You were just out of college when you were stepping up for your first job with no clue as to what your new job would be. You were not even sure who your new manager would be. Then finally you settle down seeing the world around doing that repetitive task over and over again. That’s truly Business as Usual(BAU). Then you started calling that job/task as monotonous. The Mondays started becoming blue and the Fridays started becoming shiny. Wednesdays were always the BAU day. AND THEN YOU DECIDE TO QUIT AND EVOLVE or vice versa.
The industry was diverse giving you the length and breadth to evolve and that’s where you started slipping and getting confused at each step. WHERE TO BEGIN? How to begin still looked easy. So many skill sets to learn, with infinite material available on the internet on each topic. Someone speaking for, while the other contemplating the against. The hyperlinks are a curse for they take you to places new though and leave you in the dark (not enough enlightened) for the topic that you began with.
Now you decided to change your stream altogether. The skill sets that you owed investing months/years working in the corporate culture looks dizzy and you want to forget all those and start afresh. This is the first wrong step in Evolution. That’s where I would suggest that it's good to learn new things in life but not leave the past experience behind. Look out for competencies that you possess and the milestones that you wish for. Take small but firm steps towards the objective and be regular!
- Make a checklist and note down the skill sets that are missing from your experience.
- You can also note down the skills that you owe as well if you lack expertise and you look forward to upgrading.
- Now take a look at the industry perspective as to which skill sets are highly recognized. You can do this by seeing the number of jobs for each category of skill sets that you made a note of.
- You can take help from the job portals like Naukri.com Monster.com Indeed.com IIMJobs.com.
- Now we will prioritize our skill sets and would sort them in decreasing order of their recognition & demands.
For: This tool captures the 96% of the BFSI industry
Against: This is a dying technology
Author: The easiest tool to go with if you are choosing to begin within the field of data. This is the leader of all tools and pioneer too. This is here to stay
For: This is the best tool for statistics where a small amount of data is involved
Against: This is not here to stay
Author: This is a tool for statistics and data-driven approach and the R open source community is expanding. Still, need to evolve.
For: This is the future
Against: This is for programmers and fails for big data
Author: A powerful language but when applied in the right direction. This requires a lot of effort in understanding the backend and the techniques involved
For: This is the best tool for dashboards and data visualization.
Against: This tool is overhyped and there are a lot of technicalities involved.
Author: This tool is bizarre but only in terms of data visualization and can do wonders in presenting data. But one should know what to picture based on your data.
For: The only way to big data infrastructure.
Against: This changes too often and comes up with new modules too frequently.
Author: This is the change from the data warehouses to the file systems. A lot of technicalities involved and miles to go.
For: The most basic tool.
Against: It's going to be outdated soon.
Author: Must have for this is real basic and easy to understand. This only can’t get you job in analytics but can help you end up in reporting profile jobs.Comments