SIZ EDUCATION || ROADMAP TO DATA SCIENCE || by @musfirah-m
Today, I thought of sharing with you all the road map for learning data science.
As data science is one of the most in-demand area of technology,many people are now choosing this field as their career.
Earlier ,when internet was emerging, data collected through the internet was not large enough ,therefore it was quite easy for companies to hire individuals who could look up for patterns in their data which could help the company to progress and prosper.But as the world of internet grew larger and larger,it was no more possible that data can be analyzed by a human being or a group of human beings.This huge amount of data is called BIG DATA. For this, data should be analyzed by computer systems.And this whole thing is related to data science.
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What is data science?
Data Science is simply preparing raw data for analysis so that after manipulating the data certain patterns can be achieved on the basis of which many predictions could be made which could help in the progress of businesses.
Machine learning, Artificial intelligence,Deep learning etc come under the umbrella of Data Science.
There are three parts in Data Science.
- Data collection
- Analyzing data
- Taking insights from analyzed data for solving different buisness problems.
Road map to data science:
Five steps can lead to give an overview of how to learn data science
###1) Learning a Programming Language:
The most extensive language used in Data Science is Python.It is more popular in data science as it offers the facilities of a lot of libraries and in-built resources.A basic knowledge of Python and libraries such as "Pandas" and "Numpy" can help while learning data science.
2) Learning Statistics:
Concepts of mean, median , mode, standard deviation etc are to be learned for data science.
3) Data visualization:
As in data science, we are dealing with huge amount of data, which could not be observed without using graphs and pi charts ,learning these is a basic necessity of data science. Python also offers libraries such as Matplotlib and Seaborn so one must need to learn this as well.
4) Machine learning:
A basic knowledge of machine learning and its basic algorithms is useful while working on data science.
5)Making Projects:
As we know,"Practice makes man perfect", it is also true in this case. Making projects related to data science can help you learn more and more as compared to learning theoretically, different errors and their solution tend to produce a better understanding of data science and develops problem solving skills. A website named Kaggle has a huge amount of sample data which can be used for many projects.
I hope you were able to take something useful out of this!
Thank you for your time:)
Steem on!!