Training for data science jobs has spiked particular interest amidst COVID-19
Ours is an ordinary world that is driven by extraordinary problems. And these problems whether socio-technical or techno-cultural demand extraordinary solutions. It is not an oversimplification when one claims that data is a valuable resource more than ever; a tool to address problems and create solutions. And just like all the other tools which need experts for optimal use, data also needs experts to make the best use of it. Here’s where the role of a data scientist becomes increasingly relevant. From asking the right questions, gathering and analysing voluminous data to sharing vibrant insights with stakeholders, data science jobs are all about becoming impeccable at extrapolation. And as more and more data science jobs are created, learners must not fall behind on upgrading
Data science jobs are in great demand in our technologically-rich world. What does this mean for new learners? That competition is also inevitable amidst affordable and accessible data science learning. According to a 2020 study by the US Bureau of Labour Science, the average pay of a data scientist is around US$100,560 while in India it is Rs 10,00,000 (Glassdoor). While the high salaries do not solely correspond to having an advanced degree in data science or related fields, it is also about addressing the challenges of an ever-changing world by becoming the best amongst the pool. This one-stop account can help you figure how to enhance data science skills and reap the best out of data science jobs.
Breathing and Blogging
If breathing is fundamental to life, then blogging is fundamental to learning. Today, the blogosphere is ever-bigger and ever-brighter. It is also one of the best spaces to delve deeper into the world of technology. What blogging ultimately does is not only strengthen writing but also research skills; the latter being highly relevant in data science jobs. Writing about what you already know and researching gaps in your knowledge of the field is one of the basic ways to become better at data science jobs.
Additionally, developing a healthy blogging habit is a great way to maintain a learning routine. Whether it is citing sources or reading up on data trends and studies, blogging is a great way to maintain a research discipline while developing critical thinking, reading, and writing skills in data science and analytics. After all, exceptional research skills go a long way be in data science jobs; Google, IBM, Numerator, Splunk, or data science start-ups alike. Here’s a window into the world of everything related to data science and beyond.
Feeding Passion into Projects
Passion projects (or side projects) are an excellent way to chase creative pursuits outside of the data science job routine. This would not only help you gain exposure to nuances of data applications but also help you create a portfolio.
Let’s be honest: we all wish that work was a little more fun. And let’s be more honest, we need more than jobs to stimulate our brains. While we have been spoilt for good and bad by instant gratifying powers of technology, the latter has also enhanced productivity. Carrying outside projects in the domain of data science can add that much need dimension to data science learning. Fake News Detection and Credit Card Fraud Detection are some of the most popular side project ideas that you may try out.
Practice Makes You Ready (if not perfect)
Programming skills are non-negotiable when it comes to learning and making money from data science jobs. The Internet, today, is a virtual ocean that provides programmers from all over the world to use self-assessment tests and interactive machine learning tools to further their practice. Recently, a plethora of websites has emerged that provide challenging technical tasks to both old and new learners. From algorithmic challenges, algorithm tutorials, to applying for data science jobs and preparing for interviews, these websites have it all. Platforms like HackerRank and CoderByte are ways to stimulate an appetite for technical challenges and shine in data science jobs out there.
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