subject : Introduction of business analytics

Question 1 ( 1 point) Saved Cause-and-effect modeling is the process of analyzing databases to identify natural associations among variables and create rules for target marketing or buying recommendations. True False Question 2 (1 point) Saved Data mining can be only considered part of predictive analytics. True False Question 3 (1 point) Saved Cause-and-effect modeling is the process of developing analytic models to describe the relationship between metrics that drive business performance (e.g., profitability, customer satisfaction, or employee satisfaction). Understanding the drivers of performance can lead to better decisions to improve performance. True False Question 4 (1 point) Saved The CRoss Industry Standard Process for Data Mining (CRISP-DM) is a process model with six phases that naturally describes the data science life cycle. It's like a set of guardrails to help plan, organize, and implement data science projects. True False Classification refers to some basic techniques in data mining that involve data exploration and "data reduction" (breaking down large sets of data into moremanageable groups or segments that provide better insight). True False Question 6 (1 point) Saved is a combination of structured, semistructured and unstructured data collected by organizations that can be mined for information and used in machine learning projects, predictive modeling and other advanced analytics applications. CRISP DM Data mining Big Data All of the above Machine Learning is a set of algorithms that helps Al systems see patterns in data sets. True False Question 8 ( 1 point) Saved In Unsupervised machine learning, a human operator creates the first training sets. True False Question 9 (1 point) Saved Intuitive Decisions: follow predictable patterns; follow data and statistical analysis (e.g., when a flight is cancelled, Al can use the available data and make an analytical decision about rebooking your flight). True False Internet of Things (loT) are small internet-connected devices designed to create realtime data True False Question 13 (1 point) Saved What is the most popular way to develop Al tools? Natural Language processing Machine Learning Automated decision making All of the above Question 10 ( 1 point ) Saved Analytical Decisions: slower & complex; relies on humans' intuition to solve the problems. Decisions about things that have never happened before; so, there is no pre-existing data (e.g., airlines shutdown in March 2020 due to pandemic, as a result, many of the Al systems didn't work well after the crisis and many of the problems had to be fixed by human customer service agents). True False Question 11(1 point ) Saved Al is concerned with two basic ideas: (1) the study of human thought processes (to understand what intelligence is) and (2) the representation and duplication of those thought processes in machines (e.g., computers, robots). That is, the machines are expected to have humanlike thought processes. True False Which one is the definition of natural language processing? When machine identify spoken words and converts them to text When machine interacts with a language Allows artificial intelligence systems to read and write using natural language When a machine tries to speak as similar as possible to humans Question 15 (1 point) saved What are the strengths of artificial neural network? Seeing associations or regularities in complex patterns. This is especially true when you have a lot of diverse data with patterns that are hard to recognize If can do really well when humans don't understand the relationships, or the relationships might be very difficult to describe A \& 8 None of the above To diagnose the need for back surgery when looking at x-rays (i.e. replace radiologists), what type of machine learning systems might be used? Supervised Unsupervised Either Supervised or Unsupervised, but most of the work in this area has been using supervised machine learning All of the above Question 17(1 point) Saved What are the typical types of decision making in organizations? A:Emotional B:Cognitive C:Analytical D:Intuitive are loosely inspired by the physiology of the human brain. Neural networks Supervised machine learning Unsupervised machine learning All of the above