Data Science vs. Data Analytics
Olga Pak, Ani Tekawade, September 2022
In recent years, it cannot be easy to keep up with the rapid proliferation of various data- and technology-related terms. In this article, we offer some explanations of the most commonly used words.
Data science and analytics are the terms sometimes used interchangeably by the scientific and practitioner communities. Although quite similar, there are some profound differences between the two - 1) the depth of scientific and computing training and 2) the amount of data.
Data Analytics
Data analytics (also known as business analytics) uses available datasets to draw business insights. In close collaboration with other organization members, a data analyst uses various visualization tools and statistical modeling to identify business trends and patterns or describe what happened in the past business period. A data analyst is responsible for identifying and explaining a business problem, seeking a meaningful solution, and guiding the decision process using excellent communication, visualization, and presentation skills. Data analysts have excellent knowledge of visualization and database tools such as Tableau, Alteryx, R, Python, Power BI, SAS BA, and many others. Data analysts typically have a master's degree or certification in business analytics. Budget forecasting and revenue prediction, risk evaluation, lead-to-customer conversion rate calculation, and sales projection are examples of data analytics.
Data Science
Data science is what we call data analytics on steroids. It is built on in-depth statistical, mathematical, and computing knowledge to deal with vast amounts of raw, disorganized data. Data scientists will use their immense coding and computing knowledge to "mine the data" - collect raw data, organize it, and build meaningful descriptive, predictive, and prescriptive models. One of the main capabilities of a data scientist is their ability to work with very large amounts of different data types. They can use cloud computing to work with millions of observations at once and analyze numerical, categorical, image, audio, and video data types. They leverage their extensive academic training in statistics, mathematics, probability theory, machine learning, and artificial intelligence to draw actionable business insights. Data scientists' problems are typically very complex, difficult to identify, and require sophisticated statistical analysis or machine learning modeling. Commercial tools used by data scientists include Python, TensorFlow, PyTorch, TPUs (tensor processing units), Amazon SageMaker, Azure Machine Learning, Databricks, and so on. Many data scientists are trained PhDs in math- and coding-heavy fields, including biostatistics, economics, and business. An in-depth understanding of customers' demographics and behavior, detection and prediction of fraudulent activity, movie recommendation system, sentiment analysis, and customer segmentation are examples of data science use.
NumberSci uses many data analytics tools to help its clients identify and solve the most challenging problems.