Human Resource

6 reasons why I love data and analytics as a fresher

03rd Aug `15, 11:07 AM in Human Resource

I must confess that big data analysis wasn’t exactly on my radar when I started engineering at IIT…

Soham Sinha
Soham Sinha Contributor
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I must confess that big data analysis wasn’t exactly on my radar when I started engineering at IIT Roorkee. In fact, I started out as a paper and pulp technology major.  I soon lost interest in my regular classes and started to work on other projects instead. My first experience with handling big data was during a competition organized by American Express. It was like love at first sight. I even started cleaning data just for fun. Soon, I evolved to learning languages and coding. Today, I’m fortunate to say that I have found my passion and am doing what I love – working as a data miner at Crayon Data.

Why do I claim this is my calling, you ask? Let me tell you why.

1. Make informed decisions

I’m not a very decisive person. I don’t like making decisions based on gut feel because I feel like my gut is very moody! It says one thing one day and something completely different the next. Data never lies though. Data analysis allows you to take informed decisions.

2. Learn new (programming) languages

I’ve always been fascinated by programming languages. I programmed in C and C++ during my college days and now, as a data miner, I need to know a lot more programming languages. Right now, I’m in the process of learning R and it’s just so much fun! Programming helps me think up of solutions that solve some really complex business problems. To add to this, I also like to build things people use. It’s amazing to type up some code, press a button, and suddenly thousands of people on the Internet are using apps that I have built. After R, I am planning to learn Python, since these two are the most popular programming languages used in data science.

3. Dive into data bases

A data miner should know how to query and retrieve data from data bases. I currently use HiveQL to query and manage large data sets residing in large distributed storage systems. As of now, I am familiar only with SQL. I would like to learn the hugely popular Mongodb.

data base technologies

4. The power of Predictive Analytics

Predictive analytics is the use of statistics, machine learning, data mining, and modeling to analyze current and historical facts to make predictions about future events. In layman’s terms, it gives us mere mortals the ability to predict the future, like Nostradamus or Carnac the Magnificent (but without the funny hats). The power to predict who will click, buy, lie or die is fascinating.

5. Experiment with Machine Learning and Statistics

Data mining is a field where one applies machine learning and statistical techniques to some concrete problems. Every new project covers a different field. This gives me the opportunity to discover and learn new domains without changing my job profile.  Recently, I have developed an interest in deep learning. A concept of teaching computers how to learn, this really excites me!

6. And most importantly, impress friends and family 

Data scientist is termed as the sexiest job of 21st century-by HBR. There is a lot of hype around the terms big data and data science these days.  When I tell my friends that I work in the field of data analytics, they are curious to know more, such as: What tools I use, my area of work, remuneration etc.  It feels great when people ask questions like “how can I get into big data analytics?”

Now you know what makes me tick and drives me to love data analytics. Yes, I am a complete data junkie and I will never change. All of you out there who publish content like this blog just end up fueling my desire to learn more, be more creative and innovative as well as be the best data analyst I can be. To that, I say thank you.

This article originally appeared on Crayon Data.

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