Azhar Uddin

Years of Experience in Python Training classes

4

Teaching Experience in detail in Python Training classes

Python training classes for beginners and working professionals with project, certification, and placement assistance.

Years of Experience in django

3

Teaching Experience in detail in 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: Complete Django follows the "Batteries included" philosophy and provides almost everything developers might want to do "out of the box". Because everything you need is part of the one "product", it all works seamlessly together, follows consistent design principles, and has extensive and up-to-date documentation. Versatile Django can be (and has been) used to build almost any type of website — from content management systems and wikis, through to social networks and news sites. It can work with any client-side framework, and can deliver content in almost any format (including HTML, RSS feeds, JSON, XML, etc). The site you are currently reading is based on Django! Internally, while it provides choices for almost any functionality you might want (e.g. several popular databases, templating engines, etc.), it can also be extended to use other components if needed. Secure Django helps developers avoid many common security mistakes by providing a framework that has been engineered to "do the right things" to protect the website automatically. For example, Django provides a secure way to manage user accounts and passwords, avoiding common mistakes like putting session information in cookies where it is vulnerable (instead cookies just contain a key, and the actual data is stored in the database) or directly storing passwords rather than a password hash. A password hash is a fixed-length value created by sending the password through a cryptographic hash function. Django can check if an entered password is correct by running it through the hash function and comparing the output to the stored hash value. However due to the "one-way" nature of the function, even if a stored hash value is compromised it is hard for an attacker to work out the original password. Django enables protection against many vulnerabilities by default, including SQL injection, cross-site scripting, cross-site request forgery and clickjacking (see Website security for more details of such attacks). Scalable Django uses a component-based “shared-nothing” architecture (each part of the architecture is independent of the others, and can hence be replaced or changed if needed). Having a clear separation between the different parts means that it can scale for increased traffic by adding hardware at any level: caching servers, database servers, or application servers. Some of the busiest sites have successfully scaled Django to meet their demands (e.g. Instagram and Disqus, to name just two). Maintainable Django code is written using design principles and patterns that encourage the creation of maintainable and reusable code. In particular, it makes use of the Don't Repeat Yourself (DRY) principle so there is no unnecessary duplication, reducing the amount of code. Django also promotes the grouping of related functionality into reusable "applications" and, at a lower level, groups related code into modules (along the lines of the Model View Controller (MVC) pattern). Portable Django is written in Python, which runs on many platforms. That means that you are not tied to any particular server platform, and can run your applications on many flavours of Linux, Windows, and Mac OS X. Furthermore, Django is well-supported by many web hosting providers, who often provide specific infrastructure and documentation for hosting Django sites.

Years of Experience in Data Science Classes

2

Teaching Experience in detail in Data Science Classes

Why choose us? ? Indepth and practical learning ? Best in industry and full-time trainers ? Online practice portal and self-evaluation ? Projects to match industry demands ? Certification and placement support ? Convenience of classroom, online or virtual learning Data Science Syllabus 1. Data Science Overview • Introduction to Data Science • Different Sectors Using Data Science • Purpose and Components of Python • Key Takeaways 2. Data Analytics Overview • Data Analytics Process • Knowledge Check • Exploratory Data Analysis(EDA) • EDA-Quantitative Technique • EDA Graphical Technique • Data Analytics Conclusion or Predictions • Data Analytics Communication • Data Types for Plotting • Data Types and Plotting 3. Statistical Analysis and Business Applications • Introduction to Statistics • Statistical and Non-statistical Analysis • Major Categories of Statistics • Statistical Analysis Considerations • Population and Sample • Statistical Analysis Process • Data Distribution • Dispersion • Knowledge Check • Histogram • Knowledge Check • Testing • Knowledge Check (MCQ’S) • Correlation and Inferential Statistics 4. Mathematical Computing with Python (Numpy) • Introduction to Numpy • Activity- Sequence it Right • Demo 01-Creating and Printing an ndarray • Knowledge Check • Class and Attributes of ndarray • Basic Operations • Activity-Slice It! • Copy and Views • Mathematical Functions of Numpy • Code based Assignment as Homework. 5. Scientific Computing with Python (Scipy) • Introduction to SciPy • SciPy Sub Package - Integration and Optimization • Knowledge Check • SciPy sub package • Demo - Calculate Eigen values and Eigenvector • Knowledge Check • SciPy Sub Package - Statistics, Weave and IO 6. Data Manipulation with Pandas • Introduction to Pandas • Knowledge Check • Understanding DataFrame • View and Select Data Demo • Missing Values • Data Operations • Knowledge Check • File Read and Write Support • Activity- Sequence it Right • Pandas Sql Operation 7. Machine Learning with Scikit–Learn • Machine Learning Approach • How it Works • Supervised Learning Model Considerations • Knowledge Check • Scikit-Learn • Knowledge Check • Supervised Learning Models • Unsupervised Learning Models • Pipeline • Model Persistence and Evaluation • Knowledge Check 8. Data Visualization in Python using Matplotlib • Introduction to Data Visualization • Knowledge Check • Line Properties • (x,y) Plot and Subplots • Knowledge Check • Types of Plots • Knowledge Check (MCQ’S) 9. Web Scraping • Understanding and Searching the Tree • Navigating options • Demo Navigating a Tree • Knowledge Check • Modifying the Tree • Parsing and Printing the Document • Choose a project 10. Python Integration with Big Data Science • Why Big Data Solutions are Provided for Python • Hadoop: Core Components • Python Integration with HDFS • Demo 1 - Using Hadoop Streaming for Calculating Word Count 11. Course Recap and Two Industry based Projects • Summarize the entire course concepts • Concepts to be re-visited based on learner feedback • Recap of the entire session • Finish the test to get certified