Data Science Basic Interview Questions
Data science enables businesses to process huge amounts of structured and unstructured big data to detect patterns. This in turn allows companies to increase efficiencies, manage costs, identify new market opportunities, and boost their market advantage.
Asking a personal assistant like Alexa or Siri for a recommendation demands data science. So does operating a self-driving car, using a search engine that provides useful results, or talking to a chatbot for customer service. These are all real-life applications for data science.
Data science is a multidisciplinary approach to extracting actionable insights from the large and ever-increasing volumes of data collected and created by today’s organizations. Data science encompasses preparing data for analysis and processing, performing advanced data analysis, and presenting the results to reveal patterns and enable stakeholders to draw informed conclusions.
As a result, data scientists (as data science practitioners are called) require computer science and pure science skills beyond those of a typical data analyst. A data scientist must be able to do the following:
Apply mathematics, statistics, and the scientific method
Use a wide range of tools and techniques for evaluating and preparing data—everything from SQL to data mining to data integration methods
Extract insights from data using predictive analytics and artificial intelligence (AI), including machine learning and deep learning models
Write applications that automate data processing and calculations
Tell—and illustrate—stories that clearly convey the meaning of results to decision-makers and stakeholders at every level of technical knowledge and understanding
Explain how these results can be used to solve business problems
The data science lifecycle
The data science lifecycle—also called the data science pipeline—includes anywhere from five to sixteen (depending on whom you ask) overlapping, continuing processes. The processes common to just about everyone’s definition of the lifecycle include the following:
1. Capture: This is the gathering of raw structured and unstructured data from all relevant sources via just about any method—from manual entry and web scraping to capturing data from systems and devices in real time.
2. Prepare and maintain: This involves putting the raw data into a consistent format for analytics or machine learning or deep learning models. This can include everything from cleansing, deduplicating, and reformatting the data, to using ETL (extract, transform, load) or other data integration technologies to combine the data into a data warehouse, data lake, or other unified store for analysis.
3. Preprocess or process: Here, data scientists examine biases, patterns, ranges, and distributions of values within the data to determine the data’s suitability for use with predictive analytics, machine learning, and/or deep learning algorithms (or other analytical methods).
4. Analyze: This is where the discovery happens—where data scientists perform statistical analysis, predictive analytics, regression, machine learning and deep learning algorithms, and more to extract insights from the prepared data.
5. Communicate: Finally, the insights are presented as reports, charts, and other data visualizations that make the insights—and their impact on the business—easier for decision-makers to understand. A data science programming language such as R or Python (see below) includes components for generating visualizations; alternatively, data scientists can use dedicated visualization tools.
Data Mining vs Data Science
Data mining is a technique used in business and data science both, while data science is an actual field of scientific study or discipline. Data mining’s goal is to render data more usable for a specific business purpose. Data science, in contrast, aims to create data-driven products and outcomes—usually in a business context.
Data mining deals mostly with structured data, as exploring huge amounts of raw, unprocessed data is within the bounds of data science. However, data mining is part of what a data scientists might do, and it’s a skill that’s part of the science.
Data Science vs Artificial Intelligence
The phrase “artificial intelligence” or (AI) just means simulated human brain function in computers. The traits that signal this kind of brain function include learning, logical reasoning, and self-correction. In other words, when a machine can learn, correct itself as it learns, and reason and draw inferences on its own, it is an AI.
Artificial intelligence is either general or narrow. General AI refers to the types of intelligent computers we often see in movies. They can handle a wide range of activities almost like humans do, all of which demand reasoning, judgment, and thought. So far, this has not been achieved.
However, narrow AI involves using the same kinds of “thinking” skills, but on very specific tasks. For example, IBM’s Watson is an AI that can interpret certain kinds of medical records for diagnostic purposes as well or better than humans under the right conditions.
Scientists and engineers work to achieve artificial intelligence by creating artificial neural networks. But to teach machines to think like a human brain does, even for a very specific purpose, it takes an extraordinary amount of data. This is the intersection of data science, the field; artificial intelligence, the goal; and machine learning, the process.