1.1 Students will organize data for data analysis by the end of the second lesson.

1.2 Students will clean data using data cleaning software at the end of the second lesson.

1.3 Student will arrange data for network analysis and agent-based modeling by the end of the second lesson.

2.2 Students will understand the basics of social network analysis techniques such as path-length, degree, clustering, centrality, etc. to examine social networks by the end of the fifth lesson.

2.3 Students will know differences among random, small-world and scale-free networks at the end of the sixth lesson.

2.4. Students will able to use social network analysis in innovation studies by the end of the ninth lesson.

2.4 Students will make social network analysis by using software at the end of the tenth lesson.

3.1 Students will know basic motivations for using agent-based modeling in innovation studies by the end of the twelfth lesson.

3.2 Students will understand the differences between rational actor and actor in agent-based model at the end of the thirteenth lesson.

3.3 Students will comprehend generative, inductive and deductive approaches at the end of the thirteenth lesson.

3.4 Students will establish an agent-based model using software by the end of the fifteenth lesson.