ΠΠ½Π°Π»ΠΈΠ· ΠΈ ΡΠ°Π±ΠΎΡΠ° Ρ Π±Π°Π·Π°ΠΌΠΈ Π΄Π°Π½Π½ΡΡ
, ΠΠ°Ρ
ΠΎΠΆΠ΄Π΅Π½ΠΈΡ Π·Π°ΠΊΠΎΠ½ΠΎΠΌΠ΅ΡΠ½ΠΎΡΡΠ΅ΠΉ. ΠΠΏΠ΅ΡΠ°ΡΠΈΠΎΠ½Π½Π°Ρ ΡΠΈΡΡΠ΅ΠΌΠ°: Windows. Π’Π΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΎΠ΅ Π·Π°Π΄Π°Π½ΠΈΠ΅ Π΅ΡΡΡ. ΠΠ΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎ ΡΠ΄Π΅Π»Π°ΡΡ 3 ΡΡΠ°Π΄ΠΈΡ ΠΏΡΠΎΠ΅ΠΊΡΠ° ΠΏΠΎ ΠΏΡΠ΅Π΄ΠΌΠ΅ΡΡ Data analysys ΠΏΠΎ-Π°Π½Π³Π»ΠΈΠΉΡΠΊΠΈ. ΠΠ»Ρ ΠΏΡΠΎΡΠ»ΠΎΠΉ ΡΡΠ°Π΄ΠΈΠΈ ΠΏΡΠΎΠ΅ΠΊΡΠ° Π·Π°Π΄Π°Π½ΠΈΠ΅ Π±ΡΠ»ΠΎ: For each categorical/qualitative variable, list all its possible values, analyze its occurrence in the data and create the corresponding graphs using Excel. Suggest an optimization for further analysis. For each quantitative variable, list the descriptive characteristics of the variable, analyze it, and create appropriate graphs using Excel. Design and create database and table in an SQL database and import your data. Π‘omment on the import errors. ΠΠ°Π΄Π°Π½ΠΈΠ΅ Π΄Π»Ρ ΡΡΠΎΠΉ ΡΡΠ°Π΄ΠΈΠΈ ΠΏΡΠΎΠ΅ΠΊΡΠ°: Search for appropriate variables (it doesn't have to be just two) and relationships between them using appropriate tools in Excel, R language and Python to find an interesting pattern of data behaviour and describe it. Also try to interpret the patterns you find and put them into the concept of the original data. Summarize the methods you used in your analysis and the results you arrived at. Prepare a text that explains the significance of data analysis using your data as an example. (in word document).