Updated: Feb 23, 2020
The Master guide to build an attractive LinkedIn Profile
How do you get a dream job in data science? Knowing enough statistics, machine learning, programming, etc. It would still be difficult to fetch a dream Job as a Data scientist unless you have a great LinkedIn Profile in today’s competitive world.
Why is it very important to have a good LinkedIn profile? The answer is simple: Nowadays every employer looks into LinkedIn profiles of the candidates to filter out the best candidate who matches their requirement, as the Resumes aren’t that interactive and provide end to end details of the candidate. LinkedIn provides you the ability to show potential employers what you can do instead of just telling them you can do something that’s important.
This article briefly explains you how to build an awesome LinkedIn profile as a beginner which eventually helps you in cracking a Dream Job of yours.
1. Headline/Introduction about yourself:
LinkedIn allows you to add a brief description about yourself right under your name. As a data scientist, you need to find a way to show how you can impact someone’s business. The best way to describe is to imagine if you caught me in an elevator and you have just 30 sec to describe yourself. Please note that this should be very short and simple. Here are a few examples described “I can make your data tell a story”, “I can make your data sell more goods” or “Expert in bringing insights from your Data, etc.
2. Data Science Internship / Co-op:
Why is an internship important? The internship provides you an opportunity to work on the real-time projects and POC’s wherein you are required to do some form of data collection, analysis, models building, or visualization, the skills learned are highly transferable to any data science jobs in the market which intern counts as an experience. Internship becomes very useful for the fresher’s to find the first job since most of the employers often look for students or fresh graduates with some experience in data science work. Most importantly, they want to hire someone who can start working on real stuff on the fly with minimum training time, simply because time is money in the corporate world.
Also, having a data science internship is a big boost to your profile as a whole Regardless of your academic background, having this internship shows that you’re serious and passionate about learning the data science in deep.
3. Work on several Projects and share it across:
What if you’re just starting out and have zero experience in data science work? How do you get experience when you’re not given an opportunity to get experience?
The answer is by doing projects on your own. Basically, there are 2 types of projects, in my opinion, they are 1. School project 2. Personal Project. In most of the cases in the school projects we do not drill down and use much of technical concepts due to multiple reasons such as time constraints, Limitations of tools etc. Where in the personal projects we are not bound to any limitations and tools so we tend to dive deep into the concepts and explore a lot of technical stuff.
Hence it is highly recommended to do multiple projects by choosing the current Real-time challenges in the world. Once you complete your project don’t just save it in your computer Share it online. I suggest uploading them to Github, Kaggle and Slide share and copy-paste the links to your individual projects. Include a well-written project summary that describes the work you have done. This helps your profile to catch the attention of the interviewers.
4. Update your credentials:
If you have taken any data science courses online/offline, include them under Education and List all the topics you have learned in the course duration. This will help employers know that you are familiar with the topic they are looking for in specific. Most importantly it’s very crucial for you to undergo few necessary Certifications and update them under your profile as it gives confidence to the employer that you are a certified professional.
5. Share data science-related articles on LinkedIn:
If you have read an interesting article or tutorial related to Data Science. Post them on your profile. This helps your peers/Colleague recognize the value in the domain and also acknowledge your dedication to learning.
6. Create an “Independent activities” section under Education:
Taking a course is one thing, but applying the knowledge you get from it is a whole new ball game. I believe in this phrase - “Knowledge is wasted when not expressed”. This section should list all your independent activities - whether it is additional courses, attending workshops and webinars, or participating in hackathons, you must list them.
7. Follow Data science influencers and Companies:
The more you follow the people, the more you learn about the trends in the related field this helps you get updated.
In a nutshell, I believe that an individual who invests his time doing interesting projects/internships with the proper focus and dedication, sharing his knowledge by writing several blogs and posting them across social media would definitely help him/her to excel in his career as Data scientist.