Today’s global market is incredibly competitive. One position receives a very low application on average. And just 20% of these applications receive interviews. Finding a job as a data scientist is getting harder and harder. Every single other professional identifies as a data scientist.
To grow your career as a data science specialist, you must set yourself apart from the competition. Data analytics, data mining, artificial intelligence, and machine learning are all included in the wide category of data science. A data scientist who has earned a professional degree, such as an MBA in Business Analytics and Data Science, can assist stakeholders and businesses in using data to solve a variety of issues.
You may position yourself as a knowledgeable data scientist by acquiring these skill sets:
Statistics & Probability:
With the aid of capital processes, algorithms, or systems, one can use data science to glean knowledge, get insights, and come to wise conclusions. Consequently, solid statistical and probability understanding enables a data scientist to draw significant conclusions and estimate outcomes.
programming language R/Python:
A data scientist can alter the data using certain algorithms by using a computer language to derive valuable insights. Two of the most popular languages used by data scientists are Python and R. The abundance of tools for scientific and numerical computing is the main factor.
Data Visualisation:
Data visualisation is not a hard-wired process, but more of an art. There is no set method for using this skill. A data visualisation expert’s ability to construct storyboards is quite important. Prior to moving on to more complex charts like waterfall charts and thermometer charts, you must be comfortable with basic plots like histograms, bar charts, and pie charts.
Software Development:
Although being a warranted software engineer is not required of you, knowing the fundamentals of the field will help you work more effectively with the team. To succeed as a data scientist, one must be knowledgeable about a variety of elements, including the fundamental lifecycle of software development projects, data types, compilers, and time-space complexity.
Extracting, transforming, and loading data:
There are numerous data sources available, including MySQL DB, MongoDB, and Google Analytics. Data must be extracted from these sources and transformed before being stored in the appropriate format or structure for querying and analysis. Candidates with experience in ETL (Extract, Transform, and Load) can pursue data science and succeed in it.
Consider the following questions to determine if you are ahead of the pack:
- Have you released a Python/R package of your own?
- Have you published at least a few excellent, in-depth articles outlining your side project?
- Do you actively seek opportunities to include sound software engineering principles in your data science code, such as object-oriented programming, modularization, and unit testing?
Note: You are unquestionably on the right track if the response is yes.
By 2023, the industry will have a significant need for data scientists. Companies are searching for experts who can not only recognise problems but also organise them, provide measurements, and offer their findings of a solution with assurance. With an RCM’s MBA in Business Analytics and Data Science, you can not only pursue a rewarding career with good pay but also put the skills you learn throughout your studies to good use.
In Bhubaneswar, the Regional College of Management, a well-regarded business school, provides a fantastic MBA in Data Science program. After the programme is over, there will be job prospects and industry-relevant course material. Students have a fantastic opportunity to lead an effort, effect change, and make data-driven decisions through this programme.
