Last Updated on February 21, 2024 by Nasir Hanif
The average age of a data analyst was 35 years in 2015. In 2022, it has come down to 27-28 years. According to the latest LinkedIn data, data science is among the most listed job titles/ job skills in the industry when it comes to hiring and job search keywords, followed by market research, business analytics, and sales. The conventional route to becoming a data analyst is completing a regular graduation degree like a BE or B Tech in relevant specializations from an engineering college, and then pursuing data analytics courses online. In less than 5 years, you can easily become a data analyst by applying for an entry level job vacancy. So, ideally, the average age of a data analyst should be around 24-25 years – yes, there is a scope to bring down the average age of a data analyst further. So, why is this not happening? Is the business not allowing young data analysts? Or is it something else that professionals are missing out on? There is an urgent need to identify the various factors that force young analysts to look over opportunities in data science and stop learning altogether.
In this article, we will do exactly that. Here are the top professional tips that any data analyst should follow to improve chances of promotion and job hikes in the current and future roles, especially if you are targeting the world’s sexiest job title — “Chief Data Scientist’.
Table of Contents
Let’s breakdown these ideas here
Tip 1: Data processing is a daily job — you can’t take a day off!
Data analysis is a tough job. However, that is the best part of the job, else why would businesses pay up so much for this role knowing that the market is shifting toward data driven projects. And, by the way, 90 percent of the data analysis and data intelligence projects fail, costing businesses millions of dollars every year. In order to cut down the cost of resource acquisition, businesses expect their data science team to come up with solutions very quickly, in most cases, the idea is to cut down the duration of project completion from a few months to a few weeks. Some high profile data science teams belonging to top notch Fortune 500 companies have actually done this by leveraging technologies and data management tools that can process Exabytes of data in a few hours! But, for all this to happen, data analysts have to be “at it” all the time – can’t take breaks, leisure holidays, and all the work from home benefits that perhaps their colleagues from other teams could be enjoying while DA teams crush deadlines with superlative work management in an optimized manner.
So, if you are pursuing a data science role in a high growth company, embrace a tough but equally rewarding stint. Rewards and accolades will come your way!
Tip 2: Don’t invent what is already existing in the process
Enthusiasm is a great asset. However, data analysts from data analytics online courses are often blamed for being too overly energetic — so much so that they try to reinvent things that are already established as a protocol in the data management teams. This simple, yet important misdirection could cost the teams vital time and monies, without ignoring the fact that failures often dangle on top of projects that have a large number of data analysts with less than 2 years of experience in handling large scale data processing workflows.
It is always best to read voraciously in the first few weeks of the stint as a data analyst before you actually set your eyes on the substantial data sources and their consequent processing techniques. Reserve your energy for the planning, execution, and the final hit – the analysis of all the data that will come your way with immaculate planning and resources management.
Tip 3: Track it down to the last crumb
Analysts who have succeeded in their roles and how to lead a large number of analysts would always tell you to do one thing every day — “Make a journal. Write it down.”
The best data science online courses in Gurgaon and Delhi NCR train students in document writing and instructional designing that are related to business intelligence, programming, AI Machine Learning algorithms, etc. Analysts who keep a log of all the activities including managerial instructions, research outcomes, and analysis cohorts after each testing seemingly find more faults in the process than those who don’t do this documentation. If you want to become a reputed and reliable data analyst trainer in a short time, it’s the quality and quantity of documentation work that you take on. You can also extend this learning to online project management, open source cloud computing, and other relevant platforms where your career learning would make more sense to the whole industry than just your current or former companies.
Tip 4: Don’t trade off quality of data analysis to time or resources restraints
Data is a costly asset. However, most people who work on data don’t understand this aspect as they are somewhere in the middle of the data management life cycle where they have no clue how the data was acquired and from where, and where this analysis will head to! This is a serious mishap if you look at how data management teams have now evolved into a more comprehensive and unified family. If you are new and still learning the nuances of learning in a traditional data science setup, don’t ignore the foundations — which is what tools, policies, and frameworks are in place all ready for you to begin working. If data tools are unavailable, your job will become unsatisfactory and results will be far from expectation.
Online data science courses train students to tackle the conventional challenges associated with such situations. Work with the data you can identify and then choose the hardware or software options that would make the computing and analysis easier. Once you identify these, follow the tip 3 — document it, so that when anything goes amiss, you can show your analysis logs to the manager or auditor.
Tip 5: Learn dashboards, learn automation techniques
Automation is there for a reason — to make your job easy and help you offset the time you could be spending on mundane tasks. In the workplace, particularly in data analysis and big data intelligence roles, a lot of automation is already existing which new professionals are seldom aware of. In top courses available online, these automation subjects are given a broad brush. If you wish to set a mark for yourself as an effective and prolific analyst, focus on automation concepts and how these can help you manage workflows related to ETLs, stream and event processing, big data analytics, AI, and compliance governance documentation.
Automation can also be useful in large scale data processing where you may have to sequence all your workflows based on testing, outcomes, and ordering. If you are working with multiple sources and types of data, errors will arise. Automation will help you validate in these errors and you can then apply version controls and quality checklists to identify errors in future iterations. If you follow these tips, you will definitely succeed.
Read More: X Reasons Your Business Needs a Financial Analyst
