To understand the fundamental concepts related to Image formation and processing.
To learn feature detection, matching and detection.
To become familiar with feature based alignment and motion estimation.
To develop skills on 3D reconstruction.
To understand image based rendering and recognition.
After the completion of this course, the students will be able to:
1: Understand basic knowledge, theories and methods in image processing and computer vision. 2: Implement basic and some advanced image processing techniques in OpenCV.
3: Apply 2D a feature-based based image alignment, segmentation and motion estimations. 4: Apply 3D image reconstruction techniques.
5: Design and develop innovative image processing and computer vision applications.
Computer Vision, Geometric primitives and transformations, Photometric image formation, digital camera, Point operators, Linear filtering, More neighborhood operators, Fourier transforms, Pyramids and wavelets, Geometric transformations, Global optimization.
Points and patches, Edges, Lines, Segmentation, Active contours, Split and merge, Mean shift and mode finding, Normalized cuts, Graph cuts and energy-based methods.
2D and 3D feature-based alignment, Pose estimation, Geometric intrinsic calibration, Triangulation, Two-frame structure from motion, Factorization, Bundle adjustment, Constrained structure and motion, Translational alignment, Parametric motion, Spline-based motion, Optical flow, Layered motion.
Shape from X, Active range finding, Surface representations, Point-based representations Volumetric representations, Model-based reconstruction, Recovering texture maps and albedos.
View interpolation Layered depth images, Light fields and Lumigraphs, Environment mattes, Video-based rendering, Object detection, Face recognition, Instance recognition, Category recognition, Context and scene understanding, Recognition databases and test sets.
Richard Szeliski, “Computer Vision: Algorithms and Applications”, Springer- Texts in Computer Science, Second Edition, 2022.
D. A. Forsyth, J. Ponce, “Computer Vision: A Modern Approach”, Pearson Education, Second Edition, 2015.
Richard Hartley and Andrew Zisserman, “Multiple View Geometry in Computer Vision”, Second Edition, Cambridge University Press, March 2004.
Christopher M. Bishop, “Pattern Recognition and Machine Learning”, Springer, 2006
E. R. Davies, “Computer and Machine Vision”, Fourth Edition, Academic Press, 2012.
OpenCV Installation and working with Python
Basic Image Processing , loading images, Cropping, Resizing, Thresholding, Contour analysis, Bolb detection
Image Annotation – Drawing lines, text circle, rectangle, ellipse on images
Image Enhancement, Understanding Color spaces, color space conversion, Histogram equialization, Convolution, Image smoothing, Gradients, Edge Detection
Image Features and Image Alignment – Image transforms – Fourier, Hough, Extract ORB
Image features, Feature matching and cloning
Feature matching based image alignment
Image segmentation using Graphcut / Grabcut
Camera Calibration with circular grid
Pose Estimation
3D Reconstruction – Creating Depth map from stereo images
Explain the core concepts of the cloud computing paradigm, the characteristic and advantages of cloud computing.
Explain the concept of Virtualization, Utility Computing, Elastic Computing & grid computing.
Identify resource management fundamentals, i.e., resource abstraction, managing infrastructure in cloud computing, apply the fundamental concepts to understand the trade-offs in power, efficiency and cost in cloud paradigm.
Study Issues and Challenges while migrating to Cloud Computing technologies, applications and implementations.
Study of various Open Source and Commercial Cloud Computing Platforms.
Understand Cloud Computing, its characteristic and advantages.
Understand the concept of Virtualization Utility Computing, Elastic Computing & grid computing.
Apply cloud resource management fundamental to utilize there sources efficiently and cost effectively in cloud paradigm.
Understand Cloud security fundamentals & Issues in cloud computing
Develop real life Cloud Computing based projects.
Barrie Sosinsky:"CloudComputingBible",Wiley7India,2010
Rajkumar Buyya, James Broberg, Andrzej M. Goscinski: "Cloud Computing: Principles and Paradigms",Wiley, 2013.
RajkumarBuyya,ChristianVecchiola,S.Thamaraselvi,“MasteringCloudComputing”,McGraw
Hill, 2013.
