Fundamentals of Scalable Data Science on IBM WATSON

Python for Data Science

Statistics

Data Science Math Skills

DATAPROCESSING SPECIALIST

R programming

AI for Everyone

Machine Learning with python

Neural Networks and Deep learning

What is Data Science

President - co-ordination and Finance

student co ordinator of Tamizh mandram SJCE chennai 119

மாணவ ஒருங்கிணைப்பாளர் - தமிழ் மன்றம்

I Started My volunteer ship in my second year of Engineering, to teach and train the students from the Tamil Medium to enhance their English Proficiency. From 2017 onwards I've been working as a Chief Coordinator, training nearly 200+ students

chief Co-Ordinator at Non Formal English Club

consultant and supervisor

Student Body President

Bachelor of Engineering - BE

High School Diploma

SSLC

Master of Science - MS

Member

In the Equilibrium of Product , Engineering & Data Science 
 
-Scalable EO Data Cube Development  - ETL & DataOps Pipeline creation 

-Designing AI Semantic Segmentation Modelling and Data Fusion Modelling and Consumption

- CV models : Yolov8, Segmentation Anything , SSL models of in-domain representations : MOCO , DINO

-Building Statistical/Analytical/ML/ Deep Learning Models for Aquaculture Fisheries & Marine Resource Management

- Oceanography Data Engineering(Xarray+Dask) Processing and Modelling 

- Statistical Inference and Spatial Clustering on Ocean State and  Potential Fishing Zones 

-ML MicroServices Systems development 

-MLOPs Patterns Development & AWS MLOps deployment 

-Docker based Containerisation of Models

Data Scientist II

1.Sales Forecasting for a Pharmaceutical firm 
2.Customer Churn Analysis for a Telecom Industry.
3.Promotion and Campaign analysis for a Pharmaceutical Industry.

Junior Data Scientist (Decision sciences Intern)

• Conducted over 400 hours of online sessions focused on Natural Language Processing (NLP) and Data Science (DS) across multiple platforms.  
• Organized and facilitated 20+ offline seminars and sessions at colleges , companies and Keltron
• Mentored more than 100 students, guiding them through complex concepts and practical applications in technology.

Mentor and Tutor

- Research Collab with CRIS LAB on LLMs + Material Science ;

Research Collaborator

1. Data Science Team leading 
2. Solution and Product Designing 
3. Helping many Teams across the Org to solve the issues in Building ML pipelines , statistics , Decision Sciences. 
4. Part of Hiring and Training activities

- AI for Supply Chain 
- AI for Engineering & Processing Industries

Data  Scientist - Decision Science/ Engineering/ML

1. End to End Machine learning pipeline creation for Japan-based Construction Giant.
   - Monthly payout prediction for an each Construction project based on Various Machine learning Algorithms, such as Random forest regression, Ensemble learning, Boosting and Stacking methods.
   - We have implemented this pipeline start from Data manipulation, Feature Engineering, and Variable selection  in order to decrease the RMSE and to get an optimized model
   - The technology used: Python and its various DS and ML packages
2. The descriptive analytics web application for Japan-based IT firm
  - I have built an R shiny based Web application for this firm to understand about their server failures, shipments, and Repairs
-It is about to have a decision-based Analytics to carry out the prescriptive measures about Shipments and prevent early failures.
-Technology used: Rshiny and R-DS libraries
3.Survey Data Analytics and Reporting 
-I have done a end to end analytics for a US beverage firm to unde

Trainee Decision Scientist - Level 1 - Data Science and Machine Learning Engineering

Co-Leading the OpenSource Education + Research community (NPO) for the greater cause

Leadership Team

**AI powered Product Development**

1. Real time in-situ process monitoring and  Simulation of Additive manufacturing process using Generative Neural Modeling

Data Scientist (Trainee Decision Scientist - Level 2)

Can say , this is my labs/playground for now (wecooc.com) - stay tuned for the CooCed Recipes.

