Python Training in Pune

The prerequisites for learning Python programming. Basic computer literacy and fundamental math skills are essential, while prior programming experience is not required. Familiarity with programming concepts, basic computer science, and using text editors or IDEs will be helpful but not mandatory.

Aspire Techsoft offers an online Python Classes in Pune, which is beneficial for a wide range of people. The course is aimed at aspiring programmers, professionals in various IT fields (including web development, data science and game development), and beginners interested in Python.

Previous knowledge of programming is helpful but not required.

The aim of this program is to boost career opportunities and enhance skills in the technology sector, making it suitable for students and experienced professionals alike. Our Python course is designed to be accessible and valuable to many.

Web developers, Analytics experts and professionals, Big Data experts, Game developers, Mathematicians, Data scientists, System testers, Shell scripters, Testing professionals, Project developers, ETL developers, Software professionals, BI Managers and Project Managers they should learn python course.

To enhance your skills as a great Python programmer, it is beneficial to learn a combination of programming languages, frameworks, and tools that complement Python, allowing you to work in a variety of fields such as web development, data science, machine learning, automation, or game development.

Web DevelopmentLanguages - HTML/CSS for front-end structure and styling, and JavaScript for dynamic front-end development.

Libraries/Frameworks - React, Vue.js, and Angular (based on JavaScript) are valuable for web development

SQL is required for database management and querying.

Web Frameworks - Django is a high-level Python framework for building secure websites, Flask is a lightweight micro-framework for greater flexibility, FastAPI is for building high-performance APIs, and Tornado is for handling long-term connections like WebSockets.

Tools - Docker for containerizing applications, Nginx as a web server, and PostgreSQL/MySQL for database interaction.

Languages - R is useful for advanced statistics and data analysis, complements Python, and requires SQL for large datasets.

Data Science Libraries (Python-centric) - NumPy for numerical computing, Pandas for data manipulation, Matplotlib/Seaborn for data visualization, and SciPy for scientific and technical computing.

Machine Learning Libraries - Scit-Learn for machine learning algorithms, TensorFlow/Kera for neural networks and deep learning, PyTorch for dynamic computational graphs, and XGBoost for gradient boosting models.

Tools - Docker for deploying data science applications, Git/GitHub for version control, and TensorBoard for visualizing machine learning models.

Jupyter Notebooks are powerful tools for interactive data science.

Languages/Tools - Bash/Shell scripting for automated tasks on Unix-based systems, and PowerShell for Windows environments.

Python libraries for automation - Selenium for web automation, Beautiful Soup for web scraping, Requests for HTTP requests, Pillow for image processing, and PyAutoGUI for GUI automation.

Task automation tools - Airflow for complex workflows and Celery for asynchronous tasks.

Tools - Docker for containerization, Kubernetes for container orchestration, Ansible for configuration management, and Terraform for infrastructure as code.

Tools - Git for version control, GitHub/GitLab/Bitbucket for hosting repositories, and JIRA for project management.

Our Python trainer has over 10+ years of experience in IT training, specifically focusing on Python and Django applications. This includes conducting classroom, online, and corporate training.

He is a Python certified professional and has unique teaching skills. He has deep practical knowledge of both Python 2 and Python 3.

The Python Trainer at Aspire Techsoft has experience in teaching Python for machine learning, AI and data science, as well as excellent skills in solving mathematical problems using Python programming.

Our trainer is currently working as a Senior Python Programming Developer in a top MNC. He has also worked as a Python Web and Gaming Developer and a Java Freelancer for small to medium organization.

He currently works as a Data Scientist on retail projects. This practical experience allows him to provide real-time project support during the training course.

They are experienced in handling live instructor-led online Python and Django classes and self-paced courses.

Our trainer offers the option to take demo classes via live Zoom or Goto meetings before enrolling Python course in Pune.

Full Stack Developer

Data or Quantitative Analyst

Python Developer

Data Analyst

Web Developer

Data Scientist

Machine Learning Engineer

Software Engineer

Automation Engineer

Cybersecurity Analyst

DevOps Engineer

Freelance and Consulting Opportunities

Python's popularity in India is surging, driven by its versatility and ease of use in various tech sectors. High demand for Python developers is fueled by growth in data science, machine learning, and web development.

Python's adoption by incorporating it into national digital literacy and skills programs. This creates numerous opportunities for aspiring programmers and contributes to India's technological advancement.

The strong community and readily available training also contribute to Python's bright future in India.

Python developers are responsible for developing, testing, and maintaining Python-based applications.

This includes writing efficient, reusable, and testable code, as well as maintaining and improving the existing codebase while focusing on code quality and performance.

Database management is a key responsibility, which includes integrating with databases using both relational (e.g., PostgreSQL, MySQL) and NoSQL databases, with a focus on optimal performance and data security, and handling data storage solutions.