NikosAntonopoulos,LeeGillam:"CloudComputing:Principles,Systemsand Applications",Springer,2012
Ronald L. Krutz, Russell Dean Vines: "Cloud Security: A Comprehensive Guide to Secure CloudComputing", Wiley7India,2010
TimMalhar,S.Kumaraswammy,S.Latif,“CloudSecurity&Privacy”,SPD,O’REILLY, 2009
Cloud Computing: Fundamentals, Industry Approach and Trends by Rishabh Sharma - John Wiley Publication.
Describe the basic components and fundamentals of BI.
Link data mining with business intelligence.
Understand the modeling aspects behind Business Intelligence.
Explain the data analysis and knowledge delivery stages.
Apply business intelligence methods to various situations and able to visualize the result.
Business Intelligence (BI), Scope of BI solutions and their fitting into existing infrastructure, BI Components, Future of Business Intelligence, Functional areas and description of BI tools, Data mining & warehouse, OLAP, Drawing insights from data: DIKW pyramid Business Analytics project methodology - detailed description of each phase.
Key Drivers, Key Performance Indicators and Performance Metrics, BI Architecture/Framework, Best Practices, Business Decision Making, Styles of BI-vent- Driven alerts – A cyclic process of Intelligence Creation, Ethics of Business Intelligence.
Representation of decision-making system, evolution of information system, definition and development of decision support system,Decision Taxonomy Principles of Decision Management Systems.
Definition and applications of data mining, data mining process, analysis methodologies, Typical pre-processing operations: combining values into one, handling incomplete or incorrect data, handling missing values, recoding values, sub setting, sorting, transforming scale, determining percentiles, data manipulation, removing noise, removing inconsistencies, transformations, standardizing, normalizing,min-max normalization, z-score. standardization,
rules of standardizing data. Role of visualization in analytics, different techniques for visualizing data.
Marketing models: Relational marketing, Salesforce management, Business case studies, supplychain optimization, optimization models for logistics planning, revenue management system.
Rajiv Sabherwal “Business Intelligence” Wiley Publications, 2012
Efraim Turban, Ramesh Sharda, Dursun Delen, “Decision Support and Business Intelligence Systems”, 9th Edition, Pearson 2013
S.K. Shinde and Uddagiri Chandrashekhar ,Data Mining and Business Intelligence (Includes Practicals), Dreamtech Press (1 January 2015)
Business Intelligence and Data Mining – by Anil K Maheshwari, publisher Business Expert Press- 2014.
Philo Janus, Stacia Misner, Building Integrated Business Intelligence Solutions with SQL, Server, 2008 R2 & Office 2010, TMH, 2011.
Business Intelligence Data Mining and Optimization for decision-making [Author: Carlo-Verellis][Publication: (Wiley) 2009].
After completing the course student should be able to:
Describe in-depth about theories, methods, and algorithms in computation Intelligence.
Compare and contrast traditional algorithms with nature inspired algorithms.
Examine the nature of a problem at hand and determine whether a computation intelligent technique/algorithm can solve it efficiently enough.
Understand Swarm Intelligence techniques.
Design and implement Computation Intelligence algorithms and approaches for solving real-life problems.
Russell C. Eberhart and Yuhui Shi, Computational Intelligence: Concepts to Implementations, Morgan Kaufmann Publishers.
Andries P. Engelbrecht, Computational Intelligence: An Introduction, Wiley Publishing.
Simon Haykin, Neural Networks: A Comprehensive Foundation, Prentice Hall.
David E. Goldberg, Genetic Algorithm in Search Optimization and Machine
Learning, Pearson Education.
Jagdish Chand Bansal, Pramod Kumar Singh, Nikhil R. Pal, Evolutionary and Swarm IntelligenceAlgorithms, Springer Publishing, 2019.
S. Rajeskaran, G.A. VijaylakshmiPai, “Neural Networks, Fuzzy Logic, GeneticAlgorithmsSynthesis and Applications”.
J.S. Roger Jang, C.T. Sun, E. Mizutani, “Neuro-Fuzzy and Soft Computing: A ComputationalApproach to Learning & Machine Intelligence”, PHI, 2002.
Learn the fundamental principles of Predictive analytics for business.
Visualize and explore data to better understand relationships among variables.
Examine how predictive analytics can be used in decision-making.
Apply predictive models to generate predictions for new data.
