Data Science: Introduction1

​ (a)   process of ​ (b)   , analysing, ​ (c)   to make ​ (d)  

• Data Science is the
• collecting, organising
• and interpreting data
• decisions and solve problems.

Organise these options into the right categories

Groups:

(a) Mathematics and Statistics

,

(b) Computer Science

,

(c) o Business and Enterprise

• finding patterns, trends, averages

• Data mining

• making data useful for companies or society

• databases, programming, machine learning

​ (a)   – The process of ​ (b)   from ​ (c)   websites, ​ (d)  

• Collecting Data
• collecting information
• surveys, IoT devices,
• or customer records.

​ (a)   – The ​ (b)   errors, duplicates, ​ (c)   ​ (d)   leads to wrong results and ​ (e)  

• Cleaning Data
• process of removing
• or missing values.
• Confusing and unorganise data
• bad enterprise decisions.

​ (a)   – The process of putting data ​ (b)   using ​ (c)   data is​ (d)   from ​ (e)  

• Storing Data
• into databases or cloud storage
• encryption to ensure
• protected
• unauthorised access.

​ (a)   – The process of ​ (b)   , graphs, or machine learning to ​ (c)   to help enterprises make ​ (d)  

• Analysing Data
• using statistics
• find trends
• smarter, data-driven decisions.

​ (a)   – The process of ​ (b)   , or infographics, helping ​ (c)   , data-driven desisions. (d)  

• answer option
• Visualising Data
• presenting results with charts, dashboards
• enterprise make smarter

​ (a)   – The process of enterprises​ (b)   to ​ (c)   or improve ​ (d)   (e)  

• answer option
• Making Decisions
• of analysing and visualising data
• solve problems
• strategies.

​ (a)   not just about ​ (b)   but about ​ (c)   ​ (d)   for enterprises.

• Data science is
• collecting data
• turning raw numbers
• into useful knowledge

Organise these options into the right Steps in Data Science:

Groups:

(a) 1. Collecting Data

,

(b) 2. Cleaning Data

,

(c) 3. Storing Data

,

(d) 4. Analysing Data

• Deleting repeated membership entries and fixes incorrect phone numbers.

• Collecting sales data, customer locations, and product ratings.

• Using sorting and lookup features to find customer buying patters.

• Storing customer orders in a relational database with tables for users and drivers, and meals.