Search and download datasets from the internet by topic $“$Retail$"($for example: kaggle datasets and so on$).$ Then determine what problems can be raised based on the dataset.

$2<font><font>.</font></font>$ Carry out the Big Data Life Cycle process by answering and explaining questions$-$following questions:

a$<font><font>.</font></font>$ Big Data Generation: Where did the data source in the dataset come from? $\mathrm{<font><font>(</font></font>}$include the link$\mathrm{<font><font>)</font></font>}?$ 

b$<font><font>.</font></font>$ Data Aggregation & Data Preprocessing, which includes:

$•$ Data Integration: What data is combined?

$\mathrm{<font><font>•</font></font>}$ Data Cleaning: How do you carry out the data cleaning process$<font><font>-</font></font>$inappropriate data such as errors, inconsistencies, redundancies, and so on?

$\mathrm{<font><font>•</font></font>}$ Data Reduction: Does your dataset need a data volume reduction process?

$\mathrm{<font><font>•</font></font>}$ Data Transformation: Does your dataset need to be processed to change the data format into a suitable form for further analysis?

c$<font><font>.</font></font>$ Big Data Analytics: Select one of the analysis techniques used below, then select from that technique $\mathrm{<font><font>(</font></font>}$can be one or more$\mathrm{<font><font>)</font></font>}$ jenis$/$type used:

$\mathrm{<font><font>•</font></font>}$ Quantitative analysis $\mathrm{<font><font>(</font></font>}$Nominal data$\mathrm{<font><font>/</font></font>}$ordinal data$\mathrm{<font><font>/</font></font>}$interval data$\mathrm{<font><font>/</font></font>}$ratio data$\mathrm{<font><font>)</font></font>}$

$\mathrm{<font><font>•</font></font>}$ Qualitative analysis $\mathrm{<font><font>(</font></font>}$Content analysis$\mathrm{<font><font>/</font></font>}$narrative analysis$\mathrm{<font><font>/</font></font>}$discourse analysis$\mathrm{<font><font>/</font></font>}$framework analysis$\mathrm{<font><font>/</font></font>}$grounded theory$\mathrm{<font><font>)</font></font>}$

$\mathrm{<font><font>•</font></font>}$ Statistical analysis $\mathrm{<font><font>(</font></font>}$A$<font><font>-</font></font>$B testing$\mathrm{<font><font>/</font></font>}$correlation$\mathrm{<font><font>/</font></font>}$regression$\mathrm{<font><font>)</font></font>}$ 

d$<font><font>.</font></font>$ Visualizing Big Data: Choose a visualization technique and then visualize the dataset after cycle as$.$d$<font><font>.</font></font>$ c is done.

e$<font><font>.</font></font>$ Give a conclusion in the form of insight gained from discussing the topic and$/$or data so that the problems raised at the beginning are answered in conclusion.