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Data Scientist   

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Data Scientist


Who is a Data Scientist?

Data science is a highly interdisciplinary practice. It includes information and takes the overall picture into account. The goal of data science is to provide information about consumers and campaigns. This helps companies reach their target audience and sell their products. Data scientists rely on creative insights that use big data. This is information collected through various processes such as data mining.

In the big data era, data science is a promising field for using and processing huge amounts of data. Data science consists of several elements, techniques and theories, including

  • Mathematics

  • Statistics

  • Predictive Analysis

  • Data Modelling

  • Data Technology

  • Data Mining

  • Visualization

They have been around in the form of business analytics or competitive intelligence. Only now has its true potential been recognized.

Almost every company now has the ability to collect data, and the amount of data is growing. The top jobs of data science are Core Data Scientist, Researcher and Big Data Specialist.

The data analyzed by data scientists often referred to as big data, comes from many sources. There are two types of data that come under big data: structured and unstructured data.

R is a common language for them and works best when they are given clean data to perform advanced analysis. A data scientist creates hypotheses, tests and analyzes the data. They then translate the result for the organization for easy viewing and understanding. Extracting, cleaning, and moving data is not the real job of a data scientist, but of a data engineer.

In simple terms, a data scientist is one who practices the art of data science. Data scientists solve complex data problems with their specialist knowledge. They work with various elements from the areas of

  • Mathematics

  • Statistics

  • Computer Science, etc.

(Even if they may not be experts in all these areas).

What do they do?

Data scientists use statistics and algorithms to solve a data science problem. The data scientist is able to

  • Integrate a data question into a business offering

  • Solve the business problem

  • Create predictive models

  • Answer the urgent problems the business is facing

  • Tell a little story when it comes to manifest the results

Are Data Scientists, Analysts and Engineers Same?

While both data analysts and data scientists work with data, the main difference is what they do with it. Data analysts examine data to

  • Identify trends

  • Develop graphs

  • Create visual presentations that help companies make more strategic decisions

But, Data scientists design new processes for data modelling and production using

  • Prototypes

  • Algorithms

  • Predictive models

  • Custom analysis

Data scientists ask questions, write algorithms and create statistical models. The main difference between a data analyst and a data scientist is strong coding. Data scientists can arrange undefined data sets with several tools. They create their own automation systems and frameworks. A data scientist develops hypotheses, draws conclusions and analyzes customer and market trends.

Key tasks include

  • Collecting and analyzing data

  • Using various analysis and reporting tools to identify

  • Patterns

  • Trends

  • Relationships in records

Data scientists work in teams to retrieve big data. This information predicts customer behaviour and identifies new revenue opportunities. Data scientists also establish best practices for

  • Data collection

  • Using analysis tools

  • Interpreting data

Interviewing people with a technical background can lead to disastrous results. They may not be able to learn the complex mathematics of data. It may lead to an organization's wrong conclusion about its information. Most companies need both data science and data engineering roles. You may need several data scientists and engineers in a company.

Intrigued? Here are some videos for a more detailed explanation.

How to Become a Data Scientist

How to Become a Data Scientist | Data Scientist Skills | Data Science Training | Edureka

Learn Data Science in 3 Months

What REALLY is Data Science? Told by a Data Scientist

How do I start my career in Data Science?

Earn a bachelor's degree in IT, computer science, math, physics, or another related field. Earn a master's degree in data or related field; or Gain experience in the field you intend to work in (ex: healthcare, physics, business).

Top courses where you can begin:-

  • Datatrained Education

  • Jigsaw Academy

  • Aegis School of business

  • UpGrad

  • Great Learning

  • Insofe

  • Insaid

  • Praxis Business school


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How much Data Scientists make in India?

A Data Scientist, IT earns an average salary of Rs 620,244 per year. Experience strongly influences income for this job. The highest paying skills associated with this job are Data Mining / Data Warehouse, Machine Learning, Java, Apache Hadoop, and Python.


Top Recruiters

Equifax, Gramener, Accenture, Fractal Analytics, Amazon, Deloitte, LinkedIn, MuSigma, Flipkart, IBM, Citrix, Myntra, Juniper Network.


How to prepare and what are the study material and books available?

Check out some of the useful books for pursuing chemical engineering in India:

  • Mock tests available online

  • NCERT syllabus of class 11th and 12th

  • Study material available by prestigious institutes

  • Consult your seniors (who have already given such exams) for guidance

  • Previous year question papers

  • 6 JEE Main Online 2020 Phase I Solved Papers with 10 Mock Tests

  • Advanced Problems in Organic Chemistry for JEE.

  • Problems in Physical Chemistry for JEE

Some popular books are O.P. Tandon for Physical Chemistry, P.Bahadur (G.R.Bathla & Sons) for Numerical chemistry. Concept of Physics by HC Verma (Vol 1, Vol 2); Problems In General Physics by IE Irodov. IIT Mathematics, M.L. Khanna; Trigonometry, S L Loney; Calculus & Analytic Geometry, Thomas & Finney.

Interesting Facts

The modern definition of Data Science became popular during the 2nd Jap-French statistics symposium at the University of Montpellier II (France) in 1992.

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