Course content

Core Python

Getting Started

• History
• A Python Q&A Session
• How Python Runs Programs
• How You Run Programs

Introduction to Python:

• What is Python?
• Why Python?
• Python Applications in real life
• Brief history of Python
• Versions of Python
• Installing Python
• Using IDLE
• First Python Program
• Getting help from Python Docs

Basic Syntax

• Basic syntax
• Commenting
• Indentation
• Python keywords
• Strings
• String values
• String Operations
• String slicing
• Built in string methods
• Formatted printing
• Simple Input and Output handling

Variables

• Variables
• Data type
• Number
• string
• List
• Tuple
• Dictionary

Types and Operations

• Introducing Python Object Types
• Numeric Types
• The Dynamic Typing Interlude
• Strings
• Lists and Dictionaries
• Tuples, Files and Everything Else

Variables Data types

• Intro to dynamic typing
• Variables in Python
• Naming conventions
• Basic Data types (representation of strings, integer, floats)

Decision Making – Loops 

• While loop, if loop and nested loop
• Number type conversion – int (), long (). Float ()
• Mathematical functions, Random function, Trigonometric function

Language Building blocks

• Control statements, the if, elif, else
• True and False
• Arithmetic Operators
• Relational Operators
• Logical Operators
• Bitwise Operators
• While loop
• Usage of pass, break and continue
• For each loop

String

• Strings- Escape char, String special Operator, String formatting Operator
• Build in string methods – center (), count () decode (), encode ()

List

• Python List – Accessing values in list, delete list elements, Indexing slicing & Matrices
• Built in Function – cmp(), len(), min(), max()

Dictionary

• Dictionary – Accessing values from dictionary, Deleting and updating elements in Dict.
• Properties of Dist., Built in Dist functions & Methods.
• Date & time -Time Tuple, calendar module and time module

Collections

• Lists
• Tuples
• Sets
• Dictionaries
• Sorting collections
• Operations on collections
• Discussion on real life application of above collections

Functions

• Introduction to functions
• Built in functions
• User defined functions
• Function parameters
• Variable arguments, args and kwargs
• Positional and named arguments
• Discussion scope of variables with respect to functions and namespace
• Passing function to another function

Files

• Files in Python- Reading keyboard input, input function
• Opening and closing files. Syntax and list of modes
• Files object attribute- open, close . Reading and writing files, file Position.
• Renaming and deleting files

Directories

• mkdir methid, chdir () method , getcwd method , rm dir

File Handling

Exception Handling

• Exception handling – List of exceptions – Try and exception
• Try- finally clause and user defined exceptions

Modules

• Introduction to modules
• Introduction to standard modules
• OS module
• path module
• Sys module
• sub process module
• Argument parsing using argparse module
• .csv file parsing using csv module
• .jason file paring using Jason module
• Xml file parsing using xml module
• Introduction to logging module

Object Oriented Programming

• Introduction to Classes and Objects
• Principles of OOP
• Instance methods
• Special methods
• Encapsulation
• Inheritance
• Polymorphism

Regular Expressions

• Introduction to regular exceptions
• Introduction to re module
• Simple character matches
• Match function
• Searching function
• Regular expression patterns
• Patterns in Regex
• Search And Replace

GUI Programming

• Introduction
• Tkinter programming
• Tkinter widgets

My SQL

• Data base connectivity
• Methods- MySQL, Oracle, how to install MYSQL, DB connection
• Create, insert, update and delete operation, Handling erros

Multi-Threading

• Into Multi-Threading
• Threading module
• creating thread
• Synchronizing threads
• Multithreaded Priority Queue

Optional I (For testers)

• Introduction to testing using Python
• Introduction to test automation
• Introduction to Selenium web deriver
• Web testing using selenium

Option II (   Developers)

• Generators
• Decorators
• Iterators and iterator protocol
• Debugging using PDB

Options III (Web programming)

• Introduction to web programming using Python
• Introduction to Django/Flask
• Introduction to Restful API’s using Python

Option IV (Data Science)

• Introduction to data science using python
• Introduction to panda’s module
• Introduction to data visualization using matplotlib
• Introduction to NumPy
• Introduction to SciPy

Advanced Python

Functional Programming

• Lambdas
• List Comprehensions
• Set and Dictionary Comprehensions
• Closures and Decorators
• Generators and Coroutines
• Generator Expressions
• Declarative Programming

Systems Programming

• File Descriptors
• Reading and Writing Files
• Files and Directories
• File Locking
• Memory Mapped I/O
• Creating Processes
• Process Management
• Pipes and Signals

Classes and Objects

• New Style Classes
• Inheritance and Mixins
• Properties and Slots
• Static and Class Methods
• Abstract Base Classes
• Method Overriding
• Attributes and Functors
• Decorators and Factories
• Descriptors and MetaClasses

Persistence and Databases

• Shelve and Pickle
• SQL Relational Databases
• Connection, Cursor, Row Objects
• Create, Read, Update, Delete
• Error Handing
• Query Results and Metadata
• Create and Aggregate Functions
• Exporting and Importing
• Transactions and Rollbacks
• Database Objects

Network Programming

• Sockets and Addresses
• Establishing Connections
• TCP Clients and Servers
• UDP Clients and Servers
• UDS Clients and Servers
• Network Objects
• SocketServers
• Secure Sockets Layer

Web Programming

• JSON and XML
• Using XML-RPC
• Rest Interfaces
• WSGI and HTML
• Flask Framework
• Controller Functions
• Templates and Forms
• Database ORMs

