Data Science and Data Scientist
Data Science and Data Scientist

Data Science and Data Scientist Explained –

Data science is a mixture of latest technology, worldwide algorithms and data inference. This hybrid approach can be used to solve the very complex problems analytically in very less time effectively and efficiently.  

The basic of data science is all about data. It is a combination of raw information, facts and figures which are aligned properly and stored in enterprise data warehouses. Data science is finally about using collected data in creative, effective and efficient ways to generate business value.

Data Science Opportunities – 

Now in days demand of data savvy technicians and professionals are very high. These professionals having extraordinary skills in visualisation, virtualisation, statistics, applied mathematics and computer science. According to IBM estimation, demand for data scientists and data engineers will grow by 39 percent. They predict that by 2020, 2.7 million new job openings will be based on data and analytics skills. 

The performance of the organisations becomes high while having the professionals with these deep knowledge, understanding and extraordinary skills. It will become very easy to take better decisions with high understandings for data driven organisations.

Transformations made by data science –

1.Marketing

For marketers it becomes easy to send the right messages to right people at the right times.  Now they become able to personalise – marketing offers, messages, promotions, social media engagements location and behaviour based advertisements, user tracking.

2.Energy

By using this hybrid technique the energy can be saved. It may helps in the better monitoring and forecasting of the customer’s energy consumption. Equipment failure can be easily predicted.   

3.Manufacturing

Data science helps manufacturers industries to improve various processes, high speed production, best possible quality, waste minimisation and boost profits. It may also helps in the optimisation of the firm.

4. Finance

Big industries which deals with the transaction of heavy financial services generates and uses the more data as compare to the any other business sector. Data science helps bank in real time analytics by virtue of which investment risks can be reduced. Banks now have possibility to protect the customers from any fraud or risky transactions.

5.Retail

Data science may helps to forecast demand and manage inventories. Customer experience and satisfaction may be enhanced. Better understanding of consumer trends and purchase decisions is possible by using the data science. Store layouts can be optimised by using this hybrid technique.

6.Medicine

For healthcare rise of big data is very much helpful. Now, doctors can provide faster and more accurate diagnoses by using the electronic health records. Health diagnoses become more easy and faster. Smartphone applications and wearable sensors can be used to monitor vital signs of the patients.

7. Travel and Transportation

Data scientists can collect the data from google, car cameras, CCTV cameras and GPS units. By using that data they can help keep people safer on the roads. Wear of car parts become easy to track. Weather, wind, and traffic data can be collected and analysed to predict travel delays and route changes.

8. Local and National Security

Criminals can be tracked and captured by using this approach. It can also prevent crime before it happens.

Analysis of sensors, video surveillance, GPS data, social media, online transactions etc. can help law enforcement to detect and counter terrorism, cyber attacks, and other future mishappenings.

Future in Big Data –

Data scientists in the age of big data have the vast scope and the sky is the limit for them. There are huge opportunities for everyone to make a difference by this hybrid approach.

While developing data products data scientists play a major role.

It Includes: – Creating new algorithms, refinement, testing and technical deployment etc. Data scientists works as the best technical developers.

Data Science Required Skill Set

Data science may be defined as the mixture of skills in three major fields:-

1. MathematicsThe knowledge of mathematics is compulsory for the any data scientist. Without mathematics he or she is not able to see data quantitatively. In this field data is to be expressed in mathematical form, which includes the logical symbols, figures, dimensions and interrelationships.

Any problem is first converted into the mathematical form for its analysis and optimization. Only with the help of the mathematics the solution of real life problems can be find out. Statistics is the part of mathematics which is used for data collection and analysis. Knowledge of algebra and matrix is also useful in data science.

2. Technology and HackingData scientist must be technically sound about all existing techniques so that he can analyze the problem in every possible ways. Here in field of data science hacking doesn’t refer to the breaking of the securities, steal data, breaking computers etc. The correct word is data science hacker for this field. The data science hacker is a logical thinker who uses his technical skills and creativity to find out the best optimum and clever solutions.

The knowledge of the coding is necessary for the data scientist so that he can create his own required tools whenever needed. They can’t depend upon the market tools.  Knowledge of SQL, Java, Scala, Julia, Python, R, SAS etc are required in data science. So we can say that a data scientist is the technical ninja.

3. Fast and Good Business Decisions- Data scientist should be a good decision maker. The correct decision should be made at correct time and in minimum possible time. So we can say that data scientist is good business acumen.

Data scientist is totally responsible for perfect interlinking between data science projects and required business goals. Finally the best outcome can’t come from data, math, and tech itself alone. It comes from combination of all.

What Do Data Scientists Do?
  • To discover new optimum solutions and best opportunities.
  • Creating the new models and algorithms for analysis.
  • Cleaning, optimizing, compressing and validating the data to ensure accuracy, completeness and uniformity.
  • Finding the major problems in business organizations etc. and providing the best solution.
  • Creating set of correct data sets and variables for analysis.
  • Collecting and properly storing structured and unstructured data from all possible sources.
  • Data analysis for identifying the patterns and trends.
  • To discover new optimum solutions and best opportunities.

So, it’s all about the data science, data scientist, duties of data scientists various transformations made. Stay tuned with the CRACKORSQUAD for latest technical updates and knowledge.

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