Data Analysis
Post Graduate Program in Data Science & Business Analytics
UT Austin McCombs School of Business – August 2023
Python foundations
Foundational skills for Data Analysis with Python, such as importing, reading, manipulating, and visualizing data.
business statistics
The role of statistics in helping organizations make effective decisions using analysis, data interpretation, and experiments.
supervised learning
The fundamentals of Supervised Machine Learning, its key concepts and types, and how to pre-process data for modeling.
Supervised Learning Classification
Conceptual frameworks for building classification models for accurate prediction in business contexts through popular Machine Learning approaches such as Logistic Regression and Decision Trees.
Ensemble Techniques
Combined Machine Learning techniques to improve the predictive performance of models and decrease variance and bias.
Model Tuning
Feature Engineering techniques and model selection to tune and improve Machine Learning models.
Unsupervised Learning
Common clustering techniques like K-Means Clustering and Hierarchical Clustering to discover hidden patterns or intrinsic structures in data.
Demystifying ChatGPT
Prompt engineering for ChatGPT, fine-tuning outputs, and AI’s implications for work, business, and education.
Time Series Forecasting
Time Series Analysis and its business applications for prediction problems.
Marketing and Retail Analytics
The applications of Data Analytics to Marketing and Retail to measure, improve, and predict performance.
Web and Social Media Analytics
Leveraging collected data from websites and social media to make business decisions.
Finance and Risk Analytics
The applications of Data Analytics in Finance and Risk Management such as fraud detection, credit risk, and probably of default modeling.
Explore my latest Projects
Order Analysis Using Python
Foodhub, a food aggregator company, stores the data of different orders made by registered customers in their online portal. I analyzed the data to draw actionable insights about the demand for different restaurants to enhance customer experience.
Skills and Tools Covered: Exploratory data analysis, variable identification, univariate analysis, bi-variate analysis
A/B & Hypothesis Testing
E-News Express, an online news portal, believes their current webpage is not engaging customers, leading to a decline in new monthly subscribers. I used statistical analysis, A/B testing, and data visualization to explore whether a new landing page would increase subscription rates compared to the old page.
Skills and Tools Covered: Hypothesis testing, A/B testing, data visualization, statistical inference
Linear Regression Analysis
ReCell is a startup aiming to tap into the potential of selling used and refurbished mobile devices. I built a linear regression model to help ReCell develop a dynamic pricing strategy for used and refurbished phones and identify factors that significantly influence price.
Skills and Tools Covered: Exploratory data analysis, linear regression, linear regression assumptions, drawing business insights and making recommendations
Logistic Regression Analysis
INN Hotels Group is facing problems with high numbers of booking cancellations. I created a machine learning model to predict which bookings are likely to cancel in advance to help formulate profitable policies for cancellations and refunds.
Skills and Tools Covered: Exploratory data analysis, data pre-processing, logistic regression, multicollinearity, finding the optimal threshold using AUC.ROC curve, decision trees, pruning
Ensemble Techniques
The Office of Foreign Labor Certification (OFLC) processes job certification applications for employers seeking to bring foreign workers into the United States. I developed a machine learning model to shortlist candidates that have a higher chance of VISA approval.
Skills and Tools Covered: Exploratory data analysis, data pre-processing, customer profiling, bagging classifiers, boosting classifiers, gradient boosting, XGBoost, stacking classifier, hyperparameter tuning using GridSearchCV, providing business insights
Model Tuning
ReneWind improves the machinery and processes involved in the production of wind energy. I created classification models and tuned them to discover the best model for predicting failures, saving on maintenance costs.
Skills and Tools Covered: Upsampling, downsampling, regularization, hyperperameter tuning
Unsupervised Learning
Trade&Ahead is a financial consultancy firm who provide their customers with personalized investment strategies. I analyzed the data and performed a cluster analysis to identify stocks that exhibit similar characteristics and ones which exhibit minimum correlation.
Skills and Tools Covered: Exploratory data analysis, K-means clustering, hierarchical clustering, cluster profiling
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