Unit 3

Question 1

What is Data Science?

Answer 1

multi-disciplinary field that uses scientific methods,
processes, algorithms, and systems to extract knowledge and
insights from structured, semi-structured and unstructured data.

Question 2

Data Scientist:

Answer 2

A professional that collects large amounts of data using
analytical, statistical, and programmable skills.

Responsible for using data to develop solutions tailored to meet
the organization’s unique needs.

Question 3

Vrai ou faux: Data scientists may write programs and develop new algorithms.

Answer 3

Vrai

Question 4

Data Scientist skills

Answer 4

Programming,

Communication,

Organizational,

Mathematical,

Data analysis,

Problem solving,

Analytical skills.

Question 5

Quantitative and Qualitative data

Answer 5

Quantity ~ numerical
Descriptive

Question 6

Base of the DIKW pyramid

Answer 6

Data

Question 7

What id data quality

Answer 7

check condition of data

Measured in: completeness,
uniqueness, consistency, timeliness, validity and accuracy.

Question 8

Knowledge and wisdom

Answer 8

Knowledge is understanding facts(patterns), while wisdom is the ability to apply them thoughtfully and effectively.

Question 9

Data processing

Answer 9

data ~ info

Question 10

Data processing cycle

Answer 10

Collection:

Prep(data cleaning) : removing unnecessary and inaccurate data

Input: data ~ machine readable form

Processing: raw data is processed using machine learning and AI to make a good output

Output: data to user readable format

Storage: data and metadata for quick retrieval

Question 11

Alternative cycle of data processing

Answer 11

Input: raw data ~processed

Processed: processing by a suited method

Output: the out come of the process and provides info

Question 12

Types of data processing

Answer 12

Done based on source and steps taken to generate output

Batch: collected in large amounts and in batches…accumulate and process(weekly or monthly)

Online: fed into the CPU ASAP rocky!!! when it becomes available so continuous processing(barcode)
Real-time: within seconds , small amounts of data (ATM)

Multiprocessing: data is broken down into frames within the same computer…parallel processing (weather forecasting)

Time-sharing: allocates data in time slots to several users simultaneously

Question 13

What is a data lake?

Answer 13

Centralized storage system that holds vast amounts of raw, unprocessed data in its native format, allowing it to be structured, semi-structured, or unstructured, and making it accessible for analysis, processing, and other uses when needed.

Question 14

Data warehouse?

Answer 14

centralized storage system designed to store structured and processed data from multiple sources, optimized for querying and analysis to support business intelligence and decision-making.

Question 15

Data warehouse and data lake difference

Answer 15

Data Warehouse:

Stores structured data (organized into tables and schemas)

Data is processed and cleaned before storage (ETL: Extract, Transform, Load)

Optimized for business analytics and reporting.

Example: Sales records used for generating reports and dashboards.
Data Lake:

Stores raw data in its original form (structured, semi-structured, or unstructured).

Data is processed on demand, not before storage (ELT: Extract, Load, Transform).

Flexible for big data analysis, AI, and machine learning.

Example: Sensor data, images, or logs waiting for later use