What is Data Science? Know its Applications, Tools, & Salary

Data science is an integral part of many industries because of the vast amount of data produced every day. It is also one of the most talked-about topics in IT circles.

It has become a popular tool for companies to improve customer satisfaction and grow their businesses. This article will explain the basics of data science and how you can become a data scientist.

What Is Data Science?

It is a new specialty that arose from data mining and statistical analysis. The Data Science Journal was published in 2002 by the International Council for Science.

The title of data scientist was established in 2008, and the field exploded. Since then, there has been a shortage of data scientists, even though various colleges/ universities are offering degrees for it.

They are responsible for developing data analysis strategies, visualizing, exploring, analyzing, and visualizing data. They also build models using data programming languages such as Python or R and deploy them into applications. Data scientists are responsible for developing data analysis strategies, visualizing, exploring, analyzing, and visualizing data. They also build models using data programming languages such as Python or R and deploy them into applications.

Data scientists don’t work alone. This team may include a data scientist and a business analyst.

The Data Science Lifecycle

A few life cycles are narrowly focused on data, modeling, and assessment. Some are more thorough, concentrate on business understanding and then deployment.

The one we’ll be walking through includes operations. It emphasizes agility more than any other life cycle.

This life cycle has five steps:

  • Problem Definition
  • Data Investigation and Cleaning
  • Minimal Viable Model
  • Deployment and Enhancements
  • Data Science Ops

It is better to take small, incremental steps than to go through large-scale phases. The life cycle only focuses on project steps. The process provides a comprehensive view of the project steps and a collaboration framework.

This life cycle has five steps
The Data Science Lifecycle

Prerequisites

Data Science, as its name suggests, is all about information. It can only be learned if you love data and have a solid understanding and ability to work with data.

Big data wranglers can also be used to describe data scientists. They are able to analyze large quantities of unstructured and structured data. They can use mathematics and Computer Science to model, process, analyze information, and interpret the results.

Data Science
Technical Data Science Prerequisites

To do this, they need to be knowledgeable in various disciplines. There are two types of Prerequisites.

  • Technical Prerequisites
  • Non-Technical Prerequisites

What Does a Data Scientist Do?

It can be a huge advantage to turn a sea of data into valuable insights. This includes identifying and defeating national security threats or predicting the best diabetes treatment.

Businesses and government agencies are scrambling to find data science professionals that can do this job well.

Data scientists combine computer science, modeling, and statistics with math skills and sound business sense to uncover the answers organizations need to make objective decisions.

Where Do You Fit In Data Science?

Data Scientist Role and Responsibilities:-

Data scientists collaborate closely with business stakeholders to understand and identify their goals.

They create predictive models and algorithms to extract data for businesses and help with data analysis and sharing insights with colleagues.

Although each project is unique, the general process of gathering and analyzing data follows this path:

1. Go for the right questions to start the discovery process

2. Acquire data

3. Cleanse and processes the data

4. Data integration and storage

5. Data investigation and exploratory analysis

6. Select one or more models and algorithms

7. Data science techniques such as statistical modeling, machine learning, and artificial intelligence can be applied.

8. Improve results by measuring and improving them

9. Present the final results to stakeholders

10. Based on feedback, make adjustments

11. Repetition the process for a new problem

Also Read: What is Hadoop used for? What is Big Data? How do we earn from Hadoop???

Data Scientist Salaries

Robert Half Technology’s 2020 Salary Guide states that data scientists make an average salary of $180,250 to $105,750 per year. But compensation will vary depending on where you live. The average salary in the United States includes:

  • India: $89,218
  • San Francisco: $121,836
  • Seattle: $108,399
  • New York: $101,387
  • Boston: $101,064
  • Los Angeles: $99,014
  • Austin: $96,495
  • Atlanta: $91,049
  • Washington, D.C.: $89,738
  • Chicago: $88,758
  • Charlotte: $87,306

Data scientists often rise to higher pay and experience as they gain more data science experience. These include:

  • Senior Data Scientist: $125,925
  • Data Science Manager: $135,401
  • Data Science Director: $157,273

Why Become a Data Scientist?

Technological expertise, which includes knowledge and skills, is at the heart of innovation.

It is one of the most desired technologies. Countries use it to automate the lives of their citizens.

Digital evolution and data science are closely related. This technology significantly impacts finance, education, hospitality, retail, and finance. Because of the large gap between supply and demand, data scientists have an increasing demand.

As more data science jobs are created, more businesses will use their algorithms to improve their businesses. This article lists the top five reasons to be a data scientist by 2022.

Tools

It is a rapidly growing field. Companies employ Data Science to provide insights into the market and help improve their products.

Data scientists are decision-makers. They are responsible for analyzing and managing large amounts of structured and unstructured data.

Data Science
Tools

1. SAS

2. Apache Spark

3. BigML

4. D3.js

5. MATLAB

6. Excel

7. ggplot2

8. Jupyter

9. NLTK

10. TensorFlow

Applications

It is the deep study of a large quantity of data, which involves extracting some meaning from raw, structured, and unstructured data.

Extracting meaningful data from large amounts uses data processing, which can be done using statistical techniques and algorithms, scientific techniques, different technologies, etc.

It uses various tools and techniques to extract meaningful data from raw data. Data Science is also known as the Future of Artificial Intelligence.

1. Fraud and Risk Detection

2. Healthcare

2.1. Image Analysis and Diagnosis

2.2. Genetics and Genomics Research

2.3. Drug Discovery and Development

3. Image Recognition and Speech Recognition

4. Airline Route Planning

5. Gaming

6. Banking

7. E-commerce

8. Manufacturing

9. Transport

10. Education

Data Science Use Cases     

Data science and advanced analytics have led to many applications that provide better insight and business value for enterprises.

Mainly, organizations can use data science methodologies, tools, and technologies to extract valuable information from increasing amounts of highly variable details.

1. Anomaly detection

2. Pattern recognition

3. Predictive modeling

4. Recommendation engines and personalization systems

5. Classification and categorization

6. Sentiment and behavioral analysis

7. Conversational systems

8. Autonomous systems

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