The role of a professional mentor for Big data analyst
is considered to be pivotal. Mentorship programs boost data science careers and it is no secret that best Big data analysts often attribute their success in the domain to their coach and trainers. When it comes to finding the best mentor for your data science project, it can prove to be a rather uphill task as most trainers seldom offer their time and guidance due to a lack of time and personal commitments. So, how can you go ahead with a Big Data analytics project?In this article, we have tried to put up a roadmap to find a guide for your big data analytics project before it’s too late to move on.
Mentors Re-Align Your Programming Skills
My seniors in the industry would always state: “Do your bit in professional coding before you start hunting for a mentor.”Finding a trainer is a personal journey in itself. It can take weeks and months to find your trainer, but it is worth an experience.Since it involves decision making from both parties, coach as well trainee, it is best that trainees themselves demonstrate their skills in coding, data management, and ML ops so that trainer can never say “No” to you, no matter how hard it is for them to participate.
Mentors Establish Standards in Time Management Without compromising on the quality of your practical live project, you can qualify in your Big Data analysis by simply sticking to standards of time management. Best mentors are great time managers.
A trainer can help you identify the gaps in your project that can cause delays in your deliveries and project deadlines. Big Data project timelines have shortened significantly. Most project heads expect the trainees to complete their software development and reporting completed within 8-10 weeks, which puts immense pressure on the individual. A qualified mentor will assist you with picking the right tools and solutions that help you work with the toughest of projects in a tight scenario, enabling you with the fundamental approaches required for complex tasks associated with data wrangling, data ingestion, analytics reporting, visualization, and final delivery.
Mentors Encourage Online Participation
Go online. The Sooner you do it, the better it is for your project
You can find a free mentorship program for your big data and AI projects, assisted by professionals who have an extensive network and connectivity with leading data scientists and analysts from some of the biggest IT organizations in the world.There are tons of opportunities online where you can showcase your skills and expertise. But, a majority of these are closed or walled gardens that restrict the entry of non-qualified individuals. Getting a reference check from a mentor can open doors to such places where you can participate in debates, online workshops, community discussions, and webinars. Before you head into a full time project development as a Big Data Analyst, try to sign up for online data science forums on LinkedIn, Facebook, Twitter, Quora, Reddit, and so on.
Train for the Unforeseen FutureMost big data analysts who have a trainer shine against the competition.
When we speak of competition in the big data market, we are referring to either “finding a job” or “succeeding with a live project.” Both become fairly easy if you have been trained by a certified online mentor who not just trains you for live simulation and coding tests, but also in interpersonal skills that get you through the first few levels of competition.In an industry where the chance of failure of a project is 90%, it is invariably a matter of choice that a mentor would support you if you can’t succeed. Mentors have their own set of selection processes before they agree to coaching and training. Data analytics
domains are increasingly becoming tougher and complex with all kinds of machine learning and deep learning (ML / DL) techniques coming into the mainstream every day. It is important for data scientists and big data analysts
to choose a person who can don the hat of a mentor, a coach, a trainer, and a professional guide for them at crucial stages of project development and synopsis.