Make your data science job-hunt easier and as efficient as possible WITH THESE FIVE VALUABLE LESSONS
From undergraduates to career changers, many people are looking to break into data science.
It is a very common question asked by people who want to further their careers in analytics, cloud computing, data science, and machine learning. Breaking into the field of data science has to be navigated before launching into a career. Earning a job in data science isn’t easy, especially since there are extra job seekers in this analytics jobs.
Below are five things to be kept in mind:
Keep on building your statistical and programming skills.
The top 10 skills requested in LinkedIn data scientist job postings are Python, R, and SQL, closely followed by Jupyter Notebooks, Unix Shell/Awk, AWS, and TensorFlow, Tableau, C/C++ and Hadoop/Hive/Pig. Best way to excel in these skills is to go for Hands-on training. After all, without applied experience, the academic experience is only half knowledge gained.
Frequently create unique portfolio of machine learning and analytics projects.
Just how anyone would need a good CV to get their dream job, a data scientist will be required to present a portfolio that speaks for them. From selecting data science projects to completing your own analysis, a data science portfolio should have it all. Apart from doing projects, good knowledge of storytelling and visualization of data will also work in your favor.
Make yourself a unique website.
Times are changing and having an online presence has become important more than ever. Maintaining an active profile on GitHub or Kaggle is one way to start with it. For organizations to approach you for freelance projects, interviews, and other opportunities, it is advisable to invest good time in creating an active and unique website.
Start applying for jobs available in your network.
You should not hesitate in asking your friends and colleagues, attending a career fair, or visiting a local company website for job recommendations. It is okay if you don’t have all the skills in the required skills of the company. A part of it is also enough for you to get started. Keep applying irrespective of the company’s requirements and accomplishments. Focus on growing your contacts and network.
Concentrate on improving communication skills.
To be a successful data scientist, one needs to have effective communication skills. Storytelling is a part of a data scientist’s job through which s/he will present his/her work to audiences and stakeholders. Also, collaborations are a usual thing and in order to receive better results, others have to be able to understand the insights from the data scientist.