LM 11: Introduction to Big Data Techniques

Question 1

What is fintech?

Answer 1

refers to technological innovation in the design and delivery of financial services and products

Question 2

What are the 4 characteristics or V’s of big data?

Answer 2

1. volume
2. velocity
3. variety
4. veracity (accuracy)

Question 3

What are the 6 main sources of big data? FBGISI

Answer 3

1. financial markets
2. businesses
3. governments
4. individuals
5. sensors
6. internet of things

Question 4

Who are the 3 main sources of alternative data generation? IBS

Answer 4

1. individuals
2. business processes
3. sensors

Question 5

What are 3 ways data can be organized? SSU

Answer 5

1. structured
2. semi-structured
3. unstructured

Question 6

What are the 3 challenges to big data? QVA

Answer 6

1. quality
2. volume
3. appropriateness

Question 7

What is artificial intelligence?

Answer 7

enables computers to perform tasks that traditionally have required human intelligence

Question 8

What is machine learning (ML)?

Answer 8

computer programs that learn how to complete tasks; improving with time as more data have become available

Question 9

What are the 3 types of machine learning? SUD

Answer 9

1. supervised learning (given inputs and outputs and tries to figure out the best model training data)
2. unsupervised learning (algorithm seeks to describe data and find patterns)
3. deep learning (utilizes neural networks to identify patterns, use them in image & speech recognition)

Question 10

What is the difference between underfit and overfit machine learning?

Answer 10

underfit: failure to recognize true relationships in a training data set

overfit: model generates very high accurate relationships

Question 11

What is data science?

Answer 11

uses computer science and statistics to extract information from big data

Question 12

Describe the 5 data processing methods data scientists use. DDDST

Answer 12

1. data capture (how data is collected & transformed into a usable format)
2. data curation (cleaning data to ensure high quality)
3. data storage (recording, archiving, & accessing data)
4. search (locating specific information in large datasets)
5. transfer (moving data from their source or storage location to the analytical tool)

Question 13

What is data visualization?

Answer 13

how data will be displayed & summarized in graphical form

Question 14

What is the difference between text analytics and natural language processing?

Answer 14

text analytics uses computer programs to analyze unstructured text or voice-based datasets

natural language processing (NLP) uses text analytics focus on interpreting human language

Question 15

What is corporate exhaust?

Answer 15

refers to the trail of data left by business activities and transactions. Examples include supply chain information, banking transactions, and point-of-sales data.