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In a world where everything is turning technical and technological by each passing day, the importance of learning how to code is increasing at an exponential rate. However, with the complications involved in most coding languages, most people back out from learning how to code. But Python is an exceptional coding language. Not only is it comprised of pretty simple syntax that makes it easier to learn and implement, but it’s vast series of applications make it one of the most sought-after languages. Digital Shyna Educen starts with an introductory course, and then moves on to a higher and more advanced version of Python. With each lesson full of realistic examples, we make sure each step towards mastering Python is effortless and filled with easy learning techniques.
- Python Overview
- Python Basic
- Python Softwares
- Python Programe Flow
- Classes in Python
- Frameworks vs Packages
- OOPS in Python
- The Import Statement
- Panda and Numpy
- Advanced Python
- Folium Introduction
- Python SQL Database Access
- Python for Data Science
- Python with Django
- Threads Essential
- Accessing API Essential
Intro on Modules
1. Python Overview:
2. Python Basics:
Python is a general-purpose object-oriented programming language with high-level programming capabilities. Python is a programming language that includes features of C and Java. It provides the style of writing an elegant code like C, and for object-oriented programming, it offers classes and objects like Java.
Characteristics of Python:
- Interpreted Language: Python is processed at runtime by Python Interpreter.
- Object-Oriented Language: It supports object-oriented features and techniques of programming.
- Interactive Programming Language: Users can interact with the python interpreter directly for writing programs.
- Easy language: Python is easy to learn language especially for beginners.
- Straightforward Syntax: The formation of python syntax is simple and straightforward which also makes it popular.
- Easy to read: Python source-code is clearly defined and visible to the eyes.
3. Python programme Flow:
A program’s control flow is the order in which the program’s code executes. Control flow statements are required to alter the flow of program execution based on the conditions. The control flow of a Python program is regulated by conditional statements, loops, and function calls.
Below is the list of control flow statements:
- Decision-making statements: if, else, elif
- Looping statements: for, while
- Branching statements: break, continue
4. Frameworks vs Packages:
Frameworks in Python A framework is a collection of modules or packages which helps in writing web applications. Frameworks automate the common implementation of common solutions which gives the flexibility to the users to focus on the application logic instead of the basic routine processes. Frameworks make the life of web developers easier by giving them a structure for app development. They provide common patterns in a web application that are fast, reliable and easily maintainable.
Top 5 Frameworks in Python:
Packages in Python A package is a collection of Python modules. As our application program grows larger in size with a lot of modules, we place similar modules in one package and different modules in different packages. This makes a project easy to manage and conceptually clear. Similar, as a directory can contain sub-directories and files, a Python package can have sub-packages and modules.
5. OOPS in Python:
Python is a multi-paradigm programming language. Meaning, it supports different programming approach. It allows us to develop applications using an Object-Oriented approach. One of the popular approaches to solve a programming problem is by creating objects. This is known as Object-Oriented Programming (OOP).
Overview of OOP Terminology:
- Class − A user-defined prototype for an object that defines a set of attributes that characterize any object of the class. The attributes are data members (class variables and instance variables) and methods, accessed via dot notation.
- Class variable − A variable that is shared by all instances of a class. Class variables are defined within a class but outside any of the class’s methods. Class variables are not used as frequently as instance variables are.
- Data member − A class variable or instance variable that holds data associated with a class and its objects.
- Function overloading − The assignment of more than one behaviour to a particular function. The operation performed varies by the types of objects or arguments involved.
- Instance variable − A variable that is defined inside a method and belongs only to the current instance of a class.
- Inheritance − The transfer of the characteristics of a class to other classes that are derived from it.
- Instance − An individual object of a certain class. An object obj that belongs to a class Circle, for example, is an instance of the class Circle.
- Instantiation − The creation of an instance of a class. • Method − A special kind of function that is defined in a class definition.
- Object − A unique instance of a data structure that’s defined by its class. An object comprises both data members (class variables and instance variables) and methods.
- Operator overloading − The assignment of more than one function to a particular operator.
6. Folium Introduction:
Folium is a powerful data visualization library in Python that was built primarily to help people visualize geospatial data. With Folium, users can create a map of any location in the world as long as you know its latitude and longitude values. You can also create a map and superimpose markers as well as clusters of markers on top of the map for cool and very interesting visualizations. You can also create maps of different styles such as street level map, stamen map etc. What is really interesting about the maps created by Folium is that they are interactive, so you can zoom in and out after the map is rendered, which is a super useful feature.
Collections in Python are containers that are used to store collections of data, ex: list, dict, set, tuple etc. These are built-in collections. Several modules have been developed that provide additional data structures to store collections of data. One such module is the Python collections module. Python collections module was introduced to improve the functionalities of the built-in collection containers.
6 of the most commonly used data structures from the Python collections module are as follows:
- namedtuple ()
8. Classes in Python:
A class is a code template for creating objects. It is a user-defined blueprint or prototype from which objects are created. Classes provide a means of bundling data and functionality together. Creating a new class creates a new type of object, allowing new instances of that type to be made. In python a class is created by the keyword class.
