Python is a popular high-level, open-source programming language with a wide range of applications in automation, big data, Data Science, Data Analytics development of games and web applications. It is a flexible, powerful object-oriented and interpreted language. Python is considered to be a programming language with the highest number of job opportunities.
DASVM’s Python certification course online is created by experienced professionals to match the current industry requirements and demands. This Python course will help you master Python programming concepts such as Sequences and File Operations, Conditional statements, Functions, Loops, OOPs, Modules and Handling Exceptions, various libraries such as NumPy, Pandas, Matplotlib, and also focuses on GUI Programming, Web Maps, Data Operations in python and more. Throughout this Python training course online, you will be working on real-time projects and this course prepares you to clear PCEP, PCAP and PCPP Python certification exams to become a certified developer.
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
To see the full course content Download now
Course Prerequisites
- Knowledge of basic mathematics is required
- Basic Programming knowledge
- Understanding the basics of statistics
Who can attend
- Business analysts who want to understand data science techniques
- Information architects who want to gain expertise in machine learning algorithms
- Analytics professionals who want to work in machine learning or artificial intelligence
- Graduates looking to build a career in data science and machine learning
- Experienced professionals who would like to harness machine learning in their fields to get more insights
- Big Data Professionals
- Web Developers
- Software programmers
- Research Analysts
- Data Scientists
- IT Developers
Number of Hours: 50hrs
Certification
Key features
- One to One Training
- Online Training
- Fastrack & Normal Track
- Resume Modification
- Mock Interviews
- Video Tutorials
- Materials
- Real Time Projects
- Virtual Live Experience
- Preparing for Certification
FAQs
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- One to One Training
- Online Training
- Fastrack & Normal Track
- Resume Modification
- Mock Interviews
- Video Tutorials
- Materials
- Real Time Projects
- Materials
- Preparing for Certification
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