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Distinguish Yourself From Other Data Science Candidates. Learn How.

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The job market today is extremely competitive. On average, one position has 118 applicants. And of these applicants, only 20 percent are interviewed. It is becoming increasingly difficult for a data scientist to find a job. Every other professional refers to himself/herself as a data scientist. It is imperative to distinguish yourself from the masses in order to advance your career as a data science professional. Data science is a broad term that includes data analytics, data mining, artificial intelligence, and machine learning. A data scientist with a professional degree like an  MBA in Business Analytics and Data Science can enable businesses and stakeholders to solve multifarious problems with the help of data.

Acquiring these skill sets will allow you to position yourself as a well-informed data scientist:

Probability & Statistics:

Using data science, one can work with capital processes, algorithms, or systems to extract knowledge, insights, and make informed decisions from data. Hence, the sound knowledge of probability & statistics allows a data scientist to make important inferences & estimations. 

Programming Language R/Python:

A programming language allows a data scientist to manipulate the data by resorting to certain algorithms to extract meaningful insights. Python and R are two of the most widely used languages by data scientists. The primary reason is the number of packages available for numeric and scientific computing.

Data Visualization Skills:

Data visualization is more like an art than a hard-wired step. There is no standard approach to this skill. It is of great significance for a data visualization expert to know how to create storyboards. To start with, you must be familiar with plots like histograms, bar charts, pie charts, and then move on to advanced charts like waterfall charts, thermometer charts, etc. 

Software Engineering:

While it is not expected of you to be a warranted software engineer, having the basics of software engineering allows you to collaborate with the team better. The basic lifecycle of software development projects, data types, compilers, time-space complexity are factors one needs to be aware of to excel as a data scientist.

Data Extraction, Transformation, and Loading:

We have multiple data sources like MySQL DB, MongoDB, Google Analytics – one needs to extract data from such sources, and then transform it for storing in a proper format or structure for the purposes of querying and analysis. Candidates from ETL (Extract Transform and Load) backgrounds can pursue data science and excel at it.

In order to assess if you are ahead of the league, ask yourself the following questions:

  • Have you published your own Python/R package?
  • Have you written at least a few high-quality, detailed articles describing your hobby project?
  • Do you consciously try to integrate good software engineering practices (e.g. object-oriented programming, modularization, unit testing) in your data science code at every chance you get?

If the answer is yes, you’re definitely on the correct path. 

As of 2021, there is a major demand for data scientists in the industry. Companies are looking for professionals who can not only identify issues but also structure them, devise metrics, and confidently present their findings of the solution. An MBA in Business Analytics and Data Science from BIBS can not only empower you to pursue a lucrative career with handsome salaries but also make use of these skills you develop during your learning period.  Bengal Institute of Business Studies, an award-winning business school offers an excellent program on MBA in Data Science in Kolkata. The courseware is designed to be industry-relevant coupled with job opportunities on completion of the program. This program is a great opportunity for students to spearhead an initiative, make a change, and make data-driven decisions

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