We are living in a world that is changing at a breakneck pace. It is extremely difficult to stay abreast of things that are already present in the digital world, especially the ones that are running on data. A large number of candidates that I interact with often have a grouse that the data analyst interview questions that they are asked during a job interview or project internships have nothing much to do with the real life scenarios.
In this article, I have taken an experiential approach to what data analyst interview questions should you prepare for, and why learning AI technologies is everything about this experience.
Here are the top tactical AI-focused interview questions that every data analyst should know and should be able to answer satisfactorily.
Question 1: What is AI Analytics?
A day in a data analyst’s life is certainly one of the most exciting ones. There’s a lot that happens every hour and a part of this revolves around the manner in which data analysts utilize technological capabilities to transform their business operations. AI analytics is a great example of how much work you can actually automate using advanced Machine Learning techniques.
In simple terms, AI analytics is a branch of data science that leverages AI ML tools and techniques to discover actionable insights from related or unrelated information and uncover patterns and relationships within the data sets. Many business analyst roles are now built on their understanding of various AI applications specifically designed to forecast future events based on AI analysis, historical data mining, and predictive intelligence.
If you are mastering in data science questions asked during an interview, you should be ready with at least 2 or 3 versions of questions related to AI analytics, and quote definitions and examples set up by industry leaders, AI consulting firms, and market intelligence firms such as Gartner, Forrester, McKinsey, Accenture, Deloitte, and others.
Question 2: Can you tell how chatbots are used in data analysis?
Chatbots that work on Artificial Intelligence are at the doorstep of business intelligence operations. During the pandemic, many data analysis operations have been outsourced to remotely working groups. These groups interact from their remote locations using virtual assistants or chatbots. These chatbots are specifically designed as self-service machine learning platforms that provide a holistic view of every workflow and internal communication associated with projects.
Chatbots are simply conversation generators that can answer pertinent questions based on previous conversations. A majority of the chatbots that are used for data analysis are capable of stating the email addresses, telephone numbers, addresses, the scope of data management, location of storage device, number of devices connected to the IT network, the validity of current IT maintenance cycle, and number of admins / operators in the team. This information proves vital to the effectiveness of any data analysis operation.
With minimum paperwork and back office interactions, data analysts can actually save tons of resources and hours of effort by simply adopting a chatbot or virtual assistant to perform daily activities.
Question 3: Are you familiar with Python or any other AI programming language?
This is a trick question that evaluates your likeness toward coding and data modeling. If you know a bit of Python, please don’t hesitate in letting the panel know about it. You can learn Python in 8 months if you already have some experience in coding. All it needs is a basic understanding of mathematical and statistical concepts, Python libraries, a good command of English, and the mindset of a deep coder (an individual who codes for 2 hours every day is a deep coder! genius isn’t it!)
By default, a majority of the DevOps cycles that are associated with data analysis require some degree of familiarity with top AI programming languages. Python programming language is one of the most widely used AI modeling platforms, and the recent spurt of innovations in No-code and Low-code designing has only accelerated the need to learn this language for better data analysis.
Python machine learning guarantees a successful career in upcoming and dominant industry trends including all kinds of disruptive technologies that find their foundation in AI capabilities.