Research Interests: Cybersecurity, Data Science
Mukul is an industry professional with experience in data, analytics, audit, accounting, risk management and cybersecurity.
1. MBA, Columbia Business School and London Business School (EMBA Global Program), 2003
2. B.Com (Hons.), Shriram College of Commerce, University of Delhi, 1990
1. Chartered Accountant, India, 1993
2. Cost Management Accountant, India, 1990
3. Certified Information Systems Auditor (ISACA), 1997
4. Professional Risk Manager (PRMIA), 2009
I teach Business Analytics. So what does that mean? We cover the below syllabus. We also cover six case studies, and do a final project on a real data set.
Class 1: Introduction
Overview of analytics in business, the nature of problems solved for and a brief recap of descriptive statistics with Python.
Class 2: Exploratory Data Analysis
Exploratory data analysis, data summaries, basic graphing, pivots, data profiling etc.
Class 3: Data visualization
Visualization with Python/Matplotlib covering major graph types - scatterplot, histograms and barplots, boxplots, pairplots etc.
Class 4: Data cleaning and transformations
Managing data - dealing with missing values, transforming data for analytics.
Class 5: Introduction to modeling
A gentle introduction to modeling, covering key concepts relating to approaches, the machine learning workflow and model evaluation.
Class 6: Regression
A conceptual review of linear regression (including ridge and lasso regularization), polynomial regression, logistic regression, and the nature of problems these techniques can address.
Class 7: Feature engineering
The importance of feature engineering and key transformations that help transform data to useful features.
Class 8: Modeling and machine learning - Part I
The machine learning process. We will learn the difference between supervised and unsupervised learning, tree methods, linear discriminants and SVMs etc, training and test sets, and model evaluation.
Class 9: Modeling and machine learning - Part II
We will continue the discussion from the prior class and complete our review of key algorithms.
Class 10: Deep learning
A review of neural networks, and the nature of problems they solve for. We will cover at a high level fully connected neural nets, RNNs, CNNs, backpropagation and other key concepts.
Class 11: Text data
Approaches to dealing with text data. Word counts, bag-of-word approaches, term matrices, and a high level discussion of vectorization techniques such as Glove, Word2Vec and BERT
Class 12: Time series analysis
Analyzing and forecasting time series data. We will cover practical applications of ETS decomposition, Holt-Winters, SARIMA with a view to addressing business problems.
Class 13: Excel as a business tool
Excel as a business analytics tool. Best practices for use and sharing.
Class 14: Organizational considerations
Getting analytics into production, maintaining sponsorship, managing your manager, explainability & story telling, managing change, building teams, and operating effectively.