I would like to share a few things about myself before my interest in data science. I am an engineering graduate in Computer Science but worked as a cataloging assistant which basically had no programming stuff to workaround. Later had an unfortunate career break and life seems super busy running behind my toddler. Recently thought of creating a cooking blog to make use of this lockdown. I just love cooking! But finally ended up here writing a blog on Data Science. I might sound weird but Yes I feel that I am on a right track.

What made me land on Data Science:

When I was working as a cataloging assistant, I was always passionate about becoming a Data Analyst. I dealt with a huge volume of data on a daily basis, where I would Analyze, measure, evaluate, compare data, and whatnot. I pull books from the database and find patterns of certain books, publications, authors, and collect data on which book has series, missing content, prequel, sequel, etc.

I analyze the existing data to identify trends to implement process improvements and automation and also to effectively manage resources. Eventually, I started falling in love with handling data.

Recently one of my friends was telling me about the boot camp happening in Flatiron School which is one of the best educational organizations. I was quite excited about it as I could dive deep into this data handling in different programmatic, statistical, and mathematical way. I am truly impressed with their materials, teaching, and curriculum. It gives me a lot of confidence and hopes to get into my dream job which is Data Scientist!

Old vs New :

All these days I was working on data which was handy either in some excel sheets or in the databases where I can pull and work on it. Then represent those data using bar charts and pivot tables in excel sheets. But in this world of all new emerging technologies and innovations, there are a lot more to handle.

With Data Science, we can collect data from anywhere and everywhere using some key methodologies and strategies. Those data may be complete or incomplete but still, data science uses certain mathematical and statistical analysis and makes those data meaningful. Okay, that’s all about my stuff and let’s get started.

Why Data Science is emerging:

“Data are becoming the new raw material of business.” -Craig Mundie (Senior. Advisor to the CEO at Microsoft)

It is impossible to predict, classify, or conclude on anything unless we have data in our hands. Having millions of data is still of no use unless we know :

• What the data is speaking all about.
• What to do with those data.
• How to handle it.
• What am I expecting from this data
  etc.

Hence we use data science techniques to analyze and evaluate data. It helps us to understand by giving us a clear picture and visualization. Data is everywhere, but we need the right tool to collect and handle it. Data science plays a major role in handling this data.

In this busy world with a huge population, there is a lot more to handle in any field it may matter. It is impossible to manually collect data to make any futuristic predictions and decisions. Hence data science is much needed for predicting the future in this unpredictable world.

Real-time example:

Data is ruling the world. Data helps us to make big, crucial, and critical decisions for the welfare of the country, organization, or anything that does matter. Presently the most used word in our day to day life is Covid-19 or coronavirus. It might have started in one place and as it started spreading rapidly and more people falling sick, it has been reported and declared as Global Pandemic. The data that was collected from the hospitals, doctors, or over the phone calls were used to identify how many were infected and died and what steps to be taken, etc.

The graph tells us how many have been affected and died globally. This was possible only through data. This is just one example of data science but there are a lot more.

Conclusion:

In this blog, I have shared my interest in data science and what made me choose this as my career path. I would like to share more of my knowledge of data science in my future posts as I keep strengthening my skills as a data scientist. I personally feel that becoming a data scientist is not only just sticking with the mathematical procedures and programming skills but it depends on each individual how they handle and play with it to make some meaningful prediction and analysis.