Best Online Python Programming and Spring Training Institute In Delhi (Noida).

Course Content

Module 01 - Python Environment Setup and Essentials • Introduction to Python Language • Features, the advantages of Python over other programming languages • Python installation – Windows, Mac & Linux distribution for Anaconda Python • Deploying Python IDE • Basic Python commands, data types, variables, keywords and more Hands-on Exercise – • Installing Python Anaconda for the Windows, Linux and Mac. Module 02: Python language Basic Constructs • Built-in data types in Python • Learn classes, modules, Str(String), Ellipsis Object, Null Object, Ellipsis, Debug • Basic operators, comparison, arithmetic, slicing and slice operator, logical, bitwise • Loop and control statements while, for, if, break, else, continue Hands-on Exercise – • Write your first Python program • Write a Python Function (with and without parameters) • Use Lambda expression • Write a class • Create a member function and a variable • Create an object • Write a for loop Module 03: OOP concepts in Python • How to write OOP concepts program in Python • Connecting to a database • Classes and objects in Python • OOPs paradigm, important concepts in OOP • Python functions, return types and parameters • Lambda expressions Hands-on Exercise – • Creating an application which helps to check balance, deposit money and withdraw the money using the concepts of OOPS. Module 04: Database connection • Understanding the Database, need of database • Installing MySQL on windows • Understanding Database connection using Python. Hands-on Exercise • Demo on Database Connection using python and pulling the data. Module 05: NumPy for mathematical computing • Introduction to arrays and matrices • Broadcasting of array math, indexing of array • Standard deviation, conditional probability, correlation and covariance. Hands-on Exercise – • How to import NumPy module • Creating array using ND-array • Calculating standard deviation on array of numbers • Calculating correlation between two variables. Module 06: SciPy for scientific computing • Introduction to SciPy • Functions building on top of NumPy, cluster, linalg, signal, optimize, integrate, subpackages, SciPy with Bayes Theorem. Hands-on Exercise – • Importing of SciPy • Applying the Bayes theorem on the given dataset. Module 07: MatPlotlib for data visualization • How to plot graph and chart with Python • Various aspects of line, scatter, bar, histogram, 3D, the API of MatPlotLib, subplots Hands-on Exercise – • Deploying MatPlotLib for creating Pie, Scatter, Line, Histogram. Module 08: Pandas for data analysis and machine learning • Introduction to Python dataframes • Importing data from JSON, CSV, Excel, SQL database, NumPy array to dataframe • Various data operations like selecting, filtering, sorting, viewing, joining, combining Hands-on Exercise – • Working on importing data from JSON files • Selecting record by a group • Applying filter on top, viewing records Module 09: Exception Handling • Introduction to Exception Handling • Scenarios in Exception Handling with its execution • Arithmetic exception • RAISE of Exception • What is Random List, running a Random list on Jupyter Notebook • Value Error in Exception Handling. Hands-on Exercise – • Demo on Exception Handling with an Industry-based Use Case. Module 10 : Multi Threading & Race Condition • Introduction to Thread, need of threads • What are thread functions • Performing various operations on thread like joining a thread, starting a thread, enumeration in a thread • Creating a Multithread, finishing the multithreads. • Understanding Race Condition, lock and Synchronization. Hands-on Exercise – • Demo on Starting a Thread and a Multithread and then perform multiple operations on them. Module 11: Packages and Functions • Intro to modules in Python, need of modules • How to import modules in python • Locating a module, namespace and scoping • Arithmetic operations on Modules using a function • Intro to Search path, Global and local functions, filter functions • Python Packages, import in packages, various ways of accessing the packages • Decorators, Pointer assignments, and Xldr Hands-on Exercise – • Demo on Importing the modules and performing various operation on them using arithmetic functions • Importing various packages and accessing them and then performing different operations on them. Module 12: Web scraping with Python • Introduction to web scraping in Python • Installing Python parser lxml • Various web scraping libraries, Scrapy Python packages • Searching of tree, full or partial parsing, output print Hands-on Exercise – • Installation of Beautiful soup and lxml Python parser • Making a soup object with input HTML file • Navigating using Py objects in soup tree.