Designing and implementing RESTful APIs to enable seamless communication between front-end and back-end services is also a key task.

Python developers often develop and maintain web applications using frameworks such as Django.

A python developer works closely with front-end developers to ensure seamless integration of server-side logic with user-facing components.

Troubleshoot, debug, and optimize applications to enhance performance and resolve any issues, ensuring software performance and reliability.

Performs thorough testing and debugging to identify and resolve issues for Python developers.

Creates comprehensive documentation for technical processes, architecture, and code specifications.

Collaborates with cross-functional teams, including UI/UX designers, project managers, and QA teams, to meet project goals and deadlines. They work with DevOps teams to deploy and maintain scalable applications.

Keeping up to date with the latest trends and advancements in Python and related technologies is a key part of the job.

As needed, Provides support and troubleshooting for production systems.

1.Computer Fundamental

• Fundamental of Computer Basics

• Types of Software

• Web Application

• Windows Application

• Networking Concepts

• Database

• DDL DML DCL

• What is Programming Language.

2. Python Programming

• An Introduction to Python Why Python,

• its Unique Feature, and where to use it?

• its Unique Feature, and where to use it?

• Python Environment Setup

• Discuss about IDEs like IDLE, PyCharm, and Enthought Canopy

• Python Identifiers, Keywords

• Discussion about installed modules and packages

• Access Command line arguments within programs

3. Conditional Statement, Loops, and File Handling

• Python Data Types

• Decorators

• Python Modules & Packages

• Python Files and Directories manipulations

• Use various files and directory functions for OS

• operations

4. Python Core Objects and Functions

• Built-in modules (Library Functions)

• Numeric and Math’s Module

• String/List/Dictionaries/Tuple

• Complex Data structures in Python

• Arbitrary data types and their Data Structure

• Python built-in function

• Python packages and functions

• The anonymous Functions-Lambda Functions

5. Object-Oriented Python

• OOPs Concepts

• Object, Classes, and Destroying Objects

• Accessing attributes, Built-In Class Attributes

• Inheritance and Polymorphism

• Overriding Methods, Data Hiding

• Overloading Operators

6. Exception Handling in Python

• Exceptions Handling

• Handling various exceptions using try….except…else

• Try-finally clause

• The argument of an Exception and create a self exception class

• Python Standard Exceptions

• Raising an exception, User-Defined Exceptions

7. Debugging Python Programs

• Debug Python programs using PDB debugger

• Assert for debugging

• Testing with Python using UnitTest

• Iterable and generator in Python

• Yielding from the generators

• Standard project setup in Python

8. Regular Expression

• Regular Expressions

• What are regular expressions?

• The match and search Function

• Compile and matching

• Matching vs searching

• Search and Replace feature using RE

• Extended Regular Expressions

• Wildcard characters and work with them

9. Modules & Packages

• Modules

• How to import a module…?

• Packages

• How to create packages?

10. File handling

• Reading data from a file

• Read and Write data from csv file

• OS module

• Rename and Remove files, directories

• Object, Classes, and Destroying Objects

• Accessing attributes, Built-In Class Attributes

• Inheritance and Polymorphism

• Overriding Methods, Data Hiding

• Overloading Operators

11. Database handling

• OOPs Concepts

• Create Database Connection

• Creating and accessing SQLite database

• Python with MySQL Database

• Creating Database Table

• CRUD operation on the database

• Performing Transactions

• Handling Database Errors

• Disconnecting Database

12. Statistics

• Mean Mode, Median.

• Random Variable.

• Probability, Probability Distribution of Random Variables.

• Type of Random Variables - Based on Scale of Measurement.

• Variance & Standard Deviation.

• Normal, Binomial, Poisson Distribution.

• Standard Normal Distribution and Z-Score.

• Sampling & Sampling Distribution.

• Central Limit Theorem.

• Simulation.

13. Machine Learning

• What is Machine Learning?

• Overview & Terminologies.

• Difference between AI & ML.

• Supervised & Unsupervised ML.

• Supervised ML Models.

• Linear & Logistic Regression.

• Regression methods, Classification.

• Sampling & Sampling Distribution.

• K Nearest Neighbours KNN.

• Decision Tree, Random Forest.

• Unsupervised ML Models.

• K Means Clustering.

• Under fitting and Overfitting.

• Confusion Metrix.

• SVM algorithms

• Bagging and boosting

• Ada boost algorithms, gradient boost, xg boost

• Gradient descent algorithms for regression

• Navie bias algorithms

• Hypothesis testing

• Hyperparameter tunning

• Imbalance data

• Outliers handling concept

• Multivariate and univariate data and how it handle

14. Natural Language Process

• Python - web scrapping, OCR, pyterseccart

• Vectorization, token, segmentation, limitations

• Arima model, sarima model