9. Python Software:
Python source code and installers are available for download for all versions at – https://www.python.org/downloads/
10. Panda and NumPy:
This is one of the best topic to learn pandas in python. Students can also refresh there fundamentals. Pandad ia an open-Sourca, BSD-Licensed Python library which provides high performance, its going to be easy to use in data structures and data analysis tools for the python programming. It presents a diverse range of utilities, ranging from parsing multiple file formats to converting an entire data table into a NumPy matrix array. This makes pandas a trusted ally in data science.
NumPy is an open source Python package for scientific computing. NumPy supports large, multidimensional arrays and matrices. NumPy is written in Python.. NumPy (pronounced /ˈnʌmpaɪ/ (NUM-py) or sometimes /ˈnʌmpi/ (NUM-pee)) is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.
11. The import Statement:
A module allows you to logically organize your Python code. A module is a Python object with arbitrarily named attributes that you can bind and reference. Simply, a module is a file consisting of Python code. A module can define functions, classes and variables. A module can also include runnable code. Python modules can get access to code from another module by importing the file/function using import. The import statement is the most common way of invoking the import machinery, but it is not the only way.
12. Advanced Python:
- Metaclasses & Class creation customization
- co-routines with async & wait
- magic methods – including those needed to emulate numerics
- multi-threading and multi-processing and the advantages of each
- using the re module – regular expressions are your friend
- building decorators for classes, and build decorators using classes
- Building and using descriptors
- C extensions (maybe)
- System Programming (pipes, threads, forks etc.)
- Graph Theory (pygraph, Networkx etc)
- Polynomial manipulation using python
- Linguistics (FSM, Turing machines etc)
- Numerical Computations with Python
- Creating Musical Scores with Python
- Databases with Python
- Python Generators and Iterator Protocol
- Python Meta-programming
- Python Descriptors
- Python Decorators (class and method based)
- Python Buffering Protocol
- Python Comprehensions
- Python GIL and multiprocessing and multi-threading
- Python WSGI protocol
- Python Context Managers
- Python Design Patterns
13. Python SQL Database access:
A database is a collection of tables related to each other via columns. For most real-world projects, a database is a must. We can use SQL (Structured Query Language) to create, access, and manipulate data. For database programming, Python supports many database servers- MySQL, Oracle, PostgreSQL, SQLite, Sybase, Microsoft SQL Server, mSQL, Microsoft Access, and many more. It also supports Data Query Statements, Data Definition Language (DDL), and Data Manipulation Language (DML). The standard database interface for Python is Python DB-API.
14. Python for Data Science:
Because the language is multifaceted and flexible and has easy readability, Python is an obvious language of choice in the field. However, Python usage is relatively new. As a result, Python libraries such as Pandas (fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language) help individuals clean up data and perform advanced manipulation. The growth of Python in data science has gone hand in hand with that of Pandas, which opened the use of Python for data analysis to a broader audience by enabling it to deal with row-and-column datasets, import CSV files, and much more. While Pandas may be the best-known library, there are hundreds of specialized libraries that serve a similar purpose, such as SymPy (for statistical applications), PyMC (machine learning), matplotlib (plotting and visualization), and PyTables (storage and data formatting). These and other specialized libraries aid in everything from machine learning to data pre-processing to neural networks. One of the main benefits of Python is that its flexible nature enables the data scientist to use one tool every step of the way. Another plus is the large community of data scientists, machine learning experts, and programmers who go out of their way not only to make it easy to learn Python and machine learning but also to provide datasets to test a Python student’s mastery of their newfound skills. Whether you are a social scientist who needs Python for advanced data analysis or an experienced developer interested in a growing field, a part of the Python community is ready to help you out.
15. Python with Django:
Django is a high-level Python web framework that enables rapid development of secure and maintainable websites. Built by experienced developers, Django takes care of much of the hassle of web development, so you can focus on writing your app without needing to reinvent the wheel. It is free and open source, has a thriving and active community, great documentation, and many options for free and paid-for support.
Django helps you write software that is:
16. Threads Essential:
A thread in Python can simply be defined as a separate flow of execution. What this simply means that in your program, two different processes will be executed in the same time. One interesting aspect of threading in Python is the fact that, after version 3 multiple threads in Python are not executed at the same time, but they just merely appear to. While it is an amazing feeling to be running two different processes at the same time, one needs to understand that the current version of Python 3 and above is coded in such a way, that only process can be run at any given point in time. One of the most well-known advantages of threading in Python is its ability to provide a gain in design clarity.
17. Accessing API Essential:
Application Programming Interface (API) is a standard that facilitates intercommunication between two or more computer programs. The reason we want to use API is that it is easy to access, and data keeps changing. To interact with an API, specifically a web API in python we can make use of the standard requests module to make the request, because most web service APIs return a response in a format known as JSON (JSON is a way to store data in an organized, logical manner). APIs in Python use Open Notify API (Open Notify is an open source project to provide a simple programming interface for some of NASA’s awesome data).
18. The PYTHONPATH variable:
PYTHONPATH is an environment variable which you can set to add additional directories where python will look for modules and packages. The main use of PYTHONPATH is when we are developing some code that we want to be able to import from Python, but that we have not yet made into an installable Python package.
Our New batch starts from 12th March 2020.
|Training Timings:||11:00Am – 02:00PM (Mon to Sat)|
|Training Duration:||2 Month|
|Internship:||1 Month (Our students will be working on Live Projects)|
|Internship Timings:||10:00Am – 05:00Pm (Mon to Sat)|
|Total Course Duration:||3 Months|