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Data Science: Lifecycle, Data Industry, Steps to become Data Scientists

Evolution of technology has generated a lot of data. Now we have way too much data and in order to process this much data we need more complex algorithms, we need a better process, and this is where data science comes in.


Introduction

• Data Science is all about extracting the useful insights from data and using it to grow

your business.

• Data Science is about data gathering, analysis and decision-making. Data Science is

about finding patterns in data, through analysis, and make future predictions. By

using Data Science, companies are able to make better decisions for the growth of

the company.

• The data used for analysis can come from many different sources and presented in

various formats.


The Data Science Lifecycle


Now that you know what is data science, next up let us focus on the data science lifecycle.

Data science’s lifecycle consists of five distinct stages, each with its own tasks:


1. Capture: Data Acquisition, Data Entry, Signal Reception, Data Extraction. This

stage involves gathering raw structured and unstructured data.


2. Maintain: Data Warehousing, Data Cleansing, Data Staging, Data Processing, Data

Architecture. This stage covers taking the raw data and putting it in a form that

can be used.


3. Process: Data Mining, Clustering/Classification, Data Modelling, Data

Summarization. Data scientists take the prepared data and examine its patterns,

ranges, and biases to determine how useful it will be in predictive analysis.


4. Analyse: Exploratory/Confirmatory, Predictive Analysis, R