This Curriculum is designed for developers with experience of other languages who need to get up to speed on Python. At the end of this course the students will know the essentials of the Python language, how to use Python’s module system to structure code, and how to approach the development of Python programs.
We’ll cover the core Python language and the standard library in detail. This course will cover various skills including text manipulation, modular programming, working with and retrieving data, interacting with files on your computer, and using some of the more popular third-party libraries (and getting them installed when and where we need them). The goal is to get participants up and running with Python in as short a time as possible.
Importance of the Course
- Understand how to install and use Python
- Understand the fundamental Python programming techniques and tools.
- Discover the history of Python and how it compares to other programming languages.
- Discuss its applications and the types of problems it can solve.
- File handling, Exception handling, Command line arguments, modules and packages
- Linear & Non Linear Data structures
- API’s Integration with Python
- Python for Socket programming, SMTP, Mqtt protocols
- Django Python Web frameworks
- Computer vision with python
- Algorithms with Python
|1||Introduction to algorithm Design, algorithmic problem solving.
Introduction to programming with Python, Core objects and Built-in-Functions, Conditional statements and loops
|2||Example programs :
1. Compute the GCD of two numbers.
2. Find the square root of a number (Newton’s method)
3. Exponentiation (power of a number)
|3||Functions, Strings, Lists|
1. First n prime numbers
2. Multiply matrices
|5||File handling, Exception handling, Command line arguments, modules and packages|
- Python Programming
|1||Linear & Non Linear Data structures in python
1. Find the maximum of a list of numbers
2. Linear search and Binary search
3. Selection sort, Insertion sort
4. Merge sort, etc.,
|2||Tuples, Dictionaries with exercises
1. Programs that take command line arguments (word count)
2. Find the most frequent words in a text read from a file
|3||1.Moving duplicate elements from one list to other
3.Tower of Hanoi
4.Rat in a maze
5.Travelling sales person problem
6.Chocolate and Wrapper Puzzle(0-1 Knapsack problem)
|4||Object oriented programming & its concepts in python, Program organization with modules and packages|
|5||Understanding and implementing multiprocessing and multithreading using pipes, filters, fork & sub-process.
Serialization, unit testing, and file system interaction, Debugging
- API’s Integration and database Connectivity
|1||Accessing Excel with python, Logging in python,
Programming Sql with python
|2||Introduction to IOT and role of python, Handful python API’s and Protocols Real-time examples Thing speak.|
|3||Custom python API’s to send SMS, Mail, Social networks, Sports and weather|
|4||Google API’s (Speech recognition, Vision API, Maps & navigation)|
|5||Python for Socket programming, SMTP, MQTT protocols|
The Program is mix of Theory sessions, Quizzes, Hands on Sessions, Liver Interaction with Experts, Assignments and Practical Exercises. Maximum Impetus is given to Hands on Sessions so as to enable the participants with the maximum knowledge transfer and satisfaction. The ratio of the theory, practical sessions will be 30:70.
Python Programming – Fundamentals
Types of Programming Languages – Advantages of Python – Installation of Python – Data types &Operators in Python - Numerical Operations on Python – String Operations on Python – Loops in Python
Python – Tools | Syntaxes & Data Structures
Python Lists & Tuples – Functions - Conversions – Arguments – Function Calls – Void statements – Functions with Multiple Arguments – Building Custom Functions in Python
Introduction of Pandas library using Python
Pandas Introduction - Features Of Pandas - Pandas Data Structures - Series and DataFrames with examples - Operations on a DataFrame.
Pandas Functions using Python
Pandas – Panels – Panel Creation – Different types of functions – Pandas – Descriptive Statistics – Table-wise function application – Element-wise function application.
Introduction & Uses of - Numpy library
Introduction - Pre - Requisites - Array operations - Array objects - Datatypes - Array attributes - Flags - Indexing and Slicing - Array manipulations - Transpose Operations - String Functions - Mathematical functions.
Numpy -Functions & Operations with Examples
Array Manipulations – Transpose operations – Binary Operators – String functions – Mathematical functions.
Matplotlib Library using Python
Matplotlib - Dependencies - Features - PyPlotAPI - Types Of Plots - Image Functions - Axis Functions - Figure Functions - Simple Plot Example - PyLab Module - Object Oriented Interface - Figure Class - Axes Class - Color Codes - Marker Codes - Line Styles - Multiplot - Subplot() - Subplot2Grid() - Grids - Formatting Axes.
Matplotlib Library - Introduction , Pyplot API | Types Of Plots
Introduction - Dependencies - Features of Ipython - PyplotAPI - Types Of Plots - Image Functions - Axis Functions - Figure functions - Pylab Module - Object Oriented Interface - Figure Class - Axes Class - Color codes - Marker codes - Line styles - Various types Of Plots - Grids - Formatting Axes.
Scikit - Learn using Python
ntroduction - Pre - Requisites - Installation - Features - Modelling Process - Dataset Loading - Splitting the Dataset - Sample Example.
Keras Library using Python
Keras - Features and Benefits- Installation - Layers - Examples
good training session and learning