Data Science has many branches of specialisation but among the popular ones, the major branches which can be clearly defined are Data Analytics, Machine Learning, Deep Learning and Artificial Intelligence. Yet, these branches are often mixed with one another so I thought of clarifying some of the thin differences between these topics.
Data Analytics is making sense of the data. In one sentence, describing the data and deriving predictions and conclusions from them to take better business decisions. Data analysis describes the current condition for an organization by translating data into information understandable to the business officials. Hence, data analytics requires constant improvement in methods for data collection, analysis, and reporting.
Largely, data analysts have to work for businesses to perform better based on the past and current data generated. They work with structured data to prepare reports and use it to help the business estimate market share, price products, sales, etc. However, data scientists specifically, are expected to deliver more long term prediction so involves more long term researches and deliverables. Hence, they have to use data analysis to test the outcomes of different course of actions that are taken by the business in the future. This is one of the thinnest yet important difference between these commonly confused jobs.
ML, AI and DL are related in a very interesting way as AI might be considered as a different branch it involves the other branch knowledge as well.
Deep Learning has networks capable of learning unsupervised data. It follows the working of the human brain for processing the datasets and making efficient decisions for virtual assistants, driverless cars, etc.
Machine Learning is simply training models using algorithms to convert data, visualise data, and make informed decisions based on it. It is used in image classification, speech recognition, etc.
AI is inculcating human intelligence in machines that are programmed to mimic their problem-solving and thinking abilities. Under this, training models as well as unsupervised learning, both are equally required.
Although, the three fields are interlinked largely, their functional uses do differ slightly. Some engineers and developers want to specialise under Deep Learning solely to research more on it while others want to delve deeper, which requires them to learn about its subset fields as well. Hence, these three are not stratified very differently in job descriptions as once explored, these three really subordinate one another.