Data science can be a vast subject and one cannot cover it in a single go. But then let’s try to understand it in a very simple and easy way.
Every corner of today’s world is brimming with data in its raw form. When you are shopping, taking a medical test, watching a movie or show, using the internet or taking an examination. Everything is giving birth to loads and loads of data. But why is this data so important?
Science is when one tries to understand anything using scientific tools. And data is a set of qualitative and quantitative variables regarding any subject. So comprising both these definitions one can say that; data science is a field where data is used as a raw material and then processed using scientific tools to extract an end result. This end result helps in increasing business value and customer satisfaction.
PRESENT DAY RELEVANCE OF DATA SCIENCE
You see its products every day in your day-to-day life. Products which are the result of combing huge amounts of unstructured data and using it to find solutions to business and customer related issues. Some of them are:
- Digital advertisements: at the same time two different people can see different ads on their computer screens. The reason is data science, which recognizes one’s preferences and shows ads relevant to them.
- Image and voice recognition: whether the automatic tagging option of Facebook or Alexa, Siri etc. recognizing your voice and doing exactly what you told them to do, again it’s data science.
- Recommender systems: when you go shopping on an online website or search for a show on any entertainment app, you get suggestions. These suggestions are created using data science by tracking ones past activities and likings.
- Fraud detection: many financial institutes use it to know track clients financial and credit position, to know in time whether to lend them or not. This reduces credit risk and bad loans.
- Search engines: these search engines deal with the massive amount of data, and to search the thing that you asked for in a second can be impossible if only the algorithms were not there to help in this mammoth task.
ACTIVITIES COMPRISING DATA SCIENCE
It is a big subject, it comprises of several different stages and steps before one can reach the final conclusion. They are:
- Obtaining data from several sources.
- Storing data categorically
- Cleaning the data for inconsistencies.
- Exploring the data and find trends and patterns in them.
- Machine learning that is modeling the found patterns into algorithms.
- And then lastly interpreting the algorithms and communicate it.
TOOLS USED IN DATA SCIENCE:
There are several techniques used, and all these techniques have to be learned by a data science aspirant.
- SQL or NoSQL for database management
- Hadoop, Apache Flink, and Spark for storage.
- Python, R, SAS, Hadoop, Flink, and Spark for data wrangling, scripting and processing.
- Python libraries, R libraries, statistics, experimental designing for exploring and searching the data to find needed inferences.
- Machine learning, multivariate calculus, linear algebra for modeling the data.
- Communication and presentation skills along with business acumen for making the inferences useful in strategic decision making.