Before that I failed in building a community driven idea called thozharTensor (idea is still good but the synergies are not happening) 

Then i was/am part of Communities which put FameGame First but not the outcomes 

I also took some lectures works in the name of beyond dotfit - again it drains lotta energy to teach abc of ML

CooC

Apprentice Leader 
Designing and Conceptualizing various Data driven Business Transformation Solutions for Engineering Industries 

Designed the End to End Solution to digitize the S&OP process

I am building an end-to-end data & AI  Product to enable the Autonomous Sales and Operations Planning Process for a MEAF-based Client.

Other Problems spaces that I have been consulting 
- Customer Intelligence
- Agent Based Modeling and Causal Inference 
- NLP for Pharma Clients

Leading a team of 20+ Decision Scientists / Data Analysts

Data Scientist and Apprentice Leader

400+ Hours of Online Sessions on NLP/DS
20+ Offline Seminars 
20 Learning sessions at Mu Sigma 
Mentored over 100+ Students

Mentor and Technical Speaker

Enabling Enterprise Cloud Data Operations Observability and Customer Experience via Gen AI - Decision Excellence : building a data+AI product to improve CX&EX
Conceptually I m working on : 
- Domain Adaption and Language Modeling (CLM and MLM)
- IFT and Encoders (MLM , DomainAdapted Embeddings) 
- Prompt Engineering, Synthetic Data Generation and Ops
- Adapters and PEFT
- Data Product POCss for User Adaptation
- R&D on Enterprise Observability ans ITOps
- Agents and Flow Engineering

Senior Machine Learning Engineer

Python & ML  Trainer and Mentor

As a part of my passion, I have been delivering Sessions in our company's unique weekly seminar series initiative. 

Topics : 
1. Introduction to Computer vision using deep learning.
2. Extreme Gradient Boosting - A story behind it.
3. Bio Inspired Algorithms - Evolutionary Algorithms.
4. Introduction to Reinforcement Learning. 
5. Knowledge Graphics - Theory and Applications
6. Deep Q Learning - algorithm and implementation
7. Deep learning for Music - Genre Classification and Music Generation 
8. Modern Time series forecasting -Holtswinter , ARIMA ,  Prophet and Silver kite.
9. Graph Neural Networks 
10. Art and Deep learning
11. Geo Spatial Analytics 
12. Causal Inference 
13. Conformal Prediction 
14. Prescriptive Intelligence 
15. Art of Networking

Speaker- Thurday Learning Hour Seminar Series

I am leading a team to build a Renewable Energy output prediction framework which will help the client in making optimal bidding every day power trade. 

As a Lead , I am closely working with stakeholders , designing the solution , workflow , experiments and structuring the project from the scratch to ensure the success and satisfaction of  clients.

As a Data scientist, I am building the entire Data engineering , Modeling , Continuous training and Consumption pipeline. 

TECH STACK : Google Cloud , Python , various ML algorithms , WandB, GitHub,

Along with the above Engagement , I am also providing technical support to an another team interms of build a Pricing Optimization Pipeline to avoid Anti dumping in Targeted countries

Trainee Decision Scientist - Level 2 - Data Science and Machine Learning Engineering

- led ML Practice for a Stealth Startup that focuses on Medical Document AI 
- built founding AI team of 7 Engineers 
- setup Machine Learning and Data Ops modules for end to end training,validation and operationalization of Vision Language Models, Embeddings and Agentic Workflows.

Practice Lead (fractional) - ML and Product Innovation

1. TA for a Statistical Data Analysis (for Social Sciences) Course under Institute for Social and Economic Research and Policy - coursework focuses on regression analysis, factor analysis, first difference analysis and other statistical(linear) methods/models.
2. I chose it over other CS based courses as STATS + PEOPLE is one of my favorite topics to me, and it happens as a part of QMSS founded by prof.Andrew Gelman

Teaching Assistant

- CA for the Course Applied Risk Analytics by Prof. Arora
- The course focuses on a PRACTICAL study of how to quantify & predict RISK in organizations by using learnings from: Regression analysis; Monte Carlo simulation; Factor analysis; Cohort analysis; Cluster analysis; Time series analysis; Sentiment analysis.

Course Assistant

MS in Data Science, 6+ years of Hands-on Industrial Experience as Machine Learning Engineer and Data Scientist.

Know about Prabakaran Chandran's work experience, education, and much more. Prabakaran Chandran