Section 2: Data Science Introduction

Question 1

Analysis

Answer 1

Breaking data into easier to digest, manageable pieces to study them individually and determine how they relate to each other and other parts to review past actions and events

Question 2

Analytics

Answer 2

The application of logical and computational reasoning to the component parts obtained in analysis to look for patterns to determine how to apply that to future events

Question 3

Business Case Studies

Answer 3

Examining specific business events that have happened in the past to perform analytics

Question 4

Data Science

Answer 4

A discipline reliant on quantitative data availability.

Question 5

Preliminary Data Reporting

Answer 5

An exploratory data analysis activity that involves the initial exploration and analysis of past data to identify trends, anomalies, and patterns. Typically includes cleaning and preprocessing data, exploring it using visualizations and statistical techniques, and identifying any data quality issue that need to be addressed.

Question 6

Machine Learning

Answer 6

The use of statistical algorithms to enable computers to learn from data and make predictions, analyze patterns, or decisions without being explicitly programmed. Used in a wide range of applications including image recognition, natural language processing, and fraud detection with ability to use real-time data.

Question 7

Business Intelligence

Answer 7

The preliminary step of predictive analytics involving the process of analyzing and reporting historical business data to allow end-users to make informed and tactical decisions. It aims to explain past events using business data.

Question 8

Artificial Intelligence

Answer 8

Simulating human knowledge and decision making with computers.

Question 9

Client Retention

Answer 9

Used with machine learning to help develop models that predict a client’s next purchase and keep clients coming back.

Question 10

Digital Signal Processing

Answer 10

Used to represent data in the form of discrete values, numeric data.

Question 11

Symbolic Reasoning

Answer 11

Using logic and rules to make deductions and inferences. It is a traditional approach to AI not involving machine learning algorithms.

Question 12

Data Visualization

Answer 12

Presenting data in a clear and easily understandable format, often through graphs, charts and other visual aids with the main goal being to enable stakeholders to monitor key metrics and make informed decisions based on the data.

Question 13

Traditional AI

Answer 13

AI applied to structured datasets to improve efficiency and accuracy in repetitive tasks.

Question 14

Generative AI

Answer 14

AI that generates new raw data or content such as text, code, audio, or images - by relying on patterns from a given training dataset.

Question 15

Automated Report Generation

Answer 15

The use of exploratory data analysis (preliminary data reporting), plus machine learning, to retrieve data automatically to generate reports. With AI, it can provide summaries and qualitative reporting automatically at pre-set times.

Question 16

Product Personalization

Answer 16

A marketing strategy based on user data (purchase history, preferences, interaction data, demographics, etc.) to provide personalized user offers, recommendations, and email messaging.

Question 17

Scenario Planning

Answer 17

Looking at future market trends, potential opportunities, and risks to project a company’s future in a simulated environment to challenge and solidify their strategies.

Question 18

Artificial General Intelligence

Answer 18

Machines possessing general cognitive abilities across multiple tasks instead of AI being used to perform well on a single task.