Business Analytics & Text Mining Modeling Using Python

Week 1: Introductory overview of Text Mining

- Introductory Thoughts 

- Data Mining vs. Text Mining

- Text Mining and Text Characteristics

- Predictive Text Analytics

- Text Mining Problems

- Prediction & Evaluation

- Python as a Data Science Platform

Python for Analytics

- Introduction to Python Installation

- Jupyter Notebook Introduction

Week 2: Python Basics

- Python Programming Features

- Commands for common tasks and control

- Essential Python programming concepts & language mechanics

Built in Capabilities of Python

- Data structures: tuples, lists, dicts, and sets

Week 3: Built in Capabilities of Python

- Functions, Namespaces, Scope, Local functions, Writing more reusable generic functions       

Week 4: Built in Capabilities of Python

- Generators

- Errors & Exception Handling

- Working with files

Numerical Python

- N-dimensional array objects

Week 5: Numerical Python

- Vectorized array operations

- File management using arrays

- Linear algebra operations

- Pseudo-random number generation

- Random walks

Python pandas

- Data structures: Series and DataFrame

Week 6: Python pandas

- Applying functions and methods

- Descriptive Statistics

- Correlation and Covariance

Working with Data in Python

- Working with CSV, EXCEL files

- Working with Web APIs   

Week 7: Working with Data in Python

- Filtering out missing data, Filling in the missing data, removing duplicates

- Perform transformations based on mappings

- Binning continuous variables

- Random sampling and random reordering of rows

- Dummy variables

- String and text processing

- Regular expressions

- Categorical type

Data Visualization using Python

- Matplotlib Library

- Plots & Subplots

Fundamentals of Predictive Text Mining by Sholom M. Weiss, Nitin Indurkhya, & Tong Zhang (2010/2015)

Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython by

Wes McKinney (2017)

Text Analytics with Python: A Practical Real-World Approach to Gaining Actionable Insights from Your Data by

Dipanjan Sarkar (2016)

Dr. Gaurav Dixit is an Assistant Professor in the Department of Management Studies at the Indian Institute of Technology Roorkee. He earned his doctoral degree from the Indian Institute of Management Indore and an engineering degree from Indian Institute of Technology (BHU) Varanasi. Previously, he worked in Hewlett-Packard (HP) as software engineer, and Sharda Group of Institutions as project manager on deputation.
Gaurav’s research focuses on information technology (IT) strategy, electronic commerce, electronic waste, data mining, text mining, and big data analytics and provides insights on business and social value of IT. His research has appeared in quality journals & conferences, including Resources, Conservation and Recycling, Journal of Global Information Technology Management, Sustainable Production and Consumption, Journal of Information Technology Management, ICIS conference, DIGITS conference, India Finance Conference, Indian Academy of Management conference, and Academy of Management conference

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