Achyuthuni Sri Harsha
Who am I
I am currently working as a Senior Business analyst at Tesco Bengaluru. In the space-range and merchandising domain, I am trying to solve business problems using statistical concepts and data science. I am also pursuing MSc in Business Analytics (part-time) from Imperial College London.
I am an alumnus of the Indian Institute of Management, Bangalore in Business Analytics and Intelligence. BAI is an executive on-campus one-year program with a focus on statistics, business and technology. 'Building R&R contests for one of India's largest life insurance company' which is our team's capstone project got the 'Highly commended project' award.
Previously I worked as a decision scientist at Mu-Sigma and have pursued my bachelors at Amrita Vishwa Vidyapeetham in Mechanical engineering. I have experience working with industry giants and Fortune 500 companies in retail, pharma, insurance and IT industries.
Why this blog
Sometimes, unless I do something, I don't completely understand it. After learning something new, especially in the space of data science, I try to solve a simple problem using what I just learned. This blog is a cumulation of all my notes from such learnings.
What else
I try to use problems and issues from my everyday life and try to solve them using statistical/data science lens. I have been attempting to publish some of these everyday problems which can be solved using data science as papers and conference articles, some of which are:
- Personal analytics: Time management using Google maps (presented in ICSADADS-2020)
- Personal analytics: Selection of a phone using AHP
Other publications include:
Business Analytics
Data Processing
Web scraping
- Handling Google Maps Location Data (in-time problem)
- Class size paradox and web scraping (using Amrita University placement data)
Data cleaning and imputation
Exploratory Data Analytics
- Univariate Analysis (in-time problem)
- Multivariate Analysis (in-time problem)
- Multicollinearity (in-time problem)
- Time Series EDA (in-time problem)
Factor analysis
Inferential data analytics (Hypothesis testing)
- z-test and t-test (in-time problem)
- ANOVA test (smart cities data)
- Chi-Square Goodness of fit test (in-time problem)
- Chi-Square test of independence (smart cities data)
Prediction algorithms (Supervised learning)
- Classification
- Logistic Regression (using Titanic data set)
- CHAID decision trees (using Titanic data set)
- CART classification (using Titanic data set)
- Regression
- Machine Learning
Prescriptive Analytics (Optimization)
- Linear Programming and Sensitivity analysis (basic)
- Inventory planning model (with CPLEX code)
- Gradient descent for non-linear optimization (Adoption of a new product)
- Analytic Hierarchy Process for multi-criterion optimization (Selecting a phone)
Reinforcement Learning (Stochastic modelling)
Time series forecasting
- Introduction to stationarity
- Stationarity hypothesis tests (in-time problem)
- Forecasting using ARIMA (in-time problem)
Clustering
- Hierarchical Clustering (Market segmentation using wine data)
- K means clustering (Customer segmentation using credit card data)
Deep Learning
Projects
- Personal analytics: TIme management using google maps
- Supply chain analytics: Purchase order forecasting
- Optimization of Rewards and Recognition's for a reputed insurance company using predictive and prescriptive analytics
Other interesting posts
- Review on IIMB Business Analytics and Intelligence course
- Why are basics important in data sciences
- Boy-girl paradox
Python tutorials ( Authored by Tamoghna Saha )
- Getting started with python
- A Not-so-Quick-but-Conceptual guide to Python | Intermediate | Part 1
- A not so quick but conceptual guide to python notebook intermediate | part-2
- All you ‘really’ need to know | Python Notebook | Advanced – Pandas
Papers and publications
- Parametric Study of Cantilever Plates Exposed to Supersonic and Hypersonic Flows (published in IOP Material Science)
- Purchase order forecasting (under publication- Accepted for publication in NCMLAI 2020)