New Data Scientists: Tips that will Help You to Stay Focused and Deliver Faster Results

In this post, I am providing some very easy to understand advice for junior analysts, fresh data scientists who have recently onboardedinto a career in data management and product analytics. Here, your Big Data Hadoop Certification will come handy theoretically and practically.

Yes, it’s a great institution to be part of the data science community! Congrats— it is going to serve you well as you serve it with your talent, skill, and determination to make it bigger, better and friendlier. If you are in your first data science and analyst role, there would be hundreds of doubts creeping into the mind as you take few steps wobbling into the project. But, don’t worry. These creepy thoughts are obvious to occur.

Tip 1: Get your Data and Technology in Place; Train with Best People on the Team

You will be primarily working with three things in your role – data, people, and technology. This post will focus on the extension of working with data and technology focused on junior data scientists who are primarily handling Product Innovation, Development, Execution and Marketing life cycles.

While it’s easy to work with data and technology provided, it often gets complex with people. Work with the best to deliver the best.

In terms of technology, keep reading and working with top of the block data analytics tools from Looker, Google BigQuery, Amazon, IBM Big Data, Tableau, Qubole, D&b, and Cloudera. There are countless others but I prefer to stick to those that make the most impact on my work and my customers are using already. It’s the art of creating a fine balance of learning that is relevant to your current projects.

Tip 2: Prioritize with your Product Manager

Initially, you will work with a PM who will mentor you, of sorts. Prioritization of data science projects are often tedious and take lot of effort and time to set straight. For a junior data science engineer, your PM is the best resource who can help you to get close to the project – understand the P-D-C-A timeline of the project and keep it tight to help you prioritize better.

Tip 3: Keep your Ad-Hoc Analysis Ready

As you consider a fine roadmap to build out a Big Data project, it’s OK to have a few ad hoc analyses in place as well. I often use them as fillers to fall back when my Big Data project is taking longer to develop than initially planned. This should allow Junior Big Data scientists to write a function and package this to automate complex types of analysis in the future. Secondly, it also helps me to retain and apply everything I learned during the Big Data Hadoop Certification program at the start of my career.

My secret to success is: Remember, replicate and reproduce – speed up the process, and you wouldn’t fail in the industry.

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