Thursday, 24 September 2020

Essential Concepts To Study To Become A Data Scientist!

 For a very long time I have been noticing that most people have notions about data science, they think by studying and learning to work on python, R and other tools, they can become a data scientist. Python, R, SQL, are all important in data science but these are all could be learnt easily once you start working in them, it is really the least challenging thing to learn all these tools and languages for a data scientist. What really matters is the knowledge in and about data, a data scientist should have the intelligence to vision through the raw data.

Let us focus on the real thing, we need to think beyond the tools and start concentrating on developing relationships with data because that is the key to become a data scientist. 

A data scientist must mainly possess the skill in understanding the potential of data, its value, threshold and flexibility. We call this as Data Processing. In data science, data will be the dish for which data itself will be the ingredient, which means, the main goal of data processing is to find salutary data by crunching and filtering the raw data.

The process of Data processing:

1.     Gathering of the data. By various platforms, surveys and mediums data(in all forms) will be gathered, this data will not be validated while being picked and that is why it is called as raw data. It is the data in its raw form.

2.     Cleansing of data. The gathered raw data will go through a validation process in which the useless data will be eliminated from the main data, only the useful data will be filtered through this process.

3.     Modification of data. The thoroughly validated data will be rebuilt, manipulated and will be merged with other data if necessary.

4.     Processing phase. This is the ultimate phase where the processing of data takes place, here is where the final solution for a problem will be found. Machine learning algorithms and methods are used in this phase.

5.     Interpretation of data. The final solution of data could be easily read by the data scientists but for the non-data scientists the interpretation of salutary data into an easily readable and understandable way is important. Data visualization is used in this phase of data processing.

6.     Data Storage. It is extremely necessary to store the data especially to store the statutory data so that it could be reused in future whenever necessary. But storing data was a huge concern for the businesses but due to the concept of hadoop in big data this concern has been easily resolved.

This is probably the simplest and shallow explanation on the phases of data processing.

A data scientist must have knowledge in both technical and non-technical aspects of computer science and data science.

Note: Start your journey in Data Science with Learnbay as it provides big data analytics training in Bangalore and Data Science Courses In Bangalore.

Technical aspects that one must learn to become a Data Scientist:

  1. Linear algebra- Singular value decomposition, optimization and probability theory.
  2. Mathematics, Stats, Data structure, data analytics and algorithms.
  3. Programming languages like python, R, SQL, Java, C++, but python and R are mainly necessary. Python is majorly used in various phases and processes of data science like in importing the SQL tables into code, to create tables, etc. R programming should also be necessarily learnt because 43% of data scientists believe in the potential of the language while solving statistical problems.
  4. Data visualization is the other essential part in data science, it is imperative for the data scientists to have thorough knowledge in it.
  5. Machine learning and AI algorithms ofcourse.

Always remember that you will not really be a data scientist until you enter into the field in that designation, only after years of proper experience you could be called as the ideal data scientist. So do not expect more from anything, make sure you will keep up with your perseverance towards learning the field.

Learning data science can be tricky, difficult and confusing all at the same time but with the right source of assistance you can easily follow up with the field. I recommend you the data science course of Learnbay, it is really a good source to learn data science as it provides big data analytics training in Bangalore and Data Science Courses In Bangalore.