Apply Time Series analysis for solving real world problems.
Understand the importance of predictive analytics.
Able to prepare and process data for the models.
Learn about statistical analysis techniques used in predictive models. 4.Learnabout important time series models and their applications in variousfields. 5.Formulate real life problems using multivariate time series models and its
applications.
Introduction to predictive analytics – Business analytics: types, applications- Models: predictive models – descriptive models – decision models - applications - analytical techniques.
Data types and associated techniques – complexities of data – data preparation, pre- processing – exploratory data analysis.
Measuring Performance in Regression Models - Linear Regression and Its Cousins - Non-
Linear Regression Models - Regression Trees and Rule-Based Models Case Study: Compressive Strength of Concrete Mixtures.
Unit-IV Time Series Analysis: Introduction , Examples of time series, Stationary models and autocorrelation function, Estimation and elimination of trend and seasonal components, Stationary Process and ARMA Models -- Basic properties and linear processes, Introduction to ARMA models, properties of sample mean and autocorrelation, function, Forecasting stationary time series, ARMA(p, q) processes, ACF and PACF, Modeling and Forecasting with ARMA.
Jeffrey Strickland, Predictive analytics using R, Simulation educators, Colorado Springs, 2015.
Max Kuhn and Kjell Johnson, Applied Predictive Modeling, 1st edition Springer, 2013.
Brockwell, Peter J. and Davis, Richard A. (2002). Introduction to Time Series andForecasting, 2nd edition. Springer-Verlag, New York.
Anasse Bari, Mohamed Chaouchi, Tommy Jung, Predictive analytics for dummies, 2nd edition Wiley, 2016.
Dinov, ID., Data Science and Predictive Analytics: Biomedical and Health Applications using R, Springer, 2018.
Daniel T.Larose and Chantal D.Larose, Data Mining and Predictive analytics, 2nd edition Wiley, 2015.
Data Mining and Predictive Analytics, 2ed (An Indian Adaptation) by Daniel Larose, OP Wali - John Wiley Publication
To understand how to accurately represent voluminous complex data set on the web and from other data sources.
To understand the methodologies used to visualize large data sets.
To understand the various process involved in data visualization.
To get used to using interactive data visualization.
To understand the different security aspects involved in data visualization.
Understand the representation of complex and voluminous data.
Design and use various methodologies present in data visualization.
Understand the various process and tools used for data visualization.
Use interactive data visualization to make inferences.
Ability to visualize categorical, quantitative and text data.
Overview of data visualization, Definition, Significance in AI and Data Science, Principal of Data Visualization, Methodology, Applications, Data pre-processing for visualization: Extraction, Cleaning, Transformation, Aggregation, Data Integration, Data Reduction.
Data Visualization Techniques– Pixel-Oriented Visualization Techniques- Geometric Projection Visualization Techniques- Icon-Based Visualization Techniques- Hierarchical Visualization Techniques, Visualizing Complex Data and Relations.
Visualization Techniques, Scalar and point techniques, Color maps, Contouring Height Plots
- Vector visualization techniques, Vector properties, Vector Glyphs, Vector Color Coding Stream Objects. Exploratory data analysis (EDA) Techniques
Basic and advanced charts and graphs: bar charts, line charts, scatter plots, histograms, and heat maps. Geospatial visualization: maps, choropleth maps, geospatial heat maps, Network visualization: node-link diagrams, force-directed graphs, Interactive visualization: interactivity and user engagement techniques, Introduction to programming libraries for data visualization: Matplotlib, Seaborn, Plotly.
Introduction to data visualization tools- Tableau, Visualization using R.
Multivariate visualization techniques: parallel coordinates, scatter plot matrices, Dimensionality reduction techniques: PCA (Principal Component Analysis), t-SNE (t- Distributed Stochastic Neighbour Embedding), Clustering and classification visualization: dendrograms, decision trees, confusion matrices, Visualizing high-dimensional data: glyph- based visualization, parallel coordinates, dimension stacking.
Time- Series data visualization, Big data visualization, Text data visualization Multivariate data visualization. Storytelling with data, Dashboard creation, Ethical considerations in data visualization, Case Studies for Finance-marketing, and insurance healthcare.
