Data Scientist

Organisations are utilising and gathering growing volumes of data as part of their daily operations. Your role as a data scientist is to analyse data to uncover patterns and aid in the creative and original problem-solving of organisations, from anticipating what customers will buy to addressing plastic pollution. 

Being a data scientist may be intellectually demanding, analytically fulfilling, and it can put you at the cutting edge of new technological developments. As the use of big data in organisational decision-making continues to grow, data scientists are becoming more prevalent and in demand.

To make massive amounts of data available to organisations, you'll extract, analyse, and interpret them utilising algorithmic, data mining, artificial intelligence, machine learning, and statistical technologies. After analysing the data, you'll present your findings in an appealing manner.

Data scientists are highly sought after in a variety of industries because employers need employees with the ideal mix of technical, analytical, and communication abilities.

On a daily basis, a data scientist might carry out the following tasks:

To gain insights, look for patterns and trends in datasets, Employ machine learning strategies to enhance the calibre of data or product offerings, Use data analysis tools like Python, R, SAS, or SQL;  Keep up with changes in the data science industry.

A data scientist is expected to collaborate closely with your company to identify problems, use data to suggest solutions for effective decision-making, build algorithms and plan experiments to merge, manage, interrogate, and extract data to produce customised reports for coworkers, customers, or the entire organisation, use machine learning tools and statistical techniques to produce solutions to problems, test data mining models to choose the most appropriate ones for use on a project.

In order to understand data needs and report results, a data scientist must also maintain clear and coherent verbal and written communication. They must also produce clear reports that tell compelling stories about how customers or clients interact with the business, evaluate the effectiveness of data sources and data-gathering techniques, and improve data collection methods. They must also keep an eye on the future to stay abreast of new technology, techniques, and methods, and conduct research from which they can draw conclusions.

In senior positions, you will also be expected to: hire, develop, and manage a team of data scientists; be in charge of the organization's data science strategy; establish new systems and processes and look for opportunities to improve data flow; assess new and emerging technologies; represent the business at conferences and events held outside the company; and cultivate relationships with clients.

Route to become a data scientist
 

• 10+2 in any stream with Math and Computer
• Bachelor’s degree in computer science/ data science/ Mathematics/ Economics/ Statistics or a related field 
• Master’s degree in data science or a related discipline.

Significant Statistics
 

• A degree in computer science, mathematics, or a science-related field is a must for employment as a data scientist. Computer science, data science/computer and data science, engineering, mathematics and operational research, physics, and statistics are degree fields that may be very helpful.
• You'll need to be proficient in database design and coding, as well as some programming languages like R, Python, SQL, C, or Java.
• Some major employers have graduate training programmes in data science, which typically last two years to complete. Graduates from any discipline may be accepted by some programmes. Some people will list the degree topics they will accept.
• Many data scientists have postgraduate degrees, such as a Masters or PhD, which might be helpful. It is especially beneficial if you're thinking about changing careers or want to develop your analytical skills. Employers may require a relevant Masters or PhD in a field like big data, business analytics, data analytics, or data science for some positions.
• To enrol in a course, you normally require a degree in mathematics, engineering, computer science, or a scientific field, though if you have basic programming skills and quantitative aptitude, you may also be able to succeed in business, economics, psychology, or health.
   
   

Pros

• Abundance of Positions
• A Highly Paid Career
• Data Science Involves Challenging and Varied Work
• Data Scientists are Highly Prestigious
• Data Science Has Excellent Job Prospects
• A Career in Data Science Provides Versatility

Cons

• Mastering Data Science is near to impossible/ You Cannot Master Every Element of Data Science
• Large Amount of Domain Knowledge Required
• Arbitrary Data May Yield Unexpected Results
• You Could Experience Ethical Issues
• Data Science Roles Require Continual Learning and High Commitment
• Problem of Data Privacy