week two content Flashcards
what is the data operation?
the data operation is when scientist/analysts collect and prepare information in order to present to business owners
why is statistics important in modern day business?
it helps with financial aid
it provides information about customers that could help companies strengthen their marketing plans
where can information be collected from?
online sources such as: Bloomberg or other articles
python and other coding languages.
statistical softwares
what are some challenges faced with the use of statistics?
large data sets may be complexed
data may be inherently challenging due to the advance nature of the analytical process.
data may be biased and as a result, the data may be skewed.
how can data help us?
‘numbers don’t lie’, numbers can add credibility to the data
numbers may provide insights into what is happening around the world
what are some of the unethical uses of statistics?
biasing data
leaving out information that do not align with your interest
presenting findings that could be hard for readers to analyse
what is primary data?
data that is collected directly form the source
what is secondary data?
data that is previously collected and complied by someone else
what does quantitative mean?
represents measurements or counts, it is always numeric. Arithmetic operations are meaningful only with quantitative data.
what does qualitative mean?
these are names or labels used to identify an attribute of each element, it may be numeric or non-numeric
what are the four measurement levels of data?
Nominal data:
consists of labels or names used for identification, may be qualitative.
Ordinal data:
Exhibits properties of nominal data that can be rank-ordered
Interval data:
Have the properties of ordinal data but also show uniform distances between successive values.
Ratio data:
Have all the properties of interval data and the ratio of two values is meaningful.
what does the term ‘big data’ refer to?
big data refers to a large and diverse sets of information
what are the three V’s of big data?
velocity, volume and variety
