Instructor Profile:
Mrs. Akanksha Mishra
Mrs. Akanksha Mishra, an accomplished professional serving as an Assistant Professor in the Faculty of Computer Science & Information Technology at Kalinga University, Naya Raipur, Chhattisgarh. Her academic journey includes an M.Tech. in Computer Science and a B.E. in Computer Science. With over 8 years of dedicated experience in both teaching and research, Mrs. Mishra has established herself as an expert in the field of Computer Science. Her commitment to professional development is evident through her participation in the 5-Day Residential Faculty Development Program on High-Performance Computing (HPC) organized by C-DAC, Bengaluru, from June 19 to June 23, 2023.
Demonstrating a keen interest in staying at the forefront of technological advancements, Mrs. Mishra has completed notable online courses. She achieved a commendable score of 77% in the “Foundation of Cloud IoT Edge ML” course and an impressive 93% in “Big Data Computing,” both offered by NPTEL-AICTE. Her academic contributions extend beyond the classroom, with active participation in five workshops and attendance at ten National and International conferences, where she presented valuable papers. Mrs. Mishra’s research prowess is further highlighted by her publication of one patent, thirteen Research and Review papers in esteemed National and International Journals, and three book chapters in recognized National and International Books.
Mrs. Akanksha Mishra is not only an educator but also a distinguished member of various professional societies, including the prestigious IEEE (Institute of Electrical and Electronics Engineers) and the Computer Society of India (CSI) at Kalinga University. Additionally, she is a member of the National Institute for Technical Training & Skill Development (NITTSD) since 2022. Recognizing her outstanding achievements, Mrs. Mishra was honoured with the Outstanding Performance in Academics Certificate in 2023. As a seasoned professional, she brings a wealth of knowledge and expertise, making her an ideal presenter for video lecture course content in the realm of Computer Science and Information Technology.
Glimpse of Instructor Background
Educational Background
- B. Tech. in Computer Science
- M.E. in Computer Science
Areas of Specialization:
- Computer Science
Professional Experience:
- With over 8 years of experience in teaching and research, Mrs. Mishra brings a wealth of knowledge to her role.
Recent Achievements:
- Selected for a 5-Day Residential Faculty Development Program (FDP) on High-Performance Computing (HPC) by C-DAC, Bengaluru (June 19, 2023, to June 23, 2023).
- Successful completion of the NPTEL-AICTE MOOC course on “Foundation of Cloud IoT Edge ML” (Score: 77%) and “Big Data Computing” (Score: 93%).
Research Contributions:
- Attended five workshops and participated in ten National and International conferences, presenting research papers.
- Published one patent and thirteen Research and Review papers in various National and International Journals.
- Authored three book chapters in National and International Books.
Professional Memberships:
Mrs. Mishra is an active member of several professional societies, including:
- IEEE: Institute of Electrical and Electronics Engineers
- CSI Membership: Computer Society of India, Kalinga University
- NITTSD Membership (2022): National Institute for Technical Training & Skill Development
Awards and Recognition:
- Received the Outstanding Performance in Academics Certificate in 2023.
With her extensive educational and professional background, Mrs. Akanksha Mishra is well-equipped to bring a high level of expertise and professionalism to delivering course content in the field of Computer Science and Information Technology.
Module-wise course content: Data Analytics
Module I: Fundamentals of Data Analytics
- Understanding Data Analytics: Exploring data sources, classifying data into structured, semi-structured, and unstructured, analyzing data characteristics, and an introduction to the Big Data platform.
- Significance of Data Analytics: Unveiling the need for data analytics, tracing the evolution of analytic scalability, exploring the analytic process and tools, differentiating analysis vs. reporting, and exploring modern data analytic tools and applications.
Module II: Data Analytics Lifecycle
- Navigating the Analytics Journey: Recognizing the essential phases of the data analytics lifecycle, including discovery, data preparation, model planning, model building, communicating results, and operationalization.
- Key Roles in Analytics Projects: Identifying the pivotal roles for successful analytic projects, emphasizing the significance of each role in the analytics lifecycle.
Module III: Advanced Data Analysis Techniques
- Regression Modeling and Beyond: Delving into regression modeling, multivariate analysis, Bayesian modeling, inference, Bayesian networks, support vector and kernel methods, time series analysis, rule induction, neural networks, and fuzzy logic.
- Real-time Data Analytics: Exploring the fundamentals of mining data streams, stream data model, architecture, stream computing, and applications of Real-time Analytics Platform (RTAP). Case studies include real-time sentiment analysis and stock market predictions.
Module IV: Mining Patterns and Clustering
- Extracting Patterns: Understanding the mining of frequent itemsets, market-based modeling, Apriori algorithm, and handling large datasets. Exploring clustering techniques such as hierarchical, K-means, CLIQUE, ProCLUS, and methods for clustering in non-Euclidean space.
Module V: Frameworks and Visualization
- Frameworks for Big Data: Introducing MapReduce, Hadoop, Pig, Hive, HBase, MapR, Sharding, NoSQL Databases, S3, and Hadoop Distributed File Systems.
- Visualization Techniques: Understanding visual data analysis techniques, interaction methods, and exploring systems and applications for effective data visualization.
- Introduction to R: Covering R graphical user interfaces, data import and export, attribute and data types, descriptive statistics, exploratory data analysis, and visualization before analysis. Highlighting analytics applications for unstructured data.
Each module is designed to provide a comprehensive understanding of the respective topic, ensuring a structured and engaging learning experience for the audience.
