R For Marketing Research
and Analytics
Overview
Course Objective
By the end of this course:
• You will understand why network analysis is very important
• You will understand all of the key terms in the field
• You will be able to use different types of graphs
• You will learn how to draw network graph in an efficient way
• You will learn how to analyze graph and determine the most important nodes and links in
the graph
• You will learn how to get valuable insights from the graph
Who Should Attend
Data Scientists and Analysts interested in network analysis and visualization.
Social Media Analysts looking to understand the relationships and influence within social networks.
Marketing Professionals aiming to improve targeting and engagement strategies using network insights.
Researchers working with large datasets or social media data.
Anyone interested in exploring the field of graph theory and its applications to real-world problems, particularly in social media and communication.
Prerequisites
Basic knowledge of Python programming.
Familiarity with data analysis concepts and techniques.
Understanding of basic graph theory concepts is beneficial but not required.
Experience with Python libraries like NumPy and pandas will be useful.
Training Calendar
Intake
Duration
Program Fees
Module
Module 1 - Why Study Networks and Fundamentals on NetworkX
• Networks: Definition and Why We Study Them
• Node and Edge Attributes
• Bipartite Graphs
• Loading Graphs in NetworkX
Module 2 - Graph Processing’
• Adding Attributes to Graphs
• Adding Edge Attributes to Graphs
• Creating DiGraphs
• Creating MultiGraphs
• Creating MultiDiGraphs
• Graph Generators
• Graph Metrics
• Random Graphs
• Small Famous Graphs
• Reading and Writing Graph Files
• Charting word length with nltk
Module 3 - Graph Visualizations
• Nodes Attributes
• Links Attributes
• Graph Attributes
• Graph Layout
Module 4 - Network Connectivity
• Clustering Coefficient
• Distance Measures
• Connected Components
• Network Robustness
Module 5 - Influence Measures and Network Centralization
• Degree and Closeness Centrality
• Betweenness Centrality
• Basic Page Rank
• Scaled Page Rank
• Hubs and Authorities
FAQs
General Questions:
Q: What is the Social Network Analysis with NetworkX course about?
This 2-day course introduces social network analysis and visualization using NetworkX. It covers the fundamentals of network analysis, including graph processing, visualizations, and influence measures. The course includes practical topics like clustering, network centralization, and network evolution, focusing on real-world applications such as social networks and Twitter retweets.
Q: Who should attend this course?
The course is designed for data scientists, social media analysts, marketers, and researchers interested in understanding network analysis. It is ideal for professionals in data analysis, marketing, and research who want to apply network analysis to social media and communication data.
Q: What are the prerequisites for this course?
Participants should have a basic knowledge of Python programming and data analysis concepts. Familiarity with libraries such as NumPy and pandas is beneficial. A basic understanding of graph theory concepts is helpful but not required.
Q: How long is the course?
The course lasts 2 days, with each day focusing on different aspects of network analysis and visualization using NetworkX.
Q: What key topics are covered in this course?
The course covers the following topics:
Introduction to networks and NetworkX fundamentals
Graph processing and metrics
Graph visualizations and layout
Network connectivity, clustering coefficient, and network robustness
Influence measures, centrality, and PageRank
Network evolution and link prediction
Practical project applying the techniques
Q: Will I receive a certification after completing the course?
Yes, participants will receive a certificate of completion, demonstrating their proficiency in social network analysis and visualization using NetworkX.
Program Content & Skills:
Q: What foundational NetworkX concepts will I strengthen in this course?
You’ll strengthen essential NetworkX concepts such as nodes, edges, graph attributes, and various graph types like DiGraphs and MultiGraphs. The course also covers loading, generating, and processing graphs, building a strong foundation for more advanced network analysis tasks.
Q: How does the course help me apply network analysis to real-world problems?
Through hands-on exercises and a final project, you’ll apply network analysis techniques to real-world scenarios like social networks and Twitter retweet networks. You’ll learn to identify key influencers, analyze relationships, and predict link evolution, preparing you for data-driven decision-making in social media and marketing.
Q: What skills will I develop in managing and analyzing graph structures?
You’ll learn to build, manage, and analyze complex graphs using NetworkX. This includes adding and working with node and edge attributes, calculating graph metrics, evaluating connectivity, and visualizing networks to gain actionable insights.
Q: Will I learn how to work with different graph types and network systems in NetworkX?
Yes, the course covers multiple graph types including undirected, directed, and bipartite graphs, as well as MultiGraphs and DiGraphs. You’ll learn to handle diverse network structures and datasets, enabling you to analyze various types of social and communication networks.
Q: How does this course prepare me for using NetworkX professionally in social network analysis?
This course equips you with the core skills for professional network analysis using NetworkX, including graph construction, visualization, centrality measurement, and link prediction. These skills are directly applicable to roles in data science, social media analytics, and digital marketing.
Submit your interest today !
