Teaching 

Course Details

Network Science is a new discipline of study of complex networks such as telecommunication networks, computer networks, biological networks, cognitive and semantic networks, and social networks, considering distinct elements or actors represented by nodes ( or vertices) and the connections between the elements or actors as links (or edges). This field draws on theories and methods including graph theory, statistical mechanics, data mining and information visualization, and inferential modelling.  The United States National Research council defines network science as “the study of network representations of physical, biological, and social phenomena leading to predictive models of these phenomena”.

Learning Objectives:

         This course will focus on the algorithmic, computational, and statistical methods of network science for analysing information and social networks.  To predict the behaviour of evolution of networks and to analyse complex networks, mathematical theories and computational methods are taught to the students. State of the art research topic related to network science will also be discussed.  

Pre requisites:

Basic knowledge of Graph theory, Probability Theory, Linear Algebra, and Differential Equations will be helpful.

Text Books:

1. Albert-Laszlo Barabasi,  Network Science, Cambridge University Press, 2020

2. David Easley and Jon Kleinberg, “Networks, Crowds, and Markets: Reasoning About a Highly Connected World”, Cambridge        University Press, 2010

Web Links:

1. Albert Barabasi, Northeastern University http://barabasilab.neu.edu/networksciencebook/

2. Constantine Dovrolis, Georgia Tech. http://www.cc.gatech.edu/~dovrolis/Courses/NetSci/

3. Michael Kearns, UPENN http://www.cis.upenn.edu/~mkearns/teaching/NetworkedLife/

4. Jon Kleinberg and Eva Tardos, Networks, Cornell. https://courses.cit.cornell.edu/info2040_2013fa/

5. Jure Leskovek, Social and Information Network Analysis, Stanford. http://www.stanford.edu/class/cs224w/

6. Peter Dodds, Univ. of Vermont. http://www.uvm.edu/~pdodds/teaching/courses/2014­01UVM303/index.html

Course Details

Predictive analytics looks at current and historical data patterns to determine if those patterns are likely to emerge again. This allows businesses and investors to adjust where they use their resources to take advantage of possible future events. Predictive analysis can also be used to improve operational efficiencies and reduce risks.

Learning Objectives:

         This course will focus on the algorithmic, computational, and statistical methods of predictive analytics for analysing information.  To predict the behaviour of future trends, forecasting techniques and time series analysis are taught to the students. State of the art research topic related to predictive analytics will also be discussed.  

Pre requisites:

Basic knowledge of Applied Probability Theory will be helpful.

References:

1. Max Kuhn and Kjell Johnson,  “Applied Predictive Modeling”, Springer, 2013

2.    Eric Siegel, “Predictive Analytics” John Wiley &Sons, 2013.

Web links for case studies:

1. http://appliedpredictivemodeling.com/

2. https://cran.r-project.org/web/packages/AppliedPredictiveModeling/index.html

3. http://proed.acs.org/course-catalog/courses/applied-computational-modeling/

Course Details

As the root of data analysis, the study of applied statistics prepares professionals for careers as statisticians, data scientists, data analysts, and more. Applied statistics is a foundation upon which data science has been built. Through statistical methods, analysis, and an emphasis on real-world data, applied statisticians seek concrete solutions to tangible problems. Individuals with a strong background in applied statistics may then become data scientists, but the relationship doesn’t work inversely—those who study data science exclusively would not necessarily be prepared for careers as applied statisticians.  

Learning Objectives:

         This course will focus on statistical inference theory, ANOVA, and hypothesis testing techniques for the large dataset. The data visualization is depicted through descriptive analytics tools. 

Prerequisites:

Basic knowledge of Probability Theory and descriptive analytics tools will be helpful.

TEXT BOOKS:

T1.        Jay L. Devore, “Probability and Statistics for Engineering and Sciences”, Cengage Learning, 2015.

T2.        David Forsyth, ‘Probability and Statistics for Computer Science’, Springer; 2018

T3.        Michael J. Evans, Jeffrey S. Rosenthal, ‘Probability and Statistics - The Science of Uncertainty’. W H Freeman & Co, 2010

REFERENCES:

1.     Anderson, Sweeney and Williams “Statistics for business and economics”, Cengage Learning, 2014.

2.     Douglas C Montgomery and George C Runges, “Applied Statistics and Probability for Engineers”, John Wiley &Sons,  2014.

3.     Roy D.Yates and David J. Goodman, “Probability and Stochastic Processes – A friendly Introduction for Electrical and  Computer Engineers”, John Wiley,  2014.

4.     Trivedi K.S., “Probability and Statistics with Reliability, Queueing and Computer Science Applications”, John Wiley &Sons, 2016.