Who is data scientist?
Data Scientists are professionals with the ability to collect, process, analyze, and then bring insights from millions, trillions, or even zillions of data streams. 
       Today, sensors are integrated into every phase of the industry for collecting data across procedures. These devices generate millions of data streams every second. Data scientists have an essential role in utilizing these data resources precisely to understand the information, bring insights, and uncover better growth measures. Profiles associated with Data Science include Data Analysts, Data Architects, Data Engineers, Business intelligence, or Data Experts. 
             Listed below are the main tasks that define the role of a data scientist in any organization:
1.Identify valuable data resources from zillions of data streams 
2.Build automated procedures for data acquisition and collection 
3.Implement processing techniques on unstructured and structured data 
4.Bring data analysis for developing predictive modeling and A1 algorithms 
5.Discover new patterns, factors, and reasons for growth in the markets 
6.Develop better models, procedures, tools, and techniques for data analysis 
7.Assist organizations in better decision-making and planning for the future 
8.Suggest solutions for enhancing business productivity and performance 
9.Recommend corrective changes and implementation in the policy building 
        The role of data scientists has recently evolved in the 21st century and is sure to grow in the next
few decades. They have become the driving force for top MNCs in finding unknown  patterns, clogging factors, and strategic decisions to help them grow vertically.

Skills required for data scientist
Data scientists are among the most skillful professionals in their industry. In the modern age, data scientists are playing a multi-faceted role of a computer professional, a mathematician, a statistician, and a scientist. 
 
Educational Background 
Individuals with a bachelor's degree in Mathematics, Economics, Computer  Science, Social Sciences, Physical Sciences, and Statistics can opt for a career in Data Science. Study trends show that an acquired qualification plays a crucial role in the career of a data scientist. The maximum percentage of data scientists comes from Mathematics and Statistics (32%), followed by Computer Science (19%) and Engineering (16%) backgrounds. 88 percent data scientists possess a Master's degree and 46 percent have PhDs. 
 
Technical Skills 
Data scientist profiles ask for technical skills across multiple disciplines, domain knowledge, and best industry practices. These may include:

Programming languages and Tools 
Data scientist profiles must have a fundamental understanding of multiple programming languages such as Python, Perl, Java, and SQL. Individual capability to write, modify, and develop efficient code is of primary importance. One should have expertise in one of the programming languages. According to a survey, Python was the most preferred choice among programming languages (40% share) for the role Of a data scientist. 
 
1) R programming is one of the most preferred data analytical tools in 
 Data Science, with up to 43 percent data scientists using this as a professional skill. R was built with the specific purpose of matching the complex needs Of data science. Though the R learning curve is not simple, many aspirants are still learning it for data science to match the best industry standards. 
 
2) Statistics and probability. 
 Statistics and probability leverage the mathematical concepts in the learning curve for data science. Several combinations of basic and advanced features add more predictive modeling capabilities for data scientist professions. Statistics can help conclude, understand the past, and predict the future using precise methods for understanding business procedures. 

3) Hadoop is an essential part of data science that adds the power of extra scalability for businesses. Especially when the volume of data exceeds that of the system memory, then data scientists can distribute data to multiple servers. In several procedures of data such as data exploration, sampling  and filtration, has unmatchable performance, making it ideal for several cases. 

4) SQL/ Database Management 
Though simple and easy, Structured Query Language (SQL), offers a comprehensive way to communicate, access, and modify data. Companies will expect da ta scientists to operate commands such as add, delete, and extract data as per demand. Precision in writing SQL commands can simplify complex tasks, transform database structures, and perform analytical functions at an optimum level. The SQL learning curve will also improve understanding of the relational databases and complement your data scientists profile seamlessly.

5) Apache Spark 
Apache Spark's ability to work faster than has made it to one of the top preferences for handling big data technology. Apache Spark can cache computation in memory and works seamlessly to manage complex algorithms. It can run on a cluster of machines, distribute complex unstructured data sets, disseminating data and perform analytics throughout data intake to the distributing computing procedures. 

6) Machine Learning (ML) and Artificial Intelligence (A1) 
Developing allegorizing Machine learning and Artificial Intelligence is one of the Machine learning and Artificial Intelligence for Data Science toughest skills for data scientists. Though to successful data scientist individuals have to master multiple techniques including supervised, unsupervised, reinforcement, neural networks, computer vision, natural language processing, and adversarial learning. Proficient skills in machine learning can make a data scientist profile stand out from others and catch 
the employers' eye. 
 
