Topic 1: Introduction to Data Science & Alternative Data

Question 1

What gave rise to the realm of data science?

Answer 1

1. Investments in business infrastructure
2. Volume and variety of data
3. Powerful computers

Question 2

Why is data mining used for customer relationship management?

Answer 2

To manage attrition and maximize expected customer value

Question 3

Type I and Type II Data Driven Decision (DDD) Making Problems

Answer 3

Type I: Decisions for which discoveries need to be made within the data

Type II: Increase decision making accuracy based on data analysis

Question 4

Why view data and data science capability as a strategic asset?

Answer 4

Viewing these as assets allows us to think explicitly about the extent to which one should invest in them

Question 5

How do you transition a business problem into a data mining problem?

Answer 5

Convert the business problem into subtasks and match the subtasks to known tasks for which tools are available.

Question 6

Difference between regression and classification

Answer 6

classification predicts whether something will happen, regression predicts how much something will happen.

Question 7

Define Classification

Answer 7

predict, for each individual, in a population which set of classes this individual belongs to

Question 8

What does a regression attempt to do?

Answer 8

attempts to estimate or predict, for each individual, the numeric value of some variable for that individual

Question 9

Similarity matching

Answer 9

attempts to identify similar individuals based on data known about them

Question 10

Clustering

Answer 10

attempts to group individuals in a population together by their similarity, but not driven by any specific purpose.

Question 11

Co-occurrence grouping

Answer 11

attempts to find associations between entities based on transactions involving them

“what items are often purchased together”

Question 12

Profiling (behavior description)

Answer 12

attempts to characterize the typical behavior of an individual, group, or population.

Question 13

Link prediction

Answer 13

attempts to predict connections between data items

Question 14

Data reduction

Answer 14

attempts to take a large dataset and replace it with a smaller set of data

Question 15

Causal modeling

Answer 15

helps us understand what events or actions actually influence others

Question 16

Difference between supervised and unsupervised methods

Answer 16

Unsupervised methods: no specific purpose or target has been specified for grouping

Question 17

Two main subclasses of supervised data mining

Answer 17

Classification (binary target) and regression (numerical target)

Question 18

CRISP-DM process

Answer 18

1. Business understanding
2. Data Understanding
3. Data Preparation
4. Modeling
5. Evaluation
6. Deployment

Question 19

Leakage in data preparation

Answer 19

Including variables from historical data that gives information on the target variable (predicting the future)

Question 20

Purpose of the evaluation stage (in CRISP-DM)

Answer 20

1. Assess the data mining results rigorously and

2 serves to help ensure that the model satisfies the original business goals

Question 21

Difference between Statistics and the process of Data Mining

Answer 21

Data Mining is hypothesis generation (may produce numerical estimates), while statistics focusses mainly on hypothesis testing (can we have confidence in these estimates)

Question 22

Define a query

Answer 22

A specific request for a subset/statistics of data, formulated in a technical language and posed to a database system.

Question 23

Difference between Knowledge Discovery and Data Mining (“KDD”) and Machine Learning

Answer 23

KDD is more focused on problems concerning “real world”. Also KDD tends to be more concentrated with the entire process of data analytics.

Question 24

Discuss investment managers in terms of their places on the diffusion of innovations curve.

Answer 24

Innovators - mostly hedge funds
Early adopters - aggressive long-only and PE mgrs.
Early majority - tech savvy large complex IM firms
Late majority - traditional large complex IM firms
Laggards - reluctant firms

Question 25

Name the unique challenge to alternative data as described in the paper Alternative Data for Investment Decisions?

Answer 25

Standard historical data may not exist

Question 26

Name the risk exposures to early adaptation of alternative data as described in the paper Alternative Data for Investment Decisions ?

Answer 26

1. Model risk
2. Regulatory risk
3. Data risk
4. Talent risk

Question 27

What are the four types of data risks as described in the paper Alternative Data for Investment Decisions?

Answer 27

Data provenance risk - risk related to origin of data
Accuracy or validity risk - bad trading signals
Material nonpublic information (MNPI) risk
Privacy risk - posibility of PII information in data set

Question 28

Name the risk exposures to late adaptation of alternative data as described in the paper Alternative Data for Investment Decisions ?

Answer 28

1. Positioning risk
2. Execution risk
3. Consequence risk

Question 29

Categories among the Big Data Analytic groups that are most aligned to support alpha generation

Answer 29

1. Content analytics - extract value from text
2. Advanced and predictive analytics software tools
3. Spatial information analytics (SIA) tools - geographic information software and tools

Question 30

Describe the term collective intelligence investing

Answer 30

The process of gathering insights from online communities and crowdsourcing.

Question 31

What are the four key platform types offering Collective Intelligence Investing (CII)

Answer 31

1. Open communities
2. Digital expert contribution networks
3. Digital expert communication networks
4. Crowdsourcing platforms

Question 32

What are the risk exposures of Collective Intelligence Investing and their mitigants?

Answer 32

1. Community engagement risk - adopt gamification
2. Material nonpublic information risk - Rigorous DD
3. Model risk - Sufficient testing/sturdiness checks
4. Information security risk - better security
5. Data integrity risk

Question 33

Steps to a potentially smooth takeoff for Collective Intelligence Investing

Answer 33

1. Vendor review
2. Thorough risk assessment
3. Customized technology architecture

Question 34

Key considerations when you want Wall Street to take notice of your data (in case you want to sell it)

Answer 34

• Data productization (know how the client uses the data)
• Infrastructure and delivery (how will you deliver data)
• Distribution (you need an ‘in’ to get them to look at your data)

Question 35

What are some of the factors that determine the commercial value of a dataset?

Answer 35

1. Data Edge
2. Monetization Strategy
3. Deep Market
4. Uniqueness and Replicability
5. Exclusive Access
6. Table Stakes Potential

Question 36

What is the single strongest indicator of how much a client will pay for a dataset? And what is the expected return on a data investment?

Answer 36

How big the client is in terms of AuM. 10-20x