Abstract: Data science has become a dominant tool in many different disciplines. Many methods and approaches are available and can be used in analyzing data. Network science approach is currently considered a powerful tool that is able to visualize and analyze complex data through investigating the relations among data objects. This kind of methods considers data as nodes that are connected by edges among them, and these connections are created based on a particular strategy. In this work, we generate a network model for the Iraqi parties using the IHEC public dataset. The model consists of nodes and edges connecting them. The performance of the generated network model was evaluated using two-level of measurements; Network-Level measurements (i.e., density, average degree, degree distribution, average path length, and average clustering coefficient) and Node-Level measurements (i.e., betweenness centrality and degree centrality). The visualization and the analysis of the network showed interesting facts about the collaboration patterns among the Iraqi parties. This work also showed that network science is useful in analyzing complex and highly related data. It can also reveal some hidden patterns that might be existed within the data.