7) Data Visualization 
Data Visualization consists of methods, tools, and techniques to present data graphically and bring visual insights. Especially in large and corporate organizations where constant discussions, meetings, and presentations are held, these charts, graphs, plots, etc., assisting rich insights and quicker decision-making. 
           Apart from these technical skills, data scientists' abilities to handle unstructured data can be a career-defining factor. An individual aspiring to become a data scientist must carefully choose the skills, duration, and learning curve to grasp the fundamentals for better scope and opportunities.

Non-technical Skills 
These are the personal attributes or skills that must be acquired for a vibrant in data science. You may not see these 'soft' skills written in a resume, yet, they complement the technical expertise to become a better data scientist. 
 
a)Problem Solving 
Data science involves unseen structured and unstructured data problems. Here, the fundamental goal is to get valuable information from the data. Data scientists must evaluate the current procedures to value data or information to match with organizational goals. Data scientists must have the ability to understand the problem. measure the requirements efficiently, and then apply the tools and techniques that can solve the problem. 
 
b)Teamwork 
Data science is not a one person job. Data scientists work and combine their insights and ideas to multiple departments depending on the companies'  needs and requirements. You can expect data scientists to work with almost everyone from the organization. 

c)Communication Skills 
Communication is one of the main aspects of the data scientist profession. 
He or she should be Clear and fluent in Order to implementing data -based decisions. Leveraging the effective use Of insights and assisting in implementing changes from a technical perspective to non-technical departments such as sales or account departments. Business Awareness/Acumen to be a better data scientist, individuals must possess a business awareness precise to their domain knowledge. Data scientists must have a thorough understanding of the business to define problems, corrective 
measures, and growth factors. Business acumen will help data scientists select the right model and algorithm for data analysis to get better insights and results.

Experience 
A data science profile comprises multiple skills and experience that generally can take years Of education training and working experience. Individuals must be self-starters. have definitive evaluation capabilities, and test their skills on online methods for gradually applying them to complex tasks for a broader approach. Modern data scientists work with hundreds of analytical tools, techniques, and methods to perform their tasks. Individuals must test their skills and preparation for building algorithms and models to enhance their performance regularly. 
                    Experience can be a huge factor in earning prospects. As one of the defining factors for a career, data scientists must match their skills with the best industry practice and knowledge to improve their skills to access more work opportunities. Senior profiles associated with this field may have a designation Of Senior Data Scientist, Data Science Manager, Data Science Director. With experience, you can earn high salaries for data scientists that can go up to as high as $150,000 on average.
 
Certifications 
 A career in Data science is a heavily skill-oriented profession. Working individuals can leverage their experience and combine their learning curve to get more opportunities in the industry. Individuals with the right skills can also opt for international certification and choose the data scientists profile to add more prospects. Though data science is a multidisciplinary field, you can add specific certification to add more value to the resume. 
Here are the main certifications you can add to the Data Scientist profile to 
 get greater attention from employers. 
 
*IBM Data Science Professional Certification 
*Harvard X's Data Science Professional Certificate 
*Tableau Data Scientist I Desktop Specialist Microsoft Certified Azure 
*Data Scientist Associate 
*MCSE: Data Management and Analytics by Microsoft 
*Amazon AWS Big Data Certification 
*SAS Academy for Data Science
*Advanced Business Analytics Specialization by Coursera 
*Business Analytics Specialization by' Coursera 
                      Especially for applying to MNCs and corporate organizations, certifications can enhance your chances to acquire topmost positions and earning potential.

Scope and Opportunities 
A data scientist profile comes with tremendous scope and prospects for young learners. Though. the high skilled and steep learning curve required for the data scientist profession creates a gap between demand and availability. 
          As per Pay scale, Data Scientists, on average, are earning around  $96000+ making it one of the top career choices for professionals. 
          Top global organizations that offer lucrative opportunities for Data  Scientists include Microsoft, Apple, Amazon, IBM, Facebook, FOITI Motor, Northrop Grumman Corporation and Booz, Allen, and Hamilton.