Python for Data Science

Course Outline

Introduction

Day

Modules

Sharing

Introduction to Python and Data Science
Installing and setting up the Python environment
Basic Python syntax and data types
NumPy, sets, list, and tuples data storage
Control structures in Python
Creating and using functions, date and time functions
Laboratory exercise – My first Python program

Useful References:
NumPy	Official Site Official Documentation Tutorials NumPy User Guide Official Reference

Comprehension of the use of lists and indexing data with dictionaries
Sub-setting (Lists, Matching, Handling Missing Values)
Reading and writing real-world data (text file, CSV and SQL)
Introduction to pandas and managing Data Frames
Laboratory exercise – Working with Python pandas data frame

Useful References:
Pandas	Official Site Official Documentation Tutorials User Reference

Shaping data for analysis
Handling raw text, using bag of words, TF-IDF and DTM shaping
Sub-setting observations & variables, summarizing data
Laboratory exercise – Getting, format (sub-setting) and store data.
Feature creation, combining variables
Understanding binning, discretization and data distribution
Laboratory exercise – Preparing extracted data analysis

Introduction to MatPlotLib
Exploratory graphs in Python
Visualizing data
Laboratory exercise – Visualizing data
Wrangling data and reducing dimensionality (feature selection)
Basic clustering of data and handling outliers
Laboratory exercise – Presenting analytics using Python

Useful References:
Matplotlib	Official Site Official Documentation Tutorials Official Reference
SQL	Python MySQL Tutorial SQL Tutorial T-SQL Tutorial
Others	Seaborn Official Site JSON Official site

Regression
Association Rule and Frequent Itemset Mining
Knn, Decision Trees and Random Forest
Support Vector Machines
Text processing using NLTK
Laboratory exercise – Text classifier
Wrap-up

Day5.zip

Briefying on AI

Useful References:

NLP

Tutorials:

Useful Links:

Tutorials:

Web Scraping

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