Min Tamang

Statistics | Data Science | QA Engineer

Kite Hill Inc.

My background is in applied statistics, data science and QA engineering. I am passionate in using analytics for extracting hidden insights.

I live in the Bay Area, California. I love hiking, reading, listening data science podcast and audible. I am looking to connect with the data people to gain and share insight and ideas. If you would like to connect, please leave a message.

This is a data science blog built with R package blogdown, Hugo, GitHub, and deployed through Netlify.

Recent Posts

AWS Certified Machine Learning – Specialty (MLS-C01)

In February 2021, I passed AWS Certified Machine Learning – Specialty (MLS-C01) exam with less than 3 months of preparation. In this post, I will provide you an overview of resources and strategies I used for passing the exam.

Last updated on Feb 21, 2021 7 min read Machine Learning

Exploratory Data Analysis with Fitbit Data

I thought it would be awesome to analyze my own data, therefore in June 2019, I started tracking activities with Fitbit.

Last updated on May 16, 2021 16 min read Data Viz

Exploratory Data Analysis with Fitbit Data

Word Cloud of jobs in Data Science

Word cloud is a text mining technique to visualize words that give greater prominence to the word appearing more frequently. In the following sections, we will build word clouds of data science job description in few steps using R.

Last updated on Jul 4, 2020 3 min read NLP

Sentiment Analysis with songs

Sentiment analysis is the interpretation and classification of emotions (positive, negative and neutral) within text data using natural language processing(NLP) techniques. In this session, we are going to use songs from R package genius with bing lexicon.

Last updated on Jun 27, 2020 9 min read

Topic Modeling on Gutenberg books

Topic modeling is an unsupervised machine learning technique for text data. It scans a set of documents(book chapters in this case), detecting word and phrase patterns within them, and clustering word groups and similar expressions that best characterize a set of documents.

Last updated on Aug 2, 2020 11 min read