Intro to data science 1
Data science vs statistics
Stats:
- includes operations research / economic theory / standardization / quality control
- focuses on detecting and preventing anomalies & optimal price setting
- summarizes data into few key metrics to enable manual processing and analysis
Data science:
- handles large amounts of data describing individuals
- focuses on visualizing trends and variance
Data science augments subject matter expertise
DS doesn’t replace experts augments expertise with knowledge derived from data
Data scientists help another with data hacking skills
AI requires large datasets and statistics and linear regression (within data science) work on
small and large datasets
DS in history
Dr Snow in 1854 studied cholera outbreak
linked cholera and drinking water by having all patients note their address
Average = all / amount
Median = middle value if you sort by value
Modus = most common value (often with non-comparable entities common password)
Correlation expresses whether the values of 2 variables are related
If variables related, one of values can be used to predict the other
Correlation can be:
negative: means they move in opposite directions (house price and distance to city
center)
positive: means they move in same direction (house-size and tax value)
near-zero: there is no relation
Intro to Data Science 2
Data science phases and problems
Low end / starting point: data quality issues & data handling issues
Data ready for analysis: human analysis & data science algorithms
Deeper insights: patterns, models & heuristics
Informed decisions: Ai driven & human driven
,Different data science problems:
- Classification
- Prediction
- Clustering
- Decision
- Recommendation
From technical perspective these problems are very similar, as one type of problem can be
converted into another
From user perspective problems are different with different risks and fairness requirements
Clustering:
Divide objects of dataset into different groups that are similar
Groups are not predefined, the algo discovers the groups
Possible applications: dating, recommendation and preprocessing
Decision and Classification
Classification problem: put the correct label from a finite set of labels on a datapoint
Potential labels for houses: monument, energyefficient and value-brackets
Decision problems are often binary classification problems: in/out, brake/no-brake, hire/fire
False neg: algo says no, is yes
False pos: algo says yes, is no
True neg: algo says no, is no
True pos: algo says yes, is yes
Precision and Recall
Precision is important if the cost of a false pos is high
For instance: the case in hiring for popular positions, or in case of high-risk treatment in non-
urgent cases
Recall is important if cost of false neg is high
Like medical screening
Prediction
,In prediction problem you must find a missing value based on available data
To make prediction algo, you need example data with correct answer supervised learning
In prediction, you evaluate models based on some total / average error
Recommendation
In recommendation you have a large number of items and must select a few top results
Could predict relevance for all items, sort and select top
Results should be diverse dynamic and perhaps surprising / inspiring, not just accurate
customers care about conversion
Types of value
Categorical Ordinal Numerical
Categorical (least structured):
- Column can take multiple values with no further structure:
- Yes/no
- Red/blue/green
- Supported aggregates
- Mode
Ordinal:
- Columns can take multiple values which are comparable and thus sortable
- Very low / average / high
- B-, B, A, AA, AAA
- Supported aggregates
- Mode
- Median, percentile, min/max
Numerical (most structured):
- Column can take multiple values that can be compared, averaged and subtracted
(aka numbers)
- 0%-100%
- 0.0 – 10.0
- Supported aggregates
- Mode (if few values)
- Median, percentile, min/max
- Normal average, geometric average, average without outliers
Letter grades ( A – F): ordinal, convertible to numerical
Three valued logic (yes, no, maybe): Ordinal
Colors of the rainbow: Ordinal but often treated as categorical
MBTI personality profiles: Categorical
1-10 + unknown: Numerical, ignoring unknown
Project status (successful, challenged, failed): ordinal
What is a normal distribution?
, The normal distr is a very special probability distr with a clear center and a wide base
Like a perfect circle, it does not often occur in nature
Many theorems / algos assume that data is distributed normally.
- Following theorem often assumed:
- Outcomes c=more than 2 std away from the mean occur less than 5% of the cases
Why are many distributions similar to normal?
The central limit theorem
The central limit theorem (CLT) establishes that, in many situations, when independent
random variables are summed up, their properly normalized sum tends toward a normal
distribution even if the original variables themselves are not established.
In many board games it is important to understand that the sum of 2 dice is more likely to be
6 or 7 than 2 or 12. This happens since you are adding 2 independent random variables
Other distributions:
Uniform distribution: all
equally likely (when data is generated, or you have not discovered the structure is when it
occurs)
Poisson distribution: waiting for a random event to occur. Waiting times cannot be below 0
(occurs when: predicting next event)
Intro to Data Science 3
Why data visualization?
To help data scientists with the data science:
- Understand what data is in the set
- Show data quality issues
- Support the search for patterns / features
To help communicating the data science outcomes to others:
- Good data visualizations do not just show the right answer: they convince the
audience of an important message
- Data visualizations are therefore important to get your advice implemented and your
work values
Not the same goals, but overlap: being transparent in what you did and how you found
patterns will make you more convincing
Data visualization needed to create convincing reports
- Client organizations have multiple people that all need to be convinced. This includes
people not present at the meeting
- Business decisions are often so drastic that they want to be sure. They need to be
sure. They need multiple arguments before people are convinced.
