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#Data Science

“I think of data science as more like a practice than a job. Think of the scientific method, where you have to have a problem statement, generate a hypothesis, collect data, analyze data and then communicate the results and take action…. If you just use the scientific method as a way to approach data-intensive projects, I think you’re more apt to be successful with your outcome.” ―Bob Hayes, Ph.D, Chief Research Officer at Appuri

The discipline of data science or data-driven science shares similarities with data mining, and involves scientific methods, systems, and processes which extract information from different types of data. It lays emphasis on the source of information, what it signifies, observing patterns, and use of information as an imperative resource that aids conception and implementation of IT and business strategies.

Data science amalgamates algorithm development, data inference and technology to offer solutions to analytically complicated problems. Additionally, it borrows substantially from computer science, information science, machine learning, statistics, mathematics and other fields which are cited below.

Data science amalgamates algorithm development, data inference and technology to offer solutions to analytically complicated problems. Additionally, it borrows substantially from computer science, information science, machine learning, statistics, mathematics and other fields which are cited below.

Mining a vast amount of structured and unstructured data assists organizations in aspects of cost control, enhancing efficiencies and recognizing business opportunities that afford a competitive edge. With data at its core, ultimately, this science focuses on utilizing extracted data in stupendous ways to increase business value by aiding processes like decision making, trend analysis, product development and forecasting.  

Today, tech companies around the world seek brilliant data scientists who can proficiently extract beneficial information from stored, managed, and consumed data. To qualify for junior and senior positions in this sphere potential candidates are required to be well versed in the following:

Programming skills (degree in information and computer science with vast experience in Python, Perl, C++, Java, Hive, Tableau, Spark, Matlab, R, SAS, Hadoop, SQL or other database programming language)

Statistics and Mathematics

Machine Learning (understanding appropriate use of technology and broad strokes and Deep Learning)

 

Data wrangling or Data munging (data mining, classification and data analysis)  

 

Data Visualization and Communication

Data Intuition (data exploration analysis and insights)  

Business Acumen (for business analysis, predictive analysis and predictive modeling)

Cluster Analysis (classifying and analyzing both structured and unstructured data)  

Capable data scientists who dexterously deliver results using the skills mentioned above are hired to meet mutual goals in the following positions:

Aviation Data Analyst

AVP Data Scientist  

Data Analyst

Data Analyst Automation Platform

Data Science Engineer

Data Scientist

Data Scientist – AI/ML/IoT/R/Python

Data Scientist Insight and Growth

Lead Data Analytics

Principal Data Analyst – AI, ML and Big Data

Principal Data Scientist

Senior Manager Data Scientist

Software Engineer Data Scientist

The industries listed below hire data scientists to extract vital information that helps accomplish groundbreaking progress.

Banking – for customer segregation, fraud detection, dealing with compliance requirements, offering customized services   

Finance – acquiring and storing data effectively for analyzing it in the future, integrating traditional and non-traditional data sets and creating predictive models

Manufacturing – collecting data for source inspection and quality control to deliver better products efficiently    

IT Industries

Technology

Fraud and Risk Detection – utilizing data for profiling customers before sanctioning loans, understanding past expenditures and other variables to reduce risks

Airlines – effectively predict flight timings and patterns, decide flight types and create exciting loyalty programs for customers

Image Recognition – for security, automatic tag suggestion and generation of related search results

 

Speech Recognition – to convert speech to text accurately and communicate without typing commands or messages

 

Gaming – analyze a player’s moves and customize the game accordingly

 

Biotechnology – data compilation and analysis resulting in development of new medical devices and treatment methods

 

Energy – for installation and management of clean energy producing facilities  

 

Telecommunications  

 

Transportation – for development of cost-effective methods of transportation that reduce human effort  

Data science is a thriving industry where there is no dearth of opportunities for the truly dedicated and driven professional. Apply today and get hired by industry giants to make your career as rewarding as it deserves to be.      

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