6 ways to keep your data scientist skills up to date

data-scientist-skills

Are you enthusiastic about data science? While you can learn all the concepts related to data science, after some time you will notice that your knowledge will get outdated. Even though you spend hours learning Python coding, you will find that after some time, your knowledge will be outdated. And you will have to learn new tips and tricks to write effective codes. The industry movies at a pace, and you must be acquainted with the trends to stay relevant.

The question is, how can you stay relevant in the industry?

A data scientist has to use a variety of tools to analyze data and generate insights from it. Moreover, while working in the industry you will notice that you will meet different clients with different requirements. You can get domain understanding by attending to all the different clients. But to update your knowledge base and be updated, you need some resources. In this blog, we will talk about some resources that help you move hand-in-hand with the industry’s latest trends.

Essential resources to stay updated as a Data Scientist

  1. Attend AI conferences

Attending conferences is one of the ways to stay updated as a data scientist. After the COVID-19 lockdown, you can attend AI conferences for free. There are different famous AI conferences you can attend. At such conferences, you get the chance to hear from experts from the industry and other like-minded people. It allows you to learn from their experience and learn the latest trends. Moreover, it will also help you gain professional knowledge from industry specialists who join the conference along with you.

You can also participate in deep learning live workshops. Moreover, these conferences are also helpful for getting inspired by the success stories of fellow professionals.

  1. Read online blogs and books

Reading blogs can also help you with knowing the latest trends in the industry. There are many websites where you can get access to blogs that help you stay updated and fresh. As a data scientist, blogs can be your solution banks. Blogs are great for addressing the problems that you face in your journey. Nowadays, experts try to share their knowledge and answer a lot of queries that users might face through blog posts. Moreover, industry specialists have a simple and intuitive approach to explaining concepts.

Blogs are valuable sources of information where you can find a lot of information on particular concepts. Just like blogs, books can also help gain in-depth knowledge of technical concepts as well. If you are just entering the data science industry, a book can be helpful to learn all the fundamental principles and raise the bar to the advanced level.

  1. Attend ML conferences at work

Attending a conference can be helpful to know other people from your industry. Nowadays, organizations focus on organizing such conferences as they look forward to inculcating great values in their team members.

Conferences bring together a lot of people to the same room, and through such events, you get exciting networking opportunities. Moreover, these ML conferences are also helpful when it comes to meeting people and brainstorming new things. Such brainstorming sessions are essential to invite equal participation from everybody and work toward innovative solutions. Also, these sessions also help you gain insights into what your competitors are doing and what’s working for their business.

Training programs and conferences allow you to interact and connect with your colleagues. Remember, learning together is always better than learning alone.

  1. Social media platforms

Social media and networking platforms like Twitter and LinkedIn can also turn out to be resources for staying updated in data science. There are many influencers who keep posting about new updates and what might be useful to you as a data scientist. You can find many other creators who share their knowledge on Twitter with relevant hashtags.

Also, LinkedIn is one of the most useful and popular professional networking platforms you can use to gain knowledge regarding data science. Instead of spending time on Instagram or other social media platforms which primarily don’t focus on networking and knowledge sharing, you can check out these platforms. You get a chance to read the views of industry leaders who have spent years in the data science industry and have seen the whole process before you. Moreover, you will find industry experts narrating different work experience stories, that can be helpful to you in your data science career.

Whether it’s an innovative way to learn Python programming or a new data science trend, social media platforms will help you stay updated on everything!

You get a chance to meet people with similar professional interests and expand your network.

  1. YouTube

On YouTube, you will find informative data science channels where you can get to learn a lot from the live streams and videos uploaded there. You will find the hosts explaining all the concepts simply and from scratch to improve the user’s understanding. Whether it’s Python programming for beginners or mining a large dataset, YouTube can help you learn everything.

In live streams, you will often see hosts inviting other prominent data scientists from the industry for interacting with them. Such interactions generate value and enlighten the user about the industry and its ins and outs.

There are plenty of channels that focus on providing value to the viewers. As a learner, you will find visual content has better engagement since you see what you are learning. If you want a better understanding, YouTube will always be a great platform to visit. It will present information to you in an easily digestible way.

You must always be hungry for knowledge without caring much about the platform.

  1. Podcasts

Podcasts as a content form have gained prominence in the recent past. The audio content form can be helpful to those who are enthusiastic about gaining knowledge but have busy schedules.

In your busy schedule, you can always find time to listen to podcasts. Let’s suppose you are commuting to your workplace and don’t know how you can utilize that time. You can utilize that time by listening to data science podcasts. 

The biggest benefit associated with podcasts is that they are easy to consume. Also if you believe focusing on the screen or being attentive is one of your biggest problems, podcasts will help you. Podcasts eliminate the obligation of gazing at your screen all the time. You can be busy doing your chores and still extract all the value from the content. This single benefit of podcasts has helped them gain prominence and leave other content forms behind.

Conclusion

Being a skilled data scientist requires hard work and dedication. Moreover, we are surrounded by technology which keeps changing constantly. Therefore, utilizing your resources to stay updated in the data science industry is the need of the hour. Invest your time wisely to learn all the concepts and also spend time practicing what you learn. Practise will improve your understanding and also make you familiar with the real-world understanding of data science. The data science industry has the potential to grow multi-fold, and to be a part of that revolution, you must be equipped with the right skills and knowledge. You must start now, to be prepared at the right time.

If you are keenly interested in learning data science, enrolling in an online course can be a wise decision. Online courses offer users the flexibility of learning and allow them to maintain an optimal balance between learning and other tasks. Moreover, after the pandemic, the scope of online courses has increased significantly. Learners have better focus and are more concentrated on learning. Also, students can continue their studies and working professionals can continue working in their jobs while learning. There are plenty of benefits of online courses and therefore, you must look forward to enrolling in a course that provides you with the best value for your money. Don’t just learn, but learn smarter.