Data Analytics Definitions

Question 1

Analytical skills

Answer 1

Qualities & characteristics to do with problem solving

Question 2

A Technical Mindset

Answer 2

An analytical Skill that involves breaking down processes into smaller steps & in an orderly/logical way

Question 3

Data Design

Answer 3

An analytical skill based on how info is organized

Question 4

Understanding context

Answer 4

An analytical skill based on the categorization of data

Question 5

Data Strategy

Answer 5

An analytical skill based on managing Resources & the processes used in Data Analytics

Question 6

Problem Oriented Approach

Answer 6

A key aspect of Analytical thinking , being goal oriented and addressing barriers

Question 7

Correlation

Answer 7

A key aspect of analytical thinking. Finding relationships between data points

Question 8

Big Picture Thinking

Answer 8

A key aspect of analytical thinking. Having objectivity, by zooming out. Not getting caught up on specifics but seeing the big picture.

Question 9

Analytical thinking

Answer 9

Identifying, defining, and then solving a problem

Question 10

Key Aspects of Analytical thinking

Answer 10

1. Visualization
2. Strategic mindset
3. Problem oriented approach
4. Correlation
5. Big - Picture thinking (vs. Detail oriented thinking)

Question 11

Visualization

Answer 11

A key aspect of analytical thinking. The graphical representation of information.

Question 12

Strategic Mindset

Answer 12

A key aspect of analytical thinking, to see what you want to achieve with the data & how you can get there.

Question 13

Data

Answer 13

A collection of info

Question 14

Data Analysis

Answer 14

The Collection, Conversion, & Organization of information.

Question 15

Data Analyst

Answer 15

Someone who makes informed decisions based on the collection, transformation, & organization of information

Question 16

Cleaning Data

Answer 16

Filling in gaps of info, correcting info, and formatting information to make sure its relevant

Question 17

Preparing Data

Answer 17

To identify data sources, engage stakeholders, seek feedback from industry experts, and collect data

Question 18

Processing Data

Answer 18

aka cleaning data, which is collecting, organizing, filling in gaps, verifying and formatting data

Question 19

Data Visualization

Answer 19

Using charts, graps, or maps to represent data and communicate an idea

Question 20

Programming Language R

Answer 20

A programming language used for statistical computing and graphics

Question 21

Data Analytics Process

Answer 21

1. Ask
2. Prepare
3. Process
4. Analyze
5. Share
   6.Act

Question 22

Ethics in Data

Answer 22

Ex. Collecting with permission, storing securely, managing securely, protecting data

Question 23

Ask

Answer 23

The 1st phase of data analytics, to understand the challenge.

Question 24

Prepare

Answer 24

The 2nd phase of the data analytics process, to collect, verify, & identify data sources.

Question 25

Process

Answer 25

The 3rd phase of data analytics, to clean & organize data

Question 26

Analyze

Answer 26

The 4th phase in data analytics, to find the **answers** to your question posed in phase 1.

Question 27

Share

Answer 27

The 5th phase of data analytics, to visualize the data and share with the relevant parties.

Question 28

Act

Answer 28

The last phase of Data Analytics, to implement the Answers found to your question.

Question 29

Data Science

Answer 29

Is creating new ways of **Modeling and understanding the unknown** by using **raw data** & Is made up of **Statistics, analytics & machine learning (Ai)**

Question 30

Decision Intelligence

Answer 30

Made up of 2 factions, **Applied Data Science** and **Managerial science**

Question 31

Applied data Science

Answer 31

An industry approach to Data Science. It uses the **scientific method & algorithms** to **solve business problems** and **extract actionable insights** from data

Question 32

Managerial Science

Answer 32

A field that uses the **scientific method** and machine learning (**Ai**) to **solve complex problems**

Question 33

Data Analytics

Answer 33

Extracting **insight and inspiration** from data to **solve** problems

Question 34

Statistics

Answer 34

The **aspect of Data Science** concerned with **Important decisions in uncertainty** ie. philosophy driven

Question 35

Machine Learning

Answer 35

aka. AI, used when **many decisions need to be made** in uncertainty ie. **performance driven**

Question 36

Analytics

Answer 36

The aspect of **data Science** that **addresses** and uncovers **unknown variables. ie. Speed driven**

Question 37

Data Ecosystem

Answer 37

A database where the datasets interact with each other, to produce, manage, store, organize, and share data

Question 38

Data Scientist vs. Data Analyst

Answer 38

A data scientist creates new questions using data. A data analyst uses data to answer existing questions.

Question 39

Data-driven decision making

Answer 39

using facts to guide **Business Strategy**

Question 40

Subject Matter experts

Answer 40

Someone aware of the problem/question, who can look at the results to make sense and verify data, or fill in gaps of info.

Question 41

Gut instinct

Answer 41

Intuitive Understanding of something with little or no explanation. Could also be internal bias.

Question 42

Analytical Skills

Answer 42

**Qualities and Characteristics** for **solving problems with facts.** skills that you can develop are: 1. curiosity 2. technical mindset 3. understanding context 4. data design 5. data strategy.

Question 43

Root Cause

Answer 43

The central issue causing the problem

Question 44

The 5 whys

Answer 44

Following the barriers and repeating question why 5 times to get to the root cause

Question 45

Gap analysis

Answer 45

A method used to examine & evaluate how a process works currently, to visualize and get where you want to be

Question 46

Quartile

Answer 46

Divides data sets into 4 categories

Question 47

Context

Answer 47

the condition in which something exists or happens