Pariza Kamboj
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Professor
Sarvajanik College of Engineering & Technology (SCET) |
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Dr. Pariza Kamboj is currently associated with Sarvajanik College of Engineering & Technology (SCET) Surat as Professor & HoD in Computer Engineering Department. She has more than 25 years of teaching experience in various reputed engineering colleges in India. She received her Ph. D (Computer Engineering) from MDU Rohtak in year 2011. She did her M.Tech (Comp. Sc. & Engg.) with Distinction from Kururkshetra University (KU), Kurukshetra, Haryana (India) in the year 2006. Her research interest areas are Deep Learning, Machine Learning, IoT, Big Data Analytics, Data Science, Python for Data Science, WSN, Mobile Ad-hoc Networks, Computer Networks, and Network Security. She has published a total of 47 research papers in various International, National Journals, International, National Conferences of repute. She is a member of various professional bodies like Institution of Engineers, Computer Society of India, Indian Society of Technical Education (ISTE), IFERP, ISRD and International Association of Computer Science and Information Technology (IACSIT). She has got published/filed 5 patents in India and Australia. She has done a consultancy and provided a cost effective and efficient solution to the problem titled “Optimizing IVF Predictions and Procedure for Better Outcomes”. She believes in lifelong learning and keeps on updating her knowledge base with the latest know-hows and has done approximate 33 trainings on “Deep Learning for computer vision” and “Accelerated Computing with Cuda” from Nvidia Deep Learning Institute and earned certifications on various cutting-edge technologies from NPTEL, Coursera and IBM. She is an IBM Certified Associate Developer for Rational Application Developer for WebSphere Software V6.0. She is a regular contributor on research platforms in the form of Ph.D. Supervisor at Gujarat Technological University, Sarvajanik University, and DPC members in Ph.D. panels of various universities. She is the advisory committee member, organizing committee member, and reviewer of various International Conferences of repute. She loves to share her knowledge and disseminated expert talks on topics of thrust areas in various engineering institutes and workshops. She was/is a member of Board of Studies (Computer Engg.) and Governing Body of various universities in India. She loves to share her knowledge and disseminated expert talks on topics of thrust areas in various engineering institutes, management institutes and workshops. |
Workshop: Effective and Ideal Data Presentation using Visualization Techniques and Lucid Perceptions
May 25, 2022; 8:00-8:45am, PST
May 25, 2022; 8:00-8:45am, PST
Research proves that the human brain processes visualizations better than text. And data visualizations prove that further.
Data visualization is the last phase in the data life cycle. It is the art and science of making data easy to understand and consume for the end user. Data visualizations present clusters of data in an easy-to-understand layout and that’s the reason it becomes mandatory for large amounts of complex data.
Ideal data visualization shows the right amount of data, in the right order, in the right visual form, to convey the high priority information to the right audience and for the right purpose. If the data is presented in too much detail, then the consumer of that data might lose interest and the insight.
There are innumerable types of visual graphing techniques available for visualizing data. The right visualization arises from an understanding of the totality of the situation in context of the business domain’s functioning, consumers’ needs, nature of data, and the appropriate tools and techniques to present data. Ideal data visualization should tell a true, complete and simple story backed by data effectively, while keeping it insightful and engaging.
Data visualization is the last phase in the data life cycle. It is the art and science of making data easy to understand and consume for the end user. Data visualizations present clusters of data in an easy-to-understand layout and that’s the reason it becomes mandatory for large amounts of complex data.
Ideal data visualization shows the right amount of data, in the right order, in the right visual form, to convey the high priority information to the right audience and for the right purpose. If the data is presented in too much detail, then the consumer of that data might lose interest and the insight.
There are innumerable types of visual graphing techniques available for visualizing data. The right visualization arises from an understanding of the totality of the situation in context of the business domain’s functioning, consumers’ needs, nature of data, and the appropriate tools and techniques to present data. Ideal data visualization should tell a true, complete and simple story backed by data effectively, while keeping it insightful and engaging.
Workshop: Demystifying Data Pre-processing, and Data Wrangling for Data Science -- A Comprehensive Hands on
April 27, 2022; 8:00-8:45am, PST
April 27, 2022; 8:00-8:45am, PST
In the current era, Data Science is rapidly evolving and proving very decisive in ERP (Enterprise Resource Planning). The dataset required for building the analytical model using data science, is collected from various sources such as Government, Academic, Web Scraping, API’s, Databases, Files, Sensors and many more. We cannot use such real-world data for analysis process directly because it is often inconsistent, incomplete, and more likely to contain bulk errors. We often hear the phrase “garbage in, garbage out”. Dirty data or messy data riddled with inaccuracies and errors, result in a bad/improperly trained model which in turn might result in poor business decisions and sometimes even hazardous to the domain. Any powerful algorithm is failed in providing correct analysis when applied to bad data. Therefore, data must be curated, cleaned and refined to be used in data science and products based on data science. To perform these tasks, “Data Preparation” is required which includes two methods that are: Data Pre-processing, and Data Wrangling. Most data scientists spend the majority of their time in data preparation.
Data pre-processing method converts the raw unstructured data into an understandable format that is the requirement of most machine learning algorithms. It does a pre-analysis of data, in order to transform them into a standard and normalized format.
Data Wrangling also known as data munging, is the process of discovering, cleaning, organizing, restructuring, and enriching the raw/complex data into a convenient format for the consumption of data for further analysis and visualization purposes. With more amount of unstructured data, it is essential to perform Data Wrangling for making smarter and more accurate business decisions.
Data pre-processing is used before building an analytic model, while data wrangling is used to adjust data sets interactively while analysing data and building a model.
Thus, data preparation helps in establishing the quality of data on various parameters before applying to data science like: accuracy, completeness, consistency, timeliness, believability, and interpretability, etc. Such quality data when operated upon with appropriate machine learning algorithms fetch the perfect analysis which can be utilized efficiently in taking correct decisions.
Data pre-processing method converts the raw unstructured data into an understandable format that is the requirement of most machine learning algorithms. It does a pre-analysis of data, in order to transform them into a standard and normalized format.
Data Wrangling also known as data munging, is the process of discovering, cleaning, organizing, restructuring, and enriching the raw/complex data into a convenient format for the consumption of data for further analysis and visualization purposes. With more amount of unstructured data, it is essential to perform Data Wrangling for making smarter and more accurate business decisions.
Data pre-processing is used before building an analytic model, while data wrangling is used to adjust data sets interactively while analysing data and building a model.
Thus, data preparation helps in establishing the quality of data on various parameters before applying to data science like: accuracy, completeness, consistency, timeliness, believability, and interpretability, etc. Such quality data when operated upon with appropriate machine learning algorithms fetch the perfect analysis which can be utilized efficiently in taking correct decisions.
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