As we step into 2022, all of us must be busy setting goals for our life and career. With buzzwords like AI, Analytics, Data Science, Big Data, etc. getting attention everywhere, there are many looking forward to working in that area. With a plethora of resources available online, one can often get confused about where to get started. But have you ever realized that you have been doing analytics all throughout your life knowingly or unknowingly?
Yes. You heard it right. There are many facets of our life where we apply analytics to make a decision. When you want to take an internet connection at home, you see various options available based on various parameters like bandwidth, plan term, plan cost, etc. to make a final decision. The decisions can be different for different people as some might give preference to speed while for some budget is the main concern and so on. Like this, we make various decisions in life based on different data available around us and arrive at an optimized solution. Even while writing this post, I can see my mother scanning through the kitchen shelf and fridge, collecting data about the groceries available, and making a decision on what to prepare for dinner.
Wouldn’t it be cool if you had a system that would do all these and make decisions for you? That’s exactly what analytics and data science might help you to achieve. But how or where does one begin? The best way is to start with our good old excel and learn a little bit of python programming language. This combination can do wonders for you.
The other day I wanted to analyze how I spend money in a month. I know there are quite a lot of apps in the App Store/Google play that would do the trick. But I wanted to build something from scratch with more flexibility. So, here’s what I did. I downloaded my one-month bank statement in pdf format. Then I wrote a python script to read the pdf file and extract the text out of it. With the help of a few sets of conditional statements, the extracted text was classified into various categories like particulars, credit amount, debit amount, etc, and the final result was saved as an excel file. And the remaining analysis was done in excel. Trust me. All these can be done in less than 30 lines of code.
I can go on illustrating with similar possibilities where one can make use of Python and excel to find solutions for day-to-day scenarios like these. The conclusion I’m trying to draw here is that the motivation for learning analytics need not be just for a job switch/ pay hike, but the knowledge of how to use it can help everyone solve problems in their respective fields. On that note, I encourage all to embark upon this wonderful journey of learning analytics this year.
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