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Business Analytics and Text Mining Modeling Using Python

₹2,000.00

Prof. Gaurav Dixit

IIT Roorkee

*Additional GST and optional Exam fee are applicable.

SKU: IIT Roorkee Category:

Description

Objective of this course is to impart knowledge on use of text mining techniques for deriving business intelligence to achieve organizational goals. Use of Python based software platform to build, assess, and compare models based on real datasets and cases with an easy-to-follow learning curve.

INTENDED AUDIENCE

UG & PG engineering students: all branches MBA students Professionals working in or aspiring for Business Analyst, Data Analyst, Data Scientist, and Data Engineer roles

PREREQUISITES

Relevant sessions from the courses Business Analytics & Data Mining Modelling Using R Parts I and II

INDUSTRY SUPPORT

Big Data companies, Analytics & Consultancy companies, Companies with Analytics Division

ABOUT THE INSTRUCTOR

Dr. Gaurav Dixit is an Assistant Professor in the Department of Management Studies at the IndianInstitute of Technology Roorkee. He earned his doctoral degree from the Indian Institute ofManagement 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 ofInstitutions as project manager on deputation.Gaurav’s research focuses on information technology (IT) strategy, electronic commerce, electronicwaste, data 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 andRecycling, Journal of Global Information Technology Management, Sustainable Production andConsumption, Journal of Information Technology Management, DIGITS conference, India FinanceConference, Indian Academy of Management conference, and Academy of Management conference.

Certification Process

1. Join the course
Learners may pay the applicable fees and enrol to a course on offer in the portal and get access to all of its contents including assignments. Validity of enrolment, which includes access to the videos and other learning material and attempting the assignments, will be mentioned on the course. Learner has to complete the assignments and get the minimum required marks to be eligible for the certification exam within this period.

COURSE ENROLMENT FEE: The Fee for Enrolment is Rs. 2000 + GST

2. Watch Videos+Submit Assignments
After enrolling, learners can watch lectures and learn and follow it up with attempting/answering the assignments given.

3. Get qualified to register for exams
A learner can earn a certificate in the self paced course only by appearing for the online remote proctored exam and to register for this, the learner should get minimum required marks in the assignments as given below:

CRITERIA TO GET A CERTIFICATE
Assignment score = Score more than 50% in at least 6/8 assignments.
Exam score = 50% of the proctored certification exam score out of 100
Only the e-certificate will be made available. Hard copies will not be dispatched.”

4. Register for exams
The certification exam is conducted online with remote proctoring. Once a learner has become eligible to register for the certification exam, they can choose a slot convenient to them from what is available and pay the exam fee. Schedule of available slot dates/timings for these remote-proctored online examinations will be published and made available to the learners.

EXAM FEE: The remote proctoring exam is optional for a fee of Rs.1500 + GST. An additional fee of Rs.1500 will apply for a non-standard time slot.

5. Results and Certification
After the exam, based on the certification criteria of the course, results will be declared and learners will be notified of the same. A link to download the e-certificate will be shared with learners who pass the certification exam.

CERTIFICATE TEMPLATE

Course Details

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
Week 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 machine

Books and References

  1. Fundamentals of Predictive Text Mining by Sholom M. Weiss, Nitin Indurkhya, & Tong Zhang (2010/2015)
  2. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython by
  3. Wes McKinney (2017)
  4. Text Analytics with Python: A Practical Real-World Approach to Gaining Actionable Insights from Your Data by
  5. Dipanjan Sarkar (2016)

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