What is data science?
Data science is an interdisciplinary field that uses algorithms to heave knowledge from amorphous data. Data science exists to unify statistics, data, understand actual phenomena using mathematics, computer science, and domain knowledge.
Data Science as we know it continues to grow as a career option. Glassdoor has ranked Data Science as the most demanding career option placing it on the Top 3 career options by 2026. A successful data scientist knows Machine Learning and is inclined towards business operations.
Data science is a fast-growing and in-market career due to considerable data inflation. A single person can generate 3.0 Quinton bites. So managing data can prove to be very arduous to companies. That’s where data science comes in. Data science moves past the traditional statistics and handling of large amounts of data and focuses on the Life cycle of Data science.
EARLY USAGE OF DATA SCIENCE
In 1962, John Turkey defined a field as Data Analysis resembling modern-day science. Peter Naur in 1974 suggested an alternate name for computer science. But the coining of a proper definition was still in flux. Statisticians wanted to rename statistics as data science to divest stereotypes about the field. In 1998, Hayashi Chikio introduced Data science as an independent field. He explained that data science has three aspects scilicet Data design, collecting, and analyzing.
THE LIFE CYCLE OF DATA SCIENCE
The life cycle is a set of repetitive steps that a data scientist follows to deliver their project.
Following are the life cycle of data science:
Business Understanding: In this step, it is fundamental to understand the problem and create a precise model to yield results.
Data Mining: After the problem is identified, the data has to be analyzed to discern patterns and trends. Information is extracted from the large amount of data that was available.
Preparation of Data: This step integrates data with merging data sets, cleaning and treating lacking value and inaccurate data. This is the most time-consuming step.
Exploratory Data Analysis: In this step, visual data analysis is done using bar graphs and charts. A concept about the result is obtained.
Data modeling: This step is the coronary heart of data analysis. The model takes in the organized data and gives output. In this step, selecting the suitable model is crucial, whatever the problem may be.
Model Deployment: Before the model is deployed, it has to be ensured through a rigorous evaluation that the model chosen is the right one for the problem.
PREREQUISITE FOR DATA SCIENTIST
Data Science Syllabus demands the following skills required by any data scientist:
Machine Learning: Machine Learning includes grasping ML and statistics.
Modeling: This skill required the ability to do quick calculations based on raw data and the proficiency to determine which algorithm to work on.
Statistics
Programming: Some level of Programming knowledge is required for data science. The knowledge of Python, R.Python, that supports multi libraries for data science and ML are required.
Databases: Knowing how to work, manage and extract data is important.