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:
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.
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