Mastering Python: A Comprehensive Course
Elevate Your Skills with Comprehensive Python Training at ETLHIVE Courses.
Python Training in Pune
For Best Python Training in Pune, you may enroll with ETLHIVE as it brings a training course in the widely acclaimed programming language Python, designed primarily for the budding programmers who wish to make it big in the Data Analytics Domain. Python is a high-level programming language with its powerful library, clear syntax, and high readability has emerged as one of the “must-know” languages.
Who should opt for Python Course
Software Professionals such as Programmers, Web Developers, ETL Developers, Analytics Professionals, Automation Engineers, Hadoop Programmers, Project Managers, and even beginners must learn Python to compete well and to ensure their success in the IT sector.
Pre-requisites for Python Training
Anyone having a basic knowledge of Windows or UNIX can apply for Python Classes. An additional knowledge about programming will ensure faster learning and implementation in the real-time projects.
Syllabus
- Defining Python
- History of Python and its Growing Popularity
- Features of Python and its Wide Functionality
- Python 2 vs Python 3
- Setting Up Python Environment for Development
- What and How of Python Installation?
- IDEs: IDLE, Pscharm, and Enthought Canopy
- Running a Python Script
- Writing First Python Program
- Python Scripts on UNIX and Windows
- Installation on Ubuntu-based Machines
- Programming on Interactive Shell
- Python Identifiers and Keywords
- Indentation in Python
- Comments and Writing to the Screen
- Command Line Arguments and Flow Control
- User Input
- Essentials of Hadoop
- Python Core Objects
- Defining Built-in Functions
- Objectives
- Variables and their types
- Variables – String Variables
- Variables – Numeric Types
- Variables – Boolean Variables
- Boolean Object and None Object
- Tuple Object and Operations
- Dictionary Object and Operations
- Types of Variables – Dictionary
- Comparision of Variables
- Dictionary Methods and Manipulations
- Operators and Logical Operators
- Data Structures and Data Processing
- Arithmetic Operations on Numeric Values
- Operators and Keywords for Sequences
- Understanding Conditional Statements
- Break Statements and Continue Statements
- Using Indentations for defining if & else block
- Loops in Python
- While, Nested, Demo-Create
- How to Control Loops?
- Sequence and Iterable Objects
- Objectives of Functions
- Types of Functions
- Creating UDF Functions
- Function Parameters
- Unnamed and Named Parameters
- Creating and Calling Functions
- Python user Defined Functions
- Python packages Functions
- Anonymous Lambda Function
- Understanding String Object Functions
- List and Tuple Object Functions
- Studying Dictionary Object Functions
- Defining Python Inbuilt Modules
- Studying Types of Modules
- os, sys, time, random, datetime, zip modules
- How to Create Python User Defined Modules?
- Understanding Pythonpath
- Creating Python Packages
- init File and Package Initialization
- What and How of File Handling with Python?
- How to Process Text Files using Python?
- Read/Write and Append File Object
- Test Operations: os.path
- Overview of Object Oriented Programming
- Defining Classes, Objects, and Initializers
- Attributes – Built-In Class
- Destroying Objects
- Methods – Instance, Class, Static, Private methods, and Inheritance
- Data Hiding
- Module Aliases and reloading modules
- Python Exceptions Handling
- Standard Exception Hierarchy
- .. except…else
- .. finally…clause
- Creating Self-Exception Class
- User-defined Exceptions
- Debugging Errors – Unit Tests
- Project Skeleton
- Creating and Using the Skeleton
- How to use pdb debugger?
- Using Pycharm Debugger
- Asserting Statement for Debugging
- Using UnitTest Framework for Testing
- Understanding Regular Expressions
- Match Function, Search Function, and the Comparision
- Compile and Match, Match and Search
- Search and Replace
- What and How of Extended Regular Expressions?
- Wildcard Characters
- How to Create a Database using SQLite 3?
- Understanding CRUD Operations
- Creating Database Connection
- Python MySQL Database Access
- Operations: Create, Insert, Read, Update, Delete
- What are DML and DDL Operations?
- Performing Transactions
- How to Handle Database Errors?
- What and How of Disconnecting Database?
- Machine Learning with Python
- Defining Machine Learning
- Implementation of Machine Learning
- Algorithms
- Learning NumPy and Scipy
- Learning – Supervised or Unsupervised
- Supervised, Unsupervised Learning and Classification
- Classification and k-Nearest Neighbours (kNN)
- Building, Testing, and Measuring the Performance of the Classifier
- Defining Clustering Problem
- k-Means Clustering
- Defining Panda
- Pandas – Creating and Manipulating Data
- How to Create Data Frames?
- Importance of Grouping and Sorting
- Plotting Data
- Understanding Scikit-Learn
- Algorithms for Scikit-Learn
- Understanding Parallelism
- What is Multithreading in Python?
- How to create threads with Parameters?
