SUMMARY FOUNDATIONS
OF DATA ANALYTICS
2IAB0 – 2023/2024
Biermans, Cas
,Contents
Lecture 1: (Monday 13-11-2023) ........................................................................................................ 2
Lecture 2: (Monday 20-11-2023) ...................................................................................................... 11
Lecture 3: (Monday 27-11-2023) ...................................................................................................... 26
Lecture 4: (Monday 4-12-2023) ........................................................................................................ 33
Lecture 5: (Monday 11-12-2023) ...................................................................................................... 45
Lecture 6: (Monday 18-12-2023) ...................................................................................................... 57
1
,Lecture 1: (Monday 13-11-2023)
Types of data analytics:
- Descriptive: insight into the past
- Predictive: looking into the future
- Prescriptive: data-driven advice on how to take action to influence or change the future
Data analytics life cycle:
Data: raw numbers, facts, etc.
Information: structured, meaningful, and useful numbers and facts
Data types:
- Categorical data: data that has no intrinsic value:
o Nominal: two or more outcomes that have no natural order
e.g. movie genre or hair color
o Ordinal: two or more outcomes that have a natural order
e.g. movie ratings (bad, neutral or good), level of education
- Numerical (quantitative) data: data that has an intrinsic numerical value:
o Continuous: data that can attain any value on a given measurement scale
▪ Interval data: equal intervals represent equal differences, there is no fixed
“zero point”
e.g. clock time, birth year
▪ Ratio data: both differences and ratios make sense, there is a fixed "zero
point"
e.g. movie budget, distance, time duration
o Discrete: data that can only attain certain values (typically integers)
e.g. the number of days with sunshine in a certain year, the number of traffic
incidents
Reference table: to store “all” data in a table so that it can be looked up easily.
Demonstration table: to illustrate a point (with just enough data, or with a specific summary)
EDA: exploratory data analytics
Key features of EDA:
• getting to know the data before doing further analysis
2
, • extensively using plots
• generating questions
• detecting errors in data
Plots are a useful tool to discover unexpected relations and insights. Plots help us to explore and give
clues. Numerical summaries like averages help us to document essential features of data sets.
One should use both plots and numerical summaries. They complement each other. Numerical
summaries are often called statistics or summary statistics (note the double meaning of the word:
both a scientific field and computed numbers).
Summary statistics:
- Level: location summary statistics (typical values)
- Spread: scale summary statistics (how much do values vary?)
- Relation: association summary statistics (how do values of different quantities vary
simultaneously)
Location summary statistics:
- Mean/average:
1 𝑛
𝑥̄ = ∑ 𝑥𝑖
𝑛 𝑖=1
- Median: the value separating the higher half from the lower half of a data set
o Median computation:
▪ Order series of observations from small to large.
▪ If the number of observations is odd, take the middle value.
▪ If the number of observations is even, take the average of the two middle
values.
- Mode: most frequently occurring value, may be non-unique
The mean is sensitive to “outliers”, the median is not.
The mean can be misleading and difficult to interpret for non-symmetric data sets.
3
OF DATA ANALYTICS
2IAB0 – 2023/2024
Biermans, Cas
,Contents
Lecture 1: (Monday 13-11-2023) ........................................................................................................ 2
Lecture 2: (Monday 20-11-2023) ...................................................................................................... 11
Lecture 3: (Monday 27-11-2023) ...................................................................................................... 26
Lecture 4: (Monday 4-12-2023) ........................................................................................................ 33
Lecture 5: (Monday 11-12-2023) ...................................................................................................... 45
Lecture 6: (Monday 18-12-2023) ...................................................................................................... 57
1
,Lecture 1: (Monday 13-11-2023)
Types of data analytics:
- Descriptive: insight into the past
- Predictive: looking into the future
- Prescriptive: data-driven advice on how to take action to influence or change the future
Data analytics life cycle:
Data: raw numbers, facts, etc.
Information: structured, meaningful, and useful numbers and facts
Data types:
- Categorical data: data that has no intrinsic value:
o Nominal: two or more outcomes that have no natural order
e.g. movie genre or hair color
o Ordinal: two or more outcomes that have a natural order
e.g. movie ratings (bad, neutral or good), level of education
- Numerical (quantitative) data: data that has an intrinsic numerical value:
o Continuous: data that can attain any value on a given measurement scale
▪ Interval data: equal intervals represent equal differences, there is no fixed
“zero point”
e.g. clock time, birth year
▪ Ratio data: both differences and ratios make sense, there is a fixed "zero
point"
e.g. movie budget, distance, time duration
o Discrete: data that can only attain certain values (typically integers)
e.g. the number of days with sunshine in a certain year, the number of traffic
incidents
Reference table: to store “all” data in a table so that it can be looked up easily.
Demonstration table: to illustrate a point (with just enough data, or with a specific summary)
EDA: exploratory data analytics
Key features of EDA:
• getting to know the data before doing further analysis
2
, • extensively using plots
• generating questions
• detecting errors in data
Plots are a useful tool to discover unexpected relations and insights. Plots help us to explore and give
clues. Numerical summaries like averages help us to document essential features of data sets.
One should use both plots and numerical summaries. They complement each other. Numerical
summaries are often called statistics or summary statistics (note the double meaning of the word:
both a scientific field and computed numbers).
Summary statistics:
- Level: location summary statistics (typical values)
- Spread: scale summary statistics (how much do values vary?)
- Relation: association summary statistics (how do values of different quantities vary
simultaneously)
Location summary statistics:
- Mean/average:
1 𝑛
𝑥̄ = ∑ 𝑥𝑖
𝑛 𝑖=1
- Median: the value separating the higher half from the lower half of a data set
o Median computation:
▪ Order series of observations from small to large.
▪ If the number of observations is odd, take the middle value.
▪ If the number of observations is even, take the average of the two middle
values.
- Mode: most frequently occurring value, may be non-unique
The mean is sensitive to “outliers”, the median is not.
The mean can be misleading and difficult to interpret for non-symmetric data sets.
3
