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What is Data Science?

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Data Science is the talk of the town courtesy of its growing techniques to help businesses grow and increase customer satisfaction. The popularity of Data Science can be felt and witnessed in almost every sector of life and not just the IT sector.

Edureify, the best AI Learning App, has cultivated the best method of using data science. In this article, Edureify will discuss everything related to Data Science and the prerequisites to becoming data scientists.

What is Data Science?

To go by the books,

“Data Science is the domain of study that deals with vast volumes of data and usage of modern tools and techniques that aid to find unseen patterns, deriving meaningful information, and making business decisions. The complex machine learning algorithms used by Data Science help to build predictive models.”

Data Science analyses the various data collected from different sources and implements the best method to use these data to generate the best results for a company.

How to implement the working of Data Science in an organization?

No matter how self-efficient the Data Science working is, an organization needs certain supervision to oversee the systematic working of Data Science.

The following is a list of managers who help in the supervision of Data Science-

Business Managers- The Business Managers assist the data science team to outline a problem and develop a strategy for analysis.

IT Managers- The Senior IT Managers are responsible for making the infrastructure and architecture, the blueprint that will support data science operations. The IT Managers also need to ensure that the data science team works efficiently and securely.

Data Science Managers- The Data Science Managers are the data science team leads. They overlook the daily work of the data science team and ensure the team balance, project planning, and monitoring of the work.

Stages of Data Science

Data Science has a few characteristics regarding its different stages of working. These stages can also be considered the lifecycle of Data Science.

Following are the stages of Data Science-

Capture- The first stage of Data Science is Capture where the raw structured and unstructured data is collected. The features of this stage are-

o Data Acquisition

o Data Entry

o Signal Reception

o Data Extraction

Maintain- The second stage of Data Science is Maintain where the raw data is molded into a form that can be used further for its development. The features of this stage are-

o Data Warehousing

o Data Cleansing

o Data Staging

o Data Processing

o Data Architecture

Process- Once the data is structured into a useable form, the prepared data is examined for its patterns, ranges, and biases to figure out how well the data can be used and for what. The features of this stage are-

o Data Mining

o Clustering/ Classification

o Data Modeling

o Data Summarization

Analyze- The core stage of all the Data Science stages is Analysis. Here data is properly analyzed and its use is defined after using different algorithms. The features of this stage are-

o Exploratory/ Confirmatory

o Predictive Analysis

o Regression

o Text Mining

o Qualitative Analysis

Communicate- The final stage of Data Science is Communication where the data scientists make design the collected data in easily readable forms like charts, graphs, and reports.

Uses of Data Science

So far we explored the meaning, managers, and stages of Data Science. Data Science has indeed become a new ever-growing phenomenon with its efficient result-driving uses.

The following is a list of sectors where the Data Science is used-

Fraud and Risk Detection
Healthcare
Internet Search
Targeted Advertising
Website Recommendations
Advanced Image Recognition
Speech Recognition
Airline Route Planning
Gaming
Augmented Reality

Impacts of Data Science

Now that we have covered the uses of Data Science, the following are the ways in which Data Science has placed a great impact-

Quantifiable and Data-Driven Decision Making
A better understanding of customer intent and better customer service
Recruiting and retaining skilled employees
Opportunity Identification

In these ways, Data Science has impacted in bringing greater results to an organization.

Pre-requirements of becoming a Data Scientist

Before starting to deal with Data Science one needs to have the knowledge of the following technical concepts-

Machine Learning- Machine Learning is the basis of Data Science. Candidates who are interested to become data scientists must know about Machine Learning. Read Edureify’s Artificial Intelligence and Machine Learning article to know more about the same.
Modeling- Having a good knowledge of mathematical models enables one to make quick calculations and predictions based on one’s prior knowledge of the data.
Statistics- Statistics is the core of Data Science. Having a stronghold on statistics makes you extract more intelligence and obtain more meaningful results.
Programming- Having some knowledge of programming is beneficial for Data Scientists. Read Edureify’s Top 10 Python Courses to know more about Programming courses.
Databases- An efficient data scientist knows the working of databases, how to manage them, and how to extract data from them.

Here were some important points on Data Science and what are the pre-requisites to becoming a data scientist. Edureify makes sure that students in both beginner and advanced stage has enough materials to pique their interest in coding and other technical learning. We have dedicated a particular section to coding to help budding students learn more.

Stay tuned to Edureify to know more about such technical elements.

Some FAQs on Data Science

1. What is Data Science?

Data Science is the domain of study that deals with vast volumes of data and the usage of modern tools and techniques that aid to find unseen patterns, deriving meaningful information, and making business decisions. The complex machine learning algorithms used by Data Science help to build predictive models.

2. How many stages are there of Data Science?

There are 5 stages of Data Science.

3. What are the 5 stages of Data Science?