Week 2

Question 1

Data Science

Answer 1

the exploration and quantitative analysis of all available structured and unstructured data to develop understanding, extract knowledge and formulate actionable results

Question 2

Business Intelligence

Answer 2

strategies and technologies used by enterprises for the data analysis of business information.

Question 3

CRISP-DM

Answer 3

provides useful input on ways to frame analytics problems and is popular approach for data mining. Six steps include: business understanding, data understanding, data preparation, modeling, evaluation and deployment.

Question 4

Framing a Decision

Answer 4

outline what decision is being considered, why it is important, what data is need, who will provide input. Business Understanding, Data Understanding and Data Preparation of CRISP-DM.

Question 5

Analyzing a Decision

Answer 5

what kind of analytical approach is needed, what. does it show, what does it mean. Modeling in CRISP-DM.

Question 6

Implementing a Decision

Answer 6

how do I make use of the decision, what can I expect, what else should be considered, how do I “sell” the result. Evaluation and Deployment in CRISP-DM.

Question 7

Data Modeling Blocks

Answer 7

1. Data, 2. Build Model, 3. Inter hidden variables, 4. Predict & Explore

Question 8

Interpretation Error and Inconsistencies

Answer 8

Taking the value in your data for granted and difference between data sources and company’s standardized values.

Question 9

Cleansing Data

Answer 9

Interpretation and Inconsistencies. Data Entry Errors, Redundant Whitespace, Fixing Capital Letter Mismatching, Outliers, Dealing with Missing Values, Different Units of Measurement, Different Level of Aggregation, Deviation for a Cook Book, Impossible values and Sanity Checks.

Question 10

Integrating Data

Answer 10

Combining data from different data sources. Joining/Appending Data, Appending Tables, Using Views to Simulate Data Joins and Appends, Enriching Aggregated Measures.

Question 11

Transforming Data

Answer 11

making data into a certain shape for models. Reducing the number of variables, turning variables into dummy variables.

Question 12

Data Retrieval

Answer 12

data stored within the company, data outside organization and data quality checks.

Question 13

Data Preparation

Answer 13

fix problems in the data; create derived variables.

Question 14

Exploratory Data Analysis

Answer 14

the use of graphical techniques to gain an understanding of your data and the interactions between variables.

Question 15

Joining

Answer 15

enriching an observation from one table with information from another.

Question 16

Appending/Stacking

Answer 16

Adding the observations of one table to those of another table.

Question 17

Dummy Variables

Answer 17

Can only take two values true(1) and false(0). Used to indicate the absence of a categorical effect that may explain the observation.

Question 18

Unsupervised Learning

Answer 18

Algorithm does not have past data cases, with inputs and output of interest identified. Algorithm “attempts” to learn something interesting about the data.

Question 19

Data Partitioning

Answer 19

Training 60%, Validation 30%,

Test 10%.

Question 20

Technical Data Scientist

Answer 20

designs solution from scratch.

Question 21

Business Data Scientist

Answer 21

monitors the solution from scratch. Not as knowledgeable as a Technical Data Scientist.

Question 22

Databases

Answer 22

structured with defined schema. Items are organized as a set of tables with columns and rows. Transactional.

Question 23

Data marts

Answer 23

stores data from data warehouse. Subject-oriented, partitioned segment of an enterprise data warehouse.

Question 24

Data Warehouses

Answer 24

exists on top of databases and used for business intelligence. Consumes data from databases and creates a layer optimized to perform data analytics. Schema is done on import.

Question 25

Data Lakes (Big Data)

Answer 25

centralized repository of structured/unstructured data. Store raw data without structure(schema). No ETL or transformation jobs.