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Is a Graduate Degree Necessary to Become a Data Scientist?

In 2012, data science made its breakthrough into the mainstream and was deemed by Harvard Business Review as the “sexiest job of the 21st Century.” Some may wonder what data scientists do and what the necessary requirements are to become a successful one. Data scientists make discoveries with the rapidly imported data that they receive while also bringing structure to large quantities of formless data to make analysis possible; these analyses are then used to help organizations make insightful and actionable business decisions with a high level of efficacy. Becoming a data scientist is quite a journey; some companies only look for a minimum of a bachelor’s degree in a major that has a strong focus on data and computations, while others require a master’s degree or even a PhD. There have been debates on which path should be taken to achieve one of these highly sought out jobs. Some believe that an undergraduate degree and building experience is the path, whilst others believe that graduate degrees help with a better theoretical and applicable understanding of the field. Both are valuable in the eyes of a company, but which one should be considered more useful?


What does a Data Scientist Do?

As mentioned earlier in the article, data scientists make insightful business decisions based on the data they receive. What exactly does that mean? Below are some responsibilities of data scientists:

  • Cleaning and validating data to ensure accuracy, completeness, and uniformity

  • Interpreting data to discover solutions and opportunities

  • Communicating findings to stakeholders or decision-makers using visualization and other means

  • Solving business problems through undirected research and framing open-ended industry questions

  • Extract huge volumes of structured and unstructured data

  • Employ sophisticated analytical methods, machine learning, and statistical methods to prepare data for use in predictive and prescriptive modeling

  • Thoroughly clean data to discard irrelevant information and prepare the data for preprocessing and modeling

  • Perform exploratory data analysis (EDA) to determine how to handle missing data and to look for trends and/or opportunities

  • Discovering new algorithms to solve problems and build programs to automate repetitive work

This is a brief overview of the responsibilities and tasks that are tackled by data scientists regularly. However, job descriptions vary between different companies and what they seek to achieve as a collective.


Although there is variety in the responsibilities of a data scientist, one thing that remains common amongst all their roles is the use of artificial intelligence (AI) and Machine Learning (ML). What exactly are AI and ML?


Artificial intelligence and machine learning are not sub-categories of data science, but they are closely intertwined. AI is used for the purpose of making machines execute real-time decisions to replicate human intelligence. It uses previous experience for its betterment daily and the use of inputted information is crucial for the success of the AI model. Examples of AI are Amazon Alexa, Google Assistant, chatbots, and self-driving cars.



Machine Learning is a subsection of AI and just like AI it uses well parsed data to yield quality results. It can be broken down into 3 categories: supervised, unsupervised, and semi-supervised. Below is a description of each type of ML according to mygreatlearning.com, which is a great resource to get more in-depth knowledge about ML and AI:


  1. Supervised machine learning: This model uses historical data to understand behavior and formulate future forecasts. This kind of learning algorithm analyzes any given training data set to draw inferences which can be applied to output values. Supervised learning parameters are crucial in mapping input-output pairs.

  2. Unsupervised machine learning: This type of ML algorithm does not use any classified or labelled parameters. It focuses on discovering hidden structures from unlabeled data to help systems infer a function properly. Algorithms with unsupervised learning can use both generative learning models and a retrieval-based approach.

  3. Semi-supervised machine learning: This model combines elements of supervised and unsupervised learning, yet it technically isn’t either of them. It works by using both labelled and unlabeled data to improve learning accuracy. Semi-supervised learning can be a cost-effective solution when labelling data becomes expensive.

This wing of AI aims at providing machines with independent learning methods so that they don’t have to be programmed to do so. Machine learning involves observing and studying data or experiences to identify patterns and set up a reasoning system based on the findings.


A data scientist is considered a jack-of-all-trades and needs a mixture of mathematics, computer science, and domain expertise. The role requires determination, inquisitiveness, curiosity, and the ability handle ambiguity. Having the aforementioned personality traits and mixtures of abilities is a great start to the career path.

Now that the role of a data scientist and what they do I explained, let’s discuss the pros and cons of each side of the debate.



Pros/Cons of Work Experience

Below are the listed pros and cons of having working experience over a graduate degree for the data scientist career path:


Pros

Cons

Understanding of real-world problems and solutions

Have no proven success in academia

Relevant and proven work experience

Harder to secure first job because lack of opportunity

Familiarity with how the industry works

Self-training can be daunting if unmotivated

Develop professional skills

A lot of jobs require a candidate to have a degree to be considered for the position

Less money spent on grad school



Pros/Cons of a Graduate Degree

Below are the listed pros and cons of having a graduate degree over working experience for the data scientist career path:

​​

Pros

Cons

Learn the fundamentals and in-demand skills

Schools are steadily developing proper curriculum

More hirable (88% of data scientist hold a master’s degree)

Expensive to obtain a graduate degree

More opportunities available

Graduate degree not necessarily required to become a data scientist

Earn a higher salary

Requires a minimum of a year to achieve a graduate degree

Ability to gain specialization for specific branches of data science

Learning from accredited scholastic institute, instead of self-learning


Final Verdict


What is the final verdict? Both are viable options! Depending on the circumstances and preferences, both paths will eventually lead to the end result which is becoming a data scientist. According to Teach.com the career path is projected to grow by 16% from 2018 to 2028. All companies from small startups to tech giants need quality data scientists. Honing the necessary skills to breakthrough in the career path of data science is a difficult decision to be made, but either decision will bear fruit. Although becoming a data scientist is the end goal, beginning the path of either work experience or graduate school means that a new challenging and rewarding career journey has begun.


As the field of data science continues to grow at a rapid pace, so does Bear Cognition. To keep up with the times, we have started creating and implementing ML models such as decision trees and k-means clustering. If you are looking for the aforementioned benefits of having a data scientist work for you, look no further, Bear Cognition has all the tools necessary to help you achieve data competence.







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