Everyone has a different view of big data. Some people say that this is only a stage that the scientific and technological community is experiencing. Some people say that this is a long-term stage. But all of this will be in the future, not in control. But today people can say without a doubt that data science is an ideal field of research.
Why is this sharp demand?
There is a lot of raw data stored in the business data warehouse, and they need to be sorted and understood so that it can be used for strategic use of attention. Therefore, the entire process of converting large amounts of data into usable data is data science.
Everyone knows smart watches, this is an invention. It tells us our heart rate, how many calories we burn, how healthy we are, and how many steps we need to complete our daily count. But how do you tell us all about this by clinging to our wrists? This is the perfect application of data science. It collects data such as heart rate, body temperature, etc., and uses sensors to understand motion and then process that data into insights that are meaningful to our health.
Today, every business problem requires data to solve the problem and infer future situations and develop a structural plan for it. In the past, companies were only used to analyze past data, but now it is about understanding the future.
How does data science work?
Data science has a complete workflow. A step-by-step procedure for extracting material from the original information.
- Data accumulation is usually done by database management [SQL], which retrieves semi-structured data and then stores them using Hadoop, Apache flink, etc.
- Data cleansing uses tools such as Python, R, SAS, Hadoop to eliminate inconsistencies and exceptions.
- Data analysis to understand the data, find patterns that may be useful, use Python libraries and R libraries to solve specific problem details, statistical modeling, experimental design, and more.
- Data modeling by putting in various targets and cases, and trying to get business-required algorithms through machine learning.
- Understand data by letting non-technical people understand what you find in the data so that people can gain insights using data visualization tools and the most important communication and presentation techniques.
The person who performs all these phases in the database and extracts the data products from the raw data is the data scientist. Although not easy, it is not impossible to become a data scientist. This new demand in the scientific community can be met by proper training and learning through a large number of practices in the field of practice.
To be a data scientist, you need to be curious and receive appropriate training. The purpose of the training is to learn the different skills of mathematics, technology, business strategy learning and the various tools and techniques required in the field. But the most important thing is to ask the right questions, handle difficult tasks and discover new discoveries in the process.
Orignal From: A bright and promising future in data science
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