Athira Das

Mountain View, California, United States Contact Info
6K followers 500+ connections

Join to view profile

  • Contact Athira for services

    Executive Coaching, Leadership Development, and Team Building

About

I am a Leadership Coach and Business Coach with a background in technology. I help…

Contributions

Activity

Join now to see all activity

Experience & Education

  • Meta

View Athira’s full experience

See their title, tenure and more.

or

By clicking Continue to join or sign in, you agree to LinkedIn’s User Agreement, Privacy Policy, and Cookie Policy.

Licenses & Certifications

Volunteer Experience

Courses

  • Advanced Analytics using SAS

    IDS 594

  • Advanced Predictive Models and Applications using graphical models and deep learning

    IDS 576

  • Analytics for Big Data (Auditing)

    IDS 561

  • Analytics strategy and practice

    IDS 560

  • Business Data Visualization

    IDS 567

  • Business Data Visualization using Tableau

    IDS 567

  • Business Forecasting using Time Series Methods

    IDS 476

  • Data Mining for Business

    IDS 572

  • Data science for Online Customer Analytics

    IDS 594

  • Improvisation and leadership

    MBA 590

  • Introduction to Operations Management

    IDS 532

  • Machine learning using python

    IDS 594

  • Social Media and Network Analysis

    IDS 564

  • Statistical Models and Methods for Business Analytics

    IDS 575

  • Supply Chain Management

    IDS 552

Projects

  • Automatic Labeling of restaurant images

    This problem is around image recognition and multi-label classification. Here the tag doesn’t indicate the object in the image, but rather a concept represented by the restaurant. The concept of transfer learning helped us to build the model with more efficiency. After image augmentation, we used Google’s Inception V3 to convert the images to a numpy array. Later this feature matrix was used to build a model using XGBoost, then created a model which was a combination of XGBoost and clustering…

    This problem is around image recognition and multi-label classification. Here the tag doesn’t indicate the object in the image, but rather a concept represented by the restaurant. The concept of transfer learning helped us to build the model with more efficiency. After image augmentation, we used Google’s Inception V3 to convert the images to a numpy array. Later this feature matrix was used to build a model using XGBoost, then created a model which was a combination of XGBoost and clustering following by an ensemble of the two using a custom Neural Network Model. We tested the efficiency of our model using F-score.

    See project

  • Procurement forecasting and plan optimization for Retail Energy Providers

    The project is based on applying supply chain forecasting concepts to the deregulated electrical energy market in New York State. We will attempt to forecast the energy demand and pricing for New York State and each of their 11 zones for three years. We will train the data using the last 4 years of data and determine how accurately our forecast matched the actual demand. Since the NY Independent System Operator also posts their forecast demand, we will additionally compare to their initial…

    The project is based on applying supply chain forecasting concepts to the deregulated electrical energy market in New York State. We will attempt to forecast the energy demand and pricing for New York State and each of their 11 zones for three years. We will train the data using the last 4 years of data and determine how accurately our forecast matched the actual demand. Since the NY Independent System Operator also posts their forecast demand, we will additionally compare to their initial forecast. The forecasted price will be used for the procurement planning for the Retail Energy Provider.

  • Analyzing Alaska Airline's delay and causes using Visualizations

    Tools used: Tableu

  • Analysis of SAP Knowledge Sharing Platform Network

    Analyse the interaction between individuals on SAP knowledge sharing platform. The analysis were based on centrality measures and community formation.

    Tools Used: R, Gephi

  • Clustering of Customers based on Purchase behavior

    The customers were segmented based on purchase behaviour for Target Marketing.

    Different algorithms like k-means, k-medoid, agglomerative clustering and dbscan clustering, K-means which had the best separation based was selected to segment the data. The model was validated with decision tree.

  • Credit Risk Modelling

    Develop a credit scoring rule that can be used to help determine whether a new applicant presents a good or bad credit risk.This was done as a part of IDS 572 - Data Mining course at University of Ilinois at Chicago.

    Data Used: German Credit data
    Model: Decision Tree

  • Revenue Assurance

    Analytics based rule engine that effectively sifts through Billing information of customers to spew out potential errors, catches error patterns & calculates revenue leakages. Enables Retail energy providers with 100% accurate bills before being sent out to customers.

