Pavan Potnuru

Pavan Potnuru

Assistant System Engineer

Followers of Pavan Potnuru800 followers
location of Pavan PotnuruAndhra Pradesh, India

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  • Timeline

  • About me

    Aspiring Data Scientist || Data Science Trainee at AlmaBetter || AI & ML Enthusiast

  • Education

    • Government polytechnic, visakhapatnam

      2012 - 2015
      Diploma in mechanical engineering Mechanical Engineering 90.13%
    • Gayatri Vidya Parishad College of Engineering (Autonomous), 530048(CC-13)

      2015 - 2018
      Bachelor of Technology (B.Tech.) Mechanical Engineering
  • Experience

    • Tata Consultancy Services

      Nov 2018 - now
      Assistant System Engineer

      • Analysed and evaluated various automation testing tools for different levels of testing (like Unit, Functional, etc.) and identified the most effective tool for the Business vertical on various factors. • Worked on various testing tools like Selenium, Katalon Studio, JUnit, Jmeter, utPLSQL, etc. for writing automated test scripts to test applications. • Experience in preparing detailed reports on POCs done on various tools, Test Plan and Test Strategy documents using MS office (Excel, Word, PPT, etc.)• Conducted a workshop on TDD (Test Driven Development) by leading a team of 5 which helped in reducing the code complexity by 50% and also increase in code coverage to >90%. Show less

    • AlmaBetter

      Oct 2021 - now
      Data Science and Analytics Trainee

      As a data science and analytics trainee, I have:- Developed my skills in python programming and I am proficient in working with common python frameworks; Pandas, Numpy, Matplotlib, Seaborn, Scikit Learn- Developed technical understanding on working with SQL queries, analytical tools including; Excel, Power BI, and Tableau- Worked on understanding the use cases behind different statistical hypothesis tests including; Z-test, T-test, Chi-square test, ANOVA. And the math behind different ML techniques including; Gradient descent optimization, Bayes Theorem- Learned and worked on supervised machine learning algorithms; Linear regression, Logistic Regression, Decision trees, Bagging and Boosting, KNN, SVM, Naïve Bayes Classifier- Unsupervised learning; K-means Clustering, Hierarchical Clustering, NLP, Topic Modelling, Recommender systems (Collaborative, Content Based Filtering), Anomality Detection (Isolation Forests), Time Series Analysis (SARIMA family)- Deep learning; Neural Networks- ML model deployment Show less

  • Licenses & Certifications