Introduction
This section contains my certificates and other achievements.
37 docs tagged with "Introduction"
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This is the Kaggle competition regarding Game AI and Reinforcement Learning.
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This series began with an introductory DSA course that taught the common data structures used, graph algorithms, greedy algorithms, divide-and-conquer algorithms, etc.
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This project was for the course AI: Search Methods for Problem Solving.
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These were a series of mandatory and elective courses including Deep Learning (basic framework), Computer Vision, Introduction to NLP, Speech Technology and Large Language Models.
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Although named Finance, this category included both Finance and Economics courses.
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This was the project for the course Financial Forensics, which gave us the financial statements and ratios of over 4000 stocks as well as prices at two time periods, and required us to make an investment portfolio, with a given investment budget.
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This was a group project done in a team of 5 members. The project was about researching aspects of behavioural finance and portfolio management, coming up with a strategy to manage personal finance.
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I started the BS degree in Data Science and Applications at IIT Madras in September 2021. The program consisted of trimesters (3 terms of 4 months long each every year) instead of semesters.
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This project was for the course Machine Learning Practice.
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There were mutiple courses on Machine Learning including Machine Learning Foundations (linear algebra and basic algorithms like PCA), Machine Learning Techniques (detailed mathematics of Support Vector Machines, Ensembling, etc) and Machine Learning Practice (implementation with scikit-learn, xgboost, etc).
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This is the beginner, introductory Kaggle competition that every new Kaggle member does. Since I had learned a lot of new techniques at the time, I decided to apply them all to this dataset as practice.
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This was my first undergraduate program at Indian Institute of Science, Bengaluru. I majored in Physics with a special emphasis on Quantum Physics.
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This section details my academic background. I completed schooling from St Xavier's High School, Kedargouri, Bhubaneswar with a CGPA of 10 in class 10 and 92.1 % in class 12. I had taken Physics, Chemistry, Mathematics, English and Physical Education in class 11-12.
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This project was done for the individual hackathon NetElixirAIgnition conducted by the marketing agency NetElixir digital solutions.
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This project was started as part of an intra-company (Syngenta) Gen-AI hackathon, in a team of 4 members.
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This section details the significant hackathons I have participated in and the corresponding projects.
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This portfolio provides a comprehensive record of my educational background, professional experience, and personal accomplishments.
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Causal Analysis is the study of causation as opposed to correlation. Given two events A and B which appear to be correlated, can we determine with a certain statistical significance if one of them is the cause of another?
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This section details the skills I have acquired, projects I have done and papers I have published without any academic or professional incentive or aid.
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These are the libraries and packages I have developed to automate my workflow and encapsulate my algorithms.
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This is a library encapsulating the algorithm developed by me for time series forecasting in general. It has been used for Inventory Management (particularly Sales Forecasting), Liquidation Forecasting and numerous other projects of the Syngenta INPU data science intern team.
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I learnt Algorithmic Trading from the NPTEL course Algorithmic Trading and Trading View.
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I was introduced to Quantitative Finance through the project Personal Finance.
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I used to be fascinated about Quantum Computing and Quantum Mechanics in general in my schooldays, hence deciding to do an Integrated MS with Physics major at Indian Institute of Science, Bengaluru.
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This is the Kaggle competition regarding Game AI and Reinforcement Learning.
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Reinforcement Learning was something that deeply interested me. I was fascinated by the idea of an AI that can learn from experience.
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My first full-time internship was in the supply chain department of Syngenta so I naturally learned about it and did some projects.
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This project was started as part of an intra-company (Syngenta) hackathon but I continued doing it as a personal project after the hackathon's completion.
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This was my first internship project inspired from the pre-existing Field Segmentation project for a hackathon.
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I am a Data Science intern in Production & Supply (P&S) department of Syngenta INPU (India Pune). The team, named Center of Expertise (COE), was responsible for the supply chain and logistics aspects of the AMEA (Asia Middle East Africa) region.
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This project was about optimally managing the inventory of various products, particularly Crop Protection (Insecticides, Fungicides, Herbicides, etc).
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This was a project started as an experiment to see whether using covariates like weather and economic indicators provide better forecasts than simply using historical data.
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Now that demand has been forecasted and is better than IBP forecasts for a significant number of products in many countries/regions, I decided to make use of it to further recommend how inventory should be managed.
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The data science team had made a number of end-to-end projects including the ones documented here. We needed a unified interface to interact with all of them since dashboards were cumbersome to both build and use.
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This section details my professional history. I started my professional career as a Data Science intern at Syngenta, which is ongoing (November 2023 - present).