Tamara Munzer, “Visualization Analysis and Design”, CRC Press 2014
Alexandru Telea, “Data Visualization Principles and Practice” CRC Press 2014.
Data Visualization: Storytelling Using Data by Sharada Sringeswara - John Wiley Publication
Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures Paperback – 31 March 2019 by Claus O. Wilke (Author), by O’Reilly.
Reimagining Data Visualization Using Python by Seema Acharya - John Wiley Publication.
To facilitate students to understand Android SDK
To help students to gain a basic understanding of Android application development
To inculcate working knowledge of Android Studio development tool
After the completion of this course, the students will be able to:
Identify various concepts of mobile programming that make it unique from programming for other platforms.
Critique mobile applications on their design pros and cons.
Utilize rapid prototyping techniques to design and develop sophisticated mobile interfaces.
Program mobile applications for the Android operating system that use basic and advanced phone features.
Deploy applications to the Android marketplace for distribution.
Lauren Darcey and Shane Conder, “Android Wireless Application Development”, Addison-Wesley,2009.
Reto Meier, “Professional AndroidTM Application Development”, Wiley Publishing,2014.
Mark L Murphy, “Beginning Android”, Wiley India Pvt Ltd.,2009.
Joseph Annuzzi, Jr, Lauren Darcey and Shane Conder, “Advanced AndroidTM Application Development”, Fourth Edition, Addison-Wesley,2014.
Barry Burd ,“Android Application Development All-in-One For Dummies”, Wiley, 2015.
To provide a comprehensive understanding of sampling techniques and sampling distributions.
To develop skills in correlation and regression analysis for analyzing relationships between variables.
To introduce hypothesis testing and provide knowledge of various tests for means, proportions, and variances.
To explore the concept of point estimation and develop an understanding of different estimation methods.
To introduce Bayesian statistics and its applications in data analysis.
Understand the concepts of population, sample, and different sampling techniques.
Apply various statistical methods to analyze relationships between variables using correlation and regression analysis.
Conduct hypothesis tests for means, proportions, variances, and correlation coefficients.
Estimate population parameters using different estimation methods and determine the quality of estimators.
Apply Bayesian statistics for parameter estimation and understand the concepts of hierarchical modeling and survival analysis in Bayesian inference.
Testing of Hypotheses: Null and Alternative Hypothesis, Testing Procedure (Critical region), Type I and Type II errors, Level of significance & Power of atest, p-value for symmetric null distribution. Tests for me an and proportion (single sample, two sample; exact & large sample)
Tests for variance (single sample and two samples), Tests for me an and correlation coefficient for paired sample (Exact & Large sample), Analysis of Variance (one way).
Statistical Methods by SP Gupta : 31st Edition: Sultan Chand and sons
Mathematical Statistics by S.C Gupta and VK Kapoor (10th Edition) : Sultan Chand and sons
Understanding and using Advance Statistics by Jeremy Foster Emma Barkus Christian Yavorsay, Sage Publication.
Understanding Advanced Statistical Methods (Chapman & Hall/CRC Texts in Statistical Science), by Peter Westfall, Kevin S. S. Henning ,2013
Understand the use and applications of Social media Analytics.
Apply the fundamentals of social and web analytics on various social media platforms.
Understand the fundamentals of web metrics & Analysis.
Able to perform web 2.0 Analytics.
After the completion of this course, the students will be able to:
Understand social media, web and social media analytics, and their potential impact.
Learn the usability metrics, web and social media metrics.
Identify key performance indicators for a given goal; identify data relating to the metrics and key performance indicators.
Perform web analytics on social media platform like- Facebook and Google.
Perform qualitative Analysis based on heuristic evaluation.
Type and Size of Data, Identifying Unique page Definition, Cookies, Link Coding Issues.
Matthew Ganis, Avinash Kohirkar, Social Media Analytics: Techniques and Insights for Extracting Business Value Out of Social Media Pearson 2016
Jim Sterne, Social Media Metrics: How to Measure and Optimize Your Marketing Investment Wiley Latest edition
Brian Clifton, Advanced Web Metrics with Google Analytics, John Wiley & Sons; 3rd Edition edition (30 Mar 2012)
Ganis/Kohirka, SOCIAL MEDIA ANALYTICS Paperback – 29 September 2016 by Pearson.
