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All too often companies have only the vaguest idea about what kind of data they’re holding; because such data is very often hidden deeply away in a variety of databases and fragmented across different departments. We identify this data and bring it to light, making it visible, cohesive, comparable and easy to understand so that it really does support YOU in making the right decisions. And if need be, we can also identify any lacking data and define a concept to fill in the gap.

Data Science courses completed at Osmania University

Posted by on Jun 4, 2019 in JSMNews | No Comments

It was a great honour to be a part of the teaching faculty of the Department of Statistics, University College of Science, Osmania University. Sandhya(linkedin.com/in/sandhyatanneer) and I were given the opportunity to teach the industrial electives to postgraduate students of Statistics / Applied Statistics for both the semesters in the academic year 2018-2019. We thoroughly enjoyed teaching the students who were learning what we had learnt years ago. We genuinely thank the Department of Statistics for starting this initiative and giving us this great opportunity. We believe that this initiative would really help the students to learn practical application of their theoretical knowledge.

We thank the management and our colleagues at JSM who supported us throughout the entire process.And special thanks to Rama Regulla & Bala kartik for their extended support.

We taught “Data Modelling using Machine Learning algorithms” in Semester 3 and “Text mining” in Semester 4. Along with theory, we also offered projects in both the semesters. The students were distributed in 12 batches and each of the projects were monitored closely.

In Semester 3, projects broadly covered both supervised and unsupervised Machine Learning algorithms with a proper pipeline which outlined the complete machine learning ecosystem of data cleaning, validation, tuning the parameters, etc. The projects were:

  • Predicting the outcome of an e-mail classification
  • Classifying credit risk
  • Predicting gross revenue of movies
  • Modelling for evaluating the business organisation using machine learning techniques
  • Predicting the physical examination status for the trainees of sub – inspector of police.
  • Predicting the physical examination status for the trainees of sub – inspector of police.
  • Predicting disease using machine learning
  • Predicting salaries of the professors
  • Predicting the students’ knowledge status about the subject of electrical DC machines

In Semester 4, projects covered how to work with text and the projects on Topic Modelling, Text Classification, and Sentiment Analysis. Here is the list of the projects:

  • A Project on Text classification on Amazon Product reviews
  • Topic Modelling for Statistical text Documents
  • Sentiment Analysis on Sri Lanka attacks
  • Product recommendation based on Text
  • Sentiment Analysis for Mobile
  • Text classification for Food reviews
  • Topic Modelling for various AI Speech’s
  • What Made Zelensky a comedian to become the President through text data
  • Sentiment Analysis on IPL data, Avengers, Tic-tac

We hope that these electives helped the students to understand the industry perspective of   Statistical / Machine Learning algorithms. It would be our genuine pleasure to see them placed soon in companies across industries. We wish them the absolute best.

Venugopala Rao Manneni

A doctor in statistics from Osmania University. I have been working in the fields of data analysis and research for the last 14 years. My expertise is in data mining and machine learning – in these fields I’ve also published papers. I love to play cricket and badminton.

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