- Demon/Non-Demon Processes
- Studying Multiprocessing in Python
- Defining Hadoop
- Growing Popularity of Hadoop
- Understanding the nature of BigData
- What is Hadoop Ecosystem
- Data Analysis with Python
- Studying HDFS File System
- Cloudera Cluster of Single Node
- Hadoop and MapReduce Framework
- MapReduce Job Run and Python
- PIG, HIVE, and Python
- Package Installation using Pycharm
- pip and easy_install
- XLS Interface and XLS Parsing with Python
- Web Scraping in Python
- MrJob Package
- Beautifulsoup Package
- Concepts of Testing
- Need of automation
- Automation Frameworks Types
- UI Automation – Selenium Library
- Navigating
- Locating Elements
- Waits
- APIs Basics
- Types of APIs
- API AUTOMATION – Request Module
- UNIT TEST Framework-PYTHON
- Basic Test Structure
- Running Tests Cases
- Test Outcomes
- Assert Statements-Types
- Introduction to Test Fixtures
- Introduction to Test Suites
- Test Discovery with UNIT Test Framework
- Python Nose Framework
- Installation
- Running nose
- Nose fixtures
- Testing markdown.py
- Nose assert_equals
- Test discovery
- Running unittests from nose
- Running doctests from nose
- Integration of Nose with HTML
- Robot Framework-FrameWork
- Architecture
- Test Libraries
- Installation and Configuration of Robot Framework and Ride
- Suite Test Setup and Teardown
- Tags: Tags for individual Testcases, Force Tags for Suite Level
- Reports & Logs – Creating reports with customized file names
- Creating Reports with Specified Titles
- Write Keywords in RF actually implemented in Python scripts
- Web Development Django
- Django Introduction
- Installation and Setup
- Introduction to Django Framework
- Django Principles
- Install and create virtualenv
- Install Django and production ready setup
- Creating A New project
- Running the Development Server
- Django Apps
- URLs and Views
- URL Mapping — emphasis on Python regex
- HTTP protocol Fundamentals
- Django Views — render/HttpResponse Method
- Django Templates
- Static Files — CSS, JavaScript and Images
- Model, Template and View (MTV) Design Pattern
- Django Model Classes — SQL Mapping
- Field Types
- Generating Databases
- SQL Queries
- Manage.py Database Commands
- Django Admin Interface — superuser
- Implement __str__ for your Model Classes
- The Model API
- SAVE and Delete
- Adding Login and Logout Views
- A template for the Home Page
- Authorization with Django
- Overview of all HTML Elements
- CSS Overview
- Templates: Tags and Variables
- Adding the HOME View
- URL Mapping for APPS
- Template Inheritance
- Login required — Handling issues with Login using decorators
- Template Context
- Templates – For, Include
- Django Forms — Model Class
- Views and Forms
- Templates and Forms — csrf_token tag
- Styling forms using django-crispy- forms
- Verbose Name for display in forms
- Help Text to show the text to help the user
- Make a Field nullable — null=True
- Allow empty text Field — blank=True
- Showing Invitation
- Accepting Invitation
- Named Groups in URLs
- Fat Models, skinny views
- URLs: Reverse and get_absolute_url
- Micro services
- Rest API/Framework and Test Cases
- Micro-services, concept and architecture
- Writing Micro-services
- Rest Framework and API, concept
- Writing Rest Services, sending and receiving JSON Data
- Writing Test Cases and Automated Testing Framework
Programming Languages & Tools
Certificates
Obtaining Your Certification
Upon successful completion of any course at Etlhive, participants receive a certificate attesting to their proficiency in the respective subject matter. These certificates serve as tangible evidence of the skills acquired during the training, enhancing the credibility of individuals in the job market and validating their expertise to potential employers. Etlhive certificates are recognized for their industry relevance and are highly regarded by leading organizations, providing a competitive edge to certificate holders. The validation process ensures that the certifications are earned through rigorous learning and assessment methods, reflecting real-world application and mastery of the concepts taught. With Etlhive certificates, individuals can showcase their commitment to continuous learning and professional development, opening doors to new career opportunities and advancement prospects.
Training students for leading brands
Frequently asked questions
There are no strict prerequisites for our Python course. Basic knowledge of programming concepts can be helpful, but our course caters to beginners as well.
You can download and install Python from the official Python website (python.org). Follow the instructions provided for your operating system.
Python 2 and Python 3 are two major versions of Python that have some syntactical and functional differences. Python 3 is the latest version and is actively maintained, while Python 2 has reached end-of-life. Python 3 introduces improvements such as better Unicode support, syntax enhancements, and more.
Python libraries are collections of functions and modules that extend the capabilities of Python. They allow you to perform various tasks without having to write the code from scratch. Popular libraries include NumPy for numerical computing, Pandas for data manipulation, Matplotlib for data visualization, and TensorFlow for machine learning.
Indentation is used in Python to define blocks of code. It is mandatory and serves the purpose of delimiting code blocks, such as loops, conditional statements, and function definitions. It is typically done using spaces or tabs, but consistency is important.
Python supports various data types including integers, floating-point numbers, strings, lists, tuples, dictionaries, sets, and more. Each data type has its own characteristics and use cases.
Loops are used to execute a block of code repeatedly. Python supports for loops and while loops. for loops are typically used when you know the number of iterations in advance, while while loops are used when the number of iterations is not known beforehand.
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