A bright and promising future in computer science

Everyone has their different opinions on big data. Some say it’s just a phase the tech world is going through, and others say it’s here in the long run. But all this will be in the future and not under control. But today you can say without a doubt that data science is a desired field of study.


There is a lot of raw data stored in business data warehouses, one needs to sort them out and understand them so that they can be used for the strategic use of the concern. So the whole journey of converting piles of data into usable data is data science.

Everyone is aware of smartwatches, what an invention. It can tell us our heart rate, how many calories we burn, how healthy we are, and how many more steps we need to take to complete the daily count. But how can it tell us all this just by being tied to our wrists? It is an immaculate application of data science. It gathers data such as heart rate, body temperature and uses sensors to know movement and then processes this data into meaningful insights into our health.

Today, all business relationships need data science to solve problems and derive what’s in the future and create structural plans for it. In the past, companies used to analyze past data only, but now it’s about knowing the future.


There is a whole workflow in computer science. Step by step procedure for extracting the substance from raw information.

  1. Data cumulation is usually done through database management (SQL), retrieves semi-structured data and then stores them categorically using Hadoop, Apache good, etc.

  2. Cleaning data to remove discrepancies and discrepancies using tools like Python, R, SAS, Hadoop, etc.

  3. Data analysis to understand the data, find patterns that can be useful, details that can solve a particular problem using Python libraries and R libraries, statistical modeling, experimental design, etc.

  4. Data modeling by placing different objectives and cases and trying to get an algorithm for the business needs using machine learning.

  5. Data interpretation by getting non-technical people to understand what you have discovered from the data so that you can gain insight using data visualization tools and most importantly communication and presentation skills.


Whoever performs all these phases in the pipeline and extracts the data product from raw data is a data scientist. Although not easy, but it is not impossible to become a data scientist. Proper training and learning with lots of practice in the field of practice, one can ace this new demand in the tech world.

To be a data scientist you have to be curious and have proper training. Education is about learning different skills in math, technology, business strategy learning and different tools and techniques required in the field. But the most important thing is to have the curiosity to ask the right questions, take on difficult tasks and make new discoveries along the way.