moradpourY_A12

I designed an SQLite database for a customer order tracking management system. I created four related tables that are product, customer, purchase_order and order_item; then import the data from the CSV files and check their data. I run queries to select the particular customer’s purchase and order information, which involved the use of the JOIN. Also, I tested data manipulation through creating records and editing them, and checked to confirm if modifications we made were appropriate. I liked this exercise as it gave me an opportunity to create, query and manage relational databases successfully.

moradpourY_A11

I preprocessed and analyzed game review data and then demonstrated them with bar chart and heat map. I also calculated highest and lowest scores for specific platforms and determined things like, Best platform for racing? and other things like combinations of platform & genre having the highest & lowest average rating. Furthermore, I examined Pokémon data by excluding particular columns and illustrate affinities as well as differences by making use of clustermap with annotations to the dendrogram. To that end, I employed data assembly, graphical representation, and information probing methodologies throughout.

moradpourY_A6

This is the Colab file for Pokémon battle statistics: Attack, Defense, moderate standard value, and the occasional extremely high or low number Pokémon. Thus, Water and Flying nucleate as primary and second types, which allows to reveal the Pokémon roles and their advantages.

moradpourY_A4

The script is an eukaryote genome data manipulation system based on pandas for filtering species by gene count, counting of classmammals them several times, and making a pie chart graphical representation. It gives examples such as mammals that have over 20,000 genes, species by class counting, and also modifying species names for better data visualization is an important stage in the process.

moradpourY_A10

Used the large data to achieve different data preparation and analysis tasks. I also learned how to work with CSV files with mixed content such as skipping comment lines and setting the header. I also attempted to clean the datasets by removing unwanted/not required features, resetting indices and setting them as well and also converting the data types for analysis. There were cases where I calculated statistical metrics like average of specific columns and also I filtered the data based on conditions to derive specific data. To identify the relationship, I used data visualizations indicating where one variable stands in relation to another and I used scatterplots to show cross-tabular relationships. Further, I combined two different datasets and while handling missing values, I revisited types of data integration.

moradpourY_A5

This assignment is elaborated on the wine magazine dataset using the tool of Pandas that clean the data, computes the statistics of the data, modifies the price & create P2P values, analysis the text of description, assign star rating for the wines and recognize most of the many reviewers & the most frequent country varieties pairs of the wines. It represents few of the data manipulation and data aggregation methodologies.

moradpourY_A4 (empty)

The script is an eukaryote genome data manipulation system based on pandas for filtering species by gene count, counting of classmammals them several times, and making a pie chart graphical representation. It gives examples such as mammals that have over 20,000 genes, species by class counting, and also modifying species names for better data visualization is an important stage in the process.