goodfellow are some of the biggest names in the data science community. Start following them and big tech companies on Twitter and other social media sites to stay updated.

8. Share Your Data Science Work and Insights

Sharing your work lets you get feedback and suggestions from other data scientists with different experience levels and exposure. Their comments, questions, and critiques can help you stay up-to-date with the latest trends in data science.

You can discover trendy ideas, methods, tools, or resources you may not have known before by listening to their suggestions. For example, a person may unknowingly use an outdated version of Python until he posts his work online and someone points it out.

Sites like Kaggle and Discord have several data science groups through which you can share your work and learn. After signing up and joining a group, start asking questions and interacting with other data scientists. Prioritize knowledge, remember to be humble, and try to build mutually beneficial friendships with other data scientists.

Be a Lifelong Learner in Data Science

Continuous learning is necessary to remain valuable as a data scientist, but it can be difficult to keep up all by yourself. Consequently, you’ll need to find a suitable community to help you, and Discord is one of the best platforms to find one. Find a server with people in the same field, and continue your learning with your new team.