 Threads and Concurrency

• Creating and Joining Threads
• Daemon Threads
• Thread Objects
• Timer Threads
• Locks and Semaphores
• Events and Conditions
• Thread Locals
• Thread Queues
• Process Queues and Tasks
• Process Pools

Inheritance

• The DRY Principle Revisited
• Single Inheritance
• Sub-Classing Classes from Python Packages
• Overriding Methods
• Calling the Parent Method with super()
• Multiple Inheritance
• Method Resolution Order

Extending and Embedding Python

• Calling C/C++ from Python
• Using ctypes
• Extension Modules in C/C++
• Raising Python Exceptions
• Calling Python from C/C++
• Embedding Python Interpreter
• Importing Python Modules from C/C++
• Converting Python Objects to C/C++
• Invoking Python Functions from C/C++

Data analysis using Numpy

• Introduction to Numpy arrays
• Creating and applying functions
• Numpy Indexing and selection Numpy Operations
• Exercise and assignment challenge

Pandas and advanced analysis

• Panda’s series
• Introduction to Data Frames
• Missing data
• Group by
• Merging, joining and concatenating
• Operations
• Data Input and Output

Data visualization with Python

• Plotting using Mat plot lib
• Sea born visualization
• Pandas built-in data visualization

Seaborn visualization

• Categorial Plot using Seaborn
• Distributional plots using Seaborn
• Matrix plots
• Grids
• Seaborn exercises

Django

 Introduction to Django

• Introduction
• Why Django?
• Batteries Included
• Django Principles
• What you Should Already Know
• Course Overview

Installing Django

• Intro
• Choosing your Versions
• Installing Pip and Python on Windows
• Demo: Windows Installation
• Installing Pip and Python on Mac OS X
• Demo: OS X Installation
• Installing Pip and Python on Linux
• Demo: Linux Installation
• Virtualenv
• Demo: Virtualenv
• Installing Django
• Summary

Starting a Django Project

• Introduction
• Creating a Django Project
• Demo: Creating a Django Project
• The Model-Template-View Pattern
• Demo: Hello, World!
• Mapping URLs
• Demo: URL Mapping
• Django Views
• Demo: Templates
• Summary

Models

• Introduction
• Demo: Adding Models
• Django Model Classes
• py Database Commands
• Demo: The Admin Interface
• The Django Admin Interface
• Demo: The Model API
• Save and Delete
• The Model API
• Database Relations
• Summary

Adding a User Home Page

• Introduction
• Demo: Adding Login and Logout Views
• More about URL Mappings
• Demo: A Template for the Home Page
• Authorization with Django
• More about Django Templates
• Demo: Adding the Home View
• URL Mappings for Apps
• Demo: Template Inheritance
• Template Inheritance
• Demo: Login Required
• Demo: Showing Game Data on the Home Page
• Demo: A Custom Manager Class
• The Template Context
• Templates: For and Include Tags
• Summary

Forms

• Introduction
• Demo: Adding a HTML Form
• Using Django Forms
• Demo: Adding Stypng to the Form with Crispy-Forms
• Demo: Field Options
• Field Options
• Demo: Showing Invitations in a List
• Demo: Accepting Invitations
• Demo: Named Groups
• Named Groups in URLs
• Summary

Odds and Ends

• Introduction
• Class-based Views
• Demo: Class-based Views
• Demo: Adding User Signup
• Generic Views
• Debugging Django
• Demo: The Django Debug Toolbar
• Resources
• Summary

Data Science with Python

Introduction to Data Science

• What is Data Science?
• What is Machine Learning?
• What is Deep Learning?
• What is AI?
• Data Analytics & it’s types

Python Programming Fundamentals

• Programming Basics
• Python Data Types
• Structures and conditional statements
• Python core packages
• Introduction to Jupyter Notebook

Data Science Essentials

• Data Science Introduction
• Data Science work flow
• Machine Learning Overview

Data Mugging with Numpy and Pandas

• Introduction to Numpy and Pandas
• Data filtering and selecting
• Find duplicates and treating missing values
• Concatenate and transform data

Basic Statistics

Central Tendency

• Mean
• Median
• Mode
• Skewness
• Normal Distribution

Probability Basics

• What does it mean by probability?
• Types of Probability
• ODDS Ratio?

 Standard Deviation

• Data deviation & distribution
• Variance

 Bias variance Tradeoff

• Underfitting
• Overfitting

 Distance metrics

• Euclidean Distance
• Manhattan Distance

 Outlier analysis

• What is an Outlier?
• Inter Quartile Range
• Box & whisker plot
• Upper Whisker
• Lower Whisker
• catter plot
• Cook’s Distance

Missing Value treatments

• What is an NA?
• Central Imputation
• KNN imputation
• Dummification

 Correlation

• Pearson correlation
• Positive & Negative correlation

Error Metrics Duration

• Classification
• Confusion Matrix
• Precision
• Recall
• Specificity
• F1 Score

 Regression

• MSE
• RMSE
• MAPE

Visualization, web scraping

• Creating basic charts
• Statistical Charts
• Web Scrapping tools

Introduction to Machine Learning

• Overview of Supervised and Unsupervised Machine Learning
• Linear Regression
• Clustering with K-means
• Naive Bayes Classification
• Introduction to Neural Networks

Supervised Learning  

• Linear Regression
• Linear Equation
• Slope
• Intercept
• R square value
• Logistic regression
• ODDS ratio
• Probability of success
• Probability of failure
• ROC curve
• Bias Variance Tradeoff

Unsupervised Learning

• K-Means
• K-Means ++
• Hierarchical Clustering

Other Machine Learning algorithms

• K – Nearest Neighbour
• Naïve Bayes Classifier
• Decision Tree – CART
• Decision Tree – C50
• Random Forest

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