     Developed an automated system for downloading the data from external sources using Python.
     Developed the ETL process for transforming the data using python.
     Developed the rules engine for billing assurance using…

    Analytics based rule engine that effectively sifts through Billing information of customers to spew out potential errors, catches error patterns & calculates revenue leakages. Enables Retail energy providers with 100% accurate bills before being sent out to customers.

     Developed an automated system for downloading the data from external sources using Python.
     Developed the ETL process for transforming the data using python.
     Developed the rules engine for billing assurance using SQL procedures.
     Designed and developed the interactive user interface.

    Other creators

  • Customer Micro-Segmentation based on Lifetime Value and Payment Behavior

    Customer Micro-segmentation an application which classifies the consumers with respect to their financial value to the company, timeliness of payment and their loyalty towards contract renewals

     Generated the EDAs for customer behaviour analysis.
     Ideated and developed the important KPIs.
     Developed the ETL process for data transformation.

    Other creators

  • Cerebra Signal Studio

    Cerebra signal studio is Flutura’s flagship platform to collect the real time and batch data, perform analytics and show the result through creative visualizations.

     Ideated and developed the important KPIs.
     Developed the predictor ranking algorithm for predicting the important parameters.
     Developed the template engine for the vectorization process using Python.
     Developed the ETL process using Apache Spark and SparkSQL.

    Other creators

  • Cerebra Data Products

    Cerebra Data Products are a set of 30+ products made on a common platform with a unified data model for power utility industry catering to generation, transmission and distribution segments.

     Designed the data model for the utility industry.
     Ideated and developed important KPIs for the utility industry.
     Built a real time data pipeline to store data from multiple sources, for real time analysis and Batch Processing using Apache Kafka, Apache storm, HDFS, Hive, Pig and Cassandra.

    Other creators

  • PVA Target Marketing for Fund Raising

    -

    Develop a data mining model to improve the cost- effectiveness of the direct marketing campaign of Paralyzed Veterans of America.

    Data Used: The dataset used KDD cup 1998, http://kdd.ics.uci.edu/databases/kddcup98/epsilon_mirror/cup98dic.txt
    The data was analyzed to predict the users who are going to make donations for the coming year. The model was developed to predict the rare event - "User who can donate". The final model was selected based on combining the response and donation…

    Develop a data mining model to improve the cost- effectiveness of the direct marketing campaign of Paralyzed Veterans of America.

    Data Used: The dataset used KDD cup 1998, http://kdd.ics.uci.edu/databases/kddcup98/epsilon_mirror/cup98dic.txt
    The data was analyzed to predict the users who are going to make donations for the coming year. The model was developed to predict the rare event - "User who can donate". The final model was selected based on combining the response and donation amount models to
    identify the most profitable individuals to target.

    Models used for Classification: Decision Tree, Naive Bayes, Logistic Regression, Random Forest, Boosted Trees, Support Vector Machines
    Model used for Regression: Linear regression.


  • Predict annual restaurant sales based on objective measurements

    -

    The competition was hosted in Kaggle.
    Finding a mathematical model to increase the effectiveness of investments in new restaurant sites would allow TFI to invest more in other important business areas, like sustainability, innovation, and training for new employees. Using demographic, real estate, and commercial data, this competition challenges you to predict the annual restaurant sales of 100,000 regional locations.

Languages

  • English

    Full professional proficiency

  • Malayalam

    Native or bilingual proficiency

  • Hindi

    Professional working proficiency

  • Tamil

    Limited working proficiency

Recommendations received

5 people have recommended Athira

Join now to view

More activity by Athira

View Athira’s full profile

  • See who you know in common
  • Get introduced
  • Contact Athira directly
Join to view full profile

Other similar profiles

Explore more posts

Explore collaborative articles

We’re unlocking community knowledge in a new way. Experts add insights directly into each article, started with the help of AI.

Explore More

Others named Athira Das

93 others named Athira Das are on LinkedIn

See others named Athira Das

Add new skills with these courses

See all courses