To understand the fundamental concepts related to Image formation and processing.
To learn feature detection, matching and detection.
To become familiar with feature based alignment and motion estimation.
To develop skills on 3D reconstruction.
To understand image based rendering and recognition.
After the completion of this course, the students will be able to:
1: Understand basic knowledge, theories and methods in image processing and computer vision. 2: Implement basic and some advanced image processing techniques in OpenCV.
3: Apply 2D a feature-based based image alignment, segmentation and motion estimations. 4: Apply 3D image reconstruction techniques.
5: Design and develop innovative image processing and computer vision applications.
Computer Vision, Geometric primitives and transformations, Photometric image formation, digital camera, Point operators, Linear filtering, More neighborhood operators, Fourier transforms, Pyramids and wavelets, Geometric transformations, Global optimization.
Points and patches, Edges, Lines, Segmentation, Active contours, Split and merge, Mean shift and mode finding, Normalized cuts, Graph cuts and energy-based methods.
2D and 3D feature-based alignment, Pose estimation, Geometric intrinsic calibration, Triangulation, Two-frame structure from motion, Factorization, Bundle adjustment, Constrained structure and motion, Translational alignment, Parametric motion, Spline-based motion, Optical flow, Layered motion.
Shape from X, Active range finding, Surface representations, Point-based representations Volumetric representations, Model-based reconstruction, Recovering texture maps and albedos.
View interpolation Layered depth images, Light fields and Lumigraphs, Environment mattes, Video-based rendering, Object detection, Face recognition, Instance recognition, Category recognition, Context and scene understanding, Recognition databases and test sets.
Richard Szeliski, “Computer Vision: Algorithms and Applications”, Springer- Texts in Computer Science, Second Edition, 2022.
D. A. Forsyth, J. Ponce, “Computer Vision: A Modern Approach”, Pearson Education, Second Edition, 2015.
Richard Hartley and Andrew Zisserman, “Multiple View Geometry in Computer Vision”, Second Edition, Cambridge University Press, March 2004.
Christopher M. Bishop, “Pattern Recognition and Machine Learning”, Springer, 2006
E. R. Davies, “Computer and Machine Vision”, Fourth Edition, Academic Press, 2012.
OpenCV Installation and working with Python
Basic Image Processing , loading images, Cropping, Resizing, Thresholding, Contour analysis, Bolb detection
Image Annotation – Drawing lines, text circle, rectangle, ellipse on images
Image Enhancement, Understanding Color spaces, color space conversion, Histogram equialization, Convolution, Image smoothing, Gradients, Edge Detection
Image Features and Image Alignment – Image transforms – Fourier, Hough, Extract ORB
Image features, Feature matching and cloning
Feature matching based image alignment
Image segmentation using Graphcut / Grabcut
Camera Calibration with circular grid
Pose Estimation
3D Reconstruction – Creating Depth map from stereo images
Explain the core concepts of the cloud computing paradigm, the characteristic and advantages of cloud computing.
Explain the concept of Virtualization, Utility Computing, Elastic Computing & grid computing.
Identify resource management fundamentals, i.e., resource abstraction, managing infrastructure in cloud computing, apply the fundamental concepts to understand the trade-offs in power, efficiency and cost in cloud paradigm.
Study Issues and Challenges while migrating to Cloud Computing technologies, applications and implementations.
Study of various Open Source and Commercial Cloud Computing Platforms.
Understand Cloud Computing, its characteristic and advantages.
Understand the concept of Virtualization Utility Computing, Elastic Computing & grid computing.
Apply cloud resource management fundamental to utilize there sources efficiently and cost effectively in cloud paradigm.
Understand Cloud security fundamentals & Issues in cloud computing
Develop real life Cloud Computing based projects.
Barrie Sosinsky:"CloudComputingBible",Wiley7India,2010
Rajkumar Buyya, James Broberg, Andrzej M. Goscinski: "Cloud Computing: Principles and Paradigms",Wiley, 2013.
RajkumarBuyya,ChristianVecchiola,S.Thamaraselvi,“MasteringCloudComputing”,McGraw
Hill, 2013.
