What is Data Science?
Updated: Dec 2, 2021
There are many definitions of Data science on the internet but in my understanding, data science is a field which uses data to find meaningful insights and patterns. With the help of data science, we as a human can extend our knowledge. With rapid increase in data, handling and using the data efficiently has become the need of time and thus, data science comes into picture. For example, Google has created an AI agent named as “AlphaGO” which defeated best go players in the world you can read about it on https://deepmind.com/research/alphago/
The Primary 4 Pillars
While data scientists often come from many different educational and work experience backgrounds, most should be strong in, or in an ideal case be experts in four fundamental areas. In no particular order of priority or importance, these are:
Abilities to become a Data Scientist -
Knowledge of RDBMS, NoSQL, SQL as well as integrate the data into an analytics-driven data source
Understand all statistical, programming, and library/package options available, and select the best
Ensure that the data is suitable and correct (Data mining and data engineering).
Sufficient knowledge of programming languages like Python, R, Java.
Select and implement the best tooling, algorithms, frameworks, languages, and technologies to maximize results and scale as needed
Machine Learning knowledge.
Working efficiently in collaboration with all company departments, groups
Understand customers and/or users needs, and create ideas and solutions accordingly.
Education-wise, there is no exact path to enter into data science. Many universities have created data science and analytics-specific programs, mostly at the master’s degree level. Some also offer certification programs.
Roles that'd interest you
1. Data Scientist
It is one of the best paying jobs. The Data Scientist job is to collect, analyze, interpret large amounts of data and present it in an understandable way. The Data Scientist’s task is to follow the latest data mining and visualization technologies as well.
2. Data Architect
With the huge amount of data, comes the demand for designing, creating, deploying and managing an organization’s data architecture. This is where the data architect is needed to create a structure for the DBMS which needs to be centralized and constantly maintained, protected.
3. Business Analyst
Apart from the mathematical and programming educational background, a person can be a professional in Data Science having more business understanding. As a business analyst, it’s is very important to understand business processes and to act as the bridge between the IT geeks and business stakeholders.
After choosing the right area of interest, you may want to start with LEARNING!
Data Science is a vast field with interconnected domains. You will want to learn Machine Learning, Deep Learning, Data Mining, Probability and Statistics to have greater proficiency.
This is where you can read more about how to learn machine learning for free
You can find various courses on Kaggle, Coursera, Udemy related to your area of interest.
Data Science Challenges can be found on