As the science of data acquires more and more traction in all major industries. Therefore,Things That Make You Love and Hate Data Science in this new, exciting and challenging field there are many opportunities. Here are some reasons why you should be part of this.
The data and analysis allow us to make informed decisions and to stop guessing. I was never fond of making decisions based on gut feeling, perhaps because the gut says one thing one day, and something very different the next day. The data “is what it is” – even if it can also be easily abused.
Who do you like to discuss?
Data analysis provides objective answers that can put an end to an argument. The added benefit is that, being the data scientist in the discussion, you are at a clear advantage!
Companies need to make concessions
Airlines can trade performance per load, or vice versa; Travel agencies need to spend their advertising budget to the maximum effect. Data and analytics can have a real influence on the decisions that a company takes and on the outcome.
Apart from the impact of the business, for me there are more personal reasons to love my new professional field as well.
It is exciting
With the risk of making statements too bold, the tools to handle large amounts of data and do analytics are very likely to advance faster than any other field of technology today. With better tools, comes a better understanding of how we should do analysis and how we should use the resulting information, all of which is evolving extremely fast.
Satisfy your curiosity
It is possible that you are sitting at an airport watching the planes coming and going, docking at the doors, getting on the passengers and taking off again, and wondering … Whatever your question, it is likely that the analysis of the data you Flow through Amadeus systems can respond.
What is data science? In such a new field, finding consensus around a single definition is difficult. But in my opinion, data science is the process of creating (business) value from data. One of the best definitions of Big Data I have found is: “Big Data is such a big data that its volume becomes a problem in its own right.” Not only do you understand the data, or how to make predictions based on the data, but the very fact that there is so much that even something as simple as calculating final bill rates requires specialized technology. That is one of the things that distinguishes science from statistics data.
It can be applied to many different domains
In my work, I am applying data science to travel industry data – tickets, schedules, reservations, searches – and to data that is not necessarily travel industry data but which in some way affects travel. But data science can be applied to many domains. For example, if you have investments, how are they and why?
Data Science is not just about data. The bare basics are recognizing what all data to keep, identifying how to process it for different results. It does not stop there. Data scientists need to figure out blanks in data and fill them with data that ‘may’ come up in future. Data Science essentially is about connecting dots in businesses and using existing and non-existing data to meet the demands of each business.
Data Science is one of the hottest areas in technology and so is the demand for data scientists worldwide.
What is Data Science
Most of us think Data Science is simply statistics. If you are good at statistics, you will be able to represent the numbers in any way you want: charts, info graphics, etc. Will you be able to identity the different data needs for the business in different areas? Can you ‘foresee’ data? Will you be able to fill in data pieces that are required but are not yet available? These questions don’t belong to statistics alone.
What is Data Science? Let’s check it out by listing out each step so that the overall image comes up. As such, it is difficult to explain it in one sentence, but I will try. Data science is the science that lets you identify data for different purposes, identify business needs for information, process the data using tools at hand to provide inputs necessary for a business to thrive. Thus, Data Science is a bit of everything. It includes not only statistical skills but a bit of managerial skills, some language processing, researching skills, a bit of machine learning knowledge and a complete idea of what tools are required to produce desired results.
Data Science contains all of the following, irrespective of what all is used at a business:
Creating the need for data
- Categorizing of data sets based on their possible usage
- Strategized storage of data sets on premise or the cloud; in either case, the data sets should be available on demand without delay
- Understanding of business process flows and how different data sets are useful for each
- Understanding of business decisions to help the business do better
- Ability to process data using different set of tools: spreadsheets, databases, programming languages, etc. to meet the demands of business processes
- Ability to foresee what kind of data would be incoming in the near future and using it for current processes
- Analyzing the results of a process and going back to the drawing board to make it better
The above list is not comprehensive but highlights the main points of data science. As the first point suggests, data scientists need to be able to convince businesses that all of the data is useful and hence should be stored for a long time. Maybe put on those useful old databases on some shared cloud for 10-15 years so that they can look at it and produce more effective databases? Any need may arise as the business surroundings keep changing. Laws of land change, business processes change, and data needs to be adapted. Thus, the more data you have at hand, the more effective you’ll be.