NikosAntonopoulos,LeeGillam:"CloudComputing:Principles,Systemsand Applications",Springer,2012
Ronald L. Krutz, Russell Dean Vines: "Cloud Security: A Comprehensive Guide to Secure CloudComputing", Wiley7India,2010
TimMalhar,S.Kumaraswammy,S.Latif,“CloudSecurity&Privacy”,SPD,O’REILLY, 2009
Cloud Computing: Fundamentals, Industry Approach and Trends by Rishabh Sharma - John Wiley Publication.
Describe the basic components and fundamentals of BI.
Link data mining with business intelligence.
Understand the modeling aspects behind Business Intelligence.
Explain the data analysis and knowledge delivery stages.
Apply business intelligence methods to various situations and able to visualize the result.
Business Intelligence (BI), Scope of BI solutions and their fitting into existing infrastructure, BI Components, Future of Business Intelligence, Functional areas and description of BI tools, Data mining & warehouse, OLAP, Drawing insights from data: DIKW pyramid Business Analytics project methodology - detailed description of each phase.
Key Drivers, Key Performance Indicators and Performance Metrics, BI Architecture/Framework, Best Practices, Business Decision Making, Styles of BI-vent- Driven alerts – A cyclic process of Intelligence Creation, Ethics of Business Intelligence.
Representation of decision-making system, evolution of information system, definition and development of decision support system,Decision Taxonomy Principles of Decision Management Systems.
Definition and applications of data mining, data mining process, analysis methodologies, Typical pre-processing operations: combining values into one, handling incomplete or incorrect data, handling missing values, recoding values, sub setting, sorting, transforming scale, determining percentiles, data manipulation, removing noise, removing inconsistencies, transformations, standardizing, normalizing,min-max normalization, z-score. standardization,
rules of standardizing data. Role of visualization in analytics, different techniques for visualizing data.
Marketing models: Relational marketing, Salesforce management, Business case studies, supplychain optimization, optimization models for logistics planning, revenue management system.
Rajiv Sabherwal “Business Intelligence” Wiley Publications, 2012
Efraim Turban, Ramesh Sharda, Dursun Delen, “Decision Support and Business Intelligence Systems”, 9th Edition, Pearson 2013
S.K. Shinde and Uddagiri Chandrashekhar ,Data Mining and Business Intelligence (Includes Practicals), Dreamtech Press (1 January 2015)
Business Intelligence and Data Mining – by Anil K Maheshwari, publisher Business Expert Press- 2014.
Philo Janus, Stacia Misner, Building Integrated Business Intelligence Solutions with SQL, Server, 2008 R2 & Office 2010, TMH, 2011.
Business Intelligence Data Mining and Optimization for decision-making [Author: Carlo-Verellis][Publication: (Wiley) 2009].
After completing the course student should be able to:
Describe in-depth about theories, methods, and algorithms in computation Intelligence.
Compare and contrast traditional algorithms with nature inspired algorithms.
Examine the nature of a problem at hand and determine whether a computation intelligent technique/algorithm can solve it efficiently enough.
Understand Swarm Intelligence techniques.
Design and implement Computation Intelligence algorithms and approaches for solving real-life problems.
Russell C. Eberhart and Yuhui Shi, Computational Intelligence: Concepts to Implementations, Morgan Kaufmann Publishers.
Andries P. Engelbrecht, Computational Intelligence: An Introduction, Wiley Publishing.
Simon Haykin, Neural Networks: A Comprehensive Foundation, Prentice Hall.
David E. Goldberg, Genetic Algorithm in Search Optimization and Machine
Learning, Pearson Education.
Jagdish Chand Bansal, Pramod Kumar Singh, Nikhil R. Pal, Evolutionary and Swarm IntelligenceAlgorithms, Springer Publishing, 2019.
S. Rajeskaran, G.A. VijaylakshmiPai, “Neural Networks, Fuzzy Logic, GeneticAlgorithmsSynthesis and Applications”.
J.S. Roger Jang, C.T. Sun, E. Mizutani, “Neuro-Fuzzy and Soft Computing: A ComputationalApproach to Learning & Machine Intelligence”, PHI, 2002.
Learn the fundamental principles of Predictive analytics for business.
Visualize and explore data to better understand relationships among variables.
Examine how predictive analytics can be used in decision-making.
Apply predictive models to generate predictions for new data.
