About me and Data Science

data science

Hello! I am Saurabh Mahajan and I welcome you to TinkerData.

The love story between me and data science began when I recently observed the change service and project management tracks are going through, predominantly due to emergence of automation. There are chatbots those answer to your queries, issues and even learn from new outages those occur in the production environment. Then there are these real time analytical dashboards that help you read ever messy log files like story books and let you take million dollar decisions, just so amazing. That’s when I realized that I also want to be part of this change. I also want to work on these intriguing systems.

Basically I have been part of service delivery and project management field since beginning of my career in 2007. One major reason to choose these tracks was because I was not good at coding. So I entered into support team and from there climbing up the ladder became service lead and learnt nitty-gritties of project management as well. But since being part of IT industry I also did bit of coding to automate small stuffs related to my work. However programming was never my forte.  As career progressed I understood the importance of programming and so started taking interest in Python. But I wanted to further increase and use this small bit of coding knowledge into doing some real world productive stuff. Data science gave me that opportunity. Currently I work as service manager and this has given me the opportunity to have that bird’s eye view as to where the future of service and project management lies into. It is automation or more sophisticated way to say it – artificial intelligence is the future, influencing part that made me take a plunge into data science.

Looking behind the scenes I understood that one of the main ingredients to these advancements in automation is “data”. Data is actually the power that drives them. But data is raw input, to convert it into finished product you need technical knowledge and tools which can mould this data. This is where learning the paradigm of data became important. Understanding the underlying theories and methodology of a scientific subject called data science became critical for me. I then started to look seriously into integrating python and data. On my way to learning data science I also got to know that data statistics specific tools like ‘R’ can also be useful in making the data mining process much simpler. And thus my interest in these tools and methods got deeper.

But,

Albert Einstein once said “If you can’t explain it simply, you don’t understand it well enough”. So to test my understanding I wanted to explain, in simple way, my learning on data science to others who like me wanted to get acquainted with it. Writing blog was one of the clean and easy ways to accomplish this intent. And thus TinkerData happened.

As I am still learning data science, I hope my learning and writing on this topic instigate your passion too for this fascinating field, because now a days DATA IS FUEL for businesses.

I also intent to use TinkerData to learn and share on upcoming technologies and other domains, giving all of us the chance to stay updated and be in line with the changing world.

There is always so much to learn and therefore stay tuned with me by subscribing below.

You can read about my complete professional bio on LinkedIn as well or for further queries and suggestions please contact me. I will be very happy to connect with you.

Thank you for visiting TinkerData and be in touch.

 “Data Science history”

In 1962

John W. Tukey publishes “The Future of Data Analysis”

In 1977

The International Association for Statistical Computing (IASC) is established with aim to link traditional statistical methodology, modern computer technology, and the knowledge of domain experts in order to convert data into information and knowledge.

In 1994

BusinessWeek publishes a cover story on “Database Marketing” mentioning how companies are collecting mountains of information about us, crunching it to predict how likely we are to buy a product, and using that knowledge to craft a marketing message precisely calibrated to get us to do so

In 1996

Members of the International Federation of Classification Societies (IFCS) meet in Kobe, Japan, for their biennial conference. For the first time, the term “data science” is included in the title of the conference.

In 1997

In his inaugural lecture for the H. C. Carver Chair in Statistics at the University of Michigan, Professor C. F. Jeff Wu calls for statistics to be renamed as data science and statisticians to be renamed as data scientists.

In 2002

Launch of Data Science Journal, publishing papers on the management of data and databases in Science and Technology.

In 2008

The JISC publishes the final report of a study it commissioned to examine and make recommendations on the role and career development of data scientists and the associated supply of specialist data curation skills to the research community.

In 2009

Hal Varian, Google’s Chief Economist mentions that statistician will be the sexiest job in next 10 years.

In 2012

Tom Davenport and D.J. Patil publish “Data Scientist: The Sexiest Job of the 21st Century” in the Harvard Business Review.

In 2015

Techcrunch.com in its post mentions about the global shortage of data scientists and ways to tackle it.

In 2018

TinkerData is in the making