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Indian Institute of Technology Roorkee and NPTEL via Swayam
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Business Analytics & Text Mining Modeling using Python is a comprehensive course that introduces students to the fundamentals of text mining and its application in business analytics. The course covers techniques such as natural language processing (NLP), sentiment analysis, and text classification, as well as the use of Python libraries like NLTK, spa Cy, and Scikit-learn. Students will learn how to analyze large volumes of unstructured text data and derive actionable insights to support business decision-making. The course also explores...
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 PlatformPython for Analytics- Introduction to Python Installation- Jupyter Notebook IntroductionWeek 2: Python Basics- Python Programming Features- Commands for common tasks and control- Essential Python programming concepts & language mechanicsBuilt in Capabilities of Python- Data structures: tuples, lists, dicts, and setsWeek 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 filesNumerical Python- N-dimensional array objectsWeek 5: Numerical Python- Vectorized array operations- File management using arrays- Linear algebra operations- Pseudo-random number generation- Random walksPython pandas- Data structures: Series and DataFrameWeek 6: Python pandas- Applying functions and methods- Descriptive Statistics- Correlation and CovarianceWorking with Data in Python- Working with CSV, EXCEL files- Working with Web APIsWeek 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 typeData Visualization using Python- Matplotlib Library- Plots & SubplotsWeek 8: Text mining modeling using NLTK- Text Corpus- Sentence Tokenization- Word Tokenization- Removing special Characters- Expanding contractions- Removing Stopwords- Correcting words: repeated characters- Stemming & lemmatization- Part of Speech Tagging- Feature Extraction- Bag of words model- TF-IDF model- Text classification problem- Building a classifier using support vector machin
Indian Institute of Technology Roorkee and NPTEL via Swayam
Paid Certificate Available
English
On-Demand
Prof. Gaurav Dixit
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