Eikon Club Activity - ANVEDNA

The Department of Information Science and Engineering organized an event for coding enthusiast on 26^th of August 2023, at MVJCE DEPARTMENT LABORATORY. The event started at 10:30 am and concluded at 03:00pm. A total of 140 students from IV semester and 100 students from VII semester of the Dept of ISE and Data Science attended the event.

Chief Guest Karthiban Pandiyan, Business and Digital Analyst from IIM , Bangalore, inaugurated the event, and transferred his experiences in Machine learning and Data science.

He explained the various aspects of Data Science. Data science is the study of data to extract meaningful insights for business. It is a multidisciplinary approach that combines principles and practices from the fields of mathematics, statistics, artificial intelligence, and computer engineering to analyze large amounts of data.

Data science is used to study data in four main ways:

1. Descriptive analysis

Descriptive analysis examines data to gain insights into what has happened or what is happening in the data environment. It is characterized by data visualizations such as pie charts, bar charts, line graphs, tables, or generated narratives. For example, a flight booking service may record data like the number of tickets booked each day. Descriptive analysis will reveal booking spikes, booking slumps, and high-performing months for this service.

2. Diagnostic analysis

Diagnostic analysis is a deep-dive or detailed data examination to understand why something has happened. It is characterized by techniques such as drill-down, data discovery, data mining, and correlations. Multiple data operations and transformations may be performed on a given data set to discover unique patterns in each of these techniques.For example, the flight service might drill down on a particularly high-performing month to better understand the booking spike. This may lead to the discovery that many customers visit a particular city to attend a monthly sporting event.

3. Predictive analysis

Predictive analysis uses historical data to make accurate forecasts about data patterns that may occur in the future. It is characterized by techniques such as machine learning, forecasting, pattern matching, and predictive modeling. In each of these techniques, computers are trained to reverse engineer causality connections in the data.For example, the flight service team might use data science to predict flight booking patterns for the coming year at the start of each year. The computer program or algorithm may look at past data and predict booking spikes for certain destinations in May. Having anticipated their customer’s future travel requirements, the company could start targeted advertising for those cities from February.

4. Prescriptive analysis

Prescriptive analytics takes predictive data to the next level. It not only predicts what is likely to happen but also suggests an optimum response to that outcome. It can analyze the potential implications of different choices and recommend the best course of action. It uses graph analysis, simulation, complex event processing, neural networks, and recommendation engines from machine learning.

Machine learning is a branch of artificial intelligence and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy. It is an important component of the growing field of data science. Using statistical methods, algorithms are trained to make classifications or predictions, and to uncover key insights in data mining projects. These insights subsequently drive decision making within applications and businesses, ideally impacting key growth metrics. As big data continues to expand and grow, the market demand for data scientists will increase. Its as simple as training a small baby from identification of colors to extreme.

He also spoke about chat GPT and its use in the coming days, ChatGPT is a highly advanced AI system that can produce text responses resembling human language. It operates using the GPT (Generative Pre-trained Transformer) model and has been trained on a massive corpus of conversational text. This allows it to provide information and respond to inquiries almost indistinguishable from a human conversation. The most well-known version of Chat GPT is the GPT-3.5 series.

This chatbot system uses NLP (Natural Language Processing) and deep learning to understand and process language. This allows it to perform various tasks, including customer service, which is far more advanced than traditional chatbots. The GPT-3 series is an excellent tool for researchers and developers working on NLP projects, and it has various specific tasks, domains, and applications available for use.

Open AI has already released several versions of GPT, each with increasing capabilities and performance, and will continue to develop and improve the technology behind Chat GPT. Currently, the Chat GPT AI system does not compare its results to real-world data, but we can expect real-world data integration in the near future.

About the event - ANVEDANA

ROUND -1 QUIZZ

The work shop with 240 students from Information Science Department and Data Science started with a Quiz, the 1^st round. Students were allotted with 6 different servers for 6 different classes, each server consisting of 20 objectives QNA. Questions were based on General Mathematics and Operating system. Top 10% of the students were selected from the different semesters of IS and DS. In each class, 8 students were chosen, making it 6x8. Therefore, for the second round, there were a total of 48 students selected.

IV Sem students of DS attending the quiz session

ROUND -2 DEXTER’s LAB

In this round, 48 students were selected based on their performance in the first round. These talented participants were divided into 12 groups of 4 individuals each. This round challenged the participants' programming prowess and analytical thinking.

Round 2 Details - Dexter's Lab:

• Event Name: Dexter's Lab
• Selected Participants: 48 students (8 students from each section)
• Groups: 12 groups of 4 participants each
• Challenge: Jumbled programs sorting and execution
• Time Taken: 50 minutes
• Unjumble and Execute: 20 minutes
• Treasure Hunt for Final Program: 10 minutes
• Type and Execute Final Program: 20 minutes
• Qualification: Top 4 groups were qualified for Round 3

ROUND-3 PROMPT PARADOX

In the culminating round, "Prompt Paradox", the qualified groups from Dexter's Lab faced a stimulating challenge. They were tasked with crafting a proper prompt for an AI model, such as ChatGPT, to function as desired. This round emphasized on creativity, critical thinking, and the ability to leverage AI effectively.

Round 3 Details - Prompt Paradox:

• Event Name: Prompt Paradox
• Qualified Groups: Top 4 groups from Dexter's Lab
• Challenge: Crafting an effective prompt for an AI model (e.g., ChatGPT) to fulfill a specific task (e.g., HR interviewer)
• Objectives: Showcase creative use of AI and problem-solving abilities
• Evaluation: Groups' AI models will be assessed based on their prompt's effectiveness and relevance to the assigned task.

Prompt Paradox

CONCLUSION

Anvedana culminates with three captivating rounds - ByteBurst, Dexter's Lab, and Prompt Paradox. Each round offered a unique opportunity for participants to showcase their skills, work collaboratively and push their intellectual boundaries. It encouraged all the students to embrace the challenges and actively engage in spirited competition.

Faculty Coordinator of the Event:

KAVITHA C S (Professor, Dept. of ISE)

REKHA P (Professor, Dept. of ISE)

Student Coordinators:

1. AKSHAT RAI -1MJ20IS008
2. ANJALI JADHAV -1MJ20IS012
3. SHREYA TANDON -1MJ20IS092
4. HARITHIK CHOUDHARY-1MJ20IS031
5. POOJA A PRAJAPATI-1MJ20IS065
6. DHANYA SHREE DU- 1MJ20IS024
7. SUMADHVA- 1MJ20CD036
8. VISHAKHA KASHYAP- 1MJ20CD042
9. DIVYA BHASKAR JHA-1MJ21CD012
10. LIKITH G-1MJ21CD022

Winners

Runners up

Outcome of the Event:

During this event, many participating students gained a deep understanding of the problem statement and developed innovative approaches to solve it. Additionally, the event helped students gain clarity regarding their skills, which will undoubtedly benefit them in the future. Furthermore, students also gained insights into aspects related to CHAT GPT, such as handling errors and solving problems in simpler and more logical ways.