Table of Contents

January 16, 2019 3 By Achyuthuni Sri Harsha

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 on this website instead of page numbers.

Business Analytics

Review of math fundamentals

Statistics

  1. Basics-Data measurement, measures of central tendency, variability and shape

Linear Algebra

  1. Vectors
  2. Matrices part 1

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)
  5. Combined: EDA in python

Factor analysis

  1. Curse of dimensionality
  2. Exploratory factor analysis

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 (Supervised learning)

  1. Classification
    1. Logistic Regression (using Titanic data set)
    2. CHAID decision trees (using Titanic data set)
    3. CART classification (using Titanic data set)
  2. Regression
    1. Part and partial correlation
    2. Linear regression (Boston housing problem)
  3. Machine Learning
    1. Handling Imbalanced Classes

Prescriptive Analytics (Optimization)

  1. Linear Programming and Sensitivity analysis (basic)
  2. Inventory planning model (with CPLEX code)
  3. Gradient descent for non-linear optimization (Adoption of a new product)
  4. Analytic Hierarchy Process for multi-criterion optimization (Selecting a phone)

Reinforcement Learning (Stochastic modelling)

  1. Recommendation system (associate mining)
  2. Markov Chains introduction (Customer Lifetime Value)

Time series forecasting

  1. Introduction to stationarity
  2. Stationarity hypothesis tests (in-time problem)
  3. Forecasting using ARIMA (in-time problem)

Clustering

  1. Hierarchical Clustering (Market segmentation using wine data)
  2. K means clustering (Customer segmentation using credit card data)

Deep Learning

  1. Artificial Neural Network – part 1
  2. The math behind ANN

Projects

  1. Personal analytics: TIme management using google maps
  2. Supply chain analytics: Purchase order forecasting
  3. Optimization of Rewards and Recognition’s for a reputed insurance company using predictive and prescriptive analytics

Other interesting posts

  1. Review on IIMB Business Analytics and Intelligence course
  2. Why are basics important in data sciences
  3. 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

Papers and publications

  1. Parametric Study of Cantilever Plates Exposed to Supersonic and Hypersonic Flows (published in IOP Material Science)
  2. Purchase order forecasting (under publication- Accepted for publication in NCMLAI 2020)