Lecture 1

Question 1

Data

Answer 1

A set of discrete, objective facts about events

Question 2

Dataset

Answer 2

a collection of data with a defined structure

Question 3

Data point

Answer 3

a single instance in the dataset

Question 4

Attribute

Answer 4

A single property of the dataset

Question 5

Data science

Answer 5

a collection of techniques used to extract value from data

process of building a representative model that fits the observational data

Question 6

Model

Answer 6

representation of a relationship between variable in a dataset

Question 7

modeling

Answer 7

process in which a representative abstraction is built from the observed dataset

Question 8

Data science model serves two purposes

Answer 8

1. it predicts the output (interest rate) based on the new and unseen set of input variables
2. the model can be used to understand the relationship between the output variable and all the input variables

Question 9

techniques used in the steps of a data science process

Answer 9

- descriptive statistics 
exploratory visualization 
dimensional slicing 
hypothesis-testing 
data engineering 
business intelligence

Question 10

Supervised model

Answer 10

supervised data science tries to infer a function or relationship based on labeled training data and uses this function to map new unlabeled data

Question 11

Unsupervised data

Answer 11

uncovers hidden patterns in unlabeled data

Question 12

Classification and regression techniques

Answer 12

predicting a target variables based on input variables

Question 13

Clustering

Answer 13

the process of identifying the natural groupings in a dataset

Question 14

recommendation engines

Answer 14

the systems that recommend items to the users based on individual user preference

Question 15

anomaly or outlier detection

Answer 15

identifies the data points that are significantly different from other data points in a dataset

Question 16

time-series forecasting

Answer 16

the process of predicting the future value of a variable based on past historical values that may exhibit a trend and seasonality

Question 17

text mining

Answer 17

a data science application where the input data is text which can be in the form of documents, messages, emails or web pages

Question 18

feature selection

Answer 18

A process in which attributes in a dataset are reduced to a few attributes that really matter

Question 19

association analysis

Answer 19

identifying pairs of items that are purchased together, so that specific items can be bundled or placed next to each other

Question 20

deep learning

Answer 20

increasingly used for classification and regression problems

Question 21

Big data

Answer 21

High-volume, high-velocity, and or high variety information that requires new forms of processing to enable enhanced decision making, insight discovery and process optimization

Question 22

Big data characteristics (5vs)

Answer 22

Volume, velocity, variety, veracity, and value

Question 23

volume

Answer 23

increase in data size coming from infinite sources

Question 24

velocity

Answer 24

• increase in the speed of input and output data and the ability to quickly incorporate new data
• ability to quickly add new data sources

Question 25

Variety

Answer 25

increasing the range of diversity and data structure

• structured data,
• semi-structured data,
• unstructured data

Question 26

Veracity

Answer 26

valid and truthful data that provides the right direction for future decisions and actions

• data freshness
• quality dimensions (challenges)
• trust, quality& validity of data

Question 27

Value

Answer 27

data that has high veracity provides higher value

- usefulness of data for an enterprise

Question 28

Data science tends to fall into three broad categories

Answer 28

investigating, predicting, and optimizing

Question 29

Data science tasks

Answer 29

regression 
clustering 
association analysis 
anomaly detection 
recommendation engines 
deep learning 
time series forecasting 
text mining 
feature selection 
classification