This article was written after a related course, which includes my comments and notes.

Definition of Data Science

Data Science, which was called the sexiest job in the 21st century, is widely regarded as the process of using data to analyze different things, even the world. And the definition or the name came up in the 80s and 90s when some professors were looking into the statistics curriculum, and they thought it would be better to call it Data Science. However, what is the Data Science and what kind of role it plays in our daily life?

In my point of view, first of all, it’s a efficient tool for us to learn the foundation of the world’s running. It’s a study of data, like biological sciences, or the art of undercovering the insights and trends that are hiding behind it. Nowadays, compared with decades years ago, we have tons of data available on the Internet, which can be used or fetched everywhere. We used to worry about lack of data. Now we have a data deluge. Once you get data, and you have curiosity, and you work with it, and you manipulate it, and you’re exploring it. Second, we can’t live without data. Because we need data to make conclusion, to predict the trends or weathers and to take the impossible down to earth. So we should figure out the algorithm, which should be perfected with a huge amount of data. In our social life, we use data all the time, and also create data all the time. Data Science is everywhere. In a word, I think that the methods of Data Analysis or Data Science should be a fairly important place that anybody should learn. Furthermore, in the past, the software was expansive and difficult to use, but now it’s open source and free. Data is expansive and valuable on the contrary. With the blowout growth of computer arithmetic, it’s much easier today to learn and take Data Science into life.

Advice for New Data Scientists

To be curious, extremely argumentative and judgmental.

Curiosity is absolute must. If you’re not curious, you would not know what to do with the data.

Judgmental because if you do not have preconceived notions about things you wouldn’t know where to begin with.

Argumentative because if you can argument and if you can plead a case, at least you can start somewhere and then you learn from data and then you modify your assumptions and hypotheses and your data would help you learn. And you might start at the wrong point. You may say that I thought I believed this, but now with data I know this.

The other things that would need is some comfort and flexibility with analytics platforms: some software, some computing platform, but that’s secondary. The most important thing is curiosity and the ability to take positions. Once you’ve analyzed, then you’ve got some answers.

And that’s the last thing that a data scientist need, and that is the ability to tell a story. When you have your analytics and your tabulations, you should be able to tell a great story from it. Because if you don’t tell a great story, your findings will remain hidden, remain buried, nobody would know.

A starting point would be to see what is your competitive advantage. If you want to be a data scientist and work for an IT firm or a web-based or Internet based firm, then you need a different set of skills. And if you want to be a data scientist in the health industry, then you need different sets of skills. So figure out first what you’re interested, and what is your competitive advantage. Your competitive advantage is not necessarily going to be your analytical skills. Your competitive advantage is your understanding of some aspect of life where you exceed beyond others in understanding that. Maybe it’s film, maybe it’s retail, maybe it’s health, maybe it’s computers. Once you’ve figured out where your expertise lies, then you start acquiring analytical skills. What platforms to learn and those platforms, those tools would be specific to the industry that you’re interested in. And then once you have got some proficiency in the tools, the next thing would be to apply your skills to real problems.