Data Science Terms

Question 1

What is data science

Answer 1

It is the combination of business analytical and programming skills that are used to extract meaningful insights from raw data

Question 2

Deep learning

Answer 2

The application of computational network. Deep learning is a subset of machine learning that trains a computer to perform human-like tasks, such as speech recognition, image identification and prediction making

Question 3

Artificial intelligence

Answer 3

A set of approaches to enable computer to emulate and thus automatize congnitivr behaviour - often based on learning from data

Question 4

Machine learning

Answer 4

Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.

Question 5

Benefits of data science

Answer 5

-enable organizations to make better decisions
-enhance operational efficiency, business routines and workflows
-recognize and inform companies of their target audience
Assist the automated aspect of HR

Question 6

Training set

Answer 6

The dataset used by the machine learning model that will help it to learn its desired task

Question 7

Testing set

Answer 7

These data are used to measure the performance of the developed machine learning model

Question 8

Outlier

Answer 8

A data recorded which is seen as exceptional and outside the distribution of the normal input data

Question 9

Data cleansing

Answer 9

The process of removing redundant data, handling missing data entries and removing, or at least alleviating other data quality issues

Question 10

Feature

Answer 10

An observable measure of data. E.g height, length data, other terms are also used such as properties, characteristics and attribute instead of feature

Question 11

Dimensionality reduction

Answer 11

The process of reducing dataset into less dimensions, ensuring that it conveys similar information.

Question 12

Feature selection

Answer 12

The process of selecting relevant features of the provided data set

Question 13

Supervised learning

Answer 13

The subset of machine learning that is based on data learning. It can be further distinguished in regression and classification

Question 14

Unsupervised learning

Answer 14

The subset of machine learning that is based on unlabelled data. Typical unsupervised tasks are clustering and dimensioniallity reduction.

Question 15

Probability

Answer 15

Quantification of how likely it is that a certain event occurs, or the degree of belief in given proposition

Question 16

Standard deviation

Answer 16

A measure of how spread out the data values are

Question 17

Type I error

Answer 17

False positive output, meaning that it was actually negative but predicted that it was positive

Question 18

Type II error

Answer 18

False negative output, meaning that it was actually positive but has been predicted as negative

Question 19

Decision model

Answer 19

A model assesses the relationships between the element of provided data to recommend a possible decision for given situation

Question 20

Regression

Answer 20

A forecasting technique to estimate the functional difference between input and output variables

Question 21

Cluster analysis

Answer 21

A type of unsupervised learning used to portion a set of data records into clusters. Records in a cluster are more similar to those than in another cluster

Question 22

Classification

Answer 22

A machine learning approach to categorise entities into pre defined classes

Question 23

Data science related activities

Answer 23

-understand the problem
-collect enough data
-processing raw data
-explore the data
-analyze the data
-communicate the results