Apply Time Series analysis for solving real world problems.
Understand the importance of predictive analytics.
Able to prepare and process data for the models.
Learn about statistical analysis techniques used in predictive models. 4.Learnabout important time series models and their applications in variousfields. 5.Formulate real life problems using multivariate time series models and its
applications.
Introduction to predictive analytics – Business analytics: types, applications- Models: predictive models – descriptive models – decision models - applications - analytical techniques.
Data types and associated techniques – complexities of data – data preparation, pre- processing – exploratory data analysis.
Measuring Performance in Regression Models - Linear Regression and Its Cousins - Non-
Linear Regression Models - Regression Trees and Rule-Based Models Case Study: Compressive Strength of Concrete Mixtures.
Unit-IV Time Series Analysis: Introduction , Examples of time series, Stationary models and autocorrelation function, Estimation and elimination of trend and seasonal components, Stationary Process and ARMA Models -- Basic properties and linear processes, Introduction to ARMA models, properties of sample mean and autocorrelation, function, Forecasting stationary time series, ARMA(p, q) processes, ACF and PACF, Modeling and Forecasting with ARMA.
Jeffrey Strickland, Predictive analytics using R, Simulation educators, Colorado Springs, 2015.
Max Kuhn and Kjell Johnson, Applied Predictive Modeling, 1st edition Springer, 2013.
Brockwell, Peter J. and Davis, Richard A. (2002). Introduction to Time Series andForecasting, 2nd edition. Springer-Verlag, New York.
Anasse Bari, Mohamed Chaouchi, Tommy Jung, Predictive analytics for dummies, 2nd edition Wiley, 2016.
Dinov, ID., Data Science and Predictive Analytics: Biomedical and Health Applications using R, Springer, 2018.
Daniel T.Larose and Chantal D.Larose, Data Mining and Predictive analytics, 2nd edition Wiley, 2015.
Data Mining and Predictive Analytics, 2ed (An Indian Adaptation) by Daniel Larose, OP Wali - John Wiley Publication
To understand how to accurately represent voluminous complex data set on the web and from other data sources.
To understand the methodologies used to visualize large data sets.
To understand the various process involved in data visualization.
To get used to using interactive data visualization.
To understand the different security aspects involved in data visualization.
Understand the representation of complex and voluminous data.
Design and use various methodologies present in data visualization.
Understand the various process and tools used for data visualization.
Use interactive data visualization to make inferences.
Ability to visualize categorical, quantitative and text data.
Overview of data visualization, Definition, Significance in AI and Data Science, Principal of Data Visualization, Methodology, Applications, Data pre-processing for visualization: Extraction, Cleaning, Transformation, Aggregation, Data Integration, Data Reduction.
Data Visualization Techniques– Pixel-Oriented Visualization Techniques- Geometric Projection Visualization Techniques- Icon-Based Visualization Techniques- Hierarchical Visualization Techniques, Visualizing Complex Data and Relations.
Visualization Techniques, Scalar and point techniques, Color maps, Contouring Height Plots
- Vector visualization techniques, Vector properties, Vector Glyphs, Vector Color Coding Stream Objects. Exploratory data analysis (EDA) Techniques
Basic and advanced charts and graphs: bar charts, line charts, scatter plots, histograms, and heat maps. Geospatial visualization: maps, choropleth maps, geospatial heat maps, Network visualization: node-link diagrams, force-directed graphs, Interactive visualization: interactivity and user engagement techniques, Introduction to programming libraries for data visualization: Matplotlib, Seaborn, Plotly.
Introduction to data visualization tools- Tableau, Visualization using R.
Multivariate visualization techniques: parallel coordinates, scatter plot matrices, Dimensionality reduction techniques: PCA (Principal Component Analysis), t-SNE (t- Distributed Stochastic Neighbour Embedding), Clustering and classification visualization: dendrograms, decision trees, confusion matrices, Visualizing high-dimensional data: glyph- based visualization, parallel coordinates, dimension stacking.
Time- Series data visualization, Big data visualization, Text data visualization Multivariate data visualization. Storytelling with data, Dashboard creation, Ethical considerations in data visualization, Case Studies for Finance-marketing, and insurance healthcare.
Tamara Munzer, “Visualization Analysis and Design”, CRC Press 2014
Alexandru Telea, “Data Visualization Principles and Practice” CRC Press 2014.
Data Visualization: Storytelling Using Data by Sharada Sringeswara - John Wiley Publication
Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures Paperback – 31 March 2019 by Claus O. Wilke (Author), by O’Reilly.
Reimagining Data Visualization Using Python by Seema Acharya - John Wiley Publication.
To facilitate students to understand Android SDK
To help students to gain a basic understanding of Android application development
To inculcate working knowledge of Android Studio development tool
After the completion of this course, the students will be able to:
Identify various concepts of mobile programming that make it unique from programming for other platforms.
Critique mobile applications on their design pros and cons.
Utilize rapid prototyping techniques to design and develop sophisticated mobile interfaces.
Program mobile applications for the Android operating system that use basic and advanced phone features.
Deploy applications to the Android marketplace for distribution.
Lauren Darcey and Shane Conder, “Android Wireless Application Development”, Addison-Wesley,2009.
Reto Meier, “Professional AndroidTM Application Development”, Wiley Publishing,2014.
Mark L Murphy, “Beginning Android”, Wiley India Pvt Ltd.,2009.
Joseph Annuzzi, Jr, Lauren Darcey and Shane Conder, “Advanced AndroidTM Application Development”, Fourth Edition, Addison-Wesley,2014.
Barry Burd ,“Android Application Development All-in-One For Dummies”, Wiley, 2015.
To provide a comprehensive understanding of sampling techniques and sampling distributions.
To develop skills in correlation and regression analysis for analyzing relationships between variables.
To introduce hypothesis testing and provide knowledge of various tests for means, proportions, and variances.
To explore the concept of point estimation and develop an understanding of different estimation methods.
To introduce Bayesian statistics and its applications in data analysis.
Understand the concepts of population, sample, and different sampling techniques.
Apply various statistical methods to analyze relationships between variables using correlation and regression analysis.
Conduct hypothesis tests for means, proportions, variances, and correlation coefficients.
Estimate population parameters using different estimation methods and determine the quality of estimators.
Apply Bayesian statistics for parameter estimation and understand the concepts of hierarchical modeling and survival analysis in Bayesian inference.
Testing of Hypotheses: Null and Alternative Hypothesis, Testing Procedure (Critical region), Type I and Type II errors, Level of significance & Power of atest, p-value for symmetric null distribution. Tests for me an and proportion (single sample, two sample; exact & large sample)
Tests for variance (single sample and two samples), Tests for me an and correlation coefficient for paired sample (Exact & Large sample), Analysis of Variance (one way).
Statistical Methods by SP Gupta : 31st Edition: Sultan Chand and sons
Mathematical Statistics by S.C Gupta and VK Kapoor (10th Edition) : Sultan Chand and sons
Understanding and using Advance Statistics by Jeremy Foster Emma Barkus Christian Yavorsay, Sage Publication.
Understanding Advanced Statistical Methods (Chapman & Hall/CRC Texts in Statistical Science), by Peter Westfall, Kevin S. S. Henning ,2013
Understand the use and applications of Social media Analytics.
Apply the fundamentals of social and web analytics on various social media platforms.
Understand the fundamentals of web metrics & Analysis.
Able to perform web 2.0 Analytics.
After the completion of this course, the students will be able to:
Understand social media, web and social media analytics, and their potential impact.
Learn the usability metrics, web and social media metrics.
Identify key performance indicators for a given goal; identify data relating to the metrics and key performance indicators.
Perform web analytics on social media platform like- Facebook and Google.
Perform qualitative Analysis based on heuristic evaluation.
Type and Size of Data, Identifying Unique page Definition, Cookies, Link Coding Issues.
Matthew Ganis, Avinash Kohirkar, Social Media Analytics: Techniques and Insights for Extracting Business Value Out of Social Media Pearson 2016
Jim Sterne, Social Media Metrics: How to Measure and Optimize Your Marketing Investment Wiley Latest edition
Brian Clifton, Advanced Web Metrics with Google Analytics, John Wiley & Sons; 3rd Edition edition (30 Mar 2012)
Ganis/Kohirka, SOCIAL MEDIA ANALYTICS Paperback – 29 September 2016 by Pearson.