A bright and promising future in data science
Everyone has their different opinions regarding big data. Some say it’s just a phase the tech world is going through and others say it’s here for the long haul. But all of that will be in the future and not in control. But today it can be said without any doubt that data science is a desired field of study.
WHY THIS SUDDEN DEMAND?
There is a huge amount of raw data stored in business data warehouses, it needs to be classified and understood so that it can be put to use for strategic business use. So the whole journey of turning reams of data into usable data is data science.
Everyone knows smart watches, what an invention. It can tell us our heart rate, how many calories we’re burning, how healthy we are, and how many more steps we need to take to complete the daily count. But how can he tell us all this just by being tied to our wrists? It is an immaculate application of data science. It collects data such as heart rate, body temperature, and uses sensors to understand movement and then processes this data to gain meaningful insights about our health.
Every business today needs data science to solve problems and deduce what is in the future and create structural plans for it. In the past, companies only analyzed the data of the past, but now it is about knowing the future.
HOW DOES DATA SCIENCE WORK?
There is a whole workflow in data science. Step-by-step procedure to extract the substance from the raw information.
- Data accumulation is usually done by database administration (SQL), retrieving semi-structured data and then categorically storing it using Hadoop, Apache flink, etc.
- Data cleansing to remove inconsistencies and anomalies using tools like Python, R, SAS, Hadoop, etc.
- Data analysis to understand the data, find patterns that can be useful, details that can solve a particular problem using Python and R libraries, statistical modeling, experimental design, etc.
- Data modeling by putting various targets and cases and trying to get algorithm for business need by using machine learning.
- Data Interpretation by making non-tech people understand what you have discovered from the data so that one can get an idea using data visualization tools and more importantly communication and presentation skills.
WHO ARE THE DATA SCIENTISTS?
The one who performs all these stages in the pipeline and extracts the data product from the raw data is a data scientist. Although it is not easy, but it is not impossible to become a data scientist. Correct training and learning with a lot of practice in the practical field can meet this new demand in the world of technology.
To be a data scientist, you need to be curious and have the right training. The training is about learning different skills in math, technology, business strategic learning, and various tools and techniques required in the field. But the most important thing is to be curious to ask the right questions, take on difficult tasks, and make new discoveries along the way.