Intro to Data and Data Science Flashcards by Jackson Alcock

What is Analysis?

1.2

‘how’ and ‘why’ something happened

performed on past data

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What are Analytics?

1.2

Analytics apply logical reasoning to info obtained from analysis

Explores the future and looks for patterns

2 types:
Qualitative and
Quantitative

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What are Qualitative Analytics?

1.2

The use of:
intuition
experience and
analysis

to plan the next business move

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What are Quantitative analytics?

1.2

The application of formulas and algorithms to numbers gathered from analysis

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What is Business Intellegence?

1.4

Process of analysing and reporting historical business data

Preliminary step to predictive analytics

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What is Machine Learning?

1.4

Ability of machines to predict outcomes without being programmed to do so

The machines use data to:

Make predictions
analyse patterns
give recommendations

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What are advanced analytics?

1.4

all types of analytic processes

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Symbolic reasoning is a type of AI that makes an exception and does not use ML and deep learning.
It is based on high-level human-readable representations of problems and logic.

True or False:
Symbolic reasoning is commonly used in practice

1.4

False:

Very rarely used in practice.

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5 Primary Columns om the 365 infographic

1.5

traditional data
big data
business intelligence
Applying traditional data science techniques
Using ML techniques

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What is “Data”

2.0

information stored in a digital format

used for:

a) analysis
b) decision making

2 Types:

a) Traditional
b) Big Data

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What is traditional data?

2.0

Data in the form of tables containing numeric or text values;

Data that is structured and stored in databases

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What is big data?

2.0

Extremely large data;

It can be in various formats:

structured
semi-structured
unstructured

often characterized by ‘V’ (volume, variety, velocity, etc.)

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What is Data Science?

2.0

an interdisciplinary field that combines:

statistical,

mathematical,

programming,

problem-solving, and

data-management tools.

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What are Traditional Methods?

2.0

derived from stats and adapted for business

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What is Raw Data?

4.1

AKA Primary Data

cannot be analysed immediately
accumulated and unorganized. The organization is called data collection

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What is Class labelling?

4.1

Labelling the data point to the correct data type

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What is data cleansing?

4.1

AKA Data Scrubbing

Deals with inconsistent data
-containing typos or missing info

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What is data balancing?

4.1

Ensuring the sample gives equal priority to each class

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What is Data Shuffling?

4.1

Shuffles data to ensure data is free from unwanted patterns from collection

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What is a numerical variable?

4.2

Manipulatable numbers that provide useful information

What is a categorical variable?

4.2

Numbers with no numerical value.

Dates are also considered categorical

What is text data mining?

4.3

The process of deriving valuable, unstructured data from text.

What is data masking?

4.3

data masking conceals the original data with random and false data,

allows you to conduct analysis and keep confidential information in a secure place.

What is a metric?

4.5

a value derived from obtained measures

aims at gauging business performance/progress (has business meaning)

What is a measure? 4.5

simple stats of past performance (no business meaning)

What is a KPI? 4.5

Key Performance indicator metrics + business objective

What is clustering? 4.7

grouping the data in neighbourhoods to analyse meaningful patterns

What is a time series? 4.7

used in economics and finance shows the development of certain values over time (i.e. stock prices, sales volume)

What is a model in machine learning 4.9

an algorithm to recognize certain patterns

What is an objective function? 4.9

The specification of a machine learning problem; a function to be maximized or minimized depending on the task

What is an optimization algorithm? 4.9

Algorithm that compares previous solutions until reaching the reaching the optimal solution

What are the three main types of machine learning?

Supervised Unsupervised Reinforcement

What is supervised learning? 4.10

Provides feedback whether they did ‘good’ or whether they need to improve Uses labelled data

What is unsupervised learning? 4.10

In this case, the algorithm trains itself algorithm uses unlabelled data

What is reinforcement learning? 4.10

A reward system is introduced. maximize a reward (not minimize an error)

What is deep learning? 4.10

modern state-of-the-art approach to machine learning – leverages the power of neural networks can be both supervised and unsupervised

Python and R have their limitations. They are not able to address problems specific to some domains. One example is ‘relational database management systems’. In these instances, ______ works best 5.

SQL

Data architect 6

designs the way data will be retrieved processed and consumed

Data engineer 6

processes the data for analysis

database administrator 6

– handles this control of data; works with traditional data

BI analyst 6

performs analyses and reporting of past historical data

BI consultant 6

– ‘external BI analyst’

BI developer 6

performs analyses specifically designed for the company

Data scientist 6

employs traditional statistical methods or unconventional machine learning techniques for making predictions

Data analyst 6

prepares advanced analyses

Machine learning engineer 6

applies state-of-the-art ML techniques

200,000 lines of data constitute big data -- TRUE or FALSE?

FALSE -It is not just volume that defines a data set as ‘big’ – variety, variability, velocity, veracity and other characteristics play an important role as well

Qualitative analysis such as SWOT are not used for quantitative analysis. Hence, they are not part of business intelligence --TRUE or FALSE

False