Table of contents

Welcome to my blog.

This post is like a table of contents section of a book. It provides links to the rest of the posts in this website instead of page numbers.

Data Analytics

Data Processing

Web scraping
  1. Handling Google Maps Location Data (in-time problem)
  2. Class size paradox and web scraping (using Amrita University placement data)
Data cleaning and imputation
  1. Null value imputation using KNN (mtcars data)

Exploratory Data Analytics

  1. Univariate Analysis (in-time problem)
  2. Multivariate Analysis (in-time problem)
  3. Multicollinearity (in-time problem)
  4. Time Series EDA (in-time problem)

Inferential data analytics (Hypothesis testing)

  1. z-test and t-test (in-time problem)
  2. Anova test (smart cities data)
  3. Chi-Square goodness of fit test (in-time problem)
  4. Chi Square test of independence (smart cities data)

Prediction algorithms

  1. Classification
    1. Logistic Regression (using Titanic data set)
  2. Regression
    1. Part and partial correlation
    2. Linear regression (Boston housing problem)

Prescriptive Analytics

  1. Recommendation system (associate mining)
  2. Linear Programming and Sensitivity analysis (basic)

Other interesting posts

  1. Why are basics important in data sciences
  2. Boy-girl paradox

Python tutorials ( Authored by Tamoghna Saha )

  1. Getting started with python
  2. A Not-so-Quick-but-Conceptual guide to Python  | Intermediate | Part 1
  3. A not so quick but conceptual guide to python notebook intermediate | part-2
  4. All you ‘really’ need to know | Python Notebook | Advanced – Pandas

NX Journaling basics

Extracting data from Mechanical models

  1. Selecting a part in NX Journaling
  2. Getting dimensions for the selected part
  3. Exporting dimensions to excel

Papers and publications

Parametric Study of Cantilever Plates Exposed to Supersonic and Hypersonic Flows (published in IoP Material Science)

Other Projects

Design of pilgering die and shaft

Donut chart